Guide for State Department of 

Transportation Safety Data 

Business Planning

FHWA Safety Program FHWA-SA-17-047

July 2017

http://safety.fhwa.dot.gov
Sources for cover images 

photos clockwise left to right: 

Getty Images, Cambridge 

Systematics, Inc., and Getty 

Images.
FOREWORD

The Federal Highway Administration (FHWA) has developed a Guide for State DOT Safety 

Data Business Planning. The Guide provides practical instructions for State DOTs to follow in 

developing and implementing a Safety Data Business Plan. The Guide focuses on safety data

systems, which include crash, roadway, traffic, and railway-highway grade crossing data. A Safety 

Data Business Plan provides a roadmap for States to improve their safety data management and 

governance practices. These improvements provide better quality safety data to support safety 

decision-making and improved outcomes. 

NOTICE

This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

QUALITY ASSURANCE STATEMENT

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. 

Standards and policies are used to ensure and maximize the quality, objectivity, utility, and 

integrity of its information. FHWA periodically reviews quality issues and adjusts its programs 

and processes to ensure continuous quality improvement.
TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-SA-17-047

2. Government Accession No.

No.

3. Recipient’s Catalog No.

4. Title and Subtitle

Guide for State Department of Transportation Safety Data Business 

Planning

5. Report Date

July 2017

6. Performing Organization Code

7. Author(s)

Vandervalk, A., D. Snyder, J.K. Hajek

8. Performing Organization Report No.

9. Performing Organization Name And Address

Cambridge Systematics, Inc.

4800 Hampden Ln #800

Bethesda, MD 20814

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-10-D-00020

12. Sponsoring Agency Name and Address

Federal Highway Administration, Office of Safety

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period Covered

Guide

September 2014 – September 2017

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Stuart Thompson

16. Abstract

This Guide provides practical instructions for State DOTs to follow in developing, implementing, and maintaining 

a Safety Data Business Plan (DBP). A Safety DBP describes a State’s data management challenges, vision and 

mission for safety data, framework for data governance, and actions for improving its State Safety Data System. 

Data business planning is a relevant topic as State DOTs work to advance their capabilities for safety data 

collection, integration, and analysis to support program planning and performance management. Effective data 

management practices are necessary to integrate safety related data and make it readily available to support safety 

data analysis. However, State DOTs must coordinate with a variety of internal and external partners who manage 

safety data contributing programs. Data business planning provides a framework to enhance this coordination and 

to identify action items for improving safety data management processes.

The Guide describes steps, supporting actions, and key outcomes associated with each step in developing a Safety 

DBP. The steps in the Guide are flexible in implementation. States are encouraged to conduct the steps most 

relevant to their needs as they develop and implement their own Safety DBPs. States do not have to conduct all the 

supporting actions described in the Guide to achieve their goals.

17. Key Words

Safety data, roadway inventory data, traffic data, 

crash data, data management, data governance, 

data business plan

18. Distribution Statement

No restrictions

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

174

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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TABLE OF CONTENTS

INTRODUCTION ....................................................................................... 1

SAFETY DATA BUSINESS PLANNING – WHY IS IT IMPORTANT?............... 2

DATA BUSINESS PLANNING TERMS................................................................... 8

AUDIENCE FOR THE GUIDE................................................................................11

OVERVIEW OF THE GUIDE ..................................................................................13

STEP 1. PLAN FOR SAFETY DATA MANAGEMENT 

AND GOVERNANCE ...............................................................................19

IDENTIFY STAKEHOLDERS ..................................................................................19

Core Stakeholders................................................................................................19

Stakeholders for Safety Data ..............................................................................19

Governance Stakeholders....................................................................................22

ENGAGE STAKEHOLDERS ...................................................................................22

DEFINE SAFETY DATA MANAGEMENT CHALLENGES ................................28

RESEARCH STATE EFFORTS IN DATA MANAGEMENT AND 

GOVERNANCE.........................................................................................................30

ESTABLISH VISION AND MISSION FOR SAFETY DATA 

MANAGEMENT........................................................................................................31

Vision......................................................................................................................31

Mission ...................................................................................................................32

DEVELOP OUTCOME STATEMENT ...................................................................32

SUMMARY.................................................................................................................33

STEP 2. ASSESS CURRENT STATE SAFETY DATA SYSTEM...........35

IDENTIFY DATA SYSTEMS TO INCLUDE IN THE ASSESSMENT ................35

DOCUMENT CURRENT BUSINESS PROCESSES .............................................40

RESEARCH AND SUMMARIZE CURRENT AND PAST ASSESSMENT 

EFFORTS ...................................................................................................................42

UPDATE PAST ASSESSMENTS ............................................................................44

CONDUCT CAPABILITY MATURITY ASSESSMENT.......................................45

Select Assessment Tool.......................................................................................46

Assess Current Level of Capability Maturity.....................................................49

Determine Target Level of Maturity Desired ...................................................49

Identify Gaps .........................................................................................................51
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SUMMARY.................................................................................................................51

STEP 3. ESTABLISH A DATA GOVERNANCE PROGRAM................53

DATA MANAGEMENT AND GOVERNANCE DEFINED..................................53

DEVELOP DATA PRINCIPLES ..............................................................................55

DEVELOP A GOVERNANCE MODEL ..................................................................56

ESTABLISH EOLES AND RESPONSIBILITIES FOR MANAGEMENT 

AND GOVERNANCE...............................................................................................59

DEVELOP IT PROJECT GOVERNANCE..............................................................62

DEVELOP DATA GOVERNANCE DOCUMENTATION...................................64

SUMMARY.................................................................................................................66

STEP 4. IDENTIFY NEEDS FOR SAFETY TOOLS AND 

TECHNOLOGY.........................................................................................69

IDENTIFY NEEDS FOR IMPROVED TECHNOLOGY........................................69

DEVELOP PLAN FOR IMPROVED USE OF TOOLS..........................................70

SUMMARY.................................................................................................................75

STEP 5. DEVELOP ACTION PLAN........................................................76

SUMMARIZE GAPS AND IMPROVEMENTS .......................................................76

IDENTIFY PRIORITIES............................................................................................77

DEVELOP SAFETY DATA ACTION PLAN .........................................................81

DEVELOP ROADMAP FOR IMPLEMENTATION...............................................81

SUMMARY.................................................................................................................87

STEP 6. DOCUMENT THE SAFETY DBP .............................................88

DOCUMENT THE SAFETY DATA BUSINESS PLAN........................................88

SUMMARY.................................................................................................................89

STEP 7. IMPLEMENT AND SUSTAIN THE SAFETY DBP..................90

ASSIGN RESPONSIBILITY .....................................................................................90

ESTABLISH PERFORMANCE METRICS ..............................................................90

IMPLEMENT THE SAFETY DBP ...........................................................................91

CONDUCT TRAINING ...........................................................................................92

MONITOR PROGRESS ...........................................................................................92

COMMUNICATE CHANGES .................................................................................93

SUMMARY.................................................................................................................93
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REFERENCES ...........................................................................................95

APPENDIX A. SAFETY DATA ELEMENTS ......................................... 98

APPENDIX B. SUMMARY OF CASE STUDIES .................................104

MICHIGAN DOT SAFETY DATA PROCESSES AND GOVERNANCE 

PRACTICES, CASE STUDY FHWA-SA-15-059 .................................................104

Summary .............................................................................................................104

Applicability to Other States ............................................................................105

Link.......................................................................................................................105

NEW HAMPSHIRE DOT SAFETY DATA SYSTEMS AND PROCESSES, 

CASE STUDY FHWA-SA-15-058 .........................................................................106

Summary .............................................................................................................106

Applicability to Other States ............................................................................106

Link.......................................................................................................................107

UTAH DOT SAFETY DATA PROCESSES AND GOVERNANCE 

PRACTICES, CASE STUDY FHWA-SA-15-060 .................................................108

Summary .............................................................................................................108

Applicability to Other States ............................................................................109

Link.......................................................................................................................109

APPENDIX C. SUMMARY OF PILOT STUDIES ................................. 110

KANSAS DOT DATA BUSINESS PLAN EXECUTIVE SUMMARY................110

WASHINGTON STATE DATA BUSINESS PLAN EXECUTIVE 

SUMMARY...............................................................................................................114

APPENDIX D. EXAMPLE SURVEY INSTRUMENT ON SAFETY 

DATA CHALLENGES ............................................................................118

APPENDIX E. EXAMPLE SURVEY INSTRUMENT ON

GOVERNANCE INITIATIVES...............................................................125

APPENDIX F. STATE SAFETY DATA SYSTEM CAPABILITY 

MATURITY MODEL ...............................................................................128

AREA 1: SAFETY DATA COLLECTION AND TECHNICAL 

STANDARDS ..........................................................................................................130

Element 1A: Completeness..............................................................................130

Element 1B: Timeliness ....................................................................................131
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Element 1C: Accuracy ......................................................................................132

Element 1D: Uniformity or Consistency.........................................................133

AREA 2: DATA ANALYSIS TOOLS & USES ....................................................134

Element 2A: Network Screening (Data).........................................................134

Element 2A: Network Screening (Method)....................................................135

Element 2B: Diagnosis ......................................................................................136

Element 2C: Countermeasure Selection ........................................................137

Element 2D: Evaluation (Project-Level).........................................................138

Element 2D: Evaluation (Program-Level) ......................................................139

Element 2E: Accessibility..................................................................................140

AREA 3: DATA MANAGEMENT AND GOVERNANCE ................................. 141

Element 3A: People...........................................................................................141

Element 3B: Policies..........................................................................................142

Element 3C: Technology ..................................................................................143

AREA 4: DATA INTEROPERABILITY AND EXPANDABILITY ....................144

Element 4A: Interoperability ...........................................................................144

Element 4B: Expandability ...............................................................................145

Element 4C: Integration ...................................................................................146

APPENDIX G. DATA GOVERNANCE 101..........................................147

DATA MANAGEMENT LIFE CYCLE..................................................................147

IDENTIFYING THE NEED FOR DATA MANAGEMENT AND DATA 

GOVERNANCE.......................................................................................................148

Identify and Document Needs ..........................................................................148

Demonstrate Return on Investment through a Governance Model ............ 149

Communicate the Need to Stakeholders........................................................150

Obtain Agreement on Need .............................................................................150

DATA MANAGEMENT PRACTICES ..................................................................151

APPENDIX H. STATE PRACTICES FOR IT PROJECT 

PRIORITIZATION AND SELECTION..................................................152

PROCESSES FOR SUBMITTING IT PROJECT REQUESTS ............................152

Annual Call for Projects (Michigan DOT) .......................................................152

IT Strategic Plan (Texas DOT).........................................................................152

PROJECT PRIORITIZATION AND SELECTION PROCESS ...........................153
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Pre-Review of IT Project Requests and Prioritization Meeting 

(Michigan DOT)..................................................................................................153

Bottoms Up Process (Montana DOT) .............................................................153

Decision Lens Software Methodology (Pennsylvania DOT)..........................154

Project Portfolio Management Methodology (New Mexico DOT) ..............155

IT PROJECT SELECTION CRITERIA..................................................................156

Prioritization Based on IT Priorities (Michigan DOT)...................................156

Decision Lens Software Criteria Hierarchy (Pennsylvania DOT).................157

Project Portfolio Management (PPM) Criteria (New Mexico DOT) ...........157

KEY COMPONENTS OF GOOD PRACTICES ..................................................158

APPENDIX I. GLOSSARY OF DATA BUSINESS PLANNING 

TERMS......................................................................................................160
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LIST OF TABLES

Table 1. Process for developing a safety Data Business Plan. ......................... 16

Table 2. Example stakeholder registry. ............................................................. 22

Table 3. Example stakeholder engagement plan.............................................. 24

Table 4. Inventory of safety data systems. ......................................................... 36

Table 5. Current and past assessments. ............................................................ 45

Table 6. Data quality standards........................................................................... 47

Table 7. Data governance roles and responsibilities. ....................................... 60

Table 8. Resources for identifying improvements. ........................................... 77

Table 9. Example risk assessment. ..................................................................... 78

Table 10. Example safety data action plan. ....................................................... 81

Table 11. Example roadmap for implementation. ........................................... 82

Table 12. Example performance metrics........................................................... 90

Table A.1. Summary of Model Inventory of Roadway Elements Fundamental 

Data Elements....................................................................................................... 98
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LIST OF FIGURES

Figure 1. Flow chart. Data, information, and knowledge hierarchy................. 9

Figure 2. Flow chart. State safety data system capabilities............................. 11

Figure 3. Flow chart. Linking of safety data....................................................... 11

Figure 4. Diagram. Process for developing and implementing a safety Data 

Business Plan. ........................................................................................................ 15

Figure 5.Web chart. Safety community of interest. ......................................... 21

Figure 6. Diagram. Unified modeling language use case schematic............... 40

Figure 7. Diagram. Example use case diagram for regional traffic collection.

................................................................................................................................. 41

Figure 8. Callout. Example use case narrative.................................................. 42

Figure 9. Graph. Capability assessment results. ............................................... 50

Figure 10. Flow chart. Data governance activities............................................ 54

Figure 11. Organizational chart. General data governance model. ............... 57

Figure 12. Organizational chart. Safety data governance model. .................. 58

Figure 13. Photo. LiDAR imagery....................................................................... 72

Figure 14. Venn diagram. Data governance and knowledge management... 74

Figure 15. Matrix. Level of risk............................................................................ 80

Figure 16. Gantt chart. Implementation roadmap........................................... 86

Figure C-1. Diagram. Kansas Department of Transportation Safety Data 

Governance Model. ............................................................................................. 112

Figure C-2. Gantt chart. Implementation roadmap. ...................................... 113

Figure C-3. Diagram. WSDOT safety data governance model. ...................116

Figure C-4. Gantt chart. Implementation roadmap. ...................................... 117

Figure G-1. Flow chart. Data management life cycle. .................................... 147

Figure G-2. Organizational chart. Example data governance model........... 149
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ACRONYMS

Acronym Term

DBP Data Business Plan

DOT Department of Transportation

EMS Emergency Medical Services

ETL Extract, Transform, and Load

FAST Fixing America’s Surface Transportation Act

FDE Fundamental Data Element

FHWA Federal Highway Administration

GIS Geographic Information System

HSIP Highway Safety Improvement Program

IT Information Technology

LiDAR Light Detection and Ranging

LRS Linear Referencing System

MAP-21 Moving Ahead for Progress in the 21st Century Act

MIRE Model Inventory of Roadway Elements

MMUCC Model Minimum Uniform Crash Criteria Guideline

MPO Metropolitan Planning Organization

NHTSA National Highway Traffic Safety Administration

SSDS State Safety Data System

TRCC Traffic Records Coordinating Committee
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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INTRODUCTION

State Departments of Transportation (DOT), Metropolitan Planning Organizations (MPO), 

State Highway Safety Offices, and other transportation agencies are responsible for planning, 

designing, operating, and maintaining safe transportation facilities. High quality data used to 

document crash activity, roadway elements, and traffic volumes are essential to ensure a safe 

transportation system. 

The Moving Ahead for Progress in the 21st Century Act (MAP-21) called for advancing the 

capabilities of States for safety data collection, integration, and analysis to support program 

planning and performance management and continued to allow data improvement activities as 

an eligible Highway Safety Improvement Program (HSIP) expense [23 U.S.C. 148 (a)(4)(B)(xiv)]. 

MAP-21 acknowledged the importance of using multiple data sources to understand highway 

safety problems and to make effective decisions regarding resource allocation for highway 

safety [23 U.S.C. 148 (c)(2)(A)]. To do this, State safety data systems should be sufficient to 

guide the HSIP and Strategic Highway Safety Plan (SHSP) processes, including analyses and 

evaluations identified in 23 U.S.C. 148 and 23 CFR Part 924. The Fixing America’s Surface 

Transportation (FAST) Act continued the provisions of MAP-21 and added a provision related 

to data collection on unpaved roads. [23 U.S.C. 148(k)]

There are significant challenges when collecting, integrating, and analyzing data to support safety 

programming and performance management. Safety data management poses unique challenges 

within State DOTs because extensive coordination with external stakeholders is required.

Stakeholders include law enforcement, departments of highway safety and motor vehicles, the 

State court system, departments of health, local agencies, MPOs, and a State’s traffic records 

coordinating committee (TRCC). 

Another challenge that affects a DOT’s safety program is they are not the stewards of all the 

data required for their programs. Data for safety analysis are collected by many different 

entities, some internal and some external. For example, crash data are largely collected by law 

enforcement and then collated by another entity, typically other State offices, academia, 

contractors, or others. After a quality control check, the data may reside on a DOT system or 

“in the cloud” where the DOT has access to it. Roadway inventory data are typically collected 

by a non-safety business unit for State maintained roads. For local roads, the DOT may need to 

coordinate with local government agencies and MPOs to collect data needed to meet the all 

public roads requirement of the HSIP Final Rule shown in the Guidance on State Safety Data 

Systems (SSDS). FHWA Guidance on State Safety Data Systems, March 15, 2016. Available at: 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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(https://safety.fhwa.dot.gov/legislationandpolicy/fast/docs/ssds_guidance.pdf.) Traffic data are 

also collected and maintained in one or more non-safety business units. Finally, the common 

highway basemap component required to link safety data sources is rarely under the control or 

influence of the DOT Safety Program.

In a recent peer exchange, State DOT representatives identified data management as a 

challenge where the Federal Highway Administration (FHWA) could lend assistance.

(Transportation Research Circular E-C196, Improving Safety Programs Through Data 

Governance and Data Business Planning, A Peer Exchange, March 3-4, 2015, Washington D.C.

Available at: http://onlinepubs.trb.org/onlinepubs/circulars/ec196.pdf) FHWA has initiated a 

project on data business planning, which has proven effective in other industries and sectors of 

transportation in improving data access, quality, and management. 

This Guide provides practical instructions for State DOTs 

to follow in developing, implementing, and maintaining a 

Safety Data Business Plan (DBP). A Safety DBP describes a 

State’s management challenges, vision and mission for safety 

data, framework for data governance, and actions for 

improving their SSDS. Developing a DBP at a State level will 

lead to improved management and governance of safety

data to support enhanced decision-making. Users may apply 

this Guide to develop a Safety DBP consistent with other 

enterprise (i.e., agency-wide) data management efforts. 

A Data Business Plan: 

• Guides data 

management practices

• Contains vision, goals, 

objectives, and actions

• Focuses on data systems 

and business processes

• Improves business 

operations efficiencies

This introduction documents the need for data business 

planning in the safety community, describes common terms, and includes a high-level 

description of the Guide. 

SAFETY DATA BUSINESS PLANNING – WHY IS IT IMPORTANT?

Data or information business planning is a relevant topic within State DOTs as they work to 

advance their capabilities for safety data management, performance management, and asset 

management systems. Technology for managing data is constantly improving, offering greater 

chances for more informed decision-making and better targeted investments. Despite these 

opportunities, States continue to face legacy, ‘silo’ data systems, which results in technological 

and institutional challenges related to safety data system management. 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Effective data management practices are necessary to integrate crash data with roadway and 

traffic volume data to investigate systemwide and site-specific conditions. State DOTs must 

coordinate with a variety of internal and external partners that manage data systems that 

support safety data analytics. Because data are collected by so many different programs, using 

different standards, and for different purposes, it is critical that data business planning spans the 

gaps between multiple data systems. Safety DBP efforts provide a framework to enhance this 

coordination. 

Specific data challenges fall within three categories: system (for example, data collection, 

access, interoperability, quality of data, storage, and documentation); technology (for example,

data tools, database design, system improvements, and system interfaces); and institutional

(for example, data management and governance, ownership, coordination, knowledge 

management, training, and resource availability). State DOTs experience challenges within these 

categories: 

System Challenges

• Data Collection – Budget restrictions may limit agencies’ ability to collect data elements 

required for safety analysis on all public roads. Nationally accepted data guidelines such as 

Model Inventory of Roadway Elements (MIRE) – Fundamental Data Elements (FDEs) and 

Model Minimum Uniform Crash Criteria (MMUCC) provide criteria for a minimum set of 

roadway and crash data, respectively. The proposed tools and types of analysis will also 

dictate the required set of data. 

• Data Access – Often, data is stored in unrelated systems and databases, or in legacy 

database systems. Due to current data storage methods, MPOs, local agencies, and other 

stakeholders may not have access to data or lack technical skills to use it in the native

format. Organizations may lack staff with the skills to use existing data sources and data 

analytics tools. Another challenge relates to deciding which (possibly sensitive) data should 

be accessible by the public.

• Data Interoperability – Agencies may find it challenging to link its safety data systems

due to differing database constructs. As noted in FHWA’s Data Integration Primer, legacy 

systems may utilize flat file, network, or hierarchical database structures and contain data in 

many different formats. This creates challenges in performing the Extract, Transform, and 

Load (ETL) process to move data from their native systems into a common framework to 

support safety data analytics. It also reduces access to data across systems. Comprehensive, 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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enterprise-wide data collection and data quality standards are necessary to ensure data 

interoperability across disparate data systems.

• Data Quality and Validation – Inconsistent or undocumented data collection, varying

location accuracy standards, reporting, and quality assurance techniques often compromise 

data quality. A crash record may also contain conflicting information (for example, number 

of non-motorists is none, while the contributing factor is a non-motorist action such as a 

jaywalking pedestrian). Regular verification of data quality, including temporal and spatial 

resolution, is essential to establish the integrity of input data and the subsequent analyses.

Because safety data originate from disparate sources and are generated by distinctly 

different data collection programs, the originating program needs to assess and enforce data 

quality needs. However, the standards must be developed across all data collection 

programs. For example, if spatial and temporal resolution and accuracy do not match across 

all data sources, analytical results cannot be trusted. Developing data resolution and 

accuracy standards across all data sources and enforcing them in their native datasets is the 

only way to ensure data interoperability and integrity.

• Data Storage and Delivery – Comprehensive data collection methods require more 

storage for data; one example is the use of remote sensing technology. As more agencies 

move to the cloud to accommodate ever-increasing data, new challenges occur with 

storage, processing, security, and data sharing.

• Documentation and Metadata – Agencies may have poor or missing documentation for 

its safety data systems. This makes it difficult for users to interpret data correctly. Lack of 

system documentation could also result in a loss of knowledge as the workforce turns over. 

Time is a critical factor and is often overlooked. The disparate data systems that contribute 

to safety data analysis are often out of sync. For example, as roads are built and realigned, 

pavement, crash, and other data must reflect those changes. This can only be done if all the 

databases are tracking and documenting time.

Technology Challenges

• Data Tools – States may lack statewide data tools that allows users to access and analyze 

safety data on all public roads. Safety data analysis tools may be limited to state routes and 

only certain roadway elements. Data tools should be comprehensive in scope and provide: 

1) data discovery, which allows users to access, prepare, and integrate safety data; 2) an ETL 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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process, which transforms data into the proper format for safety analysis; and 3) analytical 

tools to conduct safety data analysis on all public roads. 

• Data Reports – All users need the ability to access, search, query, analyze, visualize, and 

generate reports from safety data. The ability to generate ad hoc customized queries and 

reports, as well as standard “canned” reports is important for all users. Many DOT and 

local agency users lack the technical training, resources, or time to manipulate data. Reports 

help standardize comparisons by ensuring use of consistent data definitions. 

• Database Design – Database design is typically dictated by product vendors and 

consultants. With statewide Information Technology (IT) consolidation, database 

management is often controlled outside of the DOT. A service oriented architecture is the 

technical solution for accessing multiple data systems with different database designs. Using 

service oriented architecture; data systems can remain intact and just provide a web access 

point. This avoids costly DOT-specific vendor customization, data duplication, and other 

data management issues.

• System Improvements – Data systems may need improved functionality to support 

advanced analysis methods and to enable data sharing and reporting. Agencies must evaluate 

current systems and data structures to determine the investment needed to meet these 

requirements. Because existing data systems often adequately serve their business owners, 

there may be resistance by the business owners (whose programs fund these systems) to 

“improvements” to “their” systems, for someone else’s use. A department-level DBP, 

employing service oriented architecture, and a strong DOT-level governance program can 

overcome this.

• System Interfaces – Many organizations are developing solutions to automate data 

discovery and ETL between data systems. This may require investment in new software and 

hardware. It will likely require updating business processes, and therefore, training and 

support services. Employing service oriented architecture allows States to avoid the cost of 

modifying and maintaining customized versions of existing systems. The interface can be 

designed to find and access existing data and move it into a form that new or existing 

analytical resources can consume. In States with statewide IT consolidation, system 

improvements are subject to standards and review by entities outside of the DOT. DOTs 

may be challenged to communicate their data needs to ITs for development and 

implementation. 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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• Knowledge Management System – Agencies may have poor or missing documentation 

for its data systems, they may not have it centralized in one place, or it may be “in the 

cloud.” This makes it difficult for users to interpret data correctly. Lack of system 

documentation could also result in a loss of knowledge as the workforce turns over. In 

States with IT consolidation, system documentation may be controlled and maintained by 

personnel outside of the DOT. In these cases, DOTs may have little or no access to system 

documentation. 

• Statewide IT Consolidation – Some States are consolidating IT resources at a statewide 

level rather than having individual IT offices within each Department. Statewide IT 

consolidation may take different forms in different States. Some DOTs have retained much 

of their pre-consolidation IT capabilities, and some have not. DOTs may not have control 

over the technology used to solve their information challenges. 

Institutional Challenges

• Data Management and Governance – Roles and responsibilities for quality, access, and 

distribution of data are not always clear. Common concerns include firewalls, data silos, and 

trust issues between data owners and IT representatives. 

• Data Ownership – Data “ownership” once processed or shared is often unclear. For 

example, in many States, the State Highway Patrol owns the official crash record for legal 

purposes, but DOTs process this data to improve its usefulness for engineering purposes. 

• Transmission, Sharing, and Exchange of Safety Data – Formal data sharing 

agreements can facilitate sharing of safety data between offices within a DOT as well as with 

external stakeholders. Data standards, metadata, and data dictionaries are key to achieving 

consistency and facilitating integration of data from multiple data sources. Once these are 

implemented through a DBP, transmission, sharing, and exchange are solvable technical 

issues.

• Training – Training is essential to improve staff knowledge on data policies, procedures, 

processes, and tools for safety analysis and reporting. States should also train external safety 

stakeholders such as MPOs and local agencies as appropriate. Users of existing legacy 

systems may already have training on the use of those systems. Because safety data analytics 

require data from multiple sources, efforts should be made to utilize and build on existing 

training programs. 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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• Funding –Dedicated staff time is required to integrate safety data from multiple agencies.

However, the benefit and cost of doing so is not always easy to determine. One pilot site 

noted that high level experts familiar with data integration needs and benefits find it difficult 

to clearly articulate to executives the scope of data issues or how safety data impact the 

enterprise. This makes it challenging to obtain ongoing commitment for safety data 

improvements, particularly when staff reductions and cuts in resources occur. 

• Resources – The FHWA Office of Safety offers support to States to improve their 

capabilities in collecting, integrating, and analyzing data. Resources available to States include

the Roadway Safety Data Program Toolbox, the MIRE, Highway Safety Information System, 

Crash Data Improvement Program, the United States Roadway Safety Data Capabilities 

Assessment, and the Roadway Data Improvement Program. (FHWA Office of Safety, 

Roadway Safety Data Program website, http://safety.fhwa.dot.gov/rsdp/.); 

(https://safety.fhwa.dot.gov/rsdp/mire.aspx.); (https://safety.fhwa.dot.gov/rsdp/rdip.aspx.) The 

Office of Safety is developing data management guides in these areas: State and local data 

integration, Safety Data Business Planning for Data Management, and State Traffic Records 

Coordinating Committee Noteworthy Practices. (https://safety.fhwa.dot.gov/

rsdp/data_activities_state.aspx.); (https://safety.fhwa.dot.gov/rsdp/manage.aspx.); 

(https://safety.fhwa.dot.gov/rsdp/downloads/trcc_noteworthy.pdf.) These projects may 

include accompanying technical assistance. 

State DOTs can apply data business planning to address data management and data governance 

challenges related to operations, asset management, and transportation planning. Several

resources, including NCHRP 666, document this approach. This Guide adapts and customizes

these proven approaches for safety data business planning. (NCHRP 666: Target-Setting 

Methods and Data Management to Support Performance-Based Resource Allocation by 

Transportation Agencies, Volume II: Guide for Target-Setting and Data Management, 2010.)
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

8 

DATA BUSINESS PLANNING TERMS

NCHRP 666 defines data management and data governance as follows: (NCHRP 666: TargetSetting Methods and Data Management to Support Performance-Based Resource Allocation by 

Transportation Agencies, Volume II: Guide for Target-Setting and Data Management, 2010.)

Data management is the development, execution, and oversight of architectures, policies, 

practices, and procedures to manage the information lifecycle needs of an enterprise in an 

effective manner as it pertains to data collection, storage, security, data inventory, analysis, 

quality control, reporting, and visualization. 

Data governance is defined as the execution and enforcement of authority over the 

management of data assets and the performance of data functions. The management of data 

assets for an organization or state DOT is usually accomplished through a data governance 

board or council. This role is critical in successfully managing data programs that meet business 

needs and in supporting a comprehensive data business plan for the organization.

Data governance is a critical element of data management and data business planning. It 

provides: 

• A central focus to identify and control the collection, storage and sharing of data;

• Identification of stakeholder roles and responsibilities;

• Enterprise data standards, data dictionaries, and metadata;

• Standard data quality assurance processes;

• Knowledge management processes for sharing and retaining critical organizational 

knowledge related to data and information; and 

• Alignment of data program investments with agency needs. 

NCHRP 754 defines data, information, and knowledge as follows: (Cambridge Systematics, Inc. 

NCHRP Report 754: Improving Management of Transportation Information, Transportation 

Research Board, 2013. Available at http://onlinepubs.trb.org/onlinepubs/nchrp/

nchrp_rpt_754.pdf.)

Data is a representation of facts, concepts, or instructions in a formalized manner suitable for 

communication, interpretation, or processing by humans or computers.
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

9 

Information is data and documents given value through analysis, interpretation, or 

compilation in a meaningful form.

Knowledge is information combined with experience, context, and interpretation that make it 

possible to understand and draw implications from both data and information. Knowledge 

consists of data and information organized and processed to convey understanding, experience, 

accumulated learning, and expertise as they apply to a current problem or activity.

Figure 1 shows the relationship between data, information, and knowledge.

Figure 1. Flow chart. Data, information, and knowledge hierarchy. 

Source: Adapted from Minnesota DOT Data Business Plan, 2008. Available at 

https://www.dot.state.mn.us/tda/databusinessplan.docx. 

FHWA’s Guidance on SSDSs defines a SSDS and safety data as follows: 

As part of its State highway safety improvement program, a State shall have in place a safety 

data system that can be used to perform analyses supporting the strategic and performancebased goals in the SHSP and HSIP. [23 U.S.C. 148 (c)(2)]. This section provides guidance on 

the capabilities a State’s safety data system should have in order to support analyses and 

evaluations in 23 U.S.C. 148, including: 1) types of roadways, 2) types of data, 3) geolocation 

of safety data to a common highway basemap, 4) analysis and evaluation capabilities, and

5) the subset of Model Inventory of Roadway Elements (MIRE) to be collected. 

Types of Roadways: Consistent with the purpose and scope of the HSIP, a State shall have in 

place a safety data system to perform safety problem identification and countermeasure 

analysis. [23 U.S.C. 148 (c)(2)(A)]. The statute also specifies that a State shall advance the 

capabilities of the State for data collection, analysis, and integration in a manner that includes 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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all public roads, including non-State-owned public roads and roads on tribal land in the State. 

[23 U.S.C. 148 (c)(2)(D) and (D)(ii)]. Public road means “any road under the jurisdiction of 

and maintained by a public authority and open to public travel.” [23 CFR 460.2(a)].

Safety data means crash, roadway, and traffic data on a public road, and, includes, in the 

case of a railway-highway grade crossing, the characteristics of highway and train traffic, 

licensing, and vehicle data. [23 U.S.C. 148 (a)(9)]. Data on rail-highway grade crossing train 

traffic are available through the Federal Railroad Administration crossing inventory. 

(http://safetydata.fra.dot.gov/OfficeofSafety/PublicSite/Crossing/Crossing.aspx.)

Crash, roadway, and traffic data should be linkable by geolocation, i.e., a unique location 

identifier, on a highway basemap, which is defined as “a representation of all public roads that 

can be used to geolocate attribute data on a roadway.” [23 U.S.C. 148 (a)(2)]. States should 

put in place methodologies to assure that the location of crashes, roadway elements, and traffic 

data are consistent with the most current basemap. 

The FHWA Office of Highway Policy Information and Office of Planning, Environment, and 

Realty issued the Memorandum, Geospatial Network for All Public Roads on August 7, 2012. 

This Memorandum identified a Highway Performance Monitoring System (HPMS) requirement 

for States to update their Linear Referencing System to include all public roadways within the 

State by June 15, 2014, in accordance with the HPMS information collection approval from the 

Office of Management and Budget (2125-0028). To date, the majority of States have complied 

with this requirement. This Linear Referencing System is a means to geolocate all safety data on 

a common highway basemap that includes all public roads.

The FHWA developed the MIRE, a recommended listing of roadway and traffic elements critical 

to safety management, as a guide to help transportation agencies improve their roadway and 

traffic data inventories. MIRE was developed to enhance a State’s ability to use advanced 

safety analyses such as presented in the Highway Safety Manual. 

MAP-21 required the Secretary to establish a subset of the MIRE that are useful for the 

inventory of roadway safety and ensure that States adopt and use the subset to improve data 

collection. [23 U.S.C. 148(f)(2)]. The FHWA established a subset of the MIRE as part of the 

HSIP Final Rule changes to 23 CFR Part 924, effective April 14, 2016. This subset is referred to 

as the fundamental data elements (FDEs). The FDEs are categorized by roadway functional 

classification and surface type and include three tables, one each for non-local paved roads, 

local paved roads, and unpaved roads. They are further refined into subcategories of data 

elements for road segments, intersections and interchanges for non-local paved roads.
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Figure 2 depicts how the required capabilities for a SSDS support analysis and evaluations for

the SHSP and HSIP. Figure 3 illustrates how a State’s crash, roadway, and traffic data are 

linkable through geolocation on a common highway basemap. Appendix A summarizes the 

MIRE FDE requirements for non-local paved roads, local paved roads, and unpaved roads. 

Figure 2. Flow chart. State safety data system capabilities. 

Figure 3. Flow chart. Linking of safety data. 

AUDIENCE FOR THE GUIDE

This Guide assists States to develop a Safety DBP. The primary audience is the people leading 

safety data business planning efforts at State DOTs. This includes State safety engineers, safety 

program managers, IT professionals, planners, and data managers that use, collect, or manage 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

12

the various sources of data that contribute to safety data analysis and decision-making. 

Members of State TRCCs, local agencies, and MPOs will find its content useful to define data 

quality criteria and integration standards for traffic records data. This Guide targets State DOTs 

and uses the terms “State” and “State DOT” interchangeably. 

A State may develop its Safety DBP in coordination with its TRCC. The TRCC has these 

responsibilities:

TRCC have specific review and approval authority with respect to State highway safety data and

traffic records systems, technologies used to keep such systems current, TRCC membership, the 

TRCC coordinator, changes to the State’s multi-year Strategic Plan, and performance measures 

used to demonstrate quantitative progress. It also charges a TRCC with considering; 

coordinating and representing to outside organizations the views of the State organizations 

involved in the administration, collection, and use of highway safety data and traffic records. (23 

CFR 1200.22(b)(2))

The Safety DBP does not supplant the requirement for a State traffic records strategic plan. 

The State should use its State traffic records strategic plan as a resource to develop the Safety 

DBP, and similarly, the State could identify potential traffic records improvement projects from 

the DBP recommendations. 

Stakeholder involvement is critical throughout each step. The DBP development process is as 

important as the outcome itself. Developing a Safety DBP accomplishes the following: 

• Helps everyone in the life-cycle of safety data, from data collection to data distribution, and 

decision-makers understand data management and governance processes. This includes 

roles and responsibilities at all levels, including analysts, managers, and technical staff;

• Helps business areas understand how they can leverage data across the enterprise to 

deliver their programs supporting the overall needs of the agency;

• Promotes collaboration with IT staff, leading to improvements in software and hardware 

capabilities that support crucial program areas; 

• Identifies how each employee’s responsibilities link to the agency’s mission and goals, which 

helps them understand their role in the overall success of their program area and the DOT; 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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• Helps an agency identify risks associated with not having quality, timely data that they can 

use to demonstrate and justify the benefits of expending resources on its SSDS in the 

future; and

• Raises the perception that data is reliable and sufficient to support decision-makers and the 

agency’s mission.

OVERVIEW OF THE GUIDE 

This Guide documents each of the seven steps for developing and implementing a Safety DBP. 

The steps and their purposes are shown in Figure 4. 
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Table 1 summarizes supporting actions and key outcomes associated with each step in 

developing a Safety DBP. The work products of steps 1 through 5 culminate in the development 

of a Safety DBP in Step 6. A State implements the Safety DBP in Step 7. 

The steps in the Guide are flexible in implementation. States are encouraged to conduct the 

steps most relevant to their particular needs as they develop and implement their own Safety 

DBPs. States do not have to conduct all the supporting actions described in the Guide to 

achieve their goals.

The Guide also documents the results of case studies and pilot projects conducted as part of 

the project: 

• Case Studies. In the first phase of the project, the team researched State noteworthy 

practices in safety data management and governance. Four States – Alaska, Michigan, New 

Hampshire, and Utah – participated in detailed case studies documenting how their data 

management and IT practices have improved safety data systems and processes. Appendix B 

provides a summary of published case study results. The detailed case studies are available 

on the FHWA Roadway Safety Data Program web page at 

https://safety.fhwa.dot.gov/rsdp/safety_casestudies.aspx. 

• Pilot Studies. FHWA selected two agencies – Kansas DOT and Washington State DOT – 

to participate in pilot studies to test the Guide concepts through the development of State 

Safety DBPs. Appendix C provides a summary of pilot study results.
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Figure 4. Diagram. Process for developing and implementing a safety Data Business 

Plan.
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Table 1. Process for developing a safety Data Business Plan.

Steps Supporting Actions Key Outputs / Work 

Products

Step 1 –

Plan for 

Safety Data 

Management 

and 

Governance

• Identify stakeholders for 

safety data systems 

• Engage stakeholders 

• Define safety data 

management challenges

• Research State efforts in 

data management and 

governance 

• Establish vision and mission 

for safety data management

• Develop outcome statement

• Stakeholder registry

• Community of interest 

diagram

• Stakeholder engagement

plan 

• Survey instrument on safety 

data management challenges

• Problem statement

• Survey instrument on data 

governance initiatives

• Vision and mission for safety 

data governance

• Outcome statement for the 

Safety DBP

Step 2 – 

Assess 

Current 

SSDS

• Identify data systems to 

include in the assessment

• Document current business 

processes

• Document spatial, temporal, 

and data resolution and 

accuracy standards in each 

data source

• Research and summarize 

current and past assessment 

efforts 

• Update past assessments

• Conduct capability maturity 

assessment

• Identification of data systems 

for the assessment

• Use case diagrams and 

accompanying narratives on 

business processes and 

workflows for safety data 

systems

• Summary of similarities and 

differences in data resolution 

and accuracy standards 

across all data

• Summary of past assessment

recommendations in matrix 

form

• Update on State progress in 

implementing past 

assessment 

recommendations

• Assessment tools 

• Assessment of current and 

desired levels of maturity for 

each dimension of the 

capability maturity model

• Identification of actions 

needed to advance from 

current to desired capability
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Table 1. Process for Developing a Safety Data Business Plan. (continuation). 

Steps Supporting Actions Key Outputs / Work 

Products

Step 3 – 

Establish a 

Governance 

Program

• Develop data principles

• Develop a governance 

model

• Establish roles and 

responsibilities for 

governance 

• Develop IT project 

governance 

• Develop governance 

documentation

• Core data principles

• Governance model

• Roles and responsibilities 

• IT project selection process

• Data governance charter

• Data governance manual 

• Data catalog

• Business terms glossary

• Common resolution and 

accuracy standards for 

linking data sources

Step 4 –

Identify 

Needs for 

Safety Tools 

and 

Technology

• Identify needs for improved 

technology

• Develop plan for improved 

use of tools

• Summary of needs and 

weaknesses related to safety 

tools and technology

• Plan for enhancing or 

replacing safety tools and 

technology

• Tool training needs and 

opportunities defined

Step 5 – 

Develop 

Action Plan 

• Summarize gaps and 

improvements

• Identify priorities

• Develop action plan

• Develop roadmap for 

implementation

• Summary of system, 

technology, and institutional

gaps

• Priorities for addressing gaps

• Action plan

• Roadmap for 

implementation

Step 6 – 

Document 

the Safety 

DBP

• Document the Safety DBP • Safety DBP
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Table 1. Process for developing a safety Data Business Plan (continuation).

Steps Supporting Actions Key Outputs / Work 

Products

Step 7 – 

Implement 

and Sustain 

the Safety 

DBP

• Assign roles and

responsibility

• Establish performance 

metrics

• Implement the Safety DBP

• Conduct training 

• Monitor progress 

• Communicate changes 

• Designation of governance 

champion or small team to 

guide implementation

• Performance metrics for 

measuring success

• Implementation of the Safety 

DBP

• Training program on data 

governance 

• Progress updates
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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STEP 1. PLAN FOR SAFETY DATA MANAGEMENT

AND GOVERNANCE

The first step in safety data business planning is the initial design for data management and 

governance. This section guides a State to identify the data problems, challenges and issues, 

symptoms, root causes, and opportunities to improve safety data management. Key actions 

include identifying and involving stakeholders, defining safety data management challenges, 

researching other data governance initiatives, and establishing a vision, mission, and goals for 

data business planning. After completing this step, States will understand their current data 

management challenges, data governance efforts, and goals for managing and governing data.

IDENTIFY STAKEHOLDERS 

Core Stakeholders 

Initial planning involves identifying a core team of stakeholders willing to help champion the 

DBP effort. This team should consist of a small group of data managers or data owners whose 

daily tasks involve collecting, maintaining, or updating data elements needed for safety analysis. 

This team will help define challenges associated with managing, governing, and using safety data 

and complete the steps in this Guide. 

Stakeholders for Safety Data

Next, the State should identify and document the stakeholders for safety data. A stakeholder is 

any internal or external person or organization that collects, owns, maintains, uses, interfaces 

with, accesses, or benefits from a safety data system. Stakeholders may include any internal or 

external agency statutorily required to collect, work with, or contribute to a safety data system. 

These stakeholders, collectively known as the Community of Interest, play a vital role in 

identifying needs and business uses for safety data from the perspective of their individual 

offices or agencies (if external to the State DOT). They typically dictate the policies, 

procedures, and business processes associated with a safety data system. 

Internal stakeholders for a safety data system may include traffic safety engineers, safety 

program managers, asset managers, Highway Performance Monitoring System (HPMS) 

managers, design engineers, pavement management and bridge management engineers, planners 

responsible for statewide transportation improvement programs and long-range plans, and data 

managers from other offices that provide safety-related data. External stakeholders are also 

common users of safety data. These may include local and State law enforcement agencies, 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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MPOs, local government agencies (cities/counties), TRCC member agencies, the National 

Highway Traffic Safety Administration (NHTSA), FHWA, other Federal agencies, insurance 

companies, and university research entities. The following bullets briefly describe how some of 

these groups use safety data. 

• State and local transportation agencies safety engineers and planners analyze safety data to 

identify safety problems, recommend appropriate improvements, evaluate the effectiveness 

of implemented improvements, track mandated performance measures, and conduct 

strategic planning to support the Strategic Highway Safety Plan and HSIP. Appropriate data 

management and governance practices are essential for a State to link safety data to a 

common LRS to support these activities. 

• Local and State law enforcement agencies collect crash data and share it with the State

DOT and other agencies. Law enforcement agencies may also use the data internally to 

determine the most effective methods for deploying law enforcement (based on NHTSA’s 

Data Driven Approaches to Crime and Traffic Safety operational model) to improve traffic 

safety. (https://www.nhtsa.gov/staticfiles/nti/ddacts/811185_DDACTS_OpGuidelines.pdf.)

• Local government agencies may collect and maintain traffic volume, roadway inventory, and 

LRS data on local roads. They may provide the data to the State to maintain local roadway 

information in its statewide data system. 

• State TRCCs help develop data definitions and data standards for the collection of traffic 

records data, including the State’s crash, vehicle, driver, roadway, citation and adjudication, 

and injury surveillance data systems. This group does not necessarily use the data, but 

rather supports the State in improving the timeliness, accuracy, completeness, uniformity, 

integration, and accessibility of traffic records data to meet the needs of multiple safety 

program areas. The TRCC also develops a multi-year State Traffic Records Strategic Plan. 

This plan describes planned improvements for its core safety databases, provides an update 

on implementing traffic records assessment recommendations, and includes performance 

measures to demonstrate quantifiable and measurable progress.

Figure 5 illustrates a typical Safety Community of Interest. The shaded oval represents State

DOT safety data managers who may serve on the Core Stakeholder Team. In planning for a 

Safety DBP, the State should develop a similar Community of Interest diagram that includes all 

stakeholders specific to their agency.
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Figure 5.Web chart. Safety community of interest. 

The State should develop a stakeholder registry that identifies the individual stakeholders 

involved in developing the Safety DBP, as shown in Table 2. The registry should document the 

stakeholder’s organization, name, title, and contact information. The registry should also 

identify the stakeholder’s role concerning safety data (for example, whether they are a system 

owner, data provider, or data user of a specific safety data system).
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Table 2. Example stakeholder registry. 

Agency

Stakeholder 

Name Office Title

Safety 

Data 

System Role Email Phone

State 

DOT

Stakeholder 

1 

Office 1 Title 1 Data 

System 1

System 

Owner

Email 1 Phone 1

State 

DOT

Stakeholder 

2 

Office 1 Title 2 Data 

System 2

Data 

User

Email 2 Phone 2

... ... ... ... ... ... ... ... 

Governance Stakeholders

Finally, the State should assemble a list of managers of other DOT business offices or divisions 

that may have data business planning or governance initiatives in place. This may include State

DOT offices such as the safety office, planning, traffic engineering and operations, design, asset 

management, HPMS managers, pavement management, bridge management, maintenance, or 

construction. It may also include administrative and IT offices. These stakeholders will be 

critical in gathering information on governance initiatives underway in other business areas 

within the DOT. 

ENGAGE STAKEHOLDERS 

The State should involve stakeholders throughout all steps of developing the Safety DBP. The 

degree of stakeholder involvement will vary for each step of the Safety DBP development 

process. For example, during initial planning (Step 1), a State may engage a small group of 

stakeholders to identify data challenges and needs for safety data governance, making sure the 

group includes representatives from all safety data business areas. When the State is ready to 

move forward with other steps, the State should involve other internal and external 

stakeholders as needed. 

The State should develop a stakeholder engagement plan that identifies the stakeholders 

involved in each step of the development process, the purpose of engaging stakeholders, and 

potential engagement mechanisms. Table 3 provides an example stakeholder engagement plan. 

The plan could also include details such as meeting dates, associated meeting materials, and how 

and when to distribute materials to stakeholders. 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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The plan should be tailored based on agency size and the resources and funding available for 

gathering feedback. Potential outreach mechanisms include the following:

• Surveys: Surveys are used to obtain feedback on how well individual data systems meet

safety business needs, identify gaps in data systems or needs, and identify potential solutions

to address gaps. Surveys provide an opportunity to assess data systems (agency wide or

specific to the safety program area, depending on the scope of the DBP).

• Focus Groups: Focus groups engage stakeholders at a more detailed level. Focus groups

may include data providers and data users of a specific data system. A State can conduct

focus groups in person or via online webinar. Focus group topics could include identifying

critical data gaps and issues in meeting safety business needs; documenting current business

processes for safety analysis; and identifying short- and long-term improvements for the

Action Plan. A State should designate a champion to lead the focus group and keep

actionable items moving forward.

• Workshops: Workshops engage internal stakeholders such as IT staff, data program

managers, and other agency staff who represent data providers and users. Workshops may

capture needs for a group of data systems or a single data system. Potential topics include

discussing specific data improvement strategies; identifying technology improvements; and

collaborating on new data applications to integrate existing data into an enterprise model.

• Research Studies: Independent research studies help assess data system performance or

identify the requirements for implementing specific analysis tools. Research studies offer an

unbiased assessment of data systems and can identify best practices across both the public

and private sectors.

• Briefings: Briefings engage larger stakeholder groups within the Community of Interest or

senior management at a high level. The purpose of a briefing is to convey information, reach

consensus and buy-in, or obtain dedicated resources (such as funding, personnel, and

training) for safety data business planning efforts.
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Table 3. Example stakeholder engagement plan. 

Process 

Decisions Supporting Actions

Relevant 

Stakeholders

Purpose of Stakeholder 

Engagement

Potential 

Stakeholder 

Engagement 

Mechanisms

Step 1 

Plan for Safety 

Data Management

and Governance

• Identify Stakeholders for 

Safety Data Systems

• Engage Stakeholders

• Define Safety Data 

Management Challenges

• Research State Efforts in 

Data Management and 

Governance 

• Establish Vision and 

Mission for safety data 

management

• Develop Outcome 

Statement

• State DOT Data 

Managers

• State DOT Offices

• Other State 

Departments (if 

relevant to the 

scope of the Data 

Business Plan)

• Obtain critical input on 

current data management 

and governance challenges 

and root causes

• Identify needs and business 

uses for safety data from 

the perspective of their 

individual offices or 

agencies

• Obtain information and 

supporting documentation 

on other State efforts in 

data management and 

governance 

• Obtain consensus on vision 

and mission to ensure they 

are realistic, specific to the 

agency, achievable over 

different periods of time, 

and linked to overall agency 

vision, mission, and goals

• Obtain consensus on the 

outcome statement for the 

Safety DBP

• Surveys

• Focus groups

• Workshops

• Research 

study 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Table 3. Example stakeholder engagement plan (continuation). 

Process 

Decisions Supporting Actions

Relevant 

Stakeholders

Purpose of Stakeholder 

Engagement

Potential 

Stakeholder 

Engagement 

Mechanisms

Step 2 

Assess Current 

SSDS

• Identify Data Systems to 

Include in the 

Assessment

• Document Current 

Business Processes

• Research and Summarize 

Current and Past 

Assessment Efforts

• Update Past Assessments 

• Conduct Capability 

Maturity Assessment

• State DOT Data 

Managers

• State TRCC

• Obtain input on relevant 

safety data systems to 

include in the assessment

• Obtain input on current 

business processes and 

workflows for safety data

systems 

• Obtain an update on 

progress made in 

implementing current and 

past assessments

• Achieve consensus on 

current and desired 

maturity levels using the 

capability maturity model

• Focus groups

• Workshops

• Research 

study 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Table 3. Example stakeholder engagement plan (continuation).

Process 

Decisions Supporting Actions

Relevant 

Stakeholders

Purpose of Stakeholder 

Engagement

Potential 

Stakeholder 

Engagement 

Mechanisms

Step 3 

Establish a 

Governance 

Program

• Develop Data Principles

• Develop a Governance 

Model

• Establish Roles and 

Responsibilities for 

Governance 

• Develop IT Project 

Governance 

• Develop Governance 

Documentation 

• State DOT Offices

• State DOT Data 

Managers

• IT Office

• Other State 

Departments (if 

relevant to the 

scope of the Data 

Business Plan)

• Obtain consensus on the 

data principles, Governance 

Model, data governance 

roles and responsibilities, 

and IT project governance

• Obtain input on data 

governance documentation

• Focus groups

• Workshops

• Briefings to 

senior 

management

Step 4 

Identify Needs for 

Safety Tools and 

Technology

• Identify Needs for 

Improved Technology

• Develop Plan for 

Improved Use of Tools

• State DOT Data 

Managers

• State TRCC

• IT Office

• Other State 

Departments (if 

relevant to the 

scope of the Data 

Business Plan)

• Obtain input on business 

needs for improved 

technology

• Obtain technical input on 

IT requirements for 

software, hardware, system 

interfaces, compatibility, IT 

tools, and network 

requirements

• Workshops

• Research 

study 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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Table 3. Example stakeholder engagement plan (continuation).

Process 

Decisions

Supporting Actions

Relevant 

Stakeholders

Purpose of Stakeholder 

Engagement

Potential 

Stakeholder 

Engagement 

Mechanisms

Step 5 

Develop Action

Plan 

• Summarize Gaps and 

Improvements 

• Identify Priorities

• Develop Action Plan

• Develop Roadmap for 

Implementation

• State DOT Data 

Managers

• IT Office

• Obtain input on action plan 

steps and priorities

• Commit resources and 

assign responsibilities for 

plan implementation

• Workshops

Step 6 

Document the 

Safety DBP

• Document the Safety 

DBP

• State DOT Offices

• State DOT Data 

Managers

• Other State 

Departments (if 

relevant to the 

scope of the Data 

Business Plan)

• Obtain feedback on the 

Safety DBP

• Briefings 

Step 7 

Implement and 

Sustain the Safety 

DBP

• Assign Responsibility

• Establish Performance 

Metrics

• Implement the Safety 

DBP

• Conduct Training 

• Monitor Progress 

• Communicate Changes

• All stakeholders in 

the Community of 

Interest 

• Obtain feedback on 

proposed revisions of the 

Safety DBP

• Obtain feedback on training 

needs and plan 

effectiveness

• Surveys

• Focus groups

• Briefings 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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DEFINE SAFETY DATA MANAGEMENT CHALLENGES

In planning for safety data management, the State must document current challenges in 

managing and governing its safety data systems. The introduction section listed examples of 

common data challenges categorized by system, technology, and institutional. The State should 

use the following questions to help identify specific issues, symptoms, and root causes to 

address in the Safety DBP. 

• System:

− Are there gaps in required data elements needed for safety analysis? Are data 

elements consistent with national guidelines such as MIRE FDEs (summarized in 

Appendix C)? 

− Do current data collection efforts meet the State’s business needs? 

− Are there redundancies in current data collection or data management efforts? 

− Is data easily accessible? 

− Can users find the data they need in the format they need? 

− Are there difficulties integrating safety data within the agency or across multiple 

agencies? 

− What are the challenges with integrating safety data (such as lack of geographical 

coordinates, differing segment identification, different data formats, varying temporal 

resolution)? 

− Are system owners entering data in a timely manner?

− Is the available history of crashes adequate for safety analysis? 

− Is documentation complete and up-to-date?

• Technology:

− Can users access the tools they need to conduct safety analysis?

− Can users achieve consistent results when using safety analysis tools?

− Do users need tools such as dashboards, scorecards, and data visualization to help 

with reporting, performance tracking, and analysis of safety data? 
GUIDE FOR STATE DOT SAFETY DATA BUSINESS PLANNING

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− Do users need geospatial location capabilities to facilitate efficient visualization and 

analysis of data when using safety analysis tools? 

− Do users need additional functionality within applications such as enhanced modeling 

and reporting capabilities to help identify and prioritize safety improvements?

− Can users generate customizable reports to support safety analysis?

− Are safety related data sets stored in older legacy systems that require substantial 

investment to meet current and future business needs? 

− Do users need more streamlined access to data and information? 

− Are there data “silos,” especially for critical data sets needed to support safety 

programs? 

− Can staff access the latest information about safety systems, data, policies, reports,

tools, and training resources through a centralized knowledge management system?

• Institutional:

− Is there a data governance structure for the SSDS? 

− Are roles, responsibilities, and processes for safety data governance formalized and 

documented? 

− Are formal data sharing agreements in place to facilitate data sharing between 

internal and external stakeholders? 

− What policies and procedures exist for collecting, maintaining, using, and updating 

safety data?

− Do staff need training on safety data policies, procedures, and processes? 

− Do staff know how to use tools for safety analysis and reporting?

− Are SSDS investments made at the functional (that is, within the safety program) or 

enterprise level? 

− Are overall agency needs for safety data optimized? How are safety data 

improvements prioritized with respect to improvements in other program areas?

− Are safety data system needs coordinated with IT, local agencies, and MPOs? 

− Do data program investments align with business needs for safety data systems? 

The State can gather this information through in-person interviews, group meetings, phone 

calls, emails, or an online survey instrument. The pilot studies used an online survey instrument 
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to outreach with safety stakeholders and the IT office. Appendix D provides an example online 

survey instrument used in the pilot studies. The end goal is to develop a statement defining the 

current situation regarding safety data management and governance. 

RESEARCH STATE EFFORTS IN DATA MANAGEMENT AND GOVERNANCE

Next, the State should research other data management and governance efforts underway 

within the DOT by surveying managers of other business areas of the DOT to gather this 

information. Some example questions include: 

• Does your office own, develop, or maintain any data systems or databases?

• Does your office have a data business plan in place that guides the way you manage or 

govern data systems or databases?

• Does your office regularly assess its data systems to identify needs for improvement?

• Have you assessed data governance maturity or capability within your business area? 

• Does your office have formal policies and procedures in place for managing and governing 

its data systems or databases? 

• Are the workflows and business processes for managing your data systems or databases

documented? 

• Has your office defined roles and responsibilities for data management and governance? 

• Is there a governance board or working groups set up for data management and 

governance? 

The State can gather this information through in-person interviews, group meetings, phone 

calls, emails, or an online survey instrument. Regardless of the format, it is important to 

introduce the reason for the questions, define key terms (such as a Data Business Plan), keep 

questions simple to answer, and make sure the process does not require too much staff time. 

Appendix E provides an example online survey instrument used in the pilot studies. 

The intent of the survey is to determine where the DOT currently stands in its data 

governance efforts. The survey results could indicate one of the following scenarios: 

1. No data management or data governance initiatives;
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2. Other offices (non-safety) have embarked on efforts to better manage and govern data; 

or

3. There are enterprise-level efforts for data management and data governance. 

The State must determine whether the Safety DBP is a stand-alone initiative or part of a larger 

governance program. If efforts have started in other business areas or at the enterprise level, 

the Safety DBP should complement those efforts and leverage any work already done (such as 

assessments of data management and governance maturity). If no efforts exist, the Safety DBP 

could be used as an example for developing an enterprise-wide initiative. 

ESTABLISH VISION AND MISSION FOR SAFETY DATA MANAGEMENT 

Finally, the State should establish the vision and mission for safety data within the organization. 

The vision and mission describe high-level opportunities for managing and governing safety data. 

They articulate the outcome of improved safety data collection, accessibility, security, cost 

effectiveness, and other data management attributes. The vision and mission support the State’s 

broader safety goals by providing quality data for safety problem identification, countermeasure 

analysis, and target setting activities. 

Vision

A vision statement is aspirational in nature and describes a future condition of a State’s safety 

data system. It establishes the program’s identity and guides future activities. 

Effective vision statements are concise, yet provide enough content to clarify the preferred 

outcome. A vision statement for a safety data system could reference concepts such as 

performance management, data quality, integration of data across agencies, or providing 

information to the public. Some example vision statements are as follows:

• Quality data adheres to established data quality standards and supports safety decisionmaking.

• Users have access to quality safety data when, where, and in the form needed.

• Kansas DOT: The Kansas DOT and its safety data stakeholders will have a sound, 

comprehensive, and well-coordinated approach to managing, improving, and applying the 

State’s safety data and analysis resources.
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• Washington State DOT: WSDOT’s business decisions will be supported by reliable, 

timely, accessible, and complete safety data.

Mission

In contrast to the vision, a mission statement is action-oriented. The mission may reference 

concepts from the vision statement, but it also emphasizes the role of the safety data system in 

achieving the vision. It highlights the need for collaboration with internal and external 

stakeholders. Some example mission statements are as follows:

• Provide reliable, timely, and accurate safety data and information. The information must be 

accessible, shared for cross-program analysis, and integrated into the agency’s safety 

decision-making process. 

• Maximize the efficiency and effectiveness of safety data resources, collection, management, 

analysis, and reporting.

• Kansas DOT: Achieve sound governance of safety data resources, enhance integration of 

safety data systems, continually improve the quality and usability of data, and promote user 

friendly and easily accessible data by our safety users and partners for their business 

analysis.

• Washington State DOT: WSDOT will have integrated safety data systems that are user 

friendly and easily accessible (as appropriate) by our safety users and partners for their 

business analysis.

DEVELOP OUTCOME STATEMENT 

The outcome statement describes the results the Safety DBP will achieve. A Safety DBP has 

numerous possible objectives, but all States need data systems that support safety problem 

identification, countermeasure analysis, and target setting at a statewide level. States should 

relate the outcome statement to specific issues or gaps they want to address. For example, a 

State may want to improve its safety data systems to meet FAST Act requirements for data 

collection. A State may also want to implement a data governance program to improve its data 

management practices.

Some example outcome statements and objectives are as follows: 
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• Minnesota DOT: Provide a platform for stronger data management practices to: 

1) increase transparency and accountability; 2) expand the reliability and utility of data to 

meet business decision-making needs; 3) create efficiencies in accessing, sharing, and using 

data and information; 4) standardize processes and systems that reduce redundancy and 

promote consistency of data; and 5) optimize new information management and spatial data 

tools and methods.

• Kansas DOT: The objectives of the Safety DBP are to: 1) develop a governance 

framework to help better manage safety data resources and assets; 2) develop a roadmap 

for improving safety data resources; and 3) create a communication and implementation 

plan. 

• Washington State DOT: The Department wants to improve its data management and 

governance practices in order to integrate data and make it available to all State

practitioners. The objectives of the Safety DBP are to: 1) demonstrate how safety data 

impacts the enterprise; 2) develop a roadmap to address safety data linkage, association, and 

management challenges; 3) establish a strong, sustainable vision for safety data; 4) implement 

a formal safety data governance process; and 5) ensure a sustainable safety data 

improvement process.

SUMMARY

Step 1: Plan for Safety Data Management and 

Governance, guides States in initial planning 

efforts to help them understand current data 

management challenges, data governance 

initiatives, and goals for managing and governing 

data. The important actions in this step are:

• Identify stakeholders. Stakeholders for 

safety data systems play a key role in 

identifying needs and business uses for safety 

data from the perspective of their individual 

offices or agencies. There are multiple 

stakeholder groups involved in the Safety 

DBP. A core team of stakeholders helps 

champion the Safety DBP effort. This team

KEY OUTPUTS AND WORK 

PRODUCTS

□ Stakeholder registry

□ Community of interest diagram

□ Stakeholder engagement plan

□ Survey instrument on data 

management challenges

□ Problem statement

□ Survey instrument on data 

governance initiatives

□ Vision and mission statements for 

safety data governance

□ Outcome statement for the Safety 

DBP 
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consists of data managers or data owners whose daily tasks involve safety data management.

A larger group of stakeholders help define the policies, procedures, and business processes 

associated with a SSDS. This group includes any internal or external person or organization 

that collects, owns, maintains, uses, interfaces with, accesses, or benefits from a safety data

system. Governance stakeholders include managers of other DOT business offices or 

divisions that may have data business planning or governance initiatives in place. States 

should document stakeholder groups in a stakeholder registry and Community of Interest 

diagram.

• Engage stakeholders. Engaging stakeholders throughout each step of the Safety DBP 

development process is crucial to its success. A stakeholder engagement plan identifies the 

stakeholder groups involved in each step, the type of feedback desired, and outreach

mechanisms for obtaining feedback. Surveys, focus groups, workshops, research studies, and 

briefings are all potential outreach mechanisms for engaging stakeholders. 

• Define safety data management challenges. Defining the current situation regarding 

safety data management and governance helps States identify specific issues to address in its 

Safety DBP. States can use the exploratory questions related to system, technology, and 

institutional challenges to understand specific issues, symptoms, and root causes. 

• Research State efforts in data management and governance. States should survey 

business area managers to identify other governance initiatives underway at the DOT. This 

will determine whether the Safety DBP is a stand-alone initiative or part of a larger 

governance program. The State can also leverage work already done for these initiatives 

(such as assessments or best practices in data management and governance).

• Establish vision and mission for safety data management. The vision and mission 

describe high-level opportunities for managing and governing safety data. They articulate a 

vision for and the outcome of improved safety data collection, accessibility, security, cost 

effectiveness, and other data management attributes.

• Develop outcome statement. The outcome statement describes the results the Safety 

DBP will achieve. A Safety DBP has numerous possible objectives, but all States need data 

systems that support safety problem identification, countermeasure analysis, and target 

setting at a statewide level.
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STEP 2. ASSESS CURRENT STATE SAFETY DATA SYSTEM

Once initial planning is complete, the State should assess the current status of its 

safety data system. This section guides a State to conduct this assessment and 

develop an appropriate improvement plan. Key actions include: identifying data 

systems to include in the assessment, documenting current business processes for safety data 

systems, researching and summarizing past assessment efforts, updating past assessments, and 

conducting a capability maturity assessment. After completing this step, the State will 

understand its current capabilities related to safety data collection, analysis, governance, and 

interoperability.

IDENTIFY DATA SYSTEMS TO INCLUDE IN THE ASSESSMENT 

The State should include any datasets, data systems, and data programs critical for safety 

analysis and target setting. Although the focus is on safety data systems other programs such as 

asset management, HPMS, infrastructure, operations, or supporting administrative data may also

support safety decision-making. Data systems should have a clear connection to and support 

the DOT’s mission, core business services, and performance objectives related to improving 

traveler safety. 

The following criteria may help identify critical safety data systems: 

• Does the data system support safety analysis, performance measures, and targets?

• Does the State use the data system to meet Federal or State mandates?

• Are there critical risks associated with lack of access to the data system?

The State should document basic information for each data system in a data inventory. The

inventory should include significance of the data to the Safety DBP, data ownership, and linkages 

to the crash data system. The inventory should also reference supporting documentation such 

as user’s manuals, data dictionaries, and training manuals. Table 4 illustrates a simplistic

approach to documenting this information. 
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Table 4. Inventory of safety data systems. 

Data System and Content

Significance to 

Safety DBP Data Owner

Crash Data 

Linkage

Crash Data 

• Crash location

• Date

• Collision type

• Severity

• Number of fatalities

• Number of injuries

• Relationship to junction

• Initial travel direction

• Maneuvers by involved vehicles

Essential data set for 

all safety analysis; 

common reference for 

other safety data sets

State Highway Patrol; 

State DOT crash data 

manager

N/A 
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Table 4. Inventory of safety data systems (continuation). 

Data System and Content

Significance to 

Safety DBP Data Owner

Crash Data 

Linkage

Roadway Data 

• Segment identifier

• Route number 

• Route/street name

• Federal aid and route type

• Rural or urban designation

• Surface type

• Begin and end point segment descriptors 

• Segment length

• Direction of inventory

• Functional class

• Median type

• Access control

• One vs. two-way operations

• Number of through lanes

• Type of governmental ownership

Physical inventory 

needed to identify 

roadway risk factors 

and support 

countermeasure 

development

State DOT roadway 

inventory manager, 

State DOT HPMS 

manager, MPO 

information services 

manager

Linear reference 

system
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Table 4. Inventory of safety data systems (continuation). 

Data System and Content

Significance to 

Safety DBP Data Owner

Crash Data 

Linkage

Intersection Data

• Unique intersection identifier

• Location identifier for road 1 crossing point

• Location identifier for road 2 crossing point

• Intersection / junction geometry

• Intersection / junction traffic control

• Unique approach identifier

• Rural or urban designation

• Number of intersection legs

Inventory needed to 

identify roadway risk 

factors and support 

countermeasure 

development

State DOT roadway 

inventory manager; 

MPO information 

services manager

Linear reference 

system

Interchange / Ramp Data

• Unique interchange identifier

• Location identifier for roadway at beginning and 

ending ramp terminal

• Ramp length

• Roadway type at beginning and ending ramp terminal

• Interchange type

• Functional class

• Type of governmental ownership

Inventory needed to 

identify roadway risk 

factors and support

countermeasure 

development

State DOT roadway 

inventory manager; 

State DOT HPMS 

manager; MPO 

information services 

manager

Linear reference 

system
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Table 4. Inventory of safety data systems (continuation). 

Data System and Content

Significance to 

Safety DBP Data Owner

Crash Data 

Linkage

Traffic Data

• Annual average daily traffic

• Annual average daily traffic for each intersecting 

road at intersection

• Annual average daily traffic at ramp 

• Annual average daily traffic year

• Vehicle/ Road User Mix

Allows for comparison 

of locations by crash 

rate and supports 

advanced analysis 

methods 

State DOT traffic 

information manager

Linear reference 

system

Project Data

• Project location

• Project type 

• Project cost

• Project implementation dates

• Project-specific traffic volume estimates

Allows for ability to 

track project and 

program-level 

outcomes

State DOT project 

inventory manager

Linear reference 

system
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DOCUMENT CURRENT BUSINESS PROCESSES 

The State should document current business processes and workflows for safety data 

collection, processing, analysis, and reporting. One common method is to develop Unified 

Modeling Language use case diagrams and corresponding use case narratives. Use case diagrams 

are graphic depictions of the interactions among the following elements of a data system, as 

shown in Figure 6: 

• Actors are represented by a person figure or disk symbol. These are users, external 

operators, or systems interfacing with safety data. The disk symbol may represent a 

database, data process, a data system, an agency, or a web application.

• Actions or business processes are depicted as ovals. These represent the functionality a 

system or service provides to users.

• The lines connecting data users and actions represent information flows. 

Figure 6. Diagram. Unified modeling language use case schematic. 

Use case diagrams are helpful for visualizing and comparing business processes. The diagrams 

depict the full data management life cycle of a data system, including how data is collected, 

stored, analyzed, augmented, disseminated, and reported. The State should conduct extensive 

interviews with data system owners to document business processes and obtain supporting 

information such as user manuals. 

The State can use tools such as Visio or Microsoft PowerPoint to develop the use case 

diagrams. Each element should include simple text descriptions to clarify business processes. 

Figure 7 shows an example use case diagram for regional traffic data collection. 
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Figure 7. Diagram. Example use case diagram for regional traffic collection. 

Source: Cambridge Systematics, Inc. Data Business Plan: Concept of Operations. Prepared for the Alaska Department of Transportation and Public Facilities, 

September 2005.
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The State should develop an accompanying narrative to document each business process in 

detail. The narrative provides additional explanation of business processes not easily explained 

in the diagram (for example, defining acronyms or describing a particular software product).

Figure 8 shows an example narrative for regional traffic data collection. 

Documenting current business processes helps the State to maintain standards and consistency, 

train new hires, adhere to policy, and plan for data management improvements. 

Figure 8. Callout. Example use case narrative. 

Regional Traffic Collection Processes

DOT regions perform the following steps to collect traffic data Highway Analysis System (HAS): 

a. Traffic collection is done by region with reports and data sent to Headquarters Traffic staff for 

Federal reporting. Two major process flows are in place: counter traffic collection and Weighin-motion. To meet Highway Performance Monitoring System requirements, the cycle of study is 

once every three years at a minimum. Results of traffic collection are stored locally. Access and 

individual files are used as the local repository. Peak’s software is used where possible.

b. Data from permanent traffic recorders (PTR) are collected either via dial-up, Internet, or 

physically going out to site.

c. Temporary traffic counters are collected by going out to the site. Each study takes 1 to 3 

weeks. Configuration varies by location and needs.

d. Regional Traffic staff review the data collected to accept, rerun and provide data to requestors. 

Corrected data is loaded into HAS for HPMS and other reporting.

e. Data in the form of reports are made available to other ADOT&PF staff including consultants 

and Pavement Design staff.

f. ADOT&PF staff and the general public request data. The process to provide the data is a 

manual process.

Source: Cambridge Systematics, Inc. Data Business Plan: Concept of Operations. Prepared for the Alaska 

Department of Transportation and Public Facilities, September 2005. 

RESEARCH AND SUMMARIZE CURRENT AND PAST ASSESSMENT EFFORTS

The State should examine current and past evaluations of its data management and governance 

capabilities, as well as assessments of safety data quality. The DOT may have conducted these 

assessments as part of other projects to upgrade legacy data systems, implement safety analysis 

tools, plan for asset management, or conduct risk assessments. The State should include 

assessments from other business areas outside of safety as appropriate. For example, other 

business offices may have conducted assessments as part of ad hoc data governance initiatives. 
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The State should also examine results from national programs such as the Crash Data 

Improvement Program, Roadway Data Improvement Program, Roadway Safety Data 

Capabilities Assessment, Traffic Records Assessment, and others. These programs provide

independent evaluations of the strengths and weaknesses of a State’s data systems, as well as 

recommendations for the State’s consideration. Descriptions of these programs are as follows.

Crash Data Improvement Program. The Crash Data Improvement Program is a NHTSAsponsored technical assistance program that provides States an in-depth audit of their crash 

data systems and data quality. The evaluation team provides recommendations for 

improvement and performance measures that establish timeliness, accuracy, completeness, 

uniformity, integration, and accessibility to States that complete the program. NHTSA has 

updated the program and manual to reference new publications. The program incorporates a 

MMUCC mapping assessment that follows the new Mapping to MMUCC rules and leverages 

online data collection and analysis tools to enhance the process. (Mapping to MMUCC: A 

Process for Comparing Police Crash Reports and State Crash Databases to the Model 

Minimum Uniform Crash Criteria. DOT HS 812 184, July 2015, http://wwwnrd.nhtsa.dot.gov/Pubs/812184.pdf.) Any State can request a full crash data audit or the 

MMUCC assessment by sending a letter to their regional NHTSA office.

Roadway Data Improvement Program. The purpose of the Roadway Data Improvement 

Program is to help State transportation agencies improve the quality of their roadway data to 

better support safety and other DOT initiatives. This resource is patterned after the Crash 

Data Improvement Program and supports the Roadway Safety Data Program goals of providing 

guidance to State DOTs to improve the quality of roadway data needed for safety analysis. The 

Roadway Data Improvement Program provides information and guidance on:

• Roadway data collected. 

• Data collection tools and methods. 

• Transportation planning and coordination. 

• Data management and governance. 

• Establishing the baseline roadway network. 

• Roadway data quality measurement. 

• Data interoperability between State and local agencies. 
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• Use of roadway data in safety analysis.

Roadway Safety Data Capabilities Assessment. As part of its Roadway Safety Data 

Program, FHWA conducted a capabilities assessment for each State, the District of Columbia, 

and Puerto Rico on the collection, management, and use of roadway safety data. (United States 

Roadway Safety Data Capabilities Assessment, FHWA-SA-12-028, July 2012, 

http://safety.fhwa.dot.gov/rsdp/downloads/rsdp_usrsdca_final.pdf.) FHWA used a five-level 

capability maturity model to assess each State’s current capability in the following areas: 

1) Roadway Inventory Data Collection and Technical Standards; 2) Data Analysis Tools and 

Uses; 3) Data Management; and 4) Data Interoperability and Expandability. States also used the 

maturity model to identify their desired capability levels. FHWA provided States with Safety 

Data Action Plans that identified Safety Data improvement goals and discussed how to reach 

them. The initial assessment was conducted in 2011-2012. FHWA will conduct a second 

assessment in each State in 2018 to gauge progress since the 2011-2012 assessment.

Traffic Records Assessment. A traffic records assessment is a NHTSA-sponsored, 

independent peer review of a State’s traffic records systems that includes an examination of the 

six core traffic records data systems: crash, driver, vehicle, roadway, citation and adjudication, 

and injury surveillance. The evaluation also examines data integration processes and the 

characteristics of a State’s traffic records system management, including the TRCC’s strategic 

planning process, which sets the framework for improving all aspects of the traffic records 

system. States must undertake an assessment every five years as an eligibility requirement under 

MAP-21 to apply for §405(c) traffic data improvement grant funding. Evaluators assess a State’s 

responses to the questions outlined in the Traffic Records Program Assessment Advisory, 

which describes the ideal traffic records system. (NHTSA, Traffic Records Program Assessment 

Advisory, http://www-nrd.nhtsa.dot.gov/Pubs/811644.pdf.) The final report provides the State a 

clear picture of its performance in comparison to the ideal and includes both broad 

recommendations and specific, actionable considerations. States seeking further assistance may 

request free technical assistance and training programs to augment the assessment.

If a State has already completed these assessments, it should start by summarizing the results. 

UPDATE PAST ASSESSMENTS

The State should update past assessment results to reflect progress in implementing 

improvements since the assessment date. The core stakeholder team should meet to discuss 

and update the implementation status of recommendations. The team should note which
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recommendations are complete and which are still outstanding. The team should also note 

recommendations that are no longer valid or that it does not plan to pursue. 

The State may obtain this information from the custodial agency or from multi-agency groups. 

For example, the TRCC monitors progress in implementing traffic records assessment 

recommendations as part of its annual §405c grant application to NHTSA. Table 5 shows an 

example matrix summarizing the assessment program or source, relevant data system or 

assessment area, recommendation, and implementation status.

This process helps States identify future data needs and prioritize areas of concern to address 

in later steps of the Safety DBP. The State may wish to include outstanding items in its Action 

Plan for the Safety DBP.

Table 5. Current and past assessments. 

Source Data System Recommendation

Implementation 

Status

Crash Data 

Improvement 

Program

Crash Recommendation Status 

Roadway Data 

Improvement 

Program

Roadway ... ... 

... ... ... ... 

... ... ... ... 

CONDUCT CAPABILITY MATURITY ASSESSMENT 

After the State has updated past assessments, it should assess its capabilities for collecting, 

managing, governing, and using safety data. The assessment process includes these steps: 

1. Select assessment tool; 

2. Assess current level of capability maturity; 

3. Determine target level of maturity desired; and

4. Identify gaps. 
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Select Assessment Tool 

One common assessment tool is a capability maturity model. As defined in NCHRP 666, a 

capability maturity model “is used to assess how the roles of people, technology, and 

institutional arrangements help the agency to advance from an un-governed State to a governed 

State.” (NCHRP 666: Target-Setting Methods and Data Management to Support PerformanceBased Resource Allocation by Transportation Agencies, Volume II: Guide for Target-Setting 

and Data Management, 2010.) Because the model describes various levels of maturity and the 

characteristics of those levels, a State can use it to establish a baseline and identify next steps in 

moving towards its desired end State for data management and governance. The desired end 

State is to establish and maintain a governance program that supports safety problem 

identification, countermeasure analysis, and target setting. 

The recommended maturity model is adapted from the United States Roadway Safety Data 

Capabilities Assessment. (Vanasse Hangen Brustlin, Inc., United States Roadway Safety Data 

Capabilities Assessment, FHWA-SA-12-028, July 2012.) The model defines levels of maturity for 

the following dimensions of capability. 

• Safety Data Collection and Technical Standards: What safety data are collected?

Are there gaps in MIRE FDEs? How well do safety data systems meet data quality standards 

for timeliness, accuracy, completeness, consistency, integration, and accessibility, as defined 

in Table 6? Is a five-year history of crash data available for all public roadways?

• Data Analysis Tools and Uses: How well does the SSDS support the roadway safety 

management process, including network screening, diagnosis, countermeasure selection, and 

evaluation? How well does the SSDS support advanced analysis methods using tools such as 

the Highway Safety Manual, the Interactive Highway Safety Design Model, or AASHTOWare 

Safety AnalystTM. 
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Table 6. Data quality standards. 

Data 

Quality 

Standard Definition

Completeness The degree to which data is available for the public roadway network in the 

State, as well as the degree to which there are no missing or blank fields for 

critical data elements in the data system. 

Timeliness The degree to which data values or a set of values are provided at the time 

required or specified. For safety data, timeliness is the number of days between 

the event occurrence and entry of data into the appropriate database, or the 

time from when the custodial agency receives the data to the point of data entry 

into the database.

Accuracy The degree to which there are no errors in critical data elements. Accuracy 

reflects the degree to which data is error free, satisfies internal validity checks, 

and does not exist in duplicate within a single database.

Uniformity or 

Consistency

The degree to which records in a database are consistent with some 

independent standard or the degree of consistency in element definitions and 

codes across State and non-State files. Uniformity is the number or percentage 

of records that agree with nationally accepted guidelines and standards such as 

MMUCC and MIRE FDEs. 

Integration The extent to which data records are linked between two or more data systems 

using common or unique identifiers. For safety data, integration is the 

percentage of data elements or records in a safety database linked to another 

system or dataset.

Accessibility The relative ease with which users can retrieve and manipulate data to meet 

their needs. Accessibility is measured in terms of a user’s ability to obtain data 

and the timeliness of the response to their request.

Sources: Traffic Data Quality Measurement Final Report, FHWA, September 2004; NHTSA Model Performance 

Measures for State Traffic Records Systems, DOT HS 811 441, February 2011; The Six Primary Dimensions for 

Data Quality Assessment, DAMA UK Working Group on Data Quality Dimensions, October 2013
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• Data Management and Governance: Is there a data governance structure for the 

SSDS? For example, are there formally defined roles, accountability, and core capacities for 

data governance? Is there a designated data governance board, data stewards, and data 

owners? What policies and procedures exist for collecting, maintaining, using, and updating 

safety data? Are technology and tools for safety data management and analysis consistent, 

standardized, and updated? 

• Data Interoperability and Expandability: To what extent are linked data sets from 

roadway, crash, and others included in safety analysis? Are existing safety data systems 

expandable as new technologies and tools are developed?

Each dimension of capability has five distinct maturity levels as follows: 

• Level 1 – Initial or Ad Hoc. The agency is not aware of the need for capability in a 

specific dimension, or activities and relationships are taking place but largely in ad hoc, 

informal, and champion-driven efforts. There is no plan for interoperability or expandability. 

• Level 2 – Repeatable. The results of previous projects and the demands of the current 

project drive activities and actions. Individual managers decide what to do on a case-by-case 

basis during individual projects. 

• Level 3 – Defined. The agency documents technical and business processes rather than 

on a per-project basis. The agency’s standards relate to an adopted strategy, and this 

guidance determines project outcomes. However, there is limited accountability and uneven 

alignment with internal and external partners.

• Level 4 – Managed. The agency uses process management to initialize and supervise 

individual projects. Performance is measured, processes are predictable, and the 

organization can develop rules and conditions regarding the quality of the products and 

processes. Internal and external partnerships are aligned.

• Level 5 – Optimized. Safety data management and governance is a full, sustainable 

program priority, with top-level management support and formal partnerships in place. The 

whole organization focuses on continuous improvement. The organization possesses the 

means to detect weaknesses and to strengthen areas of concern proactively.

Appendix F provides a recommended capability maturity model for assessing a State’s safety 

data system. Worksheets are provided for States to note strengths, weaknesses, current 
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maturity level, desired maturity level, and actions to advance to the desired maturity level for 

each assessment area. States may wish to assign a separate maturity level for different elements 

of their safety data system. For example, there may be portions of a State’s system that are 

operating at a higher maturity level, while others are at a lower level. States may note specific 

areas within the element they wish to improve. For desired maturity level, it is acceptable for 

States to choose a lower maturity level if it is realistic for the organization.

FHWA expects to publish a revised version of the capability maturity model in 2018. NCHRP 

Report 814 provides an alternative method and spreadsheet tools for States to assess the value 

of their data programs and data management processes. (Spy Pond Partners, LLC. Data to 

Support Transportation Agency Business Needs: A Self-Assessment Guide. NCHRP Report 

814. Transportation Research Board, Washington, D.C., 2015. Available at: 

http://www.trb.org/Main/Blurbs/173470.aspx.)

Assess Current Level of Capability Maturity

The State should conduct outreach with safety data system stakeholders to assess its current 

maturity for each dimension of the capability maturity model. States can use the worksheets in 

Appendix F or the spreadsheet tools available through NCHRP Report 814 to conduct the 

assessment. 

Instruments for gathering feedback include surveys, focus groups, workshops, research studies, 

or current assessments conducted through NHTSA programs. For example, one pilot State

convened a workshop in which participants discussed each dimension and agreed on consensus 

ratings with other team members. For the second pilot, workshop participants completed the 

ratings individually (at the workshop). The State determined the consensus ratings by averaging 

the individual ratings. 

Determine Target Level of Maturity Desired

After identifying a current maturity level for each dimension of the capability maturity model, 

stakeholders should determine a target level of maturity desired. 

Figure 9 illustrates example results for a capability assessment. A hollow circle () indicates 

current level of capability. The solid circles indicate the target level of capability, and they are 

color coded to reflect the degree of gap. For example, a green circle () indicates no gap, in 

which the desired level of capability is the same as the current level. A yellow circle () 

indicates a small gap, in which there is one level difference between current and desired levels 
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of capability. A red circle () indicates a large gap, in which there are two or more levels 

between current and desired levels of capability.

After identifying target maturity levels, the State may wish to incorporate them into the vision

and mission statements established in Step 1. 

Figure 9. Graph. Capability assessment results. 
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Identify Gaps

The State should identify potential challenges or barriers preventing them from reaching the 

desired level. These could include: 1) needs and gaps within core business areas of the 

organization; and 2) needs and gaps based on primary job functions of the audience within the 

organization (for example, senior and mid-level managers, business data stewards, IT data 

stewards, users of data, data providers). They should also identify actions needed to advance 

from current to desired levels of maturity. These actions will help inform future steps in the 

Safety DBP development. 

SUMMARY

Step 2: Assess Current SSDS, addresses the 

need for States to assess the current state of its 

safety data system and understand its current 

capabilities related to safety data collection, 

analysis, governance, and interoperability. The 

important actions in this step are: 

• Identify data systems to include in the 

assessment. The State should include any 

datasets, data systems, and data programs 

critical for safety analysis and target setting. 

Although the focus is on safety data, other 

programs such as asset management, HPMS,

infrastructure, operations, or supporting 

administrative data may also support safety 

decision-making. Data systems should have a 

clear connection to and support the DOT’s 

mission, core business services, and 

performance objectives related to improving 

traveler safety.

KEY OUTPUTS AND WORK 

PRODUCTS

□ Identification of data systems for the 

assessment

□ Use case diagrams and 

accompanying narratives on business 

processes and workflows for safety 

data systems

□ Summary of similarities and 

differences in data resolution and 

accuracy standards across all data

□ Summary of past assessment 

recommendations in matrix form

□ Update on State progress in 

implementing past assessment 

recommendations

□ Assessment tool 

□ Assessment of current and desired 

levels of maturity for each dimension 

of the capability maturity model

• Document current business processes. Business process diagrams and accompanying 

narratives are helpful for visualizing and comparing business processes for safety data 

systems. The diagrams depict the full data management life cycle of a data system, including 

how data is collected, stored, analyzed, augmented, disseminated, and reported. The State

 

 
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should conduct extensive interviews with data system owners to document business 

processes and obtain supporting information such as user manuals. Documenting current 

business processes helps to maintain standards and consistency, train new hires, adhere to 

policy, and plan for data management improvements.

• Research and summarize current and past assessment efforts. The DOT may have 

conducted assessments as part of other projects to upgrade legacy data systems, implement 

safety analysis tools, plan for asset management, or conduct risk assessments. The State

should include assessments from other business areas outside of safety as appropriate. They 

should also examine results from national programs such as the Crash Data Improvement 

Program, Roadway Data Improvement Program, Roadway Safety Data Capabilities 

Assessment, Traffic Records Assessment, and others. These programs provide independent 

evaluations of the strengths and weaknesses of a State’s data systems, as well as 

recommendations for their consideration. The State can organize the assessment 

recommendations in matrix form by agency, data system, assessment program/source, and 

recommendation. 

• Update past assessments. States should update progress in implementing improvements 

since the assessment date. This helps identify future needs and prioritize areas of concern 

for the Safety DBP. 

• Conduct capability maturity assessment. The State should assess its current 

capabilities for collecting, managing, governing, and using safety data using a capability 

maturity model. The recommended maturity model is adapted from the United States 

Roadway Safety Data Capabilities Assessment. It defines levels of maturity for safety data 

collection, analysis, management and governance, and interoperability. States should also 

determine a target level of maturity desired, as well as actions needed to advance from 

current to desired levels of maturity. 
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STEP 3. ESTABLISH A DATA GOVERNANCE PROGRAM

Once the assessment is complete, the State should establish a plan for improving data management 

through a Data Governance Program. Key actions include establishing core data principles, 

developing a Governance Model, defining data governance roles and responsibilities, and 

documenting the Data Governance Program. This section defines data management and data 

governance and then discusses these key actions in more detail. Appendix G provides additional 

information on data governance. After completing this step, the State will have a roadmap for 

improving data management with a Data Governance Program and documentation to support its 

implementation. 

DATA MANAGEMENT AND GOVERNANCE DEFINED

Data management is the development, execution, and oversight of architectures, policies, 

practices, and procedures to manage the information lifecycle needs of an enterprise in an effective 

manner as it pertains to data collection, storage, security, data inventory, analysis, quality control, 

reporting, and visualization. 

Data governance is the “execution and enforcement of authority over the management of data 

assets and the performance of data management functions.” (NCHRP 666: Target-Setting 

Methods and Data Management to Support Performance-Based Resource Allocation by 

Transportation Agencies, Volume II: Guide for Target-Setting and Data Management, 2010.) The 

purpose of data governance is not to manage data, but rather to guide and monitor the proper 

management of data by establishing clear authority and policies for managing data at the agencywide level. A Data Governance Program is one of the recommended tools for addressing safety 

data management in this Guide.

Figure 10 depicts data governance activities as follows: 

• Create and align rules. Initially, the Governance Program establishes the policies and 

decision-making process for managing data. It also formalizes the roles and responsibilities of 

all stakeholders involved.

• Enforce rules and resolve conflicts. Next, the program ensures stakeholders are applying 

data management rules and processes correctly. It provides a forum for resolving conflicts if 

needed. 
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• Provide ongoing support. Finally, the program provides ongoing support to stakeholders 

who are applying data management rules and processes. It also identifies new opportunities for 

creating rules or adapting to existing ones, thus continuing the data governance lifecycle. 

Figure 10. Flow chart. Data governance activities. 

Source: C. Cabrera, “Data Governance Defined and Why It is Crucial to the Organization,” April 15, 2009.

http://www.element61.be/e/resourc-detail.asp?ResourceId=6. 

A well-defined Data Governance Program is vital to ensure the integrity of safety (and all other) 

data analyses performed in a DOT. The Data Governance Program must:

• Have executive buy-in and support. Everyone in the organization needs to know the 

importance of, and commitment the agency has to data governance.

• Be comprehensive in scope. The entire data lifecycle, from collection through 

management, analytics, and distribution is subject to the agency’s data governance program. 

Because data are collected by so many different programs, using different standards, and for 

different purposes, it is critical that data governance spans the gaps between data systems. 

Data are collected by specific DOT programs for their purposes (such as reporting, 

analytics, operations, planning, etc.) but are often required in the business processes of 

other programs in the DOT.

While the term “governance” may have negative connotations (due to new rules, regulations, and 

policies imposed on workflow processes and potential organizational changes), its benefits far 

outweigh any perceived negative impact. The benefits of data governance are evident from a 

policy, practical, and technical perspective as documented in NCHRP 666 as follows: (NCHRP 
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666: Target-Setting Methods and Data Management to Support Performance-Based Resource 

Allocation by Transportation Agencies, Volume II: Guide for Target-Setting and Data 

Management, 2010.)

From a policy standpoint, data governance promotes the understanding of data as a valuable asset 

to the organization and encourages the management of data from both a technical and business 

perspective.

On a practical level, the use of a data governance model provides for access to data standards, 

policies, and procedures on an enterprise basis. It provides a central focus for identifying and 

establishing rules for the collection, storage, and use of data in the organization.

From a technical perspective, use of data governance results in reducing the need to maintain 

duplicate data systems, improves data quality, and provides new opportunities to implement better 

tools for managing and integrating data.

The benefits of data governance also extend to a State’s data management practices. For example, 

it can help eliminate confusion over which offices are responsible for different data systems. 

Identifying the system of record in a data catalog provides a consistent source of information for 

addressing inquiries and producing reports. Finally, it can improve how States share data with the 

Community of Interest. Safety managers need to understand and communicate these benefits to 

upper management to obtain funding and resources for implementing data governance.

DEVELOP DATA PRINCIPLES

First, a State should develop data principles to guide governance practices at the DOT. The 

American Association of State Highway and Transportation Officials (AASHTO) recommend the 

following data principles: (AASHTO Subcommittee on Data, Data Subcommittee Efforts on Core 

Data Principles website, https://docs.google.com/viewer?a=v&pid=sites&srcid=

ZGVmYXVsdGRvbWFpbnx0cmJkYXRhc2VjdGlvbnxneDoxZDc3YjczNjUxZjIxZmM3.) 

• Principle 1 – VALUABLE: Data is an asset—Data is a core business asset that has value 

and is managed accordingly.

• Principle 2 – AVAILABLE: Data is open, accessible, transparent and shared—

Access to data is critical to performing duties and functions, data must be open and usable for 

diverse applications and open to all.

• Principle 3 – RELIABLE: Data quality and extent is fit for a variety of 

applications—Data quality is acceptable and meets the needs for which it is intended.
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• Principle 4 – AUTHORIZED: Data is secure and compliant with regulations—Data 

is trustworthy and is safeguarded from unauthorized access, whether malicious, fraudulent or 

erroneous

• Principle 5 – CLEAR: There is a common vocabulary and data definition—Data 

dictionaries are developed and metadata established to maximize consistency and transparency 

of data across systems.

• Principle 6 – EFFICIENT: Data is not duplicated—Data is collected once and used 

many times for many purposes.

• Principle 7 – ACCOUNTABLE: Decisions maximize the benefit of data—Timely, 

relevant, high quality data are essential to maximize the utility of data for decision-making. 

DEVELOP A GOVERNANCE MODEL

Next, the State should develop a safety data governance model (Governance Model) to establish 

the organizational framework and structure for governing its safety data system. Figure 11 

illustrates a general governance model. The oval shapes represent various stakeholders, and the 

rectangles depict strategic goals, business processes, data systems, and governance components. 

NCHRP 666 explains the hierarchy between the data management, data governance, and data 

stewardship components of a governance model: (NCHRP 666: Target-Setting Methods and Data 

Management to Support Performance-Based Resource Allocation by Transportation Agencies, 

Volume II: Guide for Target-Setting and Data Management, 2010.)

The data governance role primarily represents the individuals responsible for establishing overall 

policies, standards, and procedures that are to be followed by the organization. The data 

stewardship role represents the team of individuals throughout the organization who are 

responsible for enacting these policies and procedures on a daily basis. The data management 

program can be considered the umbrella overseeing all activities related to the management of 

core data systems.

The management of data assets is usually accomplished through a Data Governance Board. The 

Data Governance Board, typically comprised of senior level managers across all business areas, is

the authoritative body that serves in an oversight role for managing the data governance activities 

of an organization. This role ensures data programs are successfully managed to meet the business 

needs and help achieve the agency’s strategic vision, mission, and goals for data. Data Stewards 

and Custodians manage the data systems used to support core business processes and functions of 
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the division, as well as the internal and external stakeholders who share a common interest as 

users of those data systems (also known as the Community of Interest). 

Figure 11. Organizational chart. General data governance model. 

Source: Adapted from NCHRP 666, Figure 4.2, Overview of a general data governance framework. 

Figure 12 illustrates an example Governance Model for Safety Data. The model identifies specific 

business processes, safety applications and tools, and safety data systems that the data governance 

program supports. An external department of public safety (or similarly named State agency) often 

houses the crash database. However, the DOT may maintain a copy for geolocating crash data, 

conducting safety analyses, and programming projects to improve safety as part of their HSIP. 

State safety program managers are the most knowledgeable regarding their safety data systems 

and program needs; therefore, they play a critical role in developing a governance model that 

addresses those needs and provides maximum benefit to the Community of Interest. 
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Figure 12. Organizational chart. Safety data governance model. 
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The governance model should consider the organizational structure of the DOT, with the 

understanding that some organization changes may be necessary to implement data management. 

The governance model should also reflect the status of data governance initiatives within the DOT

as determined in Step 1: 

• If there are no other management or governance initiatives underway, the State should 

implement safety data management and governance as stand-alone initiatives and reflect the 

needs of safety stakeholders within the DOT, as shown in Figure 12. The governance model

should include coordination with the IT office and other business areas charged with collecting 

or managing safety related data. 

• If management and governance initiatives are underway within one or two other offices, the 

governance model should complement those efforts while meeting the needs of safety 

stakeholders within the DOT. The Safety Data Governance Board should coordinate with 

other Boards to leverage applicable policies and decision-making processes for managing data. 

• If management and governance initiatives are underway at the agency-wide level, State safety 

program managers should designate a representative on the Data Governance Board. If an 

agency-wide governance model exists, the safety program manager should work with the 

Board to incorporate the safety program’s strategic goals, business processes, and data 

systems into the larger governance model. If there is no model in place, the State should 

develop a safety-specific governance model that fits within the context of the larger initiative. 

ESTABLISH EOLES AND RESPONSIBILITIES FOR MANAGEMENT AND 

GOVERNANCE

Next, the state should establish roles and responsibilities for data management and governance. 

Establishing roles and responsibilities for data governance helps to embed these practices into the 

culture and day-to-day business operations. The State should reach out to other business areas 

responsible for safety data systems to help identify roles and responsibilities. Table 7 defines the 

roles and responsibilities recommended to support a governance model. 

Although there are distinct roles defined, it is important to note the roles do not equate to 

distinct positions or people. Once the state has adopted these roles, they should develop a staffing 

plan to identify the positions or people who will fulfill the roles. They may need to identify new 

positions to support the Governance Program. The State should formalize and institutionalize 

governance roles and responsibilities by incorporating them into staff job descriptions, job duties, 

and performance plans. 
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Table 7. Data governance roles and responsibilities. 

Role Description Responsibilities

Data 

Governance 

Board

Senior level managers across business areas 

of agency; typically includes director of the IT 

office or division. The Board may include 

representatives from external agencies 

charged with statutory authority for managing 

a specific data system.

• Implement policies and 

procedures for collecting, 

managing, and using data and 

information. Appendix G 

provides additional guidance 

on data management 

practices.

Chief Data 

Steward

An executive data steward who serves as the 

chair of the Data Governance Board and as 

the primary business champion of a data 

management program.

• Chair the Data Governance 

Board

• Serve as primary champion of 

a data management program

Data 

Stewards

Individuals who ensure data is collected, 

updated, managed, and used in accordance 

with policies established by the Board.

Data Stewards may be internal or external to 

the DOT, depending on the agency charged 

with statutory authority for managing a 

specific data system.

• Identify and manage metadata 

• Identify and resolve data 

quality issues

• Determine business and 

security needs of data

• Communicate data quality 

issues to individuals who can 

influence change

• Provide input to data analysis

Data 

Business 

Owners

Individuals who establish business 

requirements for use of data in their business 

area. They also may approve access to data 

systems supported by their business area.

Data Business Owners may be internal or 

external to the DOT, depending on the 

agency charged with statutory authority for 

managing a specific data system.

• Establish business rules for 

use of data in their business 

area

• May approve access to 

systems supported by their 

business area 

Data 

Custodians 

Individuals who provide technical support for 

data systems. This may include IT staff such as 

network administrators, database 

administrators, server administrators, and IT 

security. This may also include application 

programmers and systems analysts who work 

in business areas other than the IT office or 

division.

• Ensure safety and integrity of 

data in custody of IT

• Implement system and data 

access controls appropriate 

for security 

• Provide reasonable 

safeguards for information 

resources
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Table 7. Data governance roles and responsibilities (continuation). 

Role Description Responsibilities

Working 

Groups (subcommittees)

A group of people who collect and provide 

data and establish business rules and 

processes for a specific data system.

Working Groups may include internal and 

external stakeholders.

• Provide recommendations to

the Board regarding data

products to meet business

needs

• Provide recommendations to

the Board regarding

standards and procedures for

collecting, maintaining, and

using data systems and

products within the agency

• Provide recommendations

regarding technology tools to

support data management at

the agency

Community 

of Interest 

Association of people comprised of internal 

and external stakeholders who share a 

common interest as users of a data system.

• Communicate with Data

Business Owners regarding

their business needs for data

systems

Sources: NCHRP 666: Target-Setting Methods and Data Management to Support PerformanceBased Resource Allocation by Transportation Agencies, Volume II: Guide for Target-Setting and 

Data Management, 2010.; Data Governance, Standards, and Knowledge Management, Alaska 

Department of Transportation and Public Facilities (ADOT&PF), 2009, Appendix B – Kansas 

Department of Education Roles and Responsibilities and Appendix C – Data Governance Manual; 

The DAMA Dictionary of Data Management, the Data Management Association (DAMA), 2nd

Edition, 2011.

As with the governance model, the roles and responsibilities depend on whether there are other 

governance initiatives underway within the DOT, such as: 

• If there are no other data management or governance initiatives underway, the roles and

responsibilities should reflect the organizational structure of the safety program within the

DOT. They may also include other program areas or external agencies statutorily authorized

to collect or maintain safety related data.

• If data management or governance initiatives are underway within one or two other offices,

the roles and responsibilities should complement those efforts as appropriate. If necessary, the

State may define additional roles and responsibilities to meet the needs of safety stakeholders.
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• If data management or governance efforts are underway at the agency-wide level, the 

Governance Program should integrate those efforts. The roles and responsibilities for safety 

data governance should complement those defined for the larger enterprise initiative. 

The State should also determine the authority and organizational responsibility for data 

governance. Authority is often granted under a policy directive from executive management. 

Organizational responsibility should be a shared at the senior level via the Data Governance Board 

and at the individual office level with business area managers. The determination of organizational 

responsibility is unique for each DOT, as each has a distinct culture and characteristics in the way 

their data systems have evolved. 

DEVELOP IT PROJECT GOVERNANCE

The IT office also plays an important role in the Governance Program, and the IT Director should 

participate on the Board. Traditionally, the IT office established the data standards, data 

dictionaries, and defining requirements for data applications. Now, individual business areas often

help define requirements for data applications and customized software to meet their business 

needs. The partnership between the IT office and the business areas is vital for successful 

implementation of the Governance Program. 

The IT office may have its own process for project development and prioritization that does not

always align with safety business needs. States may need to revise their project selection process 

to balance safety data system improvements while accommodating the often-competing needs of 

other business units within the agency. 

Appendix H documents key components of good practices for States to improve their IT project 

selection process. This includes the development and enforcement of consistent processes for IT 

project identification, prioritization, and selection criteria as follows:

• Establish a formal process for submitting IT project requests. States could conduct an 

annual (or more frequent) call for IT projects in which project sponsors are required to submit 

a project questionnaire describing the nature of the project, expected benefits and costs, and 

how it supports IT priorities. Project sponsors would obtain signatures from their division 

director to establish executive support for the proposed effort.

• Establish a formal project prioritization and selection process. States could appoint an 

IT Investment Selection Board to pre-review IT project requests. This group could discuss

how proposals fit into overall agency priorities and explore alternate ways to handle project 
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requests. IT or Project Management Office staff could help develop project proposals and 

support the prioritization and selection process. 

• Establish criteria for IT project prioritization. Example criteria include the following: 

− Ranking potential projects by value and benefits

− Assessment of risks to determine investment priorities

− Inventory of resource availability and allocation

− Determination of an optimal or acceptable size of the project pipeline

− Alignment of projects with the Department’s strategic objectives, IT plan, and 

executive management input

− Balancing different types of projects by purpose and benefit

− Balancing opportunity, benefits, and risk

• Use tools to support the IT project prioritization process. This enables visibility, 

standardization, measurement, and continuous process improvement. Example tools include: 

− Spreadsheet tools

− Software tools such as Decision Lens Software

− Severity and risk assessment matrix

− Project Portfolio Management tools

• Engage executives in the IT project development and maintenance process. The 

State’s enterprise-wide data governance council (or executive steering committee) could 

review IT project requests and assess how they fit into larger priorities. This group would 

finalize project selections and determine budget and resource parameters. The governance 

council would coordinate with the IT Director on a regular basis to review prioritization and 

align new investment opportunities with business priorities. 

• Manage the IT pipeline. The IT Department could maintain a database of current and 

potential projects. IT staff could periodically evaluate the status and performance of projects 

using Earned Value Analysis techniques and identify tasks outside of targets or thresholds. Staff 

could also make project continuation or termination decision at major project stages.
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DEVELOP DATA GOVERNANCE DOCUMENTATION

Finally, the State should document data management plans in the form of a data governance 

charter, data governance manual, data catalog, and business terms glossary. While the Safety DBP 

provides a roadmap for developing data management in general, this supporting documentation 

provides accountability and rules of engagement for implementing the Governance Program. 

Therefore, they may develop and maintain this documentation separately from (and following 

implementation of) the Safety DBP. The following paragraphs briefly describe each of these 

documents. 

• Data Governance Charter: The data governance charter is a high-level document that

defines the authority of the Data Governance Council to oversee data management practices,

policies, and procedures that support the Safety Program. The charter describes the business

need, purpose, authority, goals, and membership of the Governance Program. The charter

serves as a formal announcement for the initiative. It conveys there is active support from

senior management, and there are resources assigned to fulfill the goals stated in the charter.

The charter includes:

− A high-level description of the business problem or safety data management challenge

to establish the need for data management;

− The purpose and authority for the Governance Program;

− The vision, mission, and goals for governance;

− The offices or business areas participating in the Governance Program; and

− The effective date and duration of the Governance Program.

• Data Governance Manual: The data governance manual documents the State’s policies,

standards, roles, and responsibilities for managing safety data systems under the authority of

the Governance Program. It also includes comprehensive, enterprise-wide data collection and

quality standards, which are necessary to ensure data interoperability across disparate systems.

Data interoperability promotes a single version of the truth and minimizes the problems

created by different business units and programs drawing different conclusions based on their

version of the data. The manual includes:

− An introduction defining the goals for data governance, authority for the manual, and

data governance principles that guide governance policies and actions;

− The Governance Model illustrating the organizational structure and procedures for

governing data systems;
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− Roles and responsibilities for governance, including members of the Data Governance 

Board, data stewards, data business owners, data custodians, working groups, and the 

Community of Interest;

− The IT office’s involvement in the safety program. This includes, but is not limited to, 

platforms used to warehouse safety data systems, protocols and IT tools used to link 

data from various sources, and mechanisms for sharing data both internally and 

externally; 

− The data systems included in the Governance Program; 

− Data management policies for data collection and updates, data quality procedures, data 

privacy and security, and data storage and access; and

− Data standards defining naming conventions, metadata, reference and master data 

management, and disclosure and disposal policies.

• Data Catalog: The data catalog documents the safety data systems included in the 

Governance Program and the offices responsible for maintaining those systems. The data 

catalog can be included in the data governance manual or maintained as a separate document. 

It includes:

− The system of record for specific safety data systems such as the State’s road inventory 

database (as the primary source of roadway elements), or their traffic database (as the 

primary source of traffic data); 

− Contact information for the data stewards and data custodians who provide routine 

updates and maintenance of the databases and application systems; and 

− A data dictionary of all of the elements in the roadway or traffic database. This includes 

the element name, definition, attributes, file structure, coding conventions, and any 

metadata on how to use and interpret the data. 

• Business Terms Glossary: The business terms glossary defines commonly used business 

terms for data stored in a safety data system. The glossary of terms can help safety 

professionals understand IT terminology and vice versa. It provides a single reference source 

for documenting how standard terms (such as “location”) are defined and used across a DOT. 

For example, the term “location” may be defined as the point on a specific roadway identified 

somewhere between the beginning and end of a road or segment. Valid values for defining 

location may include mile point or latitude and longitude coordinates. A glossary also helps IT 

professionals define business terms correctly when developing or enhancing application 

systems to support the safety business area. Appendix I provides an example business terms 

glossary for safety data. 
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• Data Sharing Agreements: The State should implement formal data sharing agreements or

memorandums of understanding as needed to facilitate data sharing between internal and

external stakeholders. Data sharing agreements should identify data standards and file

exchange protocols needed to facilitate data sharing, including:

− Data definitions;

− Data file structures;

− Formats used for data transmission;

− Frequency of transmission of data updates;

− Names of individuals and offices who transmit and receive data updates;

− Processes to secure the transmission of confidential data and information;

− Standards and procedures to enable linking of data between data systems; and

− Service level agreements for data quality areas such as timeliness and accessibility.

SUMMARY

Step 3: Establish a Data Governance Program, addresses the need for States to establish clear 

authority and policies for managing safety data. The important actions in this step are as follows: 
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KEY OUTPUTS AND WORK 

PRODUCTS

□ Core data principles

□ Governance model

□ Roles and responsibilities

□ IT project selection process

□ Data governance charter

□ Data governance manual

□ Data catalog

□ Business terms glossary

□ Common resolution and accuracy 

standards for linking data sources

• Develop data principles. Data principles are 

statements that help guide governance practices 

at the DOT. States may adopt AASHTO 

recommended data principles or develop their 

own.

• Develop a governance model. A 

governance model establishes the organizational 

framework and structure for governing data 

systems at a DOT. It includes a graphical 

representation of how various governance roles 

support the organization’s safety data systems, 

business processes, and strategic vision and 

mission for data. 

• Establish roles and responsibilities for 

governance. In this action, States define the 

roles and responsibilities needed to support the 

governance model. Potential governance roles at the DOT could include a Data Governance 

Council, data stewards, data business owners, data custodians, working groups, and the 

Community of Interest. It is important to note the roles do not equate to distinct positions or 

people. Once the State has adopted these roles, they should develop a staffing plan to identify 

the positions or people who will fulfill the roles. They also may need to identify new positions

to support the Governance Program. Finally, they should determine the authority and 

organizational responsibility for data governance.

• Develop IT project governance. The partnership between the IT office and the business 

areas is vital for successful implementation of the Governance Program. The IT Director 

should participate on the Data Governance Council. In addition, safety business areas should 

help define requirements for data applications and customized software to meet their business 

needs. The State may need to revise their project selection process to better accommodate 

safety business area needs. This action includes adopting best practices for IT project 

identification, prioritization, and selection criteria.

• Develop data governance documentation. The State should document its Data 

Governance Program in the form of a data governance charter, data governance manual, data 

catalog, business terms glossary, and data sharing agreements. While the Safety DBP provides a 
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roadmap for improving data management, this supporting documentation provides 

accountability and rules of engagement for implementing the Governance Program. The State 

may develop and maintain this documentation separately from the Safety DBP.
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STEP 4. IDENTIFY NEEDS FOR SAFETY TOOLS 

ANDTECHNOLOGY 

In Step 4, the State should develop a strategy to improve its tools for safety data management. Key 

actions include identifying technology needs and developing a plan for improved use of tools. After 

completing this step, the State will understand its needs and weaknesses related to tools and will 

have a plan to enhance or replace these tools. 

IDENTIFY NEEDS FOR IMPROVED TECHNOLOGY

The safety program often relies on data from other business areas, so technology that provides 

integration and sharing of data across business areas is critical for meeting the needs of the safety 

program. The State should review the assessment results from Step 2 to identify any technology 

challenges and needs. The State can use the following questions to identify additional gaps and 

areas for improvement: 

• Data Collection Technology

− Is there a need to improve roadway inventory data collection through innovative

technology such as mobile laser scanning and remote sensing?

− Is there a process for collecting and integrating data from non-state agencies?

− Are law enforcement officials able to geolocate crashes in the field?

• Data Tools

− Can the State meet the needs for the primary requirements of predictive analysis,

systemic analysis, and data visualization?

− Does the State’s data tools provide: 1) data discovery, which allows users to access,

prepare, and integrate safety data; 2) an ETL process, which transforms data into the

proper format for safety analysis; and 3) analytical tools to conduct safety data analysis

on all public roads?

− Can users achieve consistent results when using safety analysis tools?

− Do users need tools such as dashboards, scorecards, and data visualization to help with

reporting, performance tracking, and analysis of safety data?

− Do users need geospatial location capabilities to facilitate efficient visualization and

analysis of data when using safety analysis tools?
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− Do users need additional functionality within applications such as enhanced modeling

and reporting capabilities to help identify and prioritize safety improvements?

− Can users generate customizable reports to support safety analysis?

• System Improvements

− Do users need more streamlined access to data and information?

− Are there data silos, especially for critical data sets needed to support safety programs?

− Are State and non-state agencies data set compatible?

• Knowledge Management

− Can staff access the latest information about safety systems, data, policies, reports,

tools, and training resources through a centralized knowledge management system?

− Are policies and processes properly documented?

• Cost Management Solutions

− Do analysts need better tools to evaluate the best allocation of funding resources for

safety projects?

• IT Support

− Are IT staff involved in the planning process for safety data system improvements?

− Are data stewards involved in the review of bids and purchases related to IT, software,

and data management that support the applications managed by those stewards?

− Are existing safety data systems expandable as new technologies and tools are

developed?

The State should benchmark their practices against those of other States to identify additional gaps 

and ensure they are not overlooking any best practices. FHWA’s Roadway Safety Data Program 

Toolbox has many resources available to support benchmarking. (FHWA Office of Safety, 

Roadway Safety Data Program website, http://safety.fhwa.dot.gov/rsdp/.)

DEVELOP PLAN FOR IMPROVED USE OF TOOLS

Once the needs are established, the State should develop a plan for enhancing or replacing safety 

tools and technology. For each of the following improvement areas, they should determine the 

level of effort and cost of each improvement and develop a prioritized list of actions for 
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implementing technology solutions. In some cases, they may already have initiatives underway to 

address the need for improved technology.

• Data Collection Technology: The State should identify opportunities for using innovative

technology to improve safety data collection. For example, some States are using remote

sensing technology such as Light Detection and Ranging (LiDAR) to collect a three dimensional

model of roadway inventory and asset data, as shown in Figure 13. A State could add LiDAR to

their existing data collection contracts for photo log and pavement condition imagery. They

could then use the technology to automate extraction of roadway features such as pavement

markings, curve and grade, lane widths, signs, and others. Other States are using web-based

map technology to improve law enforcement’s ability to locate crashes in the field. This

technology combines a State’s official road network or linear reference system with Global

Positioning System data. It allows law enforcement officials to locate crashes in the field

accurately and transfer data to the crash database electronically. This increases the accuracy of

crash location and reduces the time needed to process crash data for use in planning and

analysis. A State may benefit from using similar field-based data collection and transfer methods

to obtain EMS and injury surveillance data from other State agencies.

• Data Standards: The State should implement comprehensive, enterprise-wide data

collection and quality standards to ensure data interoperability across disparate systems. Data

standards assure data quality is maintained at a consistent level as data is used and manipulated

on an ongoing basis. FHWA’s Data Integration Primer notes the following commonly used data

standards: 1) the content and format for how data is stored in a database; 2) the protocols for

how data are accessed and manipulated; and 3) the format in which data is transferred from its

native system into another application or database.
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Figure 13. Photo. LiDAR imagery. 

Source: Utah DOT Transportation Blog, Utah DOT Leveraging LiDAR for Asset Management Leap, February 25, 

2013. 

• Data Tools: The State should develop new data tools as needed to support advanced safety

analysis. Data tools should provide the following capabilities:

− Data discovery. Data discovery is the process by which users access, prepare, and

integrate safety data for analysis. Data integration merges two or more data sources

together. Geographic information systems (GIS) is one of the most commonly used tools

for data integration. Because of the differences in native data sources, data management

systems, and methods of data collection, geographic location is often the only common

element that links these data. The location may exist: 1) as a latitude/longitude coordinate

representation; 2) in the form of a linear referencing method that is applied to a LRS to

give it spatial representation; and 3) a spatial feature in a GIS. GIS tools allow users to link

safety datasets, visually view data inputs, make corrections if needed, and validate analytical

tool outputs.
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− ETL Process. Safety data may reside in one system or be drawn from their native

“source” systems for the immediate purpose at hand (such as analysis, management, or

reporting). The ETL process is necessary to move safety data from their native systems,

transform it into the format required by the analytic software, and load the resulting file

into the analytic software tool. ETL can be streamlined into a “one-button” application

that automates the process. This allows States to

conduct pre-processing of safety data routinely and 

consistently. However, specific IT skillset, as well as 

knowledge of the data, the source databases, and the 

analytics are required to perform the task properly 

and to ensure the integrity of the results.

“GIS is an essential 

technology to support quality 

assurance/quality control, 

ETL, and analytics necessary 

to perform and report safety 

data analyses.” 

James Mitchell

Louisiana Department. of 

Transportation & 

Development

− Analytical tools. Safety analysis includes traditional

safety analyses and advanced analyses as described in

the Highway Safety Manual, or customized analyses

that States might create for their own purposes. 

Analytical tools such as the Interactive Highway Safety 

Design Model and AASHTOWare Safety AnalystTM implement the methods shown in the 

Highway Safety Manual. 

FHWA’s Informational Guide for State, Tribal, and Local Safety Data Integration provides 

additional guidance on performing spatial data integration using GIS, performing the ETL 

process, and conducting safety analysis using integrated safety data. These capabilities must be 

implemented across different data systems to support advanced analysis. When procuring data 

tools, States should comply with State and Department IT architecture standards and 

requirements. The technology plan should include details on integration methods, standards, 

roles, responsibilities, training, and processes to support data quality. 

• System Improvements: The State should enhance or replace legacy and silo safety data

systems. In particular, they should focus on systems that support core safety business

functions. The State should eliminate manual processes and migrate to newer systems that

allow for improved functionality. These improvements will improve access to data and reduce

data management costs associated with maintaining independent systems. The technology plan

should leverage and improve upon tools, databases, and systems already working.

• Knowledge Management System: The State should implement a knowledge management

system if one does not already exist. Knowledge management is a system for sharing and

retaining critical organizational knowledge and business processes related to the creation,
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capture, storage, and dissemination of safety data. For example, they could develop an online 

knowledge portal to exchange and share safety data and analysis tools with traffic safety 

professionals in the State. The portal could also contain instructions for accessing and using the 

data for safety analysis purposes. Figure 14 illustrates the relationships between knowledge 

management, data governance, and IT tools. While data governance provides the enforcement 

of authority regarding data management policies and practices, knowledge management 

ensures those policies and practices are kept current and accurate, stored in a manner where 

they can be easily retrieved, and made accessible to agency staff who need it to perform their 

duties. Knowledge management is also a vital tool for retaining organizational knowledge 

related to the design and development of application systems. A State can use best practices 

and lessons learned from similar projects to shorten the application development life cycle.

Figure 14. Venn diagram. Data governance and knowledge management. 

Source: Adapted from Data Governance, Standards, and Knowledge Management, Alaska Department of 

Transportation and Public Facilities, 2009. 

• Cost Management Solutions: The State should use cost management tools to allocate

funding for safety projects. For example, one State developed a spreadsheet-based tool to

assess the cost effectiveness of countermeasures using safety data from their data portal. The

tool reports the economic benefits of implementing safety improvements based on crash

statistics and the economic costs of certain crash types. The tool has helped them decide

where to invest in safety improvements.

• IT Support: States must change their business processes and update data systems to meet

their programs’ business needs. However, data in older mainframe database systems is often

not compatible with new technology such as GIS data management tools. Data governance is

most successful when business offices and IT offices work as partners to ensure that:

1) business offices define their IT related needs, and 2) the IT offices procure the appropriate

hardware and software to meet the agency’s business needs.

Before investing time and resources in technology improvements, the State should involve IT 

staff in the planning process and ensure all stakeholders understand the implications for both 

their business area and IT infrastructure. Similarly, the IT Department should involve data 
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75

stewards in the review of bids and purchases related to IT, software, and data management 

that support the applications managed by those stewards.

Lack of investment in technology and lack of coordination with IT can lead to significant 

operational problems. For example, one State invested significant resources to transition

mainframe data to a new database structure and software. After dedicating significant 

resources to the effort, they discovered their IT infrastructure and standards were not

adequate to support the new system. As a result, they were not able to share data with other

business areas or the public as intended. In another case, a State invested in a robust data 

collection contract to meet Federal reporting requirements and the need for improved analysis 

capabilities in several program areas. As staff started receiving data from the contractor, they

discovered there was inadequate space for storing the data on the existing infrastructure. As a 

result, data access is limited, and they are not able to use the data to its potential for 

supporting safety and other business area needs. 

SUMMARY

Step 4: Identify Needs for Safety Tools and Technology, addresses the need for States to improve 

its tools for safety data management. The important actions in this step are: 

• Identify needs for improved technology.

States can use the assessment results from

Step 2 to identify technology challenges and

needs.

• Develop plan for improved use of tools.

States should develop a plan for enhancing or 

replacing safety tools and technology. They 

should develop a prioritized list of actions for 

implementing technology solutions, and

determine the level of effort and cost of each improvement. In some cases, States may already 

have initiatives underway to address the need for improved technology.

KEY OUTPUTS AND WORK 

PRODUCTS

□ Summary of needs and weaknesses

related to safety tools and 

technology

□ Plan for enhancing or replacing

safety tools and technology
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STEP 5. DEVELOP ACTION PLAN

After the State identifies its technology needs, it should develop a plan for implementing the Safety 

DBP. Key actions include summarizing the challenges, issues, and gaps identified in previous steps, 

identifying priorities, and developing an action plan for improving safety data. After completing this 

step, the State will know its most critical deficiencies and have an action plan to address those 

deficiencies.

SUMMARIZE GAPS AND IMPROVEMENTS 

First, the State should summarize the specific challenges, issues, and gaps identified in previous 

steps. The State should categorize gaps in the following areas:

• System: Gaps related to data collection, data access, data integration, data quality and

validation, data storage, and documentation.

• Technology: Gaps related to data collection technology, data tools, database design, system

improvements, system interfaces, knowledge management system, cost management solutions,

and IT support.

• Institutional: Gaps related to data management and governance, data ownership,

coordination across business areas, resource availability, and training needs.

The State should also identify improvements to address the gaps within each area. One approach 

is to compare the “current” situation to an ideal or future “desired” condition and identify the 

necessary improvements to close the gaps. For example, in the institutional area, the State may

need to establish business rules to enable sharing of safety data between business areas. They can 

also use the business process diagrams and narratives from Step 2 to identify gaps, overlaps, and 

inefficiencies in business processes. 

States can also leverage the resources in Table 8 to identify additional actions to resolve gaps. 

They should obtain input from data business owners or directives from senior management to 

determine critical gaps. 
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Table 8. Resources for identifying improvements. 

Resource Summary Link

NCHRP Report 814: Data to 

Support Transportation 

Agency Business Needs

Appendix D contains resources 

for identifying potential data 

improvements.

http://www.trb.org/Main/Bl

urbs/173470.aspx 

Improving Safety Data 

Programs Through Data 

Governance and Data 

Business Planning

Peer exchange summary that 

describes State practices in data 

governance and data business 

planning for safety applications. 

http://onlinepubs.trb.org/o

nlinepubs/circulars/ec196.p

df 

Informational Guide for State, 

Tribal, and Local Safety Data 

Integration

Provides guidance to States, 

Tribes, and local agencies on the 

steps and effective methods of 

safety data integration.

https://safety.fhwa.dot.gov/

rsdp/downloads/fhwasa161

18.pdf

FHWA Transportation 

Performance Management 

Toolbox

Provides guidance, selfassessment tools, and resources 

for States to enhance 

performance management 

practices.

https://www.tpmtools.org/

IDENTIFY PRIORITIES

Next, the State should prioritize the list of gaps to identify the most critical needs. One approach 

is to consider the risks associated with the gaps and their impact on achieving their safety program

objectives. They may also consider the potential impacts of an interruption in critical business 

processes, as shown in Table 9. 
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Table 9. Example risk assessment. 

Risk Statement Negative Impact

If safety data are not collected... Then the DOT could not evaluate and identify 

countermeasures for crashes

If there is a lack of a proper geospatial 

framework for data integration...

Then data could be stored in many different 

formats. Not having a single method of integration

would affect the quality and availability of different 

data sources for analysis. Data collection efforts 

may be duplicated across divisions. There would 

be no ability to account for the changing nature of 

the roadway network over time. Decisions may be 

made with limited information. 

If data systems are not maintained to 

modern standards... 

Then the IT office might be unable to support the 

system, there might be difficulty integrating with 

modern systems; integration efforts may be

fragmented, or there might be an inability to 

enhance or modify the system.

If there is no coordination across offices to 

exchange information between safety data 

systems... 

Then there could be limits to analysis and 

reporting, and there could be disconnected data 

resources. 

If resources for maintaining current safety 

data systems are reduced or eliminated...

Then there could be delayed application 

development, there could be delayed data 

publication, or there could be a reduced ability to 

address urgent safety business needs.

If staff have limited knowledge of safety 

analysis tools... 

Then there could be inconsistent approaches to 

analysis, documentation, and increased time to 

review and correct work.

If there is distrust in the quality of data for 

safety analysis tools... 

Then there could be hesitancy to use safety 

analysis tools. Staff avoiding using tools will revert 

to using crash rates and basic crash history. This 

requires greater investment of staff time in doing 

analysis because safety analysis tools can quickly 

produce summaries (analysis results that otherwise 

would take weeks to complete manually and in 

other cases would be impossible to do without the 

tool). 
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In its pilot study, Washington State DOT applied a four-step risk assessment process to identify, 

assess, and address risks for its safety data system as follows:

• Risk Identification: Collect and identify risks throughout the organization and develop a

risk-list.

• Risk Evaluation: Determine the likelihood (frequency) and severity (degree of impact) for

each risk.

• Risk Analysis: Rank and prioritize the risks, determine the level of risk (based on the

likelihood and severity scores, as shown in Figure 15), and assign responsibility for management

of risks.

• Risk Response: Determine the Risk Treatment Strategy and action to address risks; develop,

implement, and monitor risk treatment strategies; and monitor and sustain mitigation best

practices.

WSDOT’s risk assessment process helped identify additional action items for their Safety DBP. 

The level of risk also helped prioritize the action plan recommendations.

Another approach is to assign a priority for filling gaps based on a general assessment of the 

required investment and resulting value to the agency as follows:

• Low priority – The required investment and resulting benefits do not add significant value to

the agency;

• Medium priority – The agency should fill the gap as time and investment permits; however,

safety analysis can proceed without filling this gap; and

• High priority – The agency should fill this gap as soon as possible as it is associated with high

risks, it provides high value to the agency, or it is required for successful and accurate safety

analysis.
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Figure 15. Matrix. Level of risk. 

Source: Risk Management, Risk Assessment Process PowerPoint, Washington State Department of Transportation. 
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DEVELOP SAFETY DATA ACTION PLAN

The State should develop an action plan that summarizes the gaps, actions, and priorities for 

addressing the most critical gaps and needs. Table 10 provides an example format for a safety 

data action plan. 

Table 10. Example safety data action plan. 

Improvement 

Area Gap Action Priority

System / Data 

Collection

1. Gap – Description of gap 1. Action – Description

of solution

High

2. Gap – Description of gap 2. Action – Description

of solution

Medium 

... ... ... 

Technology ... ... ... 

Institutional ... ... ... 

DEVELOP ROADMAP FOR IMPLEMENTATION

Finally, States should develop a roadmap for implementation by identifying key steps and priorities

for each action in the Action Plan. They should also identify the offices or agencies responsible for 

each action, as well as the timeframe for implementation. The timeframe may include the short

term (six months to one year), medium term (one to three years), or long term (beyond three 

years). The State should review the schedule and revise as needed to reflect any shifting 

priorities. Table 11 and Figure 16 show an example roadmap for implementation. 
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Table 11. Example roadmap for implementation. 

Key Steps Action Priority Responsibility Timeframe

1. Examine

and revise

agency 

policies

a. Assign responsibility for

implementing the Safety DBP.

High DOT 

Transportation 

Information Group

6 months – 1 

year

b. Budget for implementation of the

Safety DBP and dedicate staff

resources

Medium DOT 

Transportation 

Information Group

6 months – 1 

year

2. Implement

Safety Data

Governance

Process

a. Implement Data Governance

Council

High Data Governance 

Council 

6 months – 1 

year

b. Formally adopt core principles

for data and information

management and incorporate

them into governance policies,

standards, and processes

High Data Governance 

Council 

6 months – 1 

year

c. Implement governance roles and

responsibilities

High Data Governance 

Council 

6 months – 1 

year

d. Institutionalize governance roles

and responsibilities by

incorporating them into staff job

descriptions and job

performance review criteria

Medium 

/ High

Data Governance 

Council 

6 months – 1 

year

e. Develop data governance

documentation, including a

charter, manual, data catalog, and

business terms glossary

Medium 

/ High

Data Governance 

Council 

1 – 3 years

f. Adopt or revise policies (for

example, stewardship, data

security, database recovery, data

retention)

High Data Governance 

Council 

1 – 3 years

g. Adopt or revise standards (for

example, metadata, naming

conventions, data models)

High Data Governance 

Council 

1 – 3 years
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Table 11. Example roadmap for implementation (continuation). 

Key Steps Action Priority Responsibility Timeframe

3. Improve

quality of

safety data

systems

a. Develop and implement crash

validation business rules

Medium State Highway 

Patrol

6 months – 1 

year

b. Ensure a 5+ year history of crash

data is available for all public

roadways

High DOT 

Transportation 

Information Group

1-3 years

c. Work with stakeholders and

partners to develop methods for

collecting and managing data on

roadway features not currently

available

Medium DOT 

Transportation 

Information Group

1-3 years

d. Identify and prioritize roadway

inventory data attributes most

important to traveler safety for

vehicles, bikes, and pedestrians

on State and local systems

Medium DOT 

Transportation 

Information Group

1-3 years

e. Identify opportunities to procure

and use innovative technology to

supplement current manual data

collection methods or to capture

data where needed for missing

data items

Medium DOT 

Transportation 

Information Group

1-3 years

f. Develop and implement an

automated reporting tool for

performance measures

established in the Strategic

Highway Safety Plan

Medium DOT Safety Office 1-3 years

g. Replace legacy crash data system

mainframe so it can be a reliable

and effective source of crash data

High DOT 

Transportation 

Information Group

3-5 years
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Table 11. Example roadmap for implementation (continuation). 

Key Steps Action Priority Responsibility Timeframe

3. Improve

quality of

safety data

systems

h. Work with local law

enforcement agencies to

implement electronic crash

reporting (provide grants for

equipment purchase and

technical support as needed)

High State Highway 

Patrol

3-5 years

i. Replace the roadway information

system mainframe so it can be a

reliable and effective source of

roadway inventory data

Medium 

/ High

DOT

Transportation 

Information Group

3-5 years

4. Improve

Tools for

Safety 

Analysis 

a. Develop specifications for

exporting data for safety analysis

tools

Medium 

/ High

DOT Safety Office 6 months – 1 

year

b. Ensure data stewards and safety

prediction experts are involved

in critical decisions regarding

segmentation criteria and testing

of safety analysis results prior to

release of safety analysis tool

database updates.

High DOT Safety Office 1-3 years

c. Expand the use of safety analysis

tools for systemwide analysis to

identify systematic improvements

needed to reduce fatalities and

serious injury crashes

Medium DOT Safety Office 1-3 years

d. Obtain cross-functional user

input to improve tools for safety

analysis

Low DOT Safety Office 1-3 years

e. Establish feedback mechanisms

among users, collectors, and data

managers

Low DOT Safety Office 1-3 years

f. Develop a complete inventory

and safety-project tracking

mechanism for all public roads

Low DOT Maintenance 3-5 years
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Table 11. Example roadmap for implementation (continuation). 

Key Steps Action Priority Responsibility Timeframe

5. Improve

Data

Integration 

and 

Collaboration

a. Implement a knowledge

management system to increase

safety data understanding for

DOT staff

Medium Data Governance 

Council 

1-3 years

b. Establish a Memorandum of

Understanding to formalize data

sharing agreements between the

DOT and local agencies

Medium DOT 

Transportation 

Information 

Group 

1-3 years

c. Develop process to geolocate

crashes against State LRS for

crashes on all public roadways

DOT 

Transportation 

Information Group

3-5 years

6. Initiate

Training

a. Train staff on safety data policies,

procedures, processes, and tools

for safety analysis and reporting

Medium DOT Safety Office 1-3 years

b. Train external safety

stakeholders such as MPOs and

local agencies on use of safety

data and tools

Medium DOT Safety Office 1-3 years

c. Require training of new staff and

any staff who retrieve safety

data. Create online training that

staff can access on-demand.

Require testing of concepts and

certification of staff upon

completion.

Medium DOT Safety Office 1-3 years
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Table 11. Example roadmap for implementation (continuation). 

Key Steps Action Priority Responsibility Timeframe

7. Monitor

Progress

a. Track progress in implementing

the Safety DBP and governance

program.

Medium Data Governance 

Council 

Ongoing 

b. Establish performance metrics to

measure success.

Medium Data Governance 

Council 

Ongoing 

c. Report on progress to senior

management.

Medium Data Governance 

Council 

Ongoing 

d. Update the Safety DBP to reflect

new policies, procedures, or

standards regarding data

collection methods or equipment

Medium Data Governance 

Council 

Ongoing 

Figure 16. Gantt chart. Implementation roadmap. 
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SUMMARY

Step 5: Develop Safety Data Action Plan, 

addresses the need for States to develop a plan for 

implementing the Safety DBP. The important 

actions in this step are: 

KEY OUTPUTS AND WORK 

PRODUCTS

□ Summary of system, technology, and

institutional gaps

□ Priorities for addressing gaps

□ Safety data action plan

□ Roadmap for implementation

• Summarize gaps and improvements. The

State should summarize the specific challenges,

issues, and gaps identified in previous steps.

They should also identify improvements to

address the gaps within each area.

• Identify priorities. The State should

prioritize the list of gaps to identify the most critical gaps and needs. One approach is to

consider the risks associated with the gaps and their impact on achieving the State’s safety

program objectives. Another approach is to assign a priority for filling gaps based on a general

assessment of the required investment and resulting value to the agency.

• Develop safety data action plan. The State should develop a plan that identifies actions to

address the most critical gaps and needs. The plan should list actions for each safety data

system in priority order. They should also identify the offices or agencies responsible for each

action, as well as the timeframe for implementation.

• Develop roadmap for implementation. States should develop a roadmap for

implementation by identifying key steps and priorities for each action in the Action Plan. The

state should also identify the offices or agencies responsible for each action, as well as the

timeframe for implementation.
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STEP 6. DOCUMENT THE SAFETY DBP

In Step 6, the State should document the Safety DBP. The key action is to compile the 

results and documentation from Steps 1 through 5 into a single document. Having all the 

information in a single document makes it easier to distribute the Safety DBP to internal and 

external stakeholders. 

DOCUMENT THE SAFETY DATA BUSINESS PLAN

The Safety DBP should include the following components: 

1. Introduction – Document the Safety DBP objectives or outcome statement, and the vision

and mission for safety data, as determined in Step 1.

2. Plan for Safety Data Management and Governance – Document current challenges in

managing and governing safety data, stakeholders for safety data, and the stakeholder outreach

process, as determined in Step 1.

3. Assessment of Current SSDS – Document business processes for safety data systems,

status of current and past assessment recommendations, and overall management and

governance capabilities of the agency, as determined in Step 2.

4. Safety Data Governance Program – Document core data principles, current and planned

governance initiatives within the organization, the governance model describing the

organizational structure and framework for governing a State’s safety data system, and

governance roles and responsibilities, as determined in Steps 1 and 3.

5. Needs for Safety Tools and Technology – Document current needs and weaknesses

related to technology, as well as strategies for enhancing or replacing technology tools, as

determined in Step 4. This section should also identify any high priority data systems in need of

enhancement or replacement.

6. Action Plan – Document the actions, priorities, responsibilities, and schedule for improving

the State’s safety data systems, as determined in Step 5. This section should also document

performance metrics to measure success, as determined in Step 7.

7. Appendices – Supporting documentation such as the data charter, data governance manual,

data catalog, and business terms glossary.
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SUMMARY

Step 6: Document the Safety DBP, guides States in 

assembling the Safety DBP. The important actions in 

this step are: 

KEY OUTPUTS AND WORK 

PRODUCTS

□ Safety DBP

• Document the Safety DBP. The results and

documentation from Steps 1 through 5 are compiled into a single document to form the Safety

DBP.
90

STEP 7. IMPLEMENT AND SUSTAIN THE

SAFETY DBP

In the final Step, the State should implement and sustain the Safety DBP. Key actions include

assigning responsibility, establishing performance metrics, implementing the Safety DBP and 

Governance Program, conducting training on data governance, and monitoring progress. 

Leadership and coordination are important for successful implementation. These actions will help

the State implement its Governance Program while ensuring institutional support and buy-in at all 

levels. 

ASSIGN RESPONSIBILITY 

The State should assign clear responsibilities for monitoring implementation of the Safety DBP and 

Governance Program. For example, the State may designate a governance champion or small team

to guide all activities in the plan initially. This provides a central point of responsibility for tracking 

progress and building momentum for the program. Once the program is self-sustaining, the Data 

Governance Board can assume responsibility for sustaining the program. 

ESTABLISH PERFORMANCE METRICS

The State should define performance metrics to measure success. While the State’s HSIP includes 

goals for improving safety, the DBP metrics should track how well data systems support the safety

program. Performance metrics should reflect the Safety DBP objectives, business needs, and 

challenges. Table 12 provides example performance metrics. They can also monitor performance 

through its State Traffic Records Strategic Plan. 

Table 12. Example performance metrics. 

Example Objectives, Business 

Needs & Challenges Example Performance Metric

Understand and promote the value of 

safety data as a Department-wide asset

Assessment of the value of data as a Departmentwide asset conducted among senior management

Number of executive briefings

Identify and address safety data gaps Assessment of gaps in safety data systems based on 

MIRE FDEs 
91

Table 12. Example performance metrics (continuation). 

Example Objectives, Business 

Needs & Challenges Example Performance Metric

Improve data quality management 

processes

Assessment of data quality (timeliness, accuracy, 

completeness, etc.)

Identify needs and opportunities to 

integrate safety data systems

Ongoing assessment of data interoperability and 

integration of safety data systems

Improve access to safety data Assessment of stakeholder’s satisfaction with and 

ability to access safety data

Number of users accessing safety data

Identify and address safety data gaps Assessment of gaps in safety data systems based on 

MIRE FDEs 

Improve ability to track safety 

improvements

Development of tools to assess the cost 

effectiveness of countermeasures using safety data

Implement formal data governance 

structure

Level of engagement, participation, and influence 

the Safety Data Governance Council is having

Extent to which stakeholder offices are 

implementing data standards in their data collection 

and management practices

Improve understanding and knowledge 

of safety data policies, procedures, 

processes, and tools for safety analysis 

and reporting

Development of annual training schedule

Conduct scheduled training classes

Percentage of employees who have attended 

training 

IMPLEMENT THE SAFETY DBP

The State should implement the Safety DBP by conducting the activities in the Action Plan and 

Roadmap. It should also formalize the roles and responsibilities for governance as identified in the 

Safety Data Governance Manual developed in Step 3. 

Implementation is not a one-time event, but rather the State should incorporate Safety DBP 

policies, standards, and procedures into day-to-day business practices. As such, they should 

continue to engage safety stakeholders beyond initial implementation of the Safety DBP. These 

individuals help inform and enforce safety data policies, procedures, and standards. They also 

provide input on development of tools for safety analysis. 
92

Implementation support is available through the FHWA Office of Safety’s technical assistance 

program. More information is available at https://rspcb.safety.fhwa.dot.gov/technical.aspx. 

CONDUCT TRAINING

The State should develop and implement a training program to introduce the Safety DBP principles 

and practices to agency staff, partner agencies, and consultants. Training promotes the Governance 

Program. It helps stakeholders understand data management and governance processes and their 

responsibilities within those processes. Training is a critical step in institutionalizing the 

Governance Program throughout the offices responsible for the SSDS. 

The State should train staff on safety data policies, procedures, processes, and tools for safety 

analysis and reporting. States should also train external safety stakeholders such as MPOs and local 

agencies as appropriate. Ongoing training is necessary to ensure the consistent use of data and to 

maintain data integrity over time. 

MONITOR PROGRESS 

The State should monitor implementation progress by tracking progress on action steps and 

assessing performance using the metrics defined above. Monitoring progress allows the State to 

adapt the program to changing priorities, and to capture knowledge and lessons learned from early 

governance efforts.

Initially, a State should report on progress at monthly meetings of the Data Governance Board. 

Discussion topics could include progress on action steps (such as tasks completed and tasks 

remaining) and any schedule impacts due to changes in DOT priorities, policies, standards, or 

legislative priorities. The State may need to adjust the timeline for implementing some of the 

recommendations. Once the program has gained momentum, the Board could meet on a

semiannual or annual basis. 

The TRCC may play a role in implementing some aspects of the Action Plan. Therefore, the State

should also report on progress to the TRCC.

The State may need to revise the Safety DBP if there are new policies, procedures, or standards 

regarding data collection methods or equipment. The State may also need to update the DBP if 

they have implemented new IT tools or applications. 
93

COMMUNICATE CHANGES

Finally, the State should provide relevant and timely progress updates to senior management and 

safety data managers and users. Topics for senior management briefings should include a high-level 

status update, successes achieved, new enhancements needed for existing systems, and 

recommendations for addressing issues. They should also report on cost savings and other 

benefits achieved through processes that are more efficient. This allows sufficient opportunity for 

senior managers to intervene and correct the course of the program, if needed. 

States may communicate with safety data managers and users through email updates, a newsletter, 

or safety data business planning website. Potential topics include information on safety data 

management standards, processes, members of the Data Governance Board, contact information 

for key safety data staff, and progress in implementing the governance program. States may also 

provide a mechanism for users to provide feedback and report on safety data issues. 

SUMMARY

Step 7: Implement and Sustain the Safety Data 

Business Plan, addresses the need for States to 

implement the Safety DBP and Governance 

Program. The important actions in this step are: 

KEY OUTPUTS AND WORK 

PRODUCTS

□ Designation of governance champion

or small team to guide

implementation

□ Performance metrics for measuring

success

□ Implementation of the Safety DBP

□ Training program on data

governance

□ Progress updates

• Assign responsibility. A governance

champion or small team should guide initial

efforts to implement the Safety DBP and

Governance Program. Once the program is selfsustaining, the Data Governance Council can

assume responsibility for sustaining the

program.

• Establish performance metrics.

Performance metrics track how well data systems support the safety program. Metrics should

reflect the Safety DBP objectives, business needs, and challenges.

• Implement the Safety Data Business Plan. The State should implement the Safety DBP

by conducting the activities in the Action Plan. It should also formalize the roles and

responsibilities for governance as identified in the Governance Program.
94

• Conduct training. Training is a critical step in institutionalizing the Governance Program

throughout the offices responsible for the SSDS. Ongoing training is necessary to ensure the

consistent use of data and to maintain data integrity over time.

• Monitor progress. The State should monitor implementation progress by tracking progress

on action steps and assessing performance using the metrics defined in this step. Monitoring

progress allows the State to adapt the program to changing priorities, and to capture

knowledge and lessons learned from early governance efforts. The State should report on

progress at Data Governance Council and TRCC meetings as appropriate.

• Communicate changes. The State should report on progress to senior management,

including successes achieved, new enhancements needed for existing systems, and

recommendations for addressing issues. This allows sufficient opportunity for senior managers

to intervene and correct the course of the program, if needed. The State should also

communicate with safety data managers and users via a safety data business planning website

or other means.
95

REFERENCES

References used to develop the Guide include the following:

• FHWA Guidance on State Safety Data Systems, March 15, 2016. Available at:

http://safety.fhwa.dot.gov/legislationandpolicy/fast/ssds_guidance.cfm .

• Cambridge Systematics, Inc. NCHRP Report 666: Target-Setting Methods and Data

Management to Support Performance-Based Resource Allocation by Transportation Agencies,

Volume II: Guide for Target-Setting and Data Management, Transportation Research Board,

2010. Available at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_666.pdf.

• Cambridge Systematics, Inc. NCHRP Report 754: Improving Management of Transportation

Information, Transportation Research Board, 2013. Available at

http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_754.pdf.

• Minnesota DOT Data Business Plan, 2008. Available at

https://www.dot.state.mn.us/tda/databusinessplan.docx.

• Scopatz, R., J. Benac, and N. Lefler. Crash Data Improvement Program (CDIP) Final Report.

FHWA-SA-14-001, January 2014. Available at

http://safety.fhwa.dot.gov/rsdp/downloads/fhwasa14001cdip_final.pdf.

• Mapping to MMUCC: A Process for Comparing Police Crash Reports and State Crash

Databases to the Model Minimum Uniform Crash Criteria. DOT HS 812 184, July 2015.

Available at http://www-nrd.nhtsa.dot.gov/Pubs/812184.pdf.

• Cambridge Systematics, Inc. Data Business Plan: Concept of Operations. Prepared for the

Alaska Department of Transportation and Public Facilities, September 2005.

• Vanasse Hangen Brustlin, Inc., United States Roadway Safety Data Capabilities Assessment,

FHWA-SA-12-028, July 2012. Available at

http://safety.fhwa.dot.gov/rsdp/downloads/rsdp_usrsdca_final.pdf.

• Vanasse Hangen Brustlin, Inc (VHB), Roadway Data Improvement Program: Informational

Resource, June 2012. Available at

http://safety.fhwa.dot.gov/rsdp/downloads/rdip_final061312.pdf.
96

• NHTSA, Traffic Records Program Assessment Advisory. Available at http://wwwnrd.nhtsa.dot.gov/Pubs/811644.pdf.

• Battelle, Cambridge Systematics, Inc., and Texas Transportation Institute. Traffic Data Quality

Measurement Final Report. FHWA, September 15, 2004. Available at

https://ntl.bts.gov/lib/jpodocs/repts_te/14058.htm.

• NHTSA. Model Performance Measures for State Traffic Records Systems. DOT HS 811 441,

February 2011. Available at http://www-nrd.nhtsa.dot.gov/Pubs/811441.pdf.

• DAMA UK Working Group on Data Quality Dimensions. The Six Primary Dimensions for

Data Quality Assessment. October 2013.

• FHWA, Crash Data Improvement Program Guide, April 2010. Available at

http://safety.fhwa.dot.gov/cdip/finalrpt04122010/finalrpt04122010.pdf.

• Vanasse Hangen Brustlin, Inc., Performance Measures for Roadway Inventory Data, FHWA-SA12-036, February 2013. Available at

http://safety.fhwa.dot.gov/rsdp/downloads/performancemeasures.pdf.

• C. Cabrera, “Data Governance Defined and Why It is Crucial to the Organization,” April 15,

2009. http://www.element61.be/e/resourc-detail.asp?ResourceId=6.

• Cambridge Systematics, Inc. Data Governance, Standards, and Knowledge Management.

Prepared for the Alaska Department of Transportation and Public Facilities, 2009.

• FHWA Office of Safety, Roadway Safety Data Program website,

http://safety.fhwa.dot.gov/rsdp/.

• Vanasse Hangen Brustlin, Inc. United States Roadway Safety Data Capabilities Assessment.

FHWA-SA-12-028, July 31, 2012. Available at

http://safety.fhwa.dot.gov/rsdp/downloads/rsdp_usrsdca_final.pdf .

• Vanasse Hangen Brustlin, Inc. Model Inventory of Roadway Elements (MIRE), Version 1.0,

FHWA-SA-10-018 (October 2010). Available at

http://safety.fhwa.dot.gov/tools/data_tools/mirereport/mirereport.pdf.

• Cambridge Systematics, Inc., Safety Data Management Systems and Processes, Literature

Review & Synthesis, FHWA-SA-15-061, March 2015.
97

• Cambridge Systematics, Inc. Safety Data Management Systems and Processes, Case Studies

(Draft), May 2015.

• Transportation Research Circular E-C196, Improving Safety Programs Through Data

Governance and Data Business Planning, A Peer Exchange, March 3-4, 2015, Washington D.C.

Available at: http://onlinepubs.trb.org/onlinepubs/circulars/ec196.pdf.

• AASHTO Subcommittee on Data, Data Subcommittee Efforts on Core Data Principles

website, https://docs.google.com/viewer?a=v&pid=sites&srcid=

ZGVmYXVsdGRvbWFpbnx0cmJkYXRhc2VjdGlvbnxneDoxZDc3YjczNjUxZjIxZmM3.

• Spy Pond Partners, LLC. Data to Support Transportation Agency Business Needs: A SelfAssessment Guide. NCHRP Report 814. Transportation Research Board, Washington, D.C.,

2015. Available at: http://www.trb.org/Main/Blurbs/173470.aspx.

• Cambridge Systematics, Inc. Highway Enterprise System and Asset Management Analysis

Assistance for the MassDOT Highway Division project, 2014.

• FHWA Data Integration Primer. Publication FHWA-IF-10-019, Federal Highway

Administration, Washington, D.C., August 2010. Available online at:

https://www.fhwa.dot.gov/asset/dataintegration/if10019/if10019.pdf.

• Scopatz, R., E. Goughnour, D. Abbott, E. Tang, D. Carter, S. Smith, and T. Salzer. Informational

Guide for State, Tribal, and Local Safety Data Integration. Publication FHWA-SA-16-118,

Federal Highway Administration, Washington, D.C., October 2016. Available online at:

https://safety.fhwa.dot.gov/rsdp/downloads/fhwasa16118.pdf.

• Cambridge Systematics. Inc. U.S. DOT Roadway Transportation Data Business Plan (Phase 3):

Data Business Plan Development for State and Local Departments of Transportation, May

2017. (Draft).

• The DAMA Dictionary of Data Management, the Data Management Association (DAMA), 2nd

Edition, 2011.

• Lefler, N., F. Council, D. Harkey, D. Carter, H. McGee, and M. Daul. Model Inventory of

Roadway Elements–MIRE, Version 1.0, Report No. FHWA-SA-10-018, October 2010.

Available online at: https://safety.fhwa.dot.gov/tools/data_tools/mirereport/mirereport.pdf.
98

APPENDIX A. SAFETY DATA ELEMENTS

Table A.1. Summary of Model Inventory of Roadway Elements

Fundamental Data Elements. 

Model Inventory of Roadway Elements 

Fundamental Data Elements (Model Inventory of 

Roadway Elements Number) (The number in 

parentheses ( ) identifies the data element number 

in MIRE Version 1.0.)

Applicable Roadway Network

Non-Local 

Paved 

Roads

Local 

Paved 

Roads

Unpaved 

Roads

Roadway Segment

Segment Identifier (12) Yes Yes Yes

Route Number (8) (Highway Performance Monitoring 

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes

Route/Street Name (9) (Highway Performance Monitoring 

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes

Federal Aid/Route Type (21) (Highway Performance 

Monitoring System full extent elements are required on all 

Federal-aid highways and ramps located within gradeseparated interchanges (that is, National Highway System 

(NHS) and all functional systems excluding local roads and 

rural minor collectors).)

Yes

Source: Model Inventory of Roadway Elements–MIRE, Version 1.0, Report No. FHWA-SA-10-018, October 2010, 

https://safety.fhwa.dot.gov/tools/data_tools/mirereport/mirereport.pdf. 
99

Table A.1. Summary of Model Inventory of Roadway Elements 

Fundamental Data Elements (continuation).

Model Inventory of Roadway Elements 

Fundamental Data Elements (Model Inventory of 

Roadway Elements Number) (The number in 

parentheses ( ) identifies the data element number 

in MIRE Version 1.0.)

Applicable Roadway Network

Non-Local 

Paved 

Roads

Local 

Paved 

Roads

Unpaved 

Roads

Rural/Urban Designation (20) (Highway Performance 

Monitoring System full extent elements are required on all 

Federal-aid highways and ramps located within gradeseparated interchanges (that is, National Highway System 

(NHS) and all functional systems excluding local roads and 

rural minor collectors).)

Yes Yes

Surface Type (23) (Highway Performance Monitoring 

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes Yes

Begin Point Segment Descriptor (10) (Highway 

Performance Monitoring System full extent elements are 

required on all Federal-aid highways and ramps located 

within grade-separated interchanges (that is, National 

Highway System (NHS) and all functional systems 

excluding local roads and rural minor collectors).

Yes Yes Yes

End Point Segment Descriptor (11) (Highway Performance 

Monitoring System full extent elements are required on all 

Federal-aid highways and ramps located within gradeseparated interchanges (that is, National Highway System 

(NHS) and all functional systems excluding local roads and 

rural minor collectors).)

Yes Yes Yes

Source: Model Inventory of Roadway Elements–MIRE, Version 1.0, Report No. FHWA-SA-10-018, October 2010, 

https://safety.fhwa.dot.gov/tools/data_tools/mirereport/mirereport.pdf. 
100

Table A.1. Summary of Model Inventory of Roadway Elements 

Fundamental Data Elements (continuation).

Model Inventory of Roadway Elements 

Fundamental Data Elements (Model Inventory of 

Roadway Elements Number) (The number in 

parentheses ( ) identifies the data element number 

in MIRE Version 1.0.)

Applicable Roadway Network

Non-Local 

Paved 

Roads

Local 

Paved 

Roads

Unpaved 

Roads

Segment Length (13) (Highway Performance Monitoring

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes

Direction of Inventory (18) Yes

Functional Class (19) (Highway Performance Monitoring 

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes Yes Yes

Median Type (54) Yes

Access Control (22) (Highway Performance Monitoring 

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes

One/Two-Way Operations (91) (Highway Performance 

Monitoring System full extent elements are required on all 

Federal-aid highways and ramps located within gradeseparated interchanges (that is, National Highway System 

(NHS) and all functional systems excluding local roads and 

rural minor collectors).)

Yes

Source: Model Inventory of Roadway Elements–MIRE, Version 1.0, Report No. FHWA-SA-10-018, October 2010, 

https://safety.fhwa.dot.gov/tools/data_tools/mirereport/mirereport.pdf. 
101

Table A.1. Summary of Model Inventory of Roadway Elements 

Fundamental Data Elements (continuation).

Model Inventory of Roadway Elements 

Fundamental Data Elements (Model Inventory of 

Roadway Elements Number) (The number in 

parentheses ( ) identifies the data element number 

in MIRE Version 1.0.)

Applicable Roadway Network

Non-Local 

Paved 

Roads

Local 

Paved 

Roads

Unpaved 

Roads

Number of Through Lanes (31) (Highway Performance 

Monitoring System full extent elements are required on all 

Federal-aid highways and ramps located within gradeseparated interchanges (that is, National Highway System 

(NHS) and all functional systems excluding local roads and 

rural minor collectors).)

Yes Yes

Average Annual Daily Traffic (79) (Highway Performance 

Monitoring System full extent elements are required on all 

Federal-aid highways and ramps located within gradeseparated interchanges (that is, National Highway System 

(NHS) and all functional systems excluding local roads and 

rural minor collectors).)

Yes Yes

AADT Year (80) (Highway Performance Monitoring 

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes

Type of Governmental Ownership (4) (Highway 

Performance Monitoring System full extent elements are 

required on all Federal-aid highways and ramps located 

within grade-separated interchanges (that is, National 

Highway System (NHS) and all functional systems 

excluding local roads and rural minor collectors).)

Yes Yes Yes

Source: Model Inventory of Roadway Elements–MIRE, Version 1.0, Report No. FHWA-SA-10-018, October 2010, 

https://safety.fhwa.dot.gov/tools/data_tools/mirereport/mirereport.pdf. 
102

Table A.1. Summary of Model Inventory of Roadway Elements 

Fundamental Data Elements (continuation).

Model Inventory of Roadway Elements 

Fundamental Data Elements (Model Inventory of 

Roadway Elements Number) (The number in 

parentheses ( ) identifies the data element number 

in MIRE Version 1.0.)

Applicable Roadway Network

Non-Local 

Paved 

Roads

Local 

Paved 

Roads

Unpaved 

Roads

Intersection

Unique Junction Identifier (120) Yes

Location Identifier for Road 1 Crossing Point (122) Yes

Location Identifier for Road 2 Crossing Point (123) Yes

Intersection/Junction Geometry (126) Yes

Intersection/Junction Traffic Control (131) Yes

AADT (79) [for Each Intersecting Road] Yes

AADT Year (80) [for Each Intersecting Road] Yes

Unique Approach Identifier (139) Yes

Interchange/Ramp

Unique Interchange Identifier (178) Yes

Location Identifier for Roadway at Beginning Ramp 

Terminal (197)

Yes

Location Identifier for Roadway at Ending Ramp Terminal 

(201) 

Yes

Ramp Length (187) Yes

Roadway Type at Beginning Ramp Terminal (195) Yes

Roadway Type at Ending Ramp Terminal (199) Yes

Source: Model Inventory of Roadway Elements–MIRE, Version 1.0, Report No. FHWA-SA-10-018, October 2010, 

https://safety.fhwa.dot.gov/tools/data_tools/mirereport/mirereport.pdf. 
103

Table A.1. Summary of Model Inventory of Roadway Elements 

Fundamental Data Elements (continuation).

Model Inventory of Roadway Elements 

Fundamental Data Elements (Model Inventory of 

Roadway Elements Number) (The number in 

parentheses ( ) identifies the data element number 

in MIRE Version 1.0.)

Applicable Roadway Network

Non-Local 

Paved 

Roads

Local 

Paved 

Roads

Unpaved 

Roads

Interchange Type (182) Yes

Ramp AADT (191) (Highway Performance Monitoring 

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes

Year of Ramp AADT (192) (Highway Performance 

Monitoring System full extent elements are required on all 

Federal-aid highways and ramps located within gradeseparated interchanges (that is, National Highway System 

(NHS) and all functional systems excluding local roads and 

rural minor collectors).)

Yes

Functional Class (19) (Highway Performance Monitoring 

System full extent elements are required on all Federal-aid 

highways and ramps located within grade-separated 

interchanges (that is, National Highway System (NHS) and 

all functional systems excluding local roads and rural minor 

collectors).)

Yes

Type of Governmental Ownership (4) (Highway 

Performance Monitoring System full extent elements are 

required on all Federal-aid highways and ramps located 

within grade-separated interchanges (that is, National 

Highway System (NHS) and all functional systems 

excluding local roads and rural minor collectors).)

Yes

Source: Model Inventory of Roadway Elements–MIRE, Version 1.0, Report No. FHWA-SA-10-018, October 2010, 

https://safety.fhwa.dot.gov/tools/data_tools/mirereport/mirereport.pdf. 
104

APPENDIX B. SUMMARY OF CASE STUDIES

MICHIGAN DOT SAFETY DATA PROCESSES AND GOVERNANCE PRACTICES, 

CASE STUDY FHWA-SA-15-059 

Summary

Michigan DOT incorporates data governance into their standard business operations to improve 

their safety data systems and processes. This case study examines Michigan DOT’s data 

governance practices from a policy and technical perspective.

Policy. Michigan DOT began implementing data management policies as part of their asset 

management program in the 1990s. These policies emphasize the value of data as an asset, with a 

goal to establish methods to collect data once for multiple uses. These practices have eliminated 

duplicate data collection, which has reduced costs associated with maintaining the same data in 

multiple data systems.

The DOT has a top-down data governance structure established by the State of Michigan 

Governor’s office via an Executive Directive in 2013. In response, Michigan DOT established: 

• A Data Governance Council, whose purpose is to establish policies for data governance and

develop data dictionaries and metadata for all major systems and data sources. Membership

includes representation from major business process areas and the IT Department;

• The role of Chief Data Steward, whose purpose is to implement data management within the

Department and chair the Data Governance Council; and

• Data stewards, who are ultimately responsible for establishing and using business rules that

govern data in each business area, including safety data systems.

Michigan DOT’s current focus is on improving its data management practices, including developing 

data dictionaries and metadata. The DOT is migrating its capital programming and asset 

management definitions and metadata into a single tool maintained by IT staff. Once migrated, the 

DOT will expand data stewardship roles to include systems not previously governed. Many of 

these are safety data systems or data programs that support safety.

Technical. From a technical perspective, Michigan DOT is using tools supported by a centralized 

IT Department, including: 

• An integrated Linear Reference System to maintain location data;
105

• AASHTOWare Safety AnalystTM, the Highway Safety Manual, and GIS tools to support safety

analysis.

• A work-order based maintenance system to maintain data on roadway safety features.

Applicability to Other States

Other States may develop and implement similar data governance practices for their safety 

programs as follows:

• Establish a Data Governance Council to oversee data management practices, policies, and

procedures;

• Define data stewardship roles and responsibilities for governing data in each business area;

• Develop a data dictionary to help IT staff understand how commonly used terms (for

example, location data) are used within individual business units;

• Involve the data stewards in the review of bids and purchases related to IT, software, and

data management that support the applications managed by those stewards;

• Include the IT Department at all levels;

• Update job descriptions and classifications to include the roles of data stewardship and

relate this concept to performance plans or job duties;

• Implement data governance using tools that are available and easy to understand; and

• Highway safety is a great starting point to implement data governance initiatives, since

there is widespread interest in improving safety.

Link

https://safety.fhwa.dot.gov/rsdp/downloads/miDOT_casestudy_dm_final.pdf. 
106

NEW HAMPSHIRE DOT SAFETY DATA SYSTEMS AND PROCESSES, CASE 

STUDY FHWA-SA-15-058

Summary

This case study examines how New Hampshire DOT has leveraged technology to support safety 

data management. NHDOT has invested in analytical, integration, and data sharing tools:

Analytical Tools. NHDOT uses the Highway Safety Manual, AASHTOWare Safety AnalystTM, 

the Interactive Highway Safety Design Model, and safety analysis spreadsheets to support safety 

analysis. Implementation and usage of these tools necessitated a review of data elements to 

determine gaps. They also used their safety datasets to calibrate the safety performance functions 

in Safety AnalystTM to local conditions. These processes required collaboration between the safety 

group and IT and demonstrated the need for institutionalizing methods outlined in the Highway 

Safety Manual.

Integration Tools. NHDOT uses an integrated LRS that provides a common link between safety 

data sets, including crash, traffic, and road inventory data. Their use of GIS applications helps to 

improve overall data quality by providing a method for visualization of data on maps and 

identification of data errors for correction, prior to their use with analytical tools.

Data Sharing Tools. Investments in data sharing technology are critical for sustaining and 

enhancing NHDOT’s safety program. Data sharing maximizes the potential to locate and correct 

errors reported by data users, which ultimately leads to improved data quality. Data sharing 

methods include providing quarterly snapshots of GIS data to regional planning agencies, providing 

GIS data to an online statewide GIS data warehouse, and providing GIS web maps online. NHDOT 

is also working on an initiative to share crash data with the New Hampshire Department of Safety 

within a relational database environment. 

Applicability to Other States

Other States may learn from NHDOT’s experiences in leveraging technology to improve develop 

their safety programs as follows:

• State DOT business areas, including safety program managers, should partner with IT offices to

understand technology available to support business operations;

• Safety Office staff should have a basic understanding of (or work with people who do) how IT

products can improve access to, integration of, and sharing of safety data;
107

• Think long-term about how technology fits into an overall safety data management and

governance strategy. Technology that provides integration and sharing of data across business

areas is critical for meeting the needs of the safety program;

• Ensure analytical tools support the needs of the safety program;

• Prioritize data collection efforts to focus on required data elements rather than optional data

elements;

• Use a common LRS as the foundation for safety data integration and analysis; and

• Coordinate data collection efforts with other agencies (including local) to expand data available

for safety analysis.

Link

https://safety.fhwa.dot.gov/rsdp/downloads/nh_case_study.pdf. 
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UTAH DOT SAFETY DATA PROCESSES AND GOVERNANCE PRACTICES, CASE 

STUDY FHWA-SA-15-060

Summary

This case study examines Utah DOT’s use of innovative technology to support safety data 

management and analysis. In addition to highlighting forward-thinking strategies for safety data 

management, data analysis, and reporting, the case study documents the need for a formal data 

business plan and data governance.

Data Collection. UDOT’s data collection program is coordinated among multiple business areas

to meet as many needs as possible. This includes incorporating advanced imagery (hi-resolution 

photo and LiDAR), data extraction of specified inventory, and pavement data imaging and tools for 

analysis. This innovative approach has vastly improved procedures for managing assets and 

roadway inventory.

Data Management. UDOT manages and shares data in a centralized data system that supports 

many tools and applications for planning, safety data analysis, target setting, and performance 

management. They consolidate all of their data (including those that support safety) in a 

centralized data portal called UGate. Data in UGate is integrated, downloadable, and accessible.

Most information is available to the public. At UDOT, data is available for viewing and analysis 

using web-based applications and online maps. Data is also available for download in various 

formats. UGate supports many data-driven tools used by traffic and safety engineers for project 

prioritization, planning, analysis, and reporting.

Data Governance. Currently, there is no formal data business plan for safety or enterprise data.

However, the expansion of technology has made data governance and data business planning a high 

priority need for the Department. UDOT is in the process of organizing a data governance board 

to review technology expenditures, stewardship roles, and other data governance matters.

Interagency Partnerships and Collaboration. UDOT uses an external department for 

technology delivery to handle technical details such as data item definitions and physical database 

locations, storage, and structures. Although the service comes at a cost, UDOT found it is helpful 

to have IT professionals handle the system architecture and database functionality so UDOT staff 

can focus on improving data collection and analysis capabilities to support program areas. 

Safety Data Analysis Tools. UDOT utilizes multiple tools to support safety analysis, planning, 

and decision-making

• UPlan. UPlan is a web-based decision support, mapping, and informational tool to support

complex planning and project development tasks. In the UPlan Portal for Zero Fatalities,
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UDOT links safety data to performance measures. Roadway segments link to statistics such as 

severe crash rate, crash rate per mile, and safety ratings. 

• Linear Bench. Linear Bench is a tool used to view and analyze data internally. UDOT staff

can select a roadway segment and data sets to generate a straight-line diagram, tabular report,

or map display with the selected characteristics. The tool allows users to view multiple data

sets to support detailed analysis, decision-making, and reporting needs. For example, a user can

view crash scores, traffic volume, and pavement data together and assess the need for a site

visit to check pavement surface condition.

• Report Auto Generator. This tool allows users to generate a bid estimate for roadway

improvements (for example, guardrail, overlay or mill and fill, or pavement repair) based on

roadway segment data. The tool allows users to notify other interested parties (internal or

external to the Department) regarding the project. This reduces overlap and allows for

coordination of work efforts.

• Crash Data Analysis Tool. This tool allows users to combine crash location, damage value

estimates, and asset inventory data to identify the need for safety improvements. The tool is

currently in spreadsheet format, but there are plans to transition to an online dashboard to

support both user-defined and network level analysis.

Applicability to Other States

Other States may benefit from the following lessons learned:

• Collect data once for use by many groups;

• Involve all interested parties in data management and make sure the right people are involved

in decision-making;

• Develop central applications to share/distribute data;

• Consolidate tools, reports, and data in central locations and ensure they meet as many needs

as possible; and

• Track and share cost savings achieved (or the return on investment) through more efficient

processes.

Link

https://safety.fhwa.dot.gov/rsdp/downloads/utah_case_studyFinal.pdf. 
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APPENDIX C. SUMMARY OF PILOT STUDIES

KANSAS DOT DATA BUSINESS PLAN EXECUTIVE SUMMARY

The Kansas Department of Transportation (KDOT) is responsible for building and maintaining a 

modern, safe, and sustainable transportation network for all Kansans. KDOT and its partners 

maintain a broad range of safety data systems and work together closely to promote and strive for 

safer transportation in the State. However, the 

Department needs to improve its data resources, data 

management, and governance practices to integrate 

safety data and make it available to all State 

practitioners. To address these concerns, KDOT 

developed a Safety Data Business Plan (DBP) to guide 

its safety data management practices.

”Safety Data” in this DBP refers to crash, roadway 

inventory, and traffic volume data on public roadway 

and includes, in the case of a railway-highway grade crossing, the characteristics of highway and 

train traffic, licensing, and vehicle data. The DBP also includes ancillary data systems that support 

safety analysis. 

Objectives

• Develop a governance

framework to better manage 

safety data resources and assets

• Develop a roadmap for

improving safety data resources

• Create a communication and

implementation plan

• Vision: The Kansas DOT and its safety data stakeholders will have a sound, comprehensive, and

well-coordinated approach to managing, improving, and applying the State’s safety data and

analysis resources.

• Mission: The Kansas DOT’s mission with respect to safety data management is to achieve sound

governance of safety data resources, enhance integration of safety data systems, continually

improve the quality and usability of data, and promote user friendly and easily accessible by our

safety users and partners for their business analysis.

KDOT developed the DBP through participation in the FHWA Safety Data Management and 

Governance Processes project. KDOT pilot tested the Guide for State DOT Safety Data Business 

Planning, which provides a seven-step approach to assist States in developing and implementing a 

Safety DBP. The following steps provide an overview of KDOT’s Safety DBP based on the FHWA 

guide. 
111

Step 1

Plan for Safety Data Management/Governance

Through discussions with its safety data stakeholders, KDOT identified several system, technical, 

and institutional challenges and issues to address in its Safety DBP. A stakeholder engagement plan 

identifies the stakeholders involved in each step of the DBP, the purpose of engaging stakeholders 

within that step, engagement mechanisms, and timeframe for engagement. KDOT also developed a 

vision, mission, and outcome statement for safety data management. 

Step 2

Assess Current State of Safety Data Program

In this step, KDOT identified existing data systems in place for collecting, managing, storing, and 

reporting safety data; identified business processes associated with safety data systems; 

summarized current and past assessment efforts; and assessed their current capabilities related to 

safety data collection, analysis, governance, and interoperability.

Step 3

Establish a Safety Data Governance Program

In this step, KDOT established core data principles; documented current and planned governance 

initiatives; developed a Governance Model; and defined governance roles and responsibilities. 

Figure C-1 depicts a formal structure for KDOT to govern its safety data system. The

governance model depicts the relationship between safety data programs, the various individuals 

responsible for implementing data governance, and the stakeholders for the data programs.
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Figure C-1. Diagram. Kansas Department of Transportation Safety Data Governance 

Model. 

Step 4

Develop Tools and Technology for Safety Data Management

In this step, KDOT identified technology needs and developed a plan for improved use of tools. 

Recommendations are categorized into the following categories – data collection, data tools, 

knowledge management, system improvements, training, and cost management. KDOT is 

implementing many of the recommendations through the K-HUB and Crash Data Portal initiatives.

Step 5

Develop an Action Plan

This step provides an action plan for improving KDOT’s safety data based on the challenges, 

issues, and gaps identified in previous steps. Recommendations are organized into the following 

improvement categories:

• System: Recommendations related to data systems, data collection, data access, data

integration, data quality, data storage, and documentation;
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• Technology: Recommendations related to software, hardware, system interfaces, IT

compatibility, business intelligence tools, analytical tools, knowledge management, and network

issues; and

• Institutional: Recommendations related to data management and governance, business rules

and processes, coordination across business areas, IT support, resource availability, and

training needs.

KDOT obtained stakeholder input to prioritize the recommendations. 

Step 6

Develop an Implementation Plan

Successful implementation of the Safety DBP will require continued work and dedication of 

resources over the next few years, as well as a cultural shift in how safety data assets are managed 

in the Department. KDOT should designate a governance champion or small team to oversee the 

recommendations in the action plan and lead initial safety data governance efforts. Once the 

governance program is self-sustaining, responsibility should transition to the Safety Data 

Governance Council. A roadmap for implementation is depicted in Figure C-2. 

Figure C-2. Gantt chart. Implementation roadmap. 
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WASHINGTON STATE DATA BUSINESS PLAN EXECUTIVE SUMMARY

Washington State Department of Transportation 

(WSDOT) is the steward of a multimodal transportation 

system and is responsible for ensuring people and goods 

move safely and efficiently. Washington’s State Strategic 

Highway Safety Plan: Target Zero, sets a zero goal for 

fatalities and serious injuries by 2030. WSDOT’s 2014-

2017 Strategic Plan also includes a goal to reduce the 

number of fatal and serious injuries for all transportation 

modes. Meeting this goal requires the ability to assemble 

and analyze safety data. WSDOT maintains safety data 

systems for these activities. However, the Department 

needs to improve its data management and governance

practices to integrate data and make it available to all State

practitioners. To address these concerns, WSDOT developed a Safety Data Business Plan (DBP) to guide

its safety data management practices.

Objectives

• Demonstrate how safety data

impact the enterprise

• Develop a roadmap to address

safety data linkage, association, 

and management challenges

• Establish a strong, sustainable

vision for safety data

• Implement a formal safety data

governance process

• Ensure a sustainable safety data

improvement process

• Vision: WSDOT’s business decisions will be supported by reliable, timely, accessible,

integrated, and complete safety data.

• Mission: WSDOT will manage and maintain integrated safety data systems that are user

friendly and easily accessible (as appropriate) by our safety users and partners for their business

analysis.

WSDOT developed this DBP through participation in the FHWA Safety Data Management and Governance 

Processes project. WSDOT pilot tested the Guide for State DOT Safety Data Business Planning, which 

provides a seven-step approach to assist States in developing and implementing a Safety DBP. The following 

steps provide an overview of WSDOT’s Safety DBP based on the FHWA Guide.

Step 1

Plan for Safety Data Management and Governance

Through discussions with its safety data stakeholders, WSDOT identified several system, technical, and 

institutional challenges and issues to address in its Safety DBP. Stakeholders cited institutional challenges as 

the number one risk to WSDOT. A stakeholder engagement plan identifies the stakeholders involved in 

each step of the DBP, the purpose of engaging the stakeholders within that step, engagement mechanisms, 

and timeframe for engagement. WSDOT also developed a vision, mission, and outcome statement for 

safety data management.
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Step 2

Assess Current State of Safety Data Program

In this step, WSDOT identified existing data systems in place for collecting, managing, storing, and reporting 

safety data; summarized current and past assessment efforts; and assessed their current capabilities in safety 

data collection, analysis, governance, and interoperability.

Step 3

Establish a Safety Data Governance Program

In this step, WSDOT identified core data principles; documented current and planned governance 

initiatives; developed a Governance Model; and defined governance roles and responsibilities. Figure C-3

depicts a formal structure for WSDOT to govern its safety data system. The governance model depicts the 

relationship between safety data programs, the various individuals responsible for implementing data 

governance, and the stakeholders for the data programs. 

Step 4 

Develop Tools and Technology for Safety Data Management

In this step, WSDOT identified technology needs and developed a plan for improved use of tools. 

Recommendations are categorized into the following categories – data collection technology, data tools, 

database design, knowledge management, system improvements, training, and cost management. 

Step 5

Develop an Action Plan

This step provides an action plan for improving WSDOT’s safety data based on the challenges, issues, and 

gaps identified in previous steps. Recommendations are organized into the following improvement 

categories:

• System: Recommendations related to data systems, data collection, data access, data integration, data

quality, data storage, and documentation;

• Technology: Recommendations related to software, hardware, system interfaces, IT compatibility,

business intelligence tools, analytical tools, knowledge management, and network issues; and

• Institutional: Recommendations related to data management and governance, business rules and

processes, coordination across business areas, IT support, resource availability, and training needs.

WSDOT prioritized the recommendations based on the results of a Risk Management Assessment.
116

Figure C-3. Diagram. WSDOT safety data governance model. 

Step 6

Develop an Implementation Plan 

Successful implementation of the Safety DBP will require continued work and dedication of resources over 

the next few years, as well as a cultural shift in how safety data assets are managed in the Department. The 

Highway Safety Executive Committee and Highway Safety Issues Group should assume responsibility for 

overseeing the recommendations in the Action Plan and leading initial safety data governance efforts. Once 

the enterprise governance program is self-sustaining, responsibility should transition to the Information 

Governance Council. A roadmap for implementation is depicted in Figure C-4.
117

Figure C-4. Gantt chart. Implementation roadmap. 
118

APPENDIX D. EXAMPLE SURVEY INSTRUMENT ON SAFETY DATA

CHALLENGES

Who to Survey: State DOT Safety Data Managers, Safety Data Providers, Safety Data System 

Owners, Safety Data Users

Purpose: <AGENCY NAME> is conducting a safety data business planning initiative to 

improve the management and governance of our safety data, which, for the purposes of this 

project, is defined as crash, traffic volume, roadway feature inventory, or other 

transportation/safety related location data. An initial step is to gather more information on current 

challenges the DOT is facing with regard to the management, governance, and use of safety data

by stakeholders. To assist us in the process, please respond to the survey below by <DATE>. We 

appreciate your assistance. 

Survey Questions (using Survey Monkey or similar online tool)

1. Please identify the organization or DOT division under which you are employed, and what

your title is.

a. Organization: <text box>

b. Title: <text box>

2. Do you directly collect, develop, or maintain any safety data for which your organization or

division is responsible? For the purposes of this study, safety data is defined as crash, traffic

volume, roadway feature inventory, or other transportation/safety related location data.

a. Yes (if yes, survey will continue to Q3)

b. No (if no, survey will continue to Q13)

Questions for Data Owners and Stewards:

3. Please list the safety data for which you are responsible. (select all that apply)

a. Traffic volume data

b. Crash data

c. Roadway features inventory data

d. Linear referencing data

e. Other (please specify)
119

4. Where is this data stored? (select all that apply)

a. On individual desktop computers

b. As hard copy maps or drawings

c. In a centralized, enterprise database management system such as Oracle or

Microsoft SQL server

d. Within a specialized software application

e. Other (please specify)

5. How frequently are the data records updated? Please list your data items in the

appropriate box.

a. Updated continuously <Text box>

b. Updated yearly <Text box>

c. Updated monthly <Text box>

d. Updated weekly <Text box>

e. Updated daily <Text box>

f. Updated as needed <Text box>

6. Are the databases shared with, or available for use by: (Select all that apply)

a. Other divisions or organization units within the DOT?

b. Other agencies outside of the DOT?

c. General public?

If yes, in what format are the databases shared with other entities?

d. Copies of the raw database

e. Summary tabulations

f. Periodic reports

g. Other (please specify)

7. Do the majority of users know how to properly query and perform analysis with your

safety data? If not, why do users face challenges accessing and/or using your data?

(Examples: Yes, a data dictionary is provided with our data so that users will understand
120

what exactly the data represents and how to use it. No, our data has a lot of codes and 

information that only those that generate the data know about.)

a. <Text box>

8. What current challenges do you and/or other data users face in merging the safety data

you manage with other data?

a. Lack of geographical coordinates

b. Differing segment/location identification

c. Different data formats

d. Varying temporal resolution

e. Other (please specify)

9. Which of the following types of improvements would you like to see for the DOT’s safety

data systems? Please rank them in priority order, with one being the most important and

four being the least important.

a. A “one stop shop” for all data at the Department instead of data storage on

multiple network connections.

b. A web based system where users can search, view, and query up-to-date data.

c. Data about the data. Data dictionaries or “how to use this data” directions

associated with all data.

d. Other (please specify)

10. Do you receive requests from users for additional data elements needed for safety analysis

that are not currently being collected? Is so, what types, and how often/important are the

requests?

a. Yes (please specify)

b. No

11. Are your data management responsibilities formalized and documented as part of your job

description or office standard operating procedures?

a. Yes

b. No
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c. Not sure

12. Which of the following types of additional resources would be helpful for managing and

maintaining your safety data? Please rank them in priority order, with one being the most

helpful and seven being the least helpful.

a. Staffing

b. Funding

c. Training

d. Data dictionaries

e. Guidance

f. Software/tools

g. Other (please specify)

Questions for Data Users

13. Do you require or regularly use safety data maintained by another organizational unit or

outside agencies?

a. Yes (If yes, survey will continue to Q14)

b. No (If no, survey will continue to Q24)

14. Please list the safety data that you use on a regular basis. (Select all that apply)

a. Traffic volume data

b. Crash data

c. Roadway features inventory data

d. Linear referencing data

e. Other (please specify)

15. For what are these data being used? (select all that apply)

a. Identify safety needs and recommend safety improvements

b. Evaluate the effectiveness of implemented improvements

c. Track mandated performance measures
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d. Conduct strategic planning to support development of the Strategic Highway

Safety Plan or HSIP

e. Develop safety-related maps, documents, or publications

f. Other (please specify)

16. In what format are the data obtained? (select all that apply)

a. Direct access to a shared database

b. Copies of the database

c. Summary reports

d. Other (please specify)

17. How frequently do you obtain updates of the data?

a. Updates obtained continuously

b. Updates obtained yearly

c. Updates obtained monthly

d. Updates obtained weekly

e. Updates obtained daily

f. Updates obtained as needed

18. Please describe any issues or difficulties you have in obtaining or using the data. (Examples:

cumbersome data request procedures, data quality issues, restrictions on use, data is

always located in a different place when I need to use it again, there is no one to tell me

how to get to it or use it, sometimes the data is named differently than when I used it

before)

a. <Text box>

19. Do you have a need for additional safety data elements that are not currently being

collected? Is so, what types and for what purpose?

a. <Text box>

20. How easy or difficult is it to merge safety data with other data needed to conduct safety

analysis or perform your job functions?

a. Very easily, the data are provided in the same format as our data.
123

b. Easily, the data are provided in a format that can be easily transformed to be in

the same format as our data.

c. Moderately, the data require some manual labor to get them into a usable

format.

d. Difficult, the data require extensive manual labor to get them into a usable

format.

e. Not at all, the data are unusable in the current format they are submitted in and

cannot be transformed into a usable format.

f. Other (please specify)

g. Does not apply

21. What software or tools do you use to conduct safety analysis? (select all that apply)

a. Advanced safety analysis tools such as AASHTOWare Safety AnalystTM

b. Highway Safety Manual spreadsheets

c. GIS Systems

d. Statistical Analysis Software (SAS)

e. Microsoft Excel

f. Microsoft Access

g. Other (please specify)

h. Does not apply

22. Are you able to achieve consistency in analysis methods and results?

a. Yes

b. No

c. Sometimes

d. Does not apply

23. Which of the following types of additional support would be helpful for conducting safety

analysis as part of your job function? Please rank them in priority order, with one being the

most helpful and seven being the least helpful.
124

a. Funding

b. Training

c. Data dictionaries

d. Format requirements

e. Guidance

f. Software/tools

g. Other (please specify)

Concluding questions (all respondents)

24. Please provide your contact information for follow-up if we have questions regarding your

response.

a. Name: <Text box>

b. Email: <Text box>

c. Phone: <Text box>
125

APPENDIX E. EXAMPLE SURVEY INSTRUMENT ON GOVERNANCE 

INITIATIVES 

Who to Survey: Managers (or designated representatives) from other DOT business offices or 

divisions

Purpose: <AGENCY / DIVISION NAME> is conducting safety data business planning 

initiative to improve the management and governance of our safety data, which, for the purposes 

of this project, is defined as crash, traffic volume, roadway feature inventory, or other 

transportation/safety related location data. The data business plan will describe our vision, goals, 

objectives, and actions related to improving the way we manage safety data within the agency. 

An initial step is to gather more information on data management or governance initiatives 

underway in other business areas within the DOT. To assist us in the process, please respond to 

the survey below by <DATE>. We appreciate your assistance. 

Survey Questions (using Survey Monkey or similar online tool)

1. Please identify the DOT division or office under which you are employed, and your job

title.

a. Name: <text box>

b. Division/Office: <text box>

c. Title: <text box>

2. Does your office own, develop, or maintain any data systems or databases? If yes, please

identify the names of the data systems or databases.

a. Yes (please explain) <Text box> <If yes, survey continues to Q3>

b. No <If no, survey ends>

3. Does your office have a data business plan in place that guides the way you manage or

govern your data systems or databases (or is one planned)?

a. Yes, a data business plan is in place and being implemented within our business area

b. Yes, a data business plan is in place, but has not been implemented yet

c. We are in the process of developing a data business plan

d. No, we don’t have a data business plan, but one is planned or we recognize the

need for one
126

e. No, we don’t have a data business plan in place, nor is one planned

f. Other (please specify)

4. Does your office regularly assess its data systems or databases to identify needs for

improvement? If yes, how often is the assessment conducted?

a. Yes (please explain) <Text box>

b. No

5. Have you done any assessments of data governance maturity or capability within your

business area? If yes, please provide a brief explanation.

a. Yes (please explain) <Text box>

b. No

6. Does your office have formal policies and procedures in place for managing and governing

its data systems or databases?

a. Yes, we have formal standards, policies, and procedures in place for the way we

manage and govern our data

b. Yes, we have procedures in place, but they are not standardized or incorporated

into policy, or our procedures differ each time we need to reconcile or correct

data.

c. No, we have no defined standards, policies, and procedures in place

d. Other (please explain)

7. Are the workflows and business processes for managing your data systems or databases

documented? If yes, please provide a brief explanation.

a. Yes (please explain) <Text box>

b. No

8. Are there clear roles and responsibilities (e.g., data stewards, data business owners, and

data custodians) defined for data management and governance activities?

a. Yes, roles and responsibilities are formalized and documented as part of our

employees’ job descriptions
127

b. Yes, there are clear roles and responsibilities, but they are not formalized or

incorporated into job descriptions

c. No, we do not have defined roles and responsibilities

d. Other (please explain)

9. Is there a governance board or working groups set up for data management or

governance?

a. Yes, there is a governance board or working groups within our business area

b. Yes, our office is part of a larger agency-wide governance board or working group

c. No

d. Other (please explain)

10. Please provide your (or a designated representative’s) contact information for follow-up if

we have questions regarding your response.

a. Name: <Text box>

b. Email: <Text box>

c. Phone: <Text box>
128

APPENDIX F. STATE SAFETY DATA SYSTEM

CAPABILITY MATURITY MODEL

The capability maturity model is adapted from the United States Roadway Safety Data Capabilities 

Assessment. (Vanasse Hangen Brustlin, Inc., United States Roadway Safety Data Capabilities 

Assessment, FHWA-SA-12-028, July 2012.) It defines levels of maturity for each of the following 

dimensions of capability:

• Safety Data Collection and Technical Standards: What safety data is collected? How

well do safety data programs meet data quality standards for timeliness, accuracy,

completeness, consistency, integration, and accessibility?

• Data Analysis Tools and Uses: How well does the SSDS support the roadway safety

management process, including network screening, diagnosis, countermeasure selection, and

evaluation? How well does the SSDS support advanced analysis methods using tools such as

the Highway Safety Manual, the Interactive Highway Safety Design Model, or AASHTOWare

Safety AnalystTM?

• Data Management and Governance: Is there a data governance structure for the SSDS?

For example, are there formally defined roles, accountability, and core capacities for data

governance? Is there a designated data governance board, data stewards, and data owners?

What policies and procedures exist for collecting, maintaining, using, and updating safety data?

Are technology and tools for safety data management and analysis consistent, standardized, and

updated?

• Data Interoperability and Expandability: To what extent are linked data sets from

roadway, crash, and others included in safety analysis? Are existing safety data systems

expandable as new technologies and tools are developed?

There are five distinct levels of capability for each of the dimensions:

• Level 1 – Initial or Ad Hoc. The agency is not aware of the need for capability in a specific

dimension, or activities and relationships are taking place but are largely ad hoc, informal, and

champion-driven. There is no plan for interoperability or expandability.

• Level 2 – Repeatable. The results of previous projects and the demands of the current

project drive activities and actions. Individual managers decide what to do on a case-by-case

basis during individual projects.
129

• Level 3 – Defined. The agency documents technical and business processes rather than on a

per-project basis. The agency’s standards relate to an adopted strategy, and this guidance

determines project outcomes. However, there is limited accountability and uneven alignment

with internal and external partners.

• Level 4 – Managed. The agency uses process management to initialize and supervise

individual projects. Performance is measured, processes are predictable, and the organization

can develop rules and conditions regarding the quality of the products and processes. Internal

and external partnerships are aligned.

• Level 5 – Optimized. Safety data management and governance is a full, sustainable program

priority, with top-level management support and formal partnerships in place. The whole

organization focuses on continuous improvement. The organization possesses the means to

detect weaknesses and to strengthen areas of concern proactively.

FHWA expects to publish a revised version of the capability maturity model in 2017. 

The following tables provide detailed descriptions of maturity for each dimension and element of 

the capability maturity model. The tables are provided in worksheet format for States to note 

strengths, weaknesses, current maturity level, desired maturity level, and actions to advance to the 

desired maturity level for each assessment area. 

States may wish to assign a separate maturity level for different elements of their safety data 

system. For example, there may be portions of a State’s system that are operating at a higher 

maturity level, while others are at a lower level. States may note specific areas within the element

they wish to improve. For desired maturity level, it is acceptable for States to choose a lower

maturity level if it is realistic for the organization.
130

AREA 1: SAFETY DATA COLLECTION AND TECHNICAL STANDARDS

Element 1A: Completeness

Note: States may wish to assess their crash, roadway, and traffic data systems separately.
131

Element 1B: Timeliness

Note: States may wish to assess their crash, roadway, and traffic data systems separately.
132

Element 1C: Accuracy

Note: States may wish to assess their crash, roadway, and traffic data systems separately.
133

Element 1D: Uniformity or Consistency

Note: States may wish to assess their crash, roadway, and traffic data systems separately.
134

AREA 2: DATA ANALYSIS TOOLS & USES

Element 2A: Network Screening (Data) 
135

Element 2A: Network Screening (Method)
136

Element 2B: Diagnosis
137

Element 2C: Countermeasure Selection
138

Element 2D: Evaluation (Project-Level)
139

Element 2D: Evaluation (Program-Level)
140

Element 2E: Accessibility
141

AREA 3: DATA MANAGEMENT AND GOVERNANCE

Element 3A: People
142

Element 3B: Policies
143

Element 3C: Technology
144

AREA 4: DATA INTEROPERABILITY AND EXPANDABILITY

Element 4A: Interoperability
145

Element 4B: Expandability
146

Element 4C: Integration

Note: States may wish to identify desired maturity levels for different time horizons (e.g., 1-year, 3-year, and 5-year).
147

APPENDIX G. DATA GOVERNANCE 101

This appendix provides an overview of data governance.

DATA MANAGEMENT LIFE CYCLE

Because data is a valuable asset, a State should manage it over its entire lifecycle. The lifecycle 

encompasses the time from collecting data to the time it is archived or updated/refreshed. 

Figure G.1 depicts the elements and stakeholders typically involved in each phase of the data 

management life cycle. 

Figure G-1. Flow chart. Data management life cycle. 

Source: NCHRP Report 754: Improving Management of Transportation Information, Transportation Research Board, 

2013. 

Data governance principles apply to each phase of the data management life cycle to ensure data is 

trusted and understood: 

• Collect: Standards should exist for the recording of asset name, location, and descriptive

attributes. The data collected for each asset and attribute must reflect the requirements of

each successive phase of the life cycle.

• Capture: Data capture involves the transfer of records from the collection mechanism to a

system-of-record. This is the phase to verify geographic completeness to ensure data exists for

all division and regions or, where appropriate, for each individual asset statewide.
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• Manage: Manage involves any processing or validation of data within the system-of-record to

improve ease-of-use. This includes performing quality assurance and quality control processes

on data. Standards must exist for each individual data element.

• Store: Data storage using systems with contemporary architecture to ensure data is

serviceable by data managers. Procedures should exist for identifying the people from both the

business and technical sides of the DOT responsible for maintaining the database.

• Archiving and Preservation: Archiving, preserving, or destroying data is subject to

significant regulation, particularly where sensitive financial or personal information is

concerned. Decisions around this phase of data management are subject to Department or

State standards and are made under the authority of data owners and data custodians.

• Deliver and Disseminate: Much like archiving, the delivery and dissemination of data is

subject to legal restriction and regulations. Data delivery is a distinct and separate function

from data capture, since the data owner plays a gatekeeper role.

• Retrieve: Data should be accessible to all users within the Community of Interest through

well-publicized retrieval methods, ad hoc queries, or formal reports. Users may retrieve data,

but they should not alter it or add to the system-of-record without the permission and

possible assistance of data owners.

• Refresh: Data custodians must understand user needs and obtain feedback on tools and data

to make appropriate revisions or corrections.

States can easily customize the data management lifecycle to show roles and responsibilities for 

data governance in their organization, but the basic elements are the same for almost any 

application. 

IDENTIFYING THE NEED FOR DATA MANAGEMENT AND DATA 

GOVERNANCE

This section helps States establish a need for data governance and obtain buy-in from executives 

and decision-makers.

Identify and Document Needs

Most States realize a need for data governance when they encounter issues within a specific 

program area or when complying with Federal or State requirements related to funding, reporting, 

or accessibility. This usually leads to small groups discussing issues with managers in an attempt to 
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solve problem. These small groups of data managers and stakeholders are exactly the ones who 

are best equipped to lead initial data management efforts in coordination with managers who will 

get them the support they need to do their jobs. 

A good way to get started on identifying safety data governance needs is to coordinate a focus 

group that includes (at a minimum) representatives from the traffic, crash, and roadway data 

business areas. The group should meet to discuss general data needs and requirements of their 

program areas and to identify opportunities for improvement. 

Demonstrate Return on Investment through a Governance Model

After needs are identified and documented, the next step is to relate improvements in safety data 

management to a return on investment (ROI) in planning processes, performance measures, target 

setting and prioritization of resources. 

One method is to show how safety data links to planning, performance measures, and target 

setting processes. A State should design and implement a data governance model to assist with 

this. A data governance model is a diagram that visualizes the connection between a Department’s 

mission, vision and goals, data governance practices, and the program areas. The need for 

governance is established by illustrating how individual programs provide information critical for 

decision-making. Figure G.2 shows an example data governance model.

Figure G-2. Organizational chart. Example data governance model. 

Source: Adapted from NCHRP 666, Figure 4.2, Overview of a general data governance framework. 

States can use information from the initial assessment and group discussion to customize a model 

for their DOT. This exercise fosters collaboration between business areas and leads to a better 

awareness of how specific data programs help managers make informed decisions. 
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Data governance leads to improved data within individual program areas, which provides 

management with quality analysis tools, applications, and reports to support data-driven decisionmaking. 

Data governance can help improve the following data concepts: 

• Traceability – Data governance helps align programs with DOT needs;

• Performance Measures – Improved data allows for better target setting and performance

tracking;

• Risk Assessment – Starting a data governance program requires a DOT to assess risks

associated with data management;

• Value of Data Programs – Part of data governance is demonstrating value of data programs to

those who authorize investment in them; and

• Knowledge Management – This system enables documentation and sharing of lessons learned

and experiences pertaining to data management.

Communicate the Need to Stakeholders

After the focus group has made some progress in documenting needs and developing a governance 

model, they should share the findings with colleagues, managers, decision-makers, and other 

stakeholders to obtain buy-in and support for improvement. The presentation of needs will 

depend on the group, but in general should be simple, focused, and demonstrate the benefits of 

governance. One approach is to target a problem a manager or colleague might face that is 

solvable through better data and tools. For example, in one State, a director needed a way to 

show legislators funded projects in their district so they could ensure fairness in resource 

allocation. This required getting data from several sources and manually compiling it to produce a 

table of results for a specific district. Better data integration and management could support an 

interactive tool that allows managers (or even legislators) access to do this type of analysis on the 

fly. The return on investment is reduced staff time (and expense) in manually building the report, 

more accessible data, and better decision-making regarding the allocation of resources. 

Obtain Agreement on Need 

A good practice is to share findings and the agreement on the need (if any) back to business areas 

supporting the effort. This is a good opportunity to get more staff and stakeholders involved in the 

planning process to ensure all needs for safety data management are included in the data business 

plan. 
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After high-level managers and executives agree there is value in pursuing data governance, they 

will need a detailed plan to move forward and can proceed with the steps in the Guide for State 

DOT Safety Data Business Planning. 

DATA MANAGEMENT PRACTICES

Effective data management practices are necessary for a State to collect, update, describe, 

standardize, analyze, store and protect safety data to ensure its usability. States should consider 

the following aspects of data management: (Cambridge Systematics. Inc. U.S. DOT Roadway 

Transportation Data Business Plan (Phase 3): Data Business Plan Development for State and Local 

Departments of Transportation, May 2017. (Draft).)

• Data collection. Define responsibilities for the collection, update and maintenance of data;

identify where duplicate data collection and storage exist; address data ownership and user

rights; investigate new data acquisition methods.

• Data quality. Define data quality per governance standards; adopt data quality standards and

metadata for the collection, processing, use, and reporting of safety data; document data

quality procedures for each data system, with instructions on how to process data errors; and

develop validation rules and allowable values for coded fields in data systems and repositories.

• Data standards. Define metadata standards for each type of safety data set (for example,

roadway, crash, traffic, project), data dictionaries and descriptive information for data

products; develop metadata guidelines to indicate update frequency, age of data, and

integration with other data sources; and coordinate with applicable data standards. Note that

metadata standards are different from data format standards.

• Data privacy and security. Ensure data privacy and security related to safety data in

accordance with Federal and State legislative requirements and limitations.

• Data storage and access. Define business requirements for data access, analysis and

reporting; define responsibilities for data storage, hosting, data retention (archive), and

disposal; define data ownership and dissemination rights; and explore methods to enhance

access to data. This includes developing web portals that internal and external stakeholders can

use to obtain data and information as needed.
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APPENDIX H. STATE PRACTICES FOR IT PROJECT 

PRIORITIZATION AND SELECTION

This appendix presents best practices for IT project identification/selection process. The 

information is based on brief research of IT practices at Michigan, Montana, New Mexico, 

Pennsylvania, and Texas DOTs conducted as part of another project. (Cambridge Systematics, 

Inc. IT Project Selection Criteria. Research conducted as part of the Highway Enterprise System 

and Asset Management Analysis Assistance for the MassDOT Highway Division, 2014.)

PROCESSES FOR SUBMITTING IT PROJECT REQUESTS

Annual Call for Projects (Michigan DOT)

DOT-specific software procurement at Michigan DOT is managed at the department level. In 

an annual call for IT projects, different divisions submit their needs for a two-year budget cycle.

The Call for Projects Memo includes background information on existing IT priorities to 

provide some context for incoming projects. Requestors coordinate with their automation 

manager (AM) to submit a MDOT Call for IT Projects Questionnaire Template. The AM vets 

the questionnaire with the requestor and with the bureau management. (These move no 

further if the bureau/management does not support it). The questionnaires are sent to 

MDOT’s Project Management Office, which assembles all of them, distributes them, and then 

schedules a prioritization meeting. 

IT Strategic Plan (Texas DOT)

In accordance with Legislative mandate, TxDOT develops the agency Strategic Plan, which 

includes the IT strategic plan, each biennium. To develop the IT strategic plan, the Technology 

Service Division (TSD) Business Services Section gathers IT information regarding technology 

needs, plans and alignment to agency strategic goals from each district, division, office, or region 

(D/D/O/R). TSD section directors review the resulting list of needs to identify potential 

overlaps and to assess high-level future infrastructure requirements to support the needs, and 

then compile them into the IT strategic plan. TSD also answers standard statewide questions 

about agency plans for technology consolidation, IT managed services, security and privacy and 

green IT. Director, TSD submits this plan to Administration. 
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PROJECT PRIORITIZATION AND SELECTION PROCESS

Pre-Review of IT Project Requests and Prioritization Meeting (Michigan DOT)

MDOT AMs collectively meet at prioritization meetings to discuss how the proposals fit into 

priorities and any alternative ways to handle the requests. Sometimes project requests are 

turned into maintenance requests; other times they will spawn a small project to satisfy the 

requests outside of the major project category. MDOT sets aside funding to handle these 

smaller requests.

MDOT technical staff also review the project scope in preparation for the prioritization 

meetings.

IT staff (who report to the CIO) participate in the project prioritization and selection process 

to ensure projects are consistent with current activities and priorities. The priorities for 

prioritizing projects in that particular year are established ahead of time and made available in 

the Call for Projects Questionnaire. While there is a spreadsheet that performs calculations on 

the answers, the MDOT/IT group found there is a tendency for requestors to want to answer 

positively to these questions, partly because from their points of view their projects are high 

priority. MDOT reviews the priorities each year, with an intent to identify projects that are 

needed to maintain funding level(s). MDOT notes that the priorities are frequently interpreted 

to mean that a project needs to follow some procedure in order to justify the expenditure of 

funds, which is not the same thing as their intent. 

The results of this process is a prioritized list of IT projects that is forwarded to the Steering 

Committee (several bureau chiefs) for approval. The committee reviews the projects, and the 

prioritization method is explained. Also discussed is staging and timing of the projects. Most of 

the issues/questions have already been addressed during prioritization.

MDOT has been following this process for several years, and it seems to work well. It has 

allowed them to be flexible and adapt to changing MDOT conditions, statewide IT efforts, 

varying funding levels and sources, etc. Additionally, it closely follows the process MDOT uses 

to identify and select highway projects. 

Bottoms Up Process (Montana DOT)

Within the Montana DOT, there is an Information Services Division led by a Division 

Administrator. This individual oversees procurement, initiation, and implementation of IT 

objectives within the agency. The division acts as a clearinghouse for IT investment and 

supports requests coming from other divisions in the agency (e.g., engineering, planning, 
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aviation, etc.). Investment decisions come through a bottoms-up process. Each division 

prioritizes their needed investments, which are then presented to the IT Division. The IT 

Division, with DOT leadership oversight, then helps to prioritize requests coming from all of 

the DOT divisions.

For IT investment decisions, projects are grouped into three different levels of investment, with 

increasing stringent procedures for larger projects:

• For projects that are estimated to require less than 100 hours of internal work, the request

is coded as a simple work ticket that is entered into a prioritization queue for internal

delivery.

• Projects requiring between 100 and 500 hours will involve a higher level of project

oversight and more documentation. Such projects will receive a dedicated project manager

and IT resources, and the department may decide to contract externally for some of this

work.

• Projects requiring more than 500 hours of work—for example, a request to replace a

bridge management system—must go through the most stringent oversight and approvals

process. Such projects must be presented before the IT investment selection board, which

includes all of the division administrators along with department director and deputy

director. The investment selection committee makes decisions about competing large

investments and discusses where to find funding.

Per recent legislation, State CIO approval is required for any IT investment that is projected to 

exceed $50,000 over the life of the project (5 years). The CIO may decline to approve a 

project if it does not match well with broader State IT strategy.

For projects exceeding $200,000, the investment needs to be included in the MDT investment 

plan, which will in turn be filed with State legislative plan. This enables MDT to request the 

required funding from the legislature. 

Decision Lens Software Methodology (Pennsylvania DOT)

Pennsylvania DOT Office of the CIO uses Decision Lens software to prioritize IT projects and 

allocate resources to the highest value projects in an effort to reduce advocacy-based decisionmaking. The CIO meets with a committee of representatives from each of the departments to 

develop a prioritization model for the entire organization, including departmental goals, 

objectives of IT projects, technical evaluations, and department priorities. From there, project 
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prioritization is facilitated within each department, using parts of the department model. To 

create a final prioritization list, each department’s ranking of project priorities is combined with 

an overall evaluation of both strategic and technical criteria. The office then initiates on ongoing 

process for the addition of new projects and removal of completed ones. 

The Decision Lens software includes the following steps in an IT project prioritization process:

• Criterion Development. Development of a decision model or criteria hierarchy identifies

what the organization is trying to achieve so that IT can select projects that best align with

those objectives.

• Criterion Weighting. The selected criterion are then ranked in order of importance by

members of the selection team. Decision Lens uses pairwise comparison, in which users

select priorities among pairs of criteria, and the software produces a weighted priority list.

A percentage weighting is assigned to each of the criteria based on their importance or

ranked preference. For example: Financial Returns – 0.33; Strategic Alignment – 0.18;

Platform Alignment – 0.16; Geographic Alignment – 0.15; Internal Process Improvement –

0.10; and Ability to Execute – 0.08.

• Prioritizing Projects. Each project is then rated against each criterion using a scale ranging

from zero to one, where zero = project does not meet criterion and one = project exceeds

criterion. The rating is multiplied by the criterion weighting, resulting in a value score

between zero and 1 for each project based on its rating on the criterion and the criterion’s

relative weight. The projects are then ranked in order of priority based on the value score

results.

• Allocate Resources. The priority results are used to select projects for funding. Resources

are allocated in a way that optimizes the value returned across the entire portfolio. Projects

may be fully funded, partially funded, or deferred.

Project Portfolio Management Methodology (New Mexico DOT)

New Mexico DOT has adopted the Project Portfolio Management (PPM) methodology, which 

is a strategic prioritization methodology employed to analyze and manage current or proposed 

projects within an organization. The aim of PPM is to determine the best grouping and 

sequencing of projects to achieve organizations’ business goals, in order to see them through 

from concept to completion. The Department employs Project Management Professionals 
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(PMP) within the Compliance and Project Management Program and the Administrative 

Services Division that are Project Management Institute (PMI) certified.

NMDOT is at a maturity Level 3 “Value Management.” This level requires metrics, models, and 

tools for quantifying the value to be derived from projects. The Department is in the process of 

assessing project interdependencies and portfolio risks. This analysis will allow projects to be 

ranked based on “bang-for-the-buck,” producing a good approximation of the value – 

maximizing project portfolio. 

In the PPM method, projects must be evaluated against common, weighted criteria to 

determine where they fit into the portfolio mix. Often, organizations will implement PPM 

software tools to aid in the decision-making process. PPM tools are used to enable visibility, 

standardization, measurement and process improvement. A strong IT governance structure is 

considered a crucial component of a project and portfolio management strategy as well. This 

structure typically includes a Project Management Office that supports the project development 

process, manages the IT project pipeline, evaluates project performance, and prioritizes IT 

projects. In addition, a Governance Council (executive steering team) reviews prioritized 

project list, interprets strategic plans and initiatives, provides budget and resource parameters, 

and makes final project selections. 

IT PROJECT SELECTION CRITERIA

Prioritization Based on IT Priorities (Michigan DOT)

The priorities for prioritizing projects in a particular year are contained in Section Three of 

Michigan DOT’s Call for Projects Questionnaire. These criteria include the following:

• Maintains critical or essential MDOT services through IT systems

• Migrates existing software applications from unsupported technologies

• Maintains funding

• Supports the required delivery of the Five-Year Transportation Program

• Supports IT related aspects of Federal or State legislation

• Promotes efficiencies & effectiveness of operations and/or ensures value for MDOT/DTMB

investments

• Supports safety & security and/or health & welfare of Michigan citizens

• Promotes technologies that enhance integration & connectivity of the transportation system

across & between modes
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• Promotes collaborative business relationships between MDOT, technology stakeholders,

other transportation entities, or local government agencies

• Promotes technologies that aid the public in accessibility to goods and services &

opportunities that enhance quality of life

IT project selection criteria may change from year to year and are established based on the 

Department’s strategic objectives, IT priorities, and executive management input.

Decision Lens Software Criteria Hierarchy (Pennsylvania DOT)

The Decision Lens Software used by Pennsylvania DOT allows development of a decision 

model or criteria hierarchy based on what the organization is trying to achieve. This allows an 

agency to select IT projects that best align with those objectives. Example criteria provided by 

Decision Lens include:

• Strategic Alignment – How does the project support the organization’s strategy or

objectives?

• Financial Returns – What value will the project deliver in terms of sales or margin

contribution, future cost savings or some other measurable return on investment?

• Ability to Execute – Can it be done? Are there technical, resource based, cultural, or other

potential hurdles that could affect the organization’s ability to execute the project?

• Platform Alignment – How does a proposed project or asset fit in the organization’s

current or targeted IT environment?

• Internal Process Improvement – How, or to what extent, does the project make work

easier, more effective or more efficient?

• Geographic Alignment – Whom does the project reach? Where are the targets located? Is

the focus on internal users or external customers?

Project Portfolio Management (PPM) Criteria (New Mexico DOT)

In the Project Portfolio Management (PPM) process used by New Mexico DOT, selection 

criteria typically includes:

• Ranking potential projects by value & benefits

• Appraisal of risk
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• Inventory of resource availability and allocation

• Determination of an optimal or acceptable size of the project pipeline

• Alignment of projects with strategic plans

• Balancing different types of projects by purpose and benefit

• Balancing opportunity, benefits, and risk

KEY COMPONENTS OF GOOD PRACTICES

Based on the best practices review, the following are characteristics/key components of good 

practices:

1. Establish formal process for submitting IT project requests

a. Annual call for IT projects

b. Project questionnaire to obtain information on the nature of the project,

expected benefits and costs, & support of IT priorities

c. Require sponsors to obtain signatures from office/division level directors to

establish executive support for the proposed effort

2. Establish a formal project prioritization & selection process

a. Pre-review of IT project requests to discuss how proposals fit into priorities and

identify alternate ways requests might be handled

b. Utilize IT or Project Management Office staff to support development of project

proposals, prioritize and select projects

3. Establish criteria for IT project prioritization

a. Ranking potential projects by value & benefits

b. Appraisal of risk

c. Inventory of resource availability and allocation

d. Determination of an optimal or acceptable size of the project pipeline
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e. Alignment of projects with department’s strategic objectives, IT plan, &

executive management input

f. Balancing different types of projects by purpose and benefit

g. Balancing opportunity, benefits, and risk

4. Use tools to support project prioritization process & enable visibility, standardization,

measurement, & process improvement

a. Spreadsheet tools

b. Decision Lens Software

c. Severity and Risk Assessment matrix

d. Project Portfolio Management tools

5. Manage the IT pipeline

a. Maintain a database of current and potential projects

b. Periodic measurement of project status & performance using Earned Value

Analysis techniques

c. Evaluate project status & performance against critical parameters

d. Report items outside of targets/limits/thresholds

e. Apply Stage-Gate model for continuation/termination decisions at major project

stages

6. Engage executives in the IT project development & maintenance process

a. Establish a governance council (executive steering team) to determine strategic

plans & initiatives, review recommendations, finalize project selections, &

determine budget and resource parameters

B. Meet with IT Director on a regular basis to review prioritization and align new

investment opportunities with business priorities
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APPENDIX I. GLOSSARY OF DATA BUSINESS PLANNING TERMS

This guide introduces several terms related to data management. Below is a glossary of data 

business planning terms: 

Community of Interest – Association of people comprised of internal and external 

stakeholders who share a common interest as users of a data system. 

Data – A representation of facts, concepts, or instructions in a formalized manner suitable for 

communication, interpretation, or processing by humans or computers.

Data Owner – An individual responsible for definitions, policy, and practice decisions about 

data within their area of responsibility. For business data, the individual may be called a business 

owner of the data. 

Data Business Plan (DBP) – Documents business rules and data quality standards for the 

information systems that support data programs, which will result in improved data 

management and data governance practices.

Data Governance – The execution and enforcement of authority over the management of 

data assets and the performance of data functions. This includes the people, policies, and 

procedures that govern data management and information systems. Data governance promotes 

the understanding of data as a valuable asset to the organization and encourages the 

understanding and management of data from both a technical and business perspective. 

Data Management – The development, execution, and oversight of architectures, policies, 

practices, and procedures to manage the information lifecycle needs of an enterprise in an 

effective manner as it pertains to data collection, storage, security, data inventory, analysis, 

quality control, reporting, and visualization. 

Data Program – A data program refers to specific data systems that support a business area 

of the organization. The “program” usually includes the functions of data collection, analysis, 

and reporting. It also includes policies, procedures, and resources for supporting these 

functions. In the case of a DOT, some examples of these programs include traffic, roadway 

inventory, safety, and pavement data. 

Data Set – Any organized collection of data. 

Extract, Transform, and Load – The process by which safety data are moved from their 

native systems, transformed into the format required by a specific analytic software, and loaded 

into the analytic software tool.
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Information – Data and documents given value through analysis, interpretation, or 

compilation in a meaningful form.

Knowledge – Information combined with experience, context, and interpretation that make it 

possible to understand and draw implications from both data and information. Knowledge 

consists of data and information organized and processed to convey understanding, experience, 

accumulated learning, and expertise as they apply to a current problem or activity.

Safety Data – Safety data means crash, roadway, and traffic data on a public road, and, 

includes, in the case of a railway-highway grade crossing, the characteristics of highway and train 

traffic, licensing, and vehicle data.

Service Oriented Architecture – Software or database design that is independent of 

vendors, products, and technologies. 

Silo Data System – A repository of data under the control of one department and is 

incompatible or not integrated with other data systems.

State Safety Data System – A SSDS must perform analyses supporting the goals in the SHSP 

and HSIP. The system must include 1) all public roadways, 2) crash, roadway, traffic, and 

railway-highway grade crossing data, 3) geolocation of safety data to a common basemap,

4) analysis and evaluation capabilities, and 5) the MIRE FDEs.

System Owner – IT professional(s) supporting the technical and functional aspects of data 

management and information delivery for specifically assigned business areas, subject areas, or 

databases.
For More Information:

Visit https://safety.fhwa.dot.gov/rsdp/manage.aspx

FHWA, Office of Safety

Stuart Thompson, P.E.

Stuart.Thompson@dot.gov

202-366-8090

FHWA-SA-17-047