Administration for Community Living

Office of Performance Evaluation

ACL Data Council

 Data Governance 

Primer
TABLE OF CONTENTS

Introduction 1

What is the ACL Data Governance Primer? 1

Who Should Use It? 1

How Should It Be Used? 3

How Was the Primer Developed? 3

Background 4

What is Data Governance? 4

What Is the Difference Between Data Governance, 

Data Management, and Data Stewardship? 4

Why Does Data Governance Matter? 5

Data Governance Framework 7

Data Governance Participants and Roles 7

Data governance leaders 7

Data stewards 8

Technical advisors 10

Other data stakeholders 12

Data Maturity Assessment 13

ACL Governance Guidance 16

Data principles 16

Data quality 18

Data lifecycle 22

Metadata 29

Data access 32

Next Steps 34

References 35

Appendix A. Definitions 37

Appendix B. Federal Government Data Maturity Model 40

Appendix C. Application of the Federal Government Data Maturity Model to ACL 42
Introduction

What is the ACL Data Governance Primer?

This Administration for Community Living (ACL) Data Governance Primer (hereinafter 

“Primer”) is a practical tool to guide ACL’s data policies, standards, and practices. 

It is designed to strengthen ACL’s data management and to help ensure that the 

data ACL collects through its evaluation, grant 

reporting, and administrative performance 

measures are available, usable, consistent, 

and secure. The Primer includes general 

information about data governance and its 

elements as well as specific data examples 

from ACL (provided in green call-out boxes).

Who Should Use It?

All ACL staff can benefit from the Primer. Grant Officers are both data curators, 

receiving important information from grantees and ensuring its accuracy, and 

consumers, who use the data received to draft funding opportunity announcements. 

Managers and leadership use data to define and implement policy. 

A practical tool to 

guide ACL’s data 

policies, standards, 

and practices.
EXAMPLE

Supporting families and family caregivers is at the heart of ACL’s 

mission. Without the daily efforts by family members to provide 

support and assistance to individuals of any age with a long-term need 

or disability, the long-term services and supports system would not 

be able to meet the need. Family caregivers are the bedrock of this 

system and key partners in the healthcare delivery system. Twenty 

years ago, ACL began implementing the National Family Caregiver 

Support Program (NFCSP), the first federal recognition of the need 

to support family members and ensure they are able to remain in 

their roles for as long as possible. Data are vital to the success of any 

program where understanding its impact is necessary in order to grow 

and improve the program over time. The State Program Report (SPR) 

is the primary means by which ACL gathers performance data from 

states on the operation of the NFCSP and other programs and services 

funded by the Older Americans Act. The programmatic lead for the 

NFCSP relies on the data provided by the states via the SPR to provide 

insights into trends in service delivery, potential challenges that states 

might be experiencing, and a better understanding of the needs and 

preferences of the caregivers served by the program. Ensuring the SPR 

is relevant and useful for states is a critical component of good program 

management and oversight.

2
How Should It Be Used?

The Primer should be used as

• a learning tool to become familiar with key federal data standards and to 

understand ACL’s approach to data governance; 

• a framework to guide what ACL programs do, by building data governance 

into program planning and design; and

• a practical tool to help ACL Centers determine where they are and where they 

want to be in terms of implementing good data policy and procedures.

How Was the Primer Developed?

The Primer was developed by the Data Governance Standards Working Group of the 

ACL Data Council. The content is based on current data governance frameworks, 

models, and guidance (resources) that apply to ACL and its programs. These 

resources inform what ACL is required to do and what ACL staff must consider 

when making decisions about data policies, practices, and standards. For a list 

of the resources and details about how they apply to ACL, see this annotated 

bibliography. 

The content of the Primer should not be considered static. Rather, data governance 

is an ongoing iterative process that must be responsive to current federal guidance 

as well as ACL’s data needs and related infrastructure needs. 

3
Background

What is Data Governance?

According to the Office of Management and Budget (OMB, 2020):

Data governance is the process of setting and 

enforcing priorities for managing and using 

data as a strategic asset. A Data Governance 

Body with authority and oversight over the 

management of agency data assets is a key 

piece of data infrastructure. These bodies 

are commonly called by such names as Data 

Governance Boards, Data Councils, or Data 

Strategy Teams. The Data Governance Body establishes policy, procedures, and 

roles for developing, overseeing, and coordinating data management policy and 

helps prioritize data resource allocations to answer agency key questions and meet 

stakeholder needs.

What Is the Difference Between Data Governance, 

Data Management, and Data Stewardship? 

Whereas data governance is the strategy of data—meaning the policies, 

procedures, and rules that govern the data, data management is the logistics 

Data governance 

is the strategy that 

enables stewardship.

4
of data—meaning the processes used to plan, specify, enable, create, acquire, 

maintain, use, archive, retrieve, control, and purge data. Data management 

encompasses the entire lifecycle of a data asset, from the very initial creation 

of the data to the final retirement of the data. Data stewardship is the actual 

implementation of the policies, procedures, and rules that govern the data. 

Data stewardship can be thought of as a collection of data management 

methods covering acquisition, storage, aggregation, and deidentification, 

and procedures for data release and use. The concept of a data steward is 

intended to convey a fiduciary (or trust) relationship with data that turns on 

a data manager whose loyalty is to the interests of individuals and entities 

whose data are stored in and managed by the system (Rosenbaum, 2010).

Data governance is the strategy that enables stewardship.

Why Does Data Governance Matter?

With the generation of data comes the risk of poor data quality, duplicate data, 

and data breaches. A coordinated strategy for standardizing, collecting, storing, 

analyzing, sharing, and using data helps mitigate these risks.

Thus, data governance plays a fundamental role in an enterprise data management 

approach that realizes the importance of managing information as an asset and 

turning data into valuable information. According to OMB Memorandum M-19-18: 

Federal Data Strategy – A Framework for Consistency, a data governance structure 

helps agencies use data to answer important questions while meeting legal and 

5
ethical requirements essential to maintaining public trust, including protecting 

privacy and ensuring confidentiality (OMB, 2019b).

Instituting a data governance framework provides entities coordinated and 

systemic oversight for identifying and controlling the collection, storage, and 

disposition of information resources. Thus, the Federal Data Strategy (OMB, 2019a) 

encourages agencies to take action focused on data governance:

Prioritize Data Governance: Ensure there are sufficient authorities, roles, 

organizational structures, policies, and resources in place to transparently 

support the management, maintenance, and use of strategic data assets.

 A Federal Data Strategy for Today’s Work Environment

Access, Use, and 

Augmentation

Commercialization,

Innovation, and

Public Use

Enterprise Data

Governance

Decision-Making

and Accountability

Four Cross-Cutting 

Drivers of Changes

Policy

People

Process

Platform

6
Data Governance Framework

Data Governance Participants and Roles

Effective data governance requires the participation of different types of data 

stakeholders. In addition, the specific concerns and rules that will be addressed 

will influence the mix of data stakeholders and the amount of effort required of 

stakeholders to make sound data-related decisions and take appropriate action. 

DATA GOVERNANCE LEADERS

What is the role of ACL’s data governance leaders?

ACL’s data governance leaders champion data solutions to ensure accountability 

and increase the value of data assets (OMB, 2020). Their role is to promote data 

governance within ACL, set and enforce priorities for managing and using data 

as a strategic asset, communicate data-related changes to ACL staff, and monitor 

progress on data governance tasks and projects. They may engage relevant 

technical advisors as needed. At ACL the primary responsibility for data governance 

falls with the Office of Performance and Evaluation (OPE), which focuses on data 

quality and usability issues, and the Office of Information Resource Management 

(OIRM), which ensures that ACL’s data are accessible and secure. Below are other 

important roles for ACL staff in maintaining high quality data.

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DATA STEWARDS

What is the role of data stewards?

ACL data stewards are the subject matter experts and points of contact for the 

ACL programs they oversee. Data stewards are responsible for managing each 

program’s data elements, including content and metadata (National Aeronautics 

and Space Administration, 2015). This responsibility includes informing data 

policies as determined by ACL’s data governance rules and data quality standards, 

as well as defining, managing, controlling, and preserving the integrity of the 

program data. Thus, the role of a data steward requires both technical and 

business-oriented skills.

Key Roles of Data Stewards

Ensure the accuracy, 

completeness, 

privacy, and security 

of program data

Ensure that the right 

data are available to 

the right people 

within ACL

Understand ACL’s 

business needs and 

relevant federal 

data rules

8
Program staff in the Office of the Long-Term Care Ombudsman Program serve 

as the steward for that program’s data. This role has involved close collaboration with the team conducting the process and outcomes evaluations, 

including helping to draft the program logic model, reviewing and commenting on data collection tools and approaches, getting buy-in from program 

stakeholders to increase their participation in the evaluation, ensuring high 

response rates, reviewing final reports for clarity, and promoting data dissemination. The role has also involved serving as the task lead for the development 

of a new web-based data tool for the collection of annual performance data, 

which includes working with system developers and technical assistance providers, drafting guidance for grantees that collect and report the performance 

data, and reviewing the data for final acceptance by ACL.

EXAMPLE

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EXAMPLE

TECHNICAL ADVISORS

What is the role of technical advisors?

Technical advisors assist data governance leaders in developing all formal 

documentation and providing guidance for data-related decision-making. Technical 

advisors should be selected based on their knowledge of the ACL program as well 

as data governance principles and best practices. 

The technical advisor for ACL’s Administration on Disabilities (AoD) data 

management leads several efforts, including those focused on the following: 

• how to organize and “wrangle” large data sets in order to get actionable 

insights from them, which may include finding innovative ways to combine 

fields of data that do not naturally mesh together and ensuring good data 

quality management techniques

• how to use algorithms and programming to efficiently go through large 

data sets and apply treatments, filters, and conditions to the data

• how to conduct exploratory data analyses in order to navigate a data set 

and come to broad conclusions based on some initial appraisal

• how to create meaningful data compilations and analytics that 

communicate and relate findings to AoD and ACL programs’ mission—

more specifically, possibly focusing on

10
continued

-	 using information about developmental disability populations 

and demographics so outlier data can be identified

-	 using historical data on a program to identify outlier data 

-	 maintaining trends of key data points

-	 helping to complete our understanding of underlying data 

points needed for performance measures 

-	 working closely with AoD project officers and managers to 

maintain data quality guidelines 

-	 providing contents for data calls and information requests 

-	 providing subject matter expertise to other ACL projects 

• working with ACL’s OIRM to address the following:

-	 making sure OIRM and system developers understand AoD’s 

reporting tools and the ways data sections, subsections, and 

data tables are related or interrelated

-	 making sure data structure and integrity are maintained and 

reflected in the data storage portal for an easy and seamless 

data export and for maintaining the quality of data

-	 identifying ways to collect and use data from 

different sources

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OTHER DATA STAKEHOLDERS

What are other types of data stakeholders?

In addition to data governance leaders, data stewards, and technical advisors, 

there are other people who are essential to good data governance. Other data 

stakeholders are individuals or groups who could affect or be affected by ACL’s 

data. Data stakeholders may be grouped into broad categories according to their 

data role(s), but any given stakeholder may also represent one or more categories. 

These categories include:

• producers — stakeholders or technologies that create or collect primary data

• managers (sometimes referred to as data owners) — stakeholders who 

receive the primary data, ensure its quality, and compile it into a data 

repository

• curators (sometimes referred to as data custodians) — stakeholders who 

create, manage, and reconcile data repositories and information technology 

systems 

• analysts — stakeholders who use data to conduct analyses and to develop 

data summaries and findings (e.g., reports, journal articles, briefs); may be 

ACL staff or external stakeholders

• consumers — stakeholders who receive the data summaries and findings 

generated from the data (e.g., program leaders, state agencies, legislators)

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Data Maturity Assessment 

What is a data maturity assessment?

A data maturity assessment is a high-level assessment of an organization’s current 

data capabilities and supporting processes. It is a tool to help an organization 

analyze all aspects of its policies, procedures, and operations related to data and 

data infrastructure, including data governance, data management, data culture, 

data systems and tools, data analytics, staff skills and capacity, resource capacity, 

and compliance with law and policy (OMB, 2020). 

Why is a data maturity assessment important? 

A data maturity assessment allows an organization to evaluate itself against 

documented best practices, determine gaps, and identify areas to prioritize for 

improvement (OMB, 2019a).

In addition, it helps with strategic communication between agency data 

professionals and agency leadership, and it provides a common language and 

framework to help promulgate solutions and best practices across federal agencies 

to advance data-driven decision-making. Thus, the Federal Data Strategy (OMB, 

2019a) encourages agencies to take action focused on assessing data maturity:

Assess Maturity: Evaluate the maturity of all aspects of agency data capabilities 

to inform priorities for strategic resource investment. 

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A data maturity assessment can help ACL and its Centers

• assess existing data capabilities and processes, 

• prioritize time and resources to improve data 

and related infrastructure, and 

• assess progress over time in improving data 

infrastructure and operations.

What is an example of a data maturity assessment?

Different data maturity assessment models are available.1

 When selecting a model, 

data stakeholders should choose one that is roughly aligned with ACL’s or their 

Center’s current capacity and that can measure current successes while identifying 

areas for improvement. It is important that the model balances the information 

needed and the resources available for the assessment with the types of actions 

ACL or a Center can realistically take based on the results.

1 The government-wide resources repository required by the Foundations for Evidence-Based 

Policymaking Act of 2018 (Evidence Act), resources.data.gov, includes a collection of references and 

examples of commonly used data maturity assessment models, including both open-source and 

proprietary models.

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One example of a data maturity model is the Federal Government Data Maturity 

Model (see Appendix B). This model would help ACL assess the current state of 

its data capabilities by determining where ACL’s capabilities lie on a continuum 

from low (=1) to high (=5) in six key data maturity areas (or “lanes”). The Federal 

Government Data Maturity Model would also help ACL identify future milestones to 

signify improvement in its data capabilities. This model could be applied at strategic time points to assess progress over time. Appendix C provides an 

example of how the model might be applied to assess the current state of ACL’s 

data capabilities.

High

Capability

Low 

Capability

Data Maturity Areas

• Analytics Capability

• Data Culture

• Data Management

• Data Personnel

• Data Systems and Technology

• Data Governance

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ACL Governance Guidance

DATA PRINCIPLES

What are key principles of data governance?

To ensure successful data governance, there are five core data governance 

principles to consider (EOL IT Services, 2018; Seiner, 2017; University of New 

South Wales, n.d.):

• Valued and strategic enterprise asset: In any organization, data are a 

critical asset. They have real, tangible, and measurable value and are 

the foundation for organizational decision-making. Thus, data must be 

recognized as a valued enterprise asset across different layers of an 

organization and across programs. In addition, data assets need to be 

defined, controlled, and accessed in a careful and process-driven way. 

• Data ownership and accountability: In a successful data governance process, 

ownership and accountability of data have to be clearly defined. If no one 

takes ownership of an organization’s data governance, it will be directionless, 

blunt, and ineffective. Ownership and accountability have to be applied 

across the organization, rather than being centered in just one department. To 

ensure cross-organization accountability, organizations may establish a data 

governance council that is composed of representatives from all departments 

or programs, including leadership. The council should define data procedures 

and policies to be implemented across the organization and should have the 

authority to ensure they are strictly followed.

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• Standardized rules and regulations: A data governance process should 

follow standardized rules and regulations to protect the data and to ensure 

they are used in accordance with all relevant external regulations. Rules 

and guidelines should focus on topics such as data access, data definition, 

privacy policies, and security standards. The rules and guidelines should 

be established at the data governance council level and enforced by the 

data steward(s).

• Data quality standards: The quality of data has to be managed consistently 

from the very beginning. Stakeholders can only make effective decisions 

if the data are high quality and trustworthy. Thus, enterprise data must be 

tested on a periodic basis against the defined quality standards.

• Transparency: All data governance processes throughout an organization 

should be implemented with the utmost transparency. The organization 

should retain permanent records of all relevant data governance actions and 

processes, including what the data have been used for and why they have 

been used in that way. Transparency will protect an organization in the event 

of a data breach and will also allow stakeholders to continually learn from 

the way data are used throughout the organization.

Why are data principles important?

Principles provide overarching rules or norms to guide policies and actions. For 

data governance, principles can help govern the design of new data systems and 

inform changes to existing data sets and collection efforts.

17
DATA QUALITY

What do we mean by data quality?

Data quality refers to data being useful, accurate, clear, complete, and unbiased. 

“Quality” is defined by OMB as a broad term that encompasses the notions of 

“utility,” “objectivity,” and “integrity.” 

• “Utility” refers to the usefulness of the information to the intended users. 

• “Objectivity” refers to whether the disseminated information is being 

presented in an accurate, clear, complete, and unbiased manner and, as a 

matter of substance, is accurate, reliable, and unbiased. 

• “Integrity” refers to security—the protection of information from 

unauthorized access or revision, to ensure that the information is not 

compromised through corruption or falsification (OMB, 2002).

The Transparent Reporting Project2 adopted eight dimensions of data quality 

(relevance, accuracy, reliability, timeliness, punctuality, consistency, comparability, 

and access) which the project considers compatible with the four OMB core 

concepts (Prell et al., 2019).

2 The Federal Committee on Statistical Methodology established its Working Group on 

Transparent Quality Reporting in the Integration of Multiple Data Sources in 2017. One of the key 

projects of the working group was to examine current practices for transparent reporting and 

identify improvements. For a report on the project, see Prell et al. (2019). 

18
Why does quality matter?

Quality data can be used to draw accurate conclusions and make good decisions. 

The better the data quality, the more confidence you can have in your outputs, 

outcomes, and decisions. In addition, quality data can enhance productivity, 

resulting in less time spent on correcting errors. 

Federal agencies, like ACL, are also governed 

by regulations and rules focused on 

ensuring data quality. Per OMB guidelines,3

federal agencies are directed to develop 

information resources management 

procedures for reviewing and substantiating 

the quality (including the objectivity, utility, 

and integrity) of information before it is 

disseminated (OMB, 2002).

3 Section 515 of the Treasury and General Government Appropriations Act for FY 2001 (Public Law 

106–554) directed the Office of Management and Budget to issue government-wide guidelines that 

provide policy and procedural guidance to federal agencies for ensuring and maximizing the quality, 

objectivity, utility, and integrity of information, including statistical information, disseminated by 

federal agencies.

The better the data 

quality, the more 

confidence you can 

have in your outcomes 

and decisions.

19
What are some key data quality issues?

There are many issues that can lead to poor data quality. Below are a few examples.

Data Quality Issue Description

Bias

A statistic is biased if it is calculated in such a way that it is systematically different from the population parameter being estimated. For example, 

data collection processes could result in lopsided, misleading results. 

Selection bias is present when researchers are more likely to select one 

kind of individual within the study population for study than others, and 

bias can happen when respondents have different characteristics from 

nonrespondents.

Inaccuracy 

Inaccuracy refers to whether the data values are the correct value, whether they correspond to reality. In order for data to be accurate, the data 

value must be the right value and must be represented in a consistent 

and unambiguous form; for example, when transferring data between 

data systems, the transferred data points must be identical.

Incompleteness

Data are complete when all available data elements or data points are 

included in the data set and there are no missing data. Missing data can 

occur because of nonresponse or because participants drop out of the 

study. If there is a high level of missing data, there may not be enough 

information to make sound decisions. This is especially true if the missing data are systematic, meaning that some groups or types of people are 

more likely than others to not have provided data.

Inconsistency

Data are consistent when there are no contradictions in data across variables and/or time. Inconsistency in data may include duplicate data or 

discrepancies in data definitions between data sources during analysis 

which produce unintended results.

Outliers

Outliers are data points with characteristics that are considerably different from most of the other data points in the data set. Outliers could 

indicate “interesting” cases or errors in the data.

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What are ACL examples of strategies to enhance quality of data?

Some ACL programs have made dedicated efforts to enhance the quality of their 

data. Below are two examples from ACL.

EXAMPLE

Burn Model Systems (BMS)

As part of the BMS, staff at the 

National Data & Statistical Center 

developed standard operating 

procedures (SOPs) to standardize procedures for internal and 

external use of data from the BMS 

database. SOPs are available 

for a range of topics, including data quality and resolving 

coding issues. To access the 

SOPs, use this link: https://

burndata.washington.edu/

standard-operating-procedures

Traumatic Brain Injury (TBI) 

Model Systems

As part of the TBI Model Systems, 

staff at the National Data & 

Statistical Center developed a 

data dictionary for its TBI Model 

Systems National Database. It provides information for each variable 

in the database, including variable 

group names, group IDs, codes/

keys, definitions, notes, examples, 

history, and Q&A for each variable. 

To access the data dictionary, use 

this link: https://www.tbindsc.

org/Syllabus.aspx

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DATA LIFECYCLE

What is a data lifecycle?

A data lifecycle refers to the process or stages data move through, from creation to 

distribution and use, to archive, to deletion or destruction. It consists of multiple 

stages, each of which has its own characteristics and builds on the previous stage.

Why is a data lifecycle important?

Knowing the stages of a data lifecycle will help you plan and conduct your program 

activities while, at the same time, supporting efforts to establish evidence for 

your programs.

A data lifecycle model offers a high-level overview of the individual actions, 

operations, or processes that must be undertaken at different stages. As a 

visual tool, it can assist [ACL staff] in anticipating and planning for specific 

actions that need to be taken at each stage to manage the data, and thus 

help to ensure timely, comprehensive, and secure approaches to data 

curation (United States Geological Survey, 2013).

What are the stages of a data lifecycle? 

Different data lifecycle models exist, and users may enter at any stage of the lifecycle, 

depending on their current area of need (United States Geological Survey, 2010). The 

diagram below presents a model adapted from the United States Geological Survey 

Science Data Lifecycle Model (United States Geological Survey, 2013).

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1. Plan: This should be the first stage when working with new data assets, which

helps ensure that all data-related activities are considered. During this stage,

staff should determine approaches, resources, and intended outputs for each

stage of the data lifecycle. Ideally, a data management plan should be developed as an output of the planning process. For existing data assets, planning

should focus on reviewing and evaluating outputs and outcomes and identifying actions for improving or updating activities for the subsequent stages. If

needed, a data maturity assessment may be conducted at this stage. ACL staff

from both OPE and OIRM can help with planning for new or revised data

collection activities.

Preserve

Plan

Publish/

Share

Acquire

Process

23

Analyze
EXAMPLE

Planning for the implementation and rollout of the Title VI module of the 

Older Americans Act Performance System was a partnership between the 

Office for American Indian, Alaskan Native, and Native Hawaiian Programs 

and OPE. The project would not have been successful without thoughtful and expert feedback and mutual support. Together offices worked on 

creating the data variables (e.g., service categories, service units) that 

measured what was important to grantees as well as what was required 

by statute. After securing the platform for collecting the data, they worked 

with developers to create a user-friendly system, including developing and 

revising system business and validation rules and regularly conducting user 

acceptance testing as iterations of the system were completed. The team 

met weekly, and during the height of planning, multiple times per week, 

to ensure proper coordination of the project. Later, OPE was even able to 

lead ACL to design data visualization tools that will help its grantees better 

understand and advocate for their programs.

24
EXAMPLE

2. Acquire: In this stage new or existing data are collected, generated, or considered and evaluated for use. The output for this stage is the data.

Sometimes the data that we need to answer important programmatic questions have already been collected. It is important to know what information 

may be available and how to access it, as well as how to use it responsibly. 

For example, as part of the outcomes evaluation of the Older Americans Act 

Nutrition Services Program, to describe participants’ geographic access 

to food, the research team used participant address information for each 

respondent in the outcomes survey, data from the Census Bureau, and 

address data for food retailers from the U.S. Department of Agriculture. The 

research team geocoded the address information and calculated measures 

of geographic access to food based on distance from each participant to the 

nearest store in the area and the number of retailers, by type, within three 

distances from each participant’s residential address. Using existing data 

reduced evaluation costs and greatly enhanced the resulting findings and 

recommendations.

25
EXAMPLE

3. Process: This stage consists of various activities associated with the preparation of new or previously collected data. Processing of data may entail definition 

of data elements, integration of disparate data sets, extraction and validation of 

data to prepare the data for analysis, and opportunities to improve data collection if data are part of ongoing evaluation activities. The outputs from this stage 

are data sets that are ready for integration and analysis.

Data transformation relating to the AGing, Independence, and Disability 

(AGID) database involves validation, summary calculations, and quality 

control of multiple data elements. Source data, generally from surveys, is 

validated to ensure the format remains consistent with past imports and to 

identify new or changed variables. The data are then converted and aggregated based on logical groupings (e.g., age category, race or ethnicity, 

income level) and linked to the appropriate internal database identifiers. 

Summary statistics, such as percentages and standard errors, are then calculated as needed and made available within the system. After the data have 

been prepared for the latest year, values are compared against previous 

years to identify any potential errors in the data or process. Upon review by 

data specialists, the newly updated data is made available for the public 

through the AGID website (https://agid.acl.gov/). 

26
4. Analyze: Activities in this stage are related to the exploration and interpretation 

of processed data, where hypotheses are tested, discoveries are made, and 

conclusions are drawn. Analytical activities include summarization, graphing, 

statistical analysis, and modeling, and they are used to produce data-driven 

results and information. At this stage, data sets may be modified (e.g., with construction of new variables) and new data generated (e.g., a data set created by 

linking two or more separate data sets); versions are tracked; and processes are 

documented. Data management during analysis improves the efficiency of data 

analysis activities, preserves documentation that is critical for data integrity, 

and creates a foundation for evaluation efforts. The outputs from this stage are 

new or updated data sets or interpretations/findings, which often are published 

in written reports. 

5. Preserve: This stage includes activities associated with storing data for longterm use and accessibility, including data archiving, data disposition, and the 

creation of data repositories. It is important to plan for long-term preservation 

of data, metadata, ancillary products, application-neutral storage formats, and 

any additional documentation, to ensure availability and reuse of data.

6. Publish/share: Activities in this stage are associated with making the data available for use by others through peer-reviewed publications, social media, and 

ACL’s website and data catalogs. 

27
EXAMPLE

ACL has a published evaluation policy that requires ACL to release evaluation 

results regardless of the findings. Evaluation reports describe the methods 

used, including strengths and weaknesses, and discuss the generalizability of 

the findings. Evaluation reports also present comprehensive results, including 

favorable, unfavorable, and null findings. ACL also makes the majority of its 

performance data publicly available through its AGID data portal.

In addition to these discrete stages, other activities need to be considered and 

performed across all stages of the lifecycle. These activities include the following: 

Describe (metadata, documentation): Document key information at each stage

Manage quality: Plan and implement data quality control processes

Backup and secure: Ensure the safeguarding of data

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METADATA

What are metadata?

Metadata are often thought of as data about data, or facts about data. Specifically, they 

provide structured information that describes and defines data, including their content, 

quality, and location (National Oceanic and Atmospheric Administration, 2010). 

Metadata can range from basic to advanced, from allowing one to discover 

the mere fact that a certain data asset exists and is about a general subject 

all the way to providing detailed information documenting the structure, 

[variables], definitions, processing history, quality, relationships, and other 

properties of a data set (Resources.data.gov, 2014).

Why are metadata important?

Metadata provide the data consumer with the information needed to retrieve, 

process, understand, use, analyze, and manage the described data. Good metadata 

answer the Who, What, Why, Where, When, and How about data (National Oceanic 

and Atmospheric Administration, 2010), including:

Who collected the data?

Who owns the data?

Where were the data collected?

Where are the data located?

What are the data about?

What format is used for the data?

When were the data collected?

When were the data processed?

Why were the data collected? How were the data collected?

How were the data quality assessed?

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What should I consider when writing metadata?

Before writing metadata, consider these fundamental standards of quality 

metadata:

1. Plan early: Plan metadata development during the initial planning stages of a 

project. Doing this can save time, effort, and money in the long run. 

2. Document roles: Determine and document contact information and roles of individuals or positions, if you do not want to name specific individuals, responsible 

for different aspects of the data. These individuals may include those who create data, those who process data, and those who provide access to data. 

3. Establish a protocol: Establish a format or protocol for data documentation, and 

then stick to it. Document as much as possible during the data development. 

It is much easier to capture this information in real time than to document it 

retroactively.

4. Build on existing procedures: Examine the current procedures established 

within ACL or your Center for requesting, submitting, certifying, and storing data 

to use as a foundation for writing quality metadata. 

5. Follow a style guide: Develop and apply standard grammar, style, and format 

conventions, and use them consistently throughout the document. If needed, 

create a generic style sheet for reference. ACL currently does not have a style 

guide but will develop one in the future.

30
6. Use consistent terminology: Use consistent terminology and definitions 

throughout the document. Define synonyms and abbreviations at the beginning 

of a paragraph or file if you plan to use them. Provide a rubric for sections that 

use a large amount of technical terminology.

7. Review your work: Review your metadata file for accuracy and completeness. 

Add any details or context needed to ensure the metadata can be used effectively. Have someone else conduct a second review. Proof the file for typos, 

grammar, and formatting errors before publishing.

31
DATA ACCESS

What do we mean by data access?

Data access refers to a stakeholder’s ability to read or retrieve data stored within a 

database or other data repository. Data access can help distinguish the role of data 

stakeholders. For example, data managers and curators may have the ability to remove, 

edit, and add data, while general data consumers may only have “read” rights.

Why is data access important? 

Data are critical for making informed decisions. 

However, data only have value if they can 

be accessed and used. Emphasizing this 

need, the Foundations for Evidence-Based 

Policymaking Act of 2018 (Evidence Act) 

requires agencies to readily provide access to 

data for evidence. Specifically, the act states 

that “the head of an agency shall, to the extent practicable, make any data asset 

maintained by the agency available, upon request, to any statistical agency or unit for 

purposes of developing evidence” (Foundations for Evidence-Based Policymaking Act 

of 2018, 2019).

In addition, the Open, Public, Electronic, and Necessary (OPEN) Government Data 

Act, which is Title II of the Evidence Act, requires that government data be made 

available in open, machine-readable formats, while continuing to ensure privacy 

and security.

Data only have value 

if they can be accessed 

and used.

32
By ensuring access to its data, ACL has an opportunity to further the agency’s 

mission by supporting programs that effectively maximize the independence, wellbeing, and health of older adults, people with disabilities across the lifespan, and 

their families and caregivers.

What are considerations for making data accessible?

When thinking about data access, consider these fundamental standards 

(Burwell et al., 2013):

• Make data available in convenient, modifiable, and open formats that can be 

retrieved, downloaded, indexed, and searched. 

• Ensure formats are machine readable (i.e., data are reasonably structured to 

allow automated processing). 

• Make data available to the widest range of users for the widest range of 

purposes, often by providing the data in multiple formats for consumption. 

To the extent permitted by law, these formats should be nonproprietary and 

publicly available, with minimal restrictions placed on their use.

• Describe data using common core metadata, including information about 

origin, linked data, geographic location, time series continuations, data 

quality, and other relevant indices that reveal relationships between data 

sets and allow the user to determine the fitness of the data source.

• Ensure systems are scalable and flexible and facilitate extraction of data 

in multiple formats and for a range of uses as internal and external needs 

change, including potential uses not considered in the original system design.

33
• Create a process to engage with stakeholders to solicit help in prioritizing 

the release of data sets and determining the most usable and appropriate 

formats for releases.

• Ensure that roles and responsibilities are clearly designated for the promotion 

of efficient and effective data release practices across the agency, and that 

proper authorities have been granted to execute related responsibilities.

• Incorporate privacy analyses into each stage of the data lifecycle. 

Specifically, review the information collected or created for valid restrictions 

to release in order to determine whether it can be made publicly available.

Next Steps

This document should be considered a work in progress. Later versions will include 

additional topics, such as ensuring data security, protecting respondent privacy, 

risks related to linking data sets, and tips for responsible data usage.

34
References

Burwell, S. M., VanRoekel, S., Park, T., & Mancini, D. J. (2013, May 9). Open data policy: Managing 

information as an asset (Memorandum M-13-13). Office of Management and Budget. https://www.

whitehouse.gov/sites/whitehouse.gov/files/omb/memoranda/2013/m-13-13.pdf

EOL IT Services. (2018, January 2). The 5 principles of data governance. https://www.eolitservices.

co.uk/2018/01/02/5-principles-data-governance/

Executive Office of the President. (n.d.). The federal government data maturity model. https://my.usgs.

gov/confluence/download/attachments/624464994/Federal%20Government%20Data%20

Maturity%20Model.pdf?api=v2

Foundations for Evidence-Based Policymaking Act of 2018, 5 U.S.C. § 101 et seq. (2019). https://www.

congress.gov/115/plaws/publ435/PLAW-115publ435.pdf

National Aeronautics and Space Administration. (2015). Data strategy white paper. https://files.

meetup.com/19117935/NASA%2520-%2520Data-Strategy-2015-03-17-White-Paper-Small.pdf

National Oceanic and Atmospheric Administration. (2010). Metadata. https://www.ncei.noaa.gov/

resources/metadata

Office of Management and Budget. (2002, February 22). Guidelines for ensuring and maximizing the 

quality, objectivity, utility, and integrity of information disseminated by federal agencies. Federal 

Register 67(36), 8452–8460. https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/

assets/OMB/fedreg/reproducible2.pdf

Office of Management and Budget. (2019a). Federal data strategy 2020 action plan. https://strategy.

data.gov/assets/docs/2020-federal-data-strategy-action-plan.pdf

Office of Management and Budget. (2019b). Federal data strategy: A framework for consistency

Memorandum for the Heads of Executive Departments and Agencies (M-19-18). https://www.

whitehouse.gov/wp-content/uploads/2019/06/M-19-18.pdf

Office of Management and Budget. (2020). Federal data strategy: Data governance playbook. https://

resources.data.gov/assets/documents/fds-data-governance-playbook.pdf

Prell, M., Chapman, C., Adeshiyan, S., Fixler, D., Garin, T., Mirel, L., & Phipps, P. (2019, September). 

Transparent reporting for integrated data quality: Practices of seven federal statistical agencies 

(FCSM 19-01). Federal Committee on Statistical Methodology. https://nces.ed.gov/FCSM/pdf/

Transparent_Reporting_FCSM_19.01.pdf

Resources.data.gov. (2014). DCAT-US Schema v1.1 (Project Open Data Metadata Schema). https://

resources.data.gov/resources/dcat-us/

35
Rosenbaum, S. (2010). Data governance and stewardship: Designing entities and advancing data 

access. Health Services Research, 45(5 Pt 2), 1442–1455. https://www.ncbi.nlm.nih.gov/pmc/

articles/PMC2965885/

Seiner, R. S. (2017, March 1). Defining data governance core principles. The Data Administration 

Newsletter. https://tdan.com/defining-data-governance-core-principles/17087

United States Geological Survey. (2010). Data lifecycle models and concepts. https://my.usgs.gov/

confluence/download/attachments/82935852/Data%20Lifecycle%20Models%20and%20

Concepts_sm.pdf?version=1&modificationDate=1362680814819&api=v2

United States Geological Survey. (2013). The United States Geological Survey science data lifecycle 

model. https://pubs.usgs.gov/of/2013/1265/pdf/of2013-1265.pdf

University of New South Wales. (n.d.). Data governance principles. https://www.datagovernance.unsw.

edu.au/data-governance-principles

36
Appendix A. Definitions

Data archiving A process that supports long-term storage of 

scientific data and methods used to read or interpret 

them.

Data asset A collection of data elements or data sets that make 

sense to group together. Each community of interest 

identifies the data assets specific to supporting 

the needs of their respective mission or business 

functions. Notably, a data asset is a deliberately 

abstract concept. A given data asset may represent 

an entire database consisting of multiple distinct 

entity classes, or it may represent a single entity 

class.

Data disposition The final stage in the records lifecycle, resulting 

in destruction of temporary records or the legal 

and physical transfer of permanent records to the 

National Archives and Records Administration.

Data governance The process of setting and enforcing priorities 

for managing and using data as a strategic asset. 

Refers to the strategy of data, meaning the policies, 

procedures, and rules that govern the data.

37
Data lifecycle Refers to the process or stages data move through, 

from creation to distribution and use, until deletion 

or destruction. It consists of multiples stages, each 

of which has its own characteristics and builds on the 

previous stage.

Data management Refers to the logistics of data, meaning the processes 

used to plan, specify, enable, create, acquire, 

maintain, use, archive, retrieve, control, and purge 

data.

Data management plan Describes what data twill be acquired or produced; 

how the data will be managed, described, and 

stored; what standards will be used; and how data 

will be handled and protected.

Data maturity assessment A high-level assessment of an organization’s current 

data capabilities and supporting processes.

Data quality Data that are useful, accurate, clear, complete, and 

unbiased.

Data repository A centralized place to store and maintain data. A 

repository can consist of one or more databases or 

files which can be distributed over a network. 

38
Data set A data set is an organized collection of data. The 

most basic representation of a data set is data 

elements presented in tabular form. Each column 

represents a particular variable, and each row 

corresponds to a given value of that column’s 

variable. A data set may also present information in a 

variety of nontabular formats, such as an extensible 

mark-up language (XML) file, a geospatial data file, or 

an image file.

Data steward Subject matter experts and points of contact for 

the program they oversee. They are responsible for 

the management of the program data elements, 

including content and metadata.

Data stewardship The implementation of the policies, procedures, and 

rules that govern the data. Data stewardship can 

be thought of as a collection of data management 

methods covering acquisition, storage, aggregation, 

and deidentification, and procedures for data release 

and use.

Metadata Metadata are often thought of as data about data, or 

facts about data. Specifically, they provide structured 

information that describe and define data, including 

their content, quality, and location.

39
Appendix B. Federal Government 

Data Maturity Model

Appendix B shows a data maturity model that lists the criteria for data maturity from 

low capability to high capability. Data maturity is assessed based on the following 

areas: analytics capability, data culture, data management, data personnel, systems/

technology, and data governance. Data with low maturity—or low capability—are 

characterized by disparate systems and groups, reactive data management at the 

individual system level, poor data quality, and low decision-making capability. Data 

with high maturity—or high capability—are characterized by transparency and 

mission return on investment through executive collaboration and accountability for 

data quality, government-wide standards, and automation and decision support. 

40
Source: Executive Office of the President. (n.d.). The federal government data maturity model. https://my.usgs.gov/confluence/download/attachments/624464994/Federal%20Government%20Data%20Maturity%20Model.pdf?api=v2

FEDERAL GOVERNMENT DATA MATURITY MODEL

Analytics 

Capability Summary reports Descriptive analytics Diagnostic analytics Predictive analytics Cross-functional prescriptive analytics

Data 

Culture

Data use is uncoordinated and ad-hoc. 

Quality issues limit 

usefulness

Date use is by request. 

Quality programs are 

nascent

Some data and analytics are routine and 

have quality programs 

supporting key assets

High demand for data 

across agency. Drives 

decision making

Inter-agency data communities of interest share 

analyses, best practices

Data 

Management

Data Managed in 

silos; documentation 

sparse; standards not 

regularly applied

Data managed in 

silos; some documentation exists; standards not regularly 

applied

Data managed across 

the agency; documentation is uniform; 

some standards are 

applied

Data are managed 

with cross-functional 

applications in mind; 

documentation is 

uniform; standards 

regularly applied

Data are managed considering agency-wide needs; 

documentation is unitofrm; 

standards are uniformly 

applied

Data 

Personnel

No dedicated personnel performing data 

duties

Some siloed data 

teams; no clear career 

path for data personnel

Professional development path established 

for data personnel 

Data Professionals integrated with subject 

matter experts

Multidisciplinary teams 

solving agency mission and 

operational challenges

Systems/

Technology

Data is stored in 

siloed systems; data 

are frequently copied 

to facilitate use

Data are stored in 

siloed systems; some 

data can be programmatically accessed

Some common data 

systesm’ some data 

can be programmatically accessed

Some common data 

systems; key data can 

be programmatically 

accessed; some common tools exist

Core common data systems; 

key data can be programmatically accessed; common tolls are in use across 

agency

Data 

Governance

Loose affiliations of 

technical staff

Bureau-level collaboration, data ownership 

and stewardship

Agency-level collaboration, data ownership 

and stewardship

Agency-level organization accountable for 

data governance

Multi-agency advancement 

of data ownership and 

stewardship

LOW CAPABILITY HIGH CAPABILITY

Disparate Systems and Groups, Reative Data Management at the Individual 

System Level, Poor Data Quality, Little Decision Making Capability

Transparency and Mission ROI Through Executive Collaboration and Accountability 

for Data Quality, Government-wide Standards, Automation and Decision Support

41
Appendix C. Application of the 

Federal Government Data Maturity 

Model to ACL

Following is an overview of the current state of data capabilities (on a 1–5 continuum, with 5 being highest level of capability) of ACL as an agency (capabilities may 

differ for specific programs), as shown for the six key areas listed in the Federal 

Government Data Maturity model. It also lists milestones that ACL may target to 

improve its data capabilities in each area.

• Analytic Capabilities (2): ACL is able to conduct descriptive analytics to 

assess “what happened.” Sufficient data exist to create metrics or compare 

data points from year to year. To enhance its analytic capabilities, ACL 

may build out its data collection and analytics to identify causes of poor 

performance or operational problems in real time in order to determine what 

is happening and why it is happening.

• Data Culture (3): Within ACL, some high performing programs/Centers exist 

that transform quality data into useful information that support decisionmaking. There is a defined data quality strategy within parts of ACL. Some 

user feedback is collected and incorporated into programs that produce 

data-related products and services. To enhance its data culture, ACL may 

42
implement efforts to manage data as a strategic asset across the agency. 

Customer feedback loops and accountability for data and data quality should 

be realized across all Centers/programs. Data should be used to drive 

strategic and operational decision-making agencywide.

• Data Management (2): Currently, data are managed in siloes. Although some 

documentation exists, standards may not be applied regularly and across 

the entire agency. To enhance its data management, ACL may take steps to 

ensure that data are managed across the agency, documentation is uniform, 

and standards are applied consistently. 

• Data Personnel (2): Dedicated data professionals exist within ACL, but data 

teams may not be integrated into all operations/Centers/programs. Data 

personnel may spend most of their time on data maintenance, analysis, and 

report writing, rather than helping solve operational and mission challenges 

using complex analytics. To encourage further contributions from its data 

personnel, ACL may establish professional development training for data 

professionals and program/regional staff.

• Systems/Technology (3): ACL program data can be accessed for decisionmaking, and ACL is actively working on improving its data systems (e.g., 

data restructuring project) to eliminate siloes and allow for use of data 

across programs. To further enhance its systems/technology, ACL may 

continue to build common data systems and common data tools and 

encourage their use across the agency.

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• Data Governance (2): ACL program managers and system owners assume 

accountability for data sets based on projects. Program leads work with 

technical staff on incremental policy and data management improvements 

(e.g., Office of Elder Justice and Adult Protective Services) based on specific 

business needs and program goals. To enhance its data governance, ACL 

may focus on agency-level collaboration, such as establishing a review and 

evaluation process for data governance for continuous improvement.

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For additional information contact:

Office of Performance and Evaluation

Center for Policy and Evaluation

Administration for Community Living

330 C St SW

Washington, DC 20201

www.acl.gov/contact
Administration for Community Living

Office of Performance Evaluation

ACL Data Council