Ohio Department of Transportation

Data Governance 

Ohio’s People, Processes, and Technology

SAFETY DATA CASE STUDY 

FHWA-SA-20-059 

Federal Highway Administration Office of Safety 

Roadway Safety Data Program 

http://safety.fhwa.dot.gov/rsdp
i SAFETY DATA CASE STUDY FHWA-SA-20-059

Notice 

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ii SAFETY DATA CASE STUDY FHWA-SA-20-059

1. Report No.

FHWA-SA-20-059

2. Government Accession 

No.

3. Recipient's Catalog No.

 

4. Title and Subtitle

Data Governance: Ohio’s People, Processes, and Technology

5. Report Date

August 2020

6. Performing Organization Code

7.Author(s) 

Matt Albee, Ian Hamilton, and Catherine Chestnutt

8. Performing Organization Report No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc (VHB)

940 Main Campus Drive 

Raleigh, NC 27606 

10. Work Unit No.

11. Contract or Grant No.

DTFH61-16-D-00052

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety 

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

Case Study, January 2020-December 

2020 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract managers for this report was Esther Strawder and Jerry Roche.

16. Abstract

This case study documents the institutional advances of the Ohio Department of Transportation’s (ODOT’s) data governance 

program. In 2020, ODOT is in the final year of the agency’s Data Governance Plan established in 2017; ODOT’s latest Data 

Governance Strategic Roadmap plans activities through 2025. While this effort began as an undertaking by the Division of 

Planning, the agency has since established an independent Data Governance Office (DGO) that oversees ODOT’s Data 

Governance Committee and Data Governance Framework. The DGO is headed by the newly created Chief Data Officer (CDO) 

position, and the CDO is responsible for executing data governance policies, strategic planning, and institutional change 

management. ODOT uses Prosci’s Awareness, Desire, Knowledge, Ability, and Reinforcement model for organizational change 

management related to data governance. ODOT has committed to demonstrating the effectiveness and benefits of data 

governance to the organization by calculating the return on investment that data governance provides to ODOT’s daily business 

practices. By incorporating people, processes, and technology into its approach, ODOT continues to successfully implement data 

governance as part of its overall institutional strategy.

17. Key Words: 

Data governance, safety, data, management

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

 18

22. Price

Technical Documentation Page
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Acronyms 

Acronym Description

ADKAR Awareness, Desire, Knowledge, Ability, and Reinforcement 

CDO Chief Data Officer

CMM capability maturity models

DBO Data Business Owners

DGC Data Governance Committee

DGO Data Governance Office 

DoIT Department of Information Technology 

FHWA Federal Highway Administration 

GIS geographic information system

ODOT Ohio Department of Transportation

PMO Project Management Office 

ROI return on investment 

SME subject matter expert

TAMAG Transportation Asset Management Audit Group

TSMO Transportation Systems Management and Operations
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Table of Contents 

Introduction................................................................................................................................... 1

Data Governance Framework ..................................................................................................... 2

Training .......................................................................................................................................... 5

Maturity Assessment .................................................................................................................... 5

Benefits........................................................................................................................................... 5

Challenges...................................................................................................................................... 7

Next Steps ..................................................................................................................................... 8

Conclusions and Lessons Learned ............................................................................................. 10

References .................................................................................................................................... 11

List of Figures

Figure 1. Graphic. ODOT Data Governance Framework......................................................... 2

Figure 2. Graphic. ODOT groups that support the DGC. ........................................................ 3

Figure 3. Graphic. DGC membership structure. ....................................................................... 4

Figure 4. Graphic. Examples of data governance benefits and ROI for ODOT...................... 6

Figure 5. Graphic. ODOT’s Data Governance Strategic Roadmap. ........................................ 8

Figure 6. Graphic. ODOT’s Task-Level Data Governance Plan (2020). .................................. 9

Figure 7. Graphic. ODOT’s data governance philosophy........................................................ 10
vi SAFETY DATA CASE STUDY FHWA-SA-20-059

Executive Summary

This case study documents the institutional advances of the Ohio Department of Transportation’s 

(ODOT’s) data governance program. In 2020, ODOT is in the final year of the agency’s Data 

Governance Plan established in 2017; ODOT’s latest Data Governance Strategic Roadmap plans 

activities through 2025. While this effort began as an undertaking by the Division of Planning, the agency 

has since established an independent Data Governance Office (DGO) that oversees ODOT’s Data 

Governance Committee and Data Governance Framework. The DGO is headed by the newly created 

Chief Data Officer (CDO) position, and the CDO is responsible for executing data governance policies, 

strategic planning, and institutional change management. ODOT uses Prosci’s Awareness, Desire, 

Knowledge, Ability, and Reinforcement model for organizational change management related to data 

governance. ODOT has committed to demonstrating the effectiveness and benefits of data governance 

to the organization by calculating the return on investment that data governance provides to ODOT’s 

daily business practices. By incorporating people, processes, and technology into its approach, ODOT 

continues to successfully implement data governance as part of its overall institutional strategy.
1 SAFETY DATA CASE STUDY FHWA-SA-20-059

Introduction 

The Ohio Department of Transportation (ODOT) needed guidelines and rules to develop a formal 

framework for enterprise data. In order to develop this framework, ODOT formalized data life cycle 

interactions between people, processes, and technologies to support the agency’s broader objectives. 

ODOT sees data governance as a key driver in its approach to data management. 

In 2020, ODOT is in the final year of the agency’s Data Governance Plan established in 2017. Beyond 

2020, data governance will be a permanent feature of ODOT’s data life cycle management process. 

ODOT made significant progress in data governance efforts in 2019 by establishing the Data 

Governance Office (DGO), Chief Data Officer (CDO) position, the Data Governance Committee 

(DGC), and a Data Governance Framework.

At the beginning of the data governance formulation process, ODOT staff held several beliefs and 

conceptions of what data governance entails. Through this effort, ODOT now defines data governance 

as establishing guidelines and rules of engagement for business and management activities of enterprise 

data. 

ODOT’s data governance activities began in the Division of Planning under Asset Management. Safety 

data—crash, roadway inventory, and traffic volumes—are also addressed under this agency-wide 

framework. This ensures accurate, consistent, and timely data for safety analysis and implementation of 

projects. 

Roles and Responsibilities: 

The Data Governance Framework includes four levels of responsibilities: 

• Strategic 

• Tactical

• Implementation 

• Support

Target Audience: 

• Executive Leadership

• Data Governance Committees

• Information Technology Staff

• Data Managers and Stewards

• Subject Matter Experts

 
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Data Governance Framework

Figure 1 illustrates the three-tiered framework, beginning 

with the strategic Data Governance Organizational Business 

Drivers. Governance activities support these strategies 

through planning and performance monitoring, and Data Life 

Cycle Management activities form the core day-to-day data 

management workflows. Each tier reinforces the others to 

ensure that agency activities meet agency priorities. 

Within these tiers, the framework consists of four levels of 

groups with various roles and responsibilities: Strategic, 

Tactical, Implementation, and Support.

The Strategic level consists of ODOT executive leadership

and responsibilities including: 

• Setting the overall direction of data governance at ODOT 

in alignment with core strategies.

• Supporting the data governance policy.

• Providing oversight and accountability.

• Providing human resources and funding.

• Resolving escalated data conflicts.

Figure 1. Graphic. ODOT Data Governance Framework.

ODOT’s Data Governance 

Framework, “establishes 

guidelines and rules of 

engagement for business and 

management activities of 

enterprise data. It formalizes 

data life cycle interactions 

between people, processes, and 

technologies to support positive 

outcomes” 

(Ohio Department of Transportation, 

2020, slide 2).
3 SAFETY DATA CASE STUDY FHWA-SA-20-059

Members include the Director of ODOT, along with the Assistant Directors of Business and Human 

Resources, Chief Engineer and Transportation Policy, and Field Operations.

The Tactical level consists of the CDO, DGC, Technology Council, Funding Council, and other 

disciplines like the Transportation Asset Management Audit Group (TAMAG) and Transportation 

Systems Management and Operations (TSMO). This group plans, develops, implements, supports, 

monitors, and allocates funding for data governance activities. The Tactical level provides a forum to 

address critical data issues. 

The Implementation level consists of Data Business Owners (DBO), subject matter experts (SMEs), 

ODOT Staff, the Department of Information Technology (DoIT), and the Project Management Office 

(PMO). This group manages the programs, systems, and technologies that ingest data and practically 

apply data governance. Groups in this tier are responsible for communicating between SMEs and DBOs, 

participating in training, and adhering to policies. 

The Support level consists of DGO staff and technical staff who are accountable for data governance 

through executing, enforcing, and providing feedback where needed. These staff members are experts in 

their data and systems; they advise SMEs, DBOs, and the DoIT on data governance policies and formal 

documentation for business and technical consumption.

Although the framework is new, most of these groups existed prior to a formal data governance 

structure; the DGO and DGC are the only new 

organizations created by data governance.

Data Governance Committee

The DGC held its first meeting in May of 2020. It has 

a leadership role in the oversight and perpetuation of 

ODOT’s data management strategy. The DGC’s goals 

include:

• Fostering a culture of data quality and data-driven 

decision-making.

• Creating structured roles and responsibilities. 

• Facilitating collaboration, education, and 

standardization where appropriate.

• Ensuring data policies align with ODOT’s strategic 

plans.

The DGC does not replace any existing groups; it

assists and streamlines the activities of these existing 

groups (figure 2). Figure 2. Graphic. ODOT groups that support the DGC.
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The DGC meets monthly and on an ad hoc basis as required. The committee consists of 19 core 

participants, with other business process and data experts engaged as needed. The DGC membership is 

tiered in four different levels according to their active participation and voting power (figure 3). 

• The Strategic and Tactical tiers, comprised of executive leadership and asset group managers 

respectively, have voting power and actively participate.

• The Support tier, comprised of data experts, division-level experts, the DoIT, and other support 

staff (e.g., legal, finance, human resources, etc.), involves active members that support the data 

governance program across ODOT, but do not vote on DGC business.

• The Advise tier, comprised of technical members like the TAMAG and TSMO groups, advise the 

DGC as needed.

The DGC and its relationship to other partner 

groups, the processes it oversees, and its role in 

data governance are well-defined and documented 

in the DGC Charter. 

Data Governance Office

Originally a part of the Division of Planning, the 

DGO exists outside of the DoIT. It is accountable 

for implementing the Data Governance Framework, 

allocating resources, and executing governance 

activities. As of spring 2020, the DGO consists of 

the CDO and two data analysts. Responsibilities of 

the CDO include:

• Implementing the Data Governance 

Framework. 

• Executing data governance policies and 

procedures.

• Developing a multi-year strategic roadmap for 

data governance activities. 

The CDO is responsible for organizational change 

management through communication, education, and training. The CDO also manages the agenda for 

the governance portion of the Executive Leadership Team and chairs the DGC.

The TAMAG also exists under the DGO. This allows the DGO to manage both structured and 

unstructured data systems and provides consistency in the program’s implementation. This arrangement 

provides a direct connection between ODOT’s business needs and the overall vision of the program. 

Figure 3. Graphic. DGC membership structure.
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Data Life Cycle Management

Ultimately, data governance exists to manage the collection, maintenance, and application of data and 

data-related technologies. Because data serves multiple purposes and users within the agency, ODOT 

has formalized an agency-wide data collection workflow. A data collection request is submitted to the 

TAMAG who decides: 

1. Who is the data owner?

2. Who will collect the data?

3. How will data be collected?

This process helps ODOT continually manage data collection requests, develop collection cycles, and 

ensure standards are enforced. 

Training

ODOT uses Prosci’s Awareness, Desire, Knowledge, Ability, and Reinforcement (ADKAR) model for 

organizational change management related to data governance. To raise awareness, ODOT also created 

marketing materials to communicate data governance principles and policies. ODOT developed onepage flyers and produced television advertisements to play in county garages and offices through ODOT 

TV. Data governance can be a difficult concept to practically apply and communicate, and these 

marketing materials explain the importance of data collection and management in non-technical, easy-tounderstand illustrations.

Maturity Assessment

In 2017, ODOT hired a contractor to measure the department’s maturity in data governance. This 

survey of data business owners assessed 15 dimensions of data quality, data usability, and data 

accessibility. Like other capability maturity models (CMM), this assessment assigned a score between 1 

and 5, with 1 representing an ad-hoc, informal approach to data management and 5 indicating a 

streamlined enterprise system that is continually optimizing its procedures. Now, some dimensions of 

the original assessment are no longer relevant, and the CMM needs to be updated to reflect the agency’s 

progress. ODOT plans to reassess its performance following the implementation at the end of 2020. 

Benefits

ODOT identified inefficiencies in their data lifecycle management process that could be addressed 

through better data management and data governance. These are tangible costs to ODOT, including an 

annual sync of the Structural Management System to the linear referencing system, where some assets 

have conflicting standards, regulations, and integration methods. Data integration can also be a challenge 

when common data codes are recorded inconsistently across different systems. Figure 4 illustrates some 

of the benefits associated with data governance, including:
6 SAFETY DATA CASE STUDY FHWA-SA-20-059

• Implementing standards ensures data accuracy, consistency, and completeness.

• Managing data lifecycle ensures coordination, accessibility, and flow of information.

• Establishing measures ensures compliance and reliability and allows ODOT to calculate a return 

on investment (ROI).

Return on Investment 

ODOT recognizes that data are an asset and should be tracked and managed accordingly. The use of 

data can help make more precise and concise decisions, resulting in added quality, savings, and improved 

performance. To quantify the management and use of data, ODOT has pursued an ROI approach to 

performance assessment to reinforce the value of the data governance program. ROI is calculated by 

dividing the cumulative benefits of an investment by its costs. Examples of quantifiable benefits include 

reduced labor-related costs, increased productivity with new tools and processes, reduced equipment 

costs, optimized project delivery costs, and reduced travel time for data collection. 

ODOT identified some opportunities for improvement and their cost to the agency in data collection, 

data management, applications (e.g., mobile business intelligence, preventative maintenance), and others 

in the realm of construction, legal mandates, and environmental (figure 4). Potential benefits can be in 

the form of added value to existing processes or as mitigation of existing challenges. ROI can be 

calculated at the project level, to show the benefits of implementing changes or process improvements, 

or ROI can be calculated at the program level to show benefits for the whole organization. 

Figure 4. Graphic. Examples of data governance benefits and ROI for ODOT.
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Regular self-assessments help ODOT determine opportunities for the highest ROI and increase 

efficiencies. With the assistance of a consultant, ODOT compiled these findings in a white paper (ODOT 

Data Governance: Return on Investment in Transportation Data Governance) that defined the various types of 

ROI analyses and their inherent qualitative and quantitative elements and established a potential 

framework for future ROI calculations within the data governance program.

The white paper provided various examples of how ROI can be used in action to align ODOT’s 

Transportation Asset Management Plan and data governance. For example, annual asset inspections

place an emphasis on personnel to adequately and efficiently track the assets and meet inspection 

requirements. The data collection method is manual, requires a significant amount of staff time, and is 

not standardized. As a result, collected data may not be consistent. Furthermore, maintenance is often 

reactionary to declining conditions, as opposed to planned. Implementing a formal, proactive solution 

will reduce the amount of staff and time required to complete inspections and will increase the quality of 

the collected data. This can also reduce the amount of unplanned maintenance incidents over time as 

asset conditions can be more reliably tracked and preventative maintenance can be planned. By 

automating data conversion from design files to a geographic information systems (GIS) format, ODOT 

estimates over $2.5 million in savings in a five-year period due to labor and resource costs, with a 238

percent ROI in the first year alone. ODOT will implement this process beginning in July of 2020.

In undertaking the ROI approach to data governance assessment, ODOT can evaluate potential 

projects, business processes, technologies, and overall data governance programs to prioritize those 

with the most promising ROI. 

Challenges

Executive support has been critical in moving data governance activities forward in Ohio, but 

institutional changes have occasionally provided barriers to implementation. For instance, staff turnover 

at the administrative level has led to brief periods of change in the level of support for the program.

However, program champions persistently communicated the programmatic benefits of data governance 

in terms of cost savings, ROI, and alignment with ODOT’s broader objectives. 

Legacy systems within ODOT have also posed a challenge to data governance implementation. These 

programs will take more effort to integrate into the Data Governance Framework than newer solutions; 

business rules already exist, and current data business owners may have a different understanding of 

data ownership. This challenge illustrates the critical role of the DGO in conducting change management 

at ODOT.
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Next Steps

On a programmatic timeline, ODOT’s strategic roadmap for data governance is a coordinated effort 

between the DGO and the DoIT. This plan currently schedules annual activities through 2025 (figure 5). 

Key steps for this process include adding new data systems, implementing a new data warehouse, and 

continual re-evaluation and self-assessment of the program. Additionally, ODOT will conduct regular 

ROI analyses of the data governance program to ensure cost effectiveness and maintain executive 

support for the initiative.

On a short-term, project-level timeline, ODOT’s data governance priorities involve continuing to map 

the agency’s data systems and building the workforce engaged in the day-to-day data governance (figure

6). A hurdle in data integration will be the processes and technological components that need to be 

assessed for each data asset. As a result, data business plans are a core function of the current data 

governance initiative in 2020. This entails DGO meetings with each data business owner to map legacy 

systems and plan for structured data and policies. These plans will cover all data collected and integrated 

by ODOT. 

In the future, ODOT desires to integrate data from local agencies. Currently, local agencies do not use 

ODOT’s enterprise applications. Local involvement has focused on asset management and data 

collection. Data governance has not been a core focus of local interaction as of spring 2020. 

Figure 5. Graphic. ODOT’s Data Governance Strategic Roadmap.
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Figure 6. Graphic. ODOT’s Task-Level Data Governance Plan (2020).
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Conclusions and Lessons Learned

ODOT understands that data and technology are only two components of an effective data governance 

program; people are the critical link to translate data into enterprise technology (figure 7). This allows 

the agency to effectively manage the transportation system. ODOT has focused most of its data 

governance effort to date on aligning the agency’s existing data and technology groups, DoIT, TAMAG, 

Technology Council and others, within the Data Governance Framework. The DGO exists as an 

independent body, able to negotiate the interaction between these groups in a common forum, the 

DGC.

Software and technology are not the enablers of good data governance, they are the result of good data 

governance. As ODOT continues to develop data business plans with individual data business owners, 

these conversations will allow ODOT to identify solutions that reinforce ODOT’s strategic goals, rather 

than allowing the business rules inherent in purchased software to guide the agency’s workflow.

Re-evaluation is another cornerstone of ODOT’s data governance program. As ODOT follows its 

strategic roadmap and new data systems come online, the agency has set clear milestones where it will 

evaluate its performance and program maturity. This will allow the agency to determine if it is moving 

from a more ad-hoc and siloed approach, to a program that is working to optimize its practices. It will 

take time for ODOT to reach the optimizing stage. Steady progress reinforced by change management, 

training, and a culture of data governance will allow ODOT to be more effective data stewards.

Finally, to underscore the value of data 

governance, ODOT plans to regularly 

conduct ROI analyses to quantify the 

program’s importance to the overall 

mission of the agency. These studies will 

allow data managers and business owners 

to understand the practical and tangible 

benefits of participating within the Data 

Governance Framework. 

Figure 7. Graphic. ODOT’s data governance philosophy.
11

References

 Ohio Department of Transportation. (2020). Data Governance Committee Charter, Columbus, 

Ohio.

 Ohio Department of Transportation. (2020). Design File to GIS ROI Use Case, Columbus, Ohio.

 Ohio Department of Transportation. (2020). ODOT Data Governance: Managing Information to 

Save Lives and Money [PowerPoint Slides]. 

 Ohio Department of Transportation. (2019). ROI in Transportation Data Governance, Columbus, 

Ohio.

 Ohio Department of Transportation. (2014). Technology Council Charter, Columbus, Ohio.

 Ohio Department of Transportation. (2016). TAMAG Charter, Columbus, Ohio.

 Phone Interview on May 27, 2020, with Ian Kidner and John Puente. 

Contact

Ohio Department of Transportation

John Puente, Chief Data Officer

John.Puente@dot.ohio.gov 

Ian Kidner, GIS Manager 

Ian.Kidner@dot.ohio.gov