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Component C

Data Management

This chapter provides assistance to transportation agencies with the Data Management component of Transportation Performance Management (TPM). It discusses how data management fits within the TPM Framework, describes how it interrelates with the other nine components, presents definitions for associated terminology, and includes an action plan exercise. Key implementation steps are the focus of the chapter. Guidebook users should take the TPM Capability Maturity Self-Assessment as a starting point for enhancing TPM activities. It is important to note that federal regulations for data management may differ from what is included in this chapter.

Data Management encompasses a set of coordinated activities for maximizing the value of data to an organization. It includes data collection, creation, processing, storage, backup, organization, documentation, protection, integration, dissemination, archiving and disposal. Well-managed data are essential for a robust TPM practice.

The TPM Framework showing ten components with Component C Data Management called out. Subcomponents are C.1 Data Quality, C.2 Data Accessibility, C.3 Data Standardization and Integration, C.4 Data Collection Efficiency, and C.5 Data Governance.

Introduction to Data Management

Data provide a foundation for TPM, informing decisions about how to best use available resources to maximize transportation system performance. Agencies make substantial investments in data, and seek to obtain the greatest possible return from these investments. Increasingly, agencies are recognizing that data should be managed as a valuable asset, analogous to physical assets like pavement and bridges.1 The American Association of State Highway and Transportation Officials (AASHTO) Standing Committee on Planning (SCOP) Core Data Principles recognize data as an asset and define how to protect it and maximize its value2:

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.
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.

Data management practices require coordinated agency-wide planning in order to collect, store, and provide data most efficiently and effectively. Although many transportation agencies are “data rich” and “information poor,” improved data management practices can enhance their abilities to use the data and become “information rich.”

Data management practices are crucial to TPM and can benefit an agency in a variety of ways:

  • Improving the accuracy, completeness, consistency, and timeliness of data;
  • Providing a “single version of the truth” to use in analyses and reporting;
  • Enabling new analysis possibilities through providing more accessible data and data linkages;
  • Collecting and sharing data more efficiently across an agency and with agency partners; and
  • Fostering a culture that understands and supports the value of data in business processes.

This chapter includes noteworthy practices that can be used to implement and improve data management processes and capabilities within a transportation agency.

Data management practices can be implemented both at an agency-wide level and within individual business units. For example, a business unit might implement a data quality management process for the data it collects, while an agency might have overarching standards so that data can be integrated and shared across different business units. Each of the components discussed in this chapter can similarly be addressed at different levels within an agency. Some aspects of data management may also involve cross-agency collaboration – for example, to standardize data elements for aggregation and reporting.

Subcomponents and Implementation Steps

Figure C-1: Subcomponents for Data Management

Source: Federal Highway Administration

Data Management component with subcomponents: Data Quality (requirements, validation rules, improvement processes), Data Accessibility (requirements, methods and tools), Data Standardization and Integration (assessment, integration plan implementation), Data Collection Efficiency (internal coordination, external partnerships), Data Governance (roles, governance structures and policies).

In this guidebook, Data Management is defined as a set of coordinated activities for maximizing the value of data to an organization. It includes data collection, creation, processing, storage, backup, organization, documentation, protection, integration, dissemination, archiving, and disposal. The data management subcomponents illustrated in Figure C-1 ensure delivery of integrated data of sufficient quality for use in each of the key TPM processes. These specific aspects of data management3 are important to consider for strengthening TPM:

  • Data Quality: Processes and organizational functions to ensure data are accurate, complete, timely, consistent with requirements and business rules, and relevant for a given use.
  • Data Accessibility: Processes and organizational functions to provide access to key data sets.
  • Data Standardization and Integration: Processes and organizational functions to integrate and compare data sets as needed to support transportation performance management.
  • Data Collection Efficiency: Efforts to maximize use of limited agency resources through coordination of data collection programs across business units and with partner agencies.
  • Data Governance: Establishing accountability and decision making authority for collecting, processing, protecting, and delivering data.

It is important to note that these components are interrelated. For example, data governance is the mechanism by which data quality, accessibility, and standardization are achieved. Coordinated data collection supports data standardization. Data standardization and integration efforts facilitate the provision of centralized access to agency data. A comprehensive approach to data management that considers each component and how it can be mutually reinforcing is most effective.

“One asset that is owned by virtually all transportation agencies – yet often overlooked – is data.”

Source: NCHRP Report 814, Data to Support Transportation Agency Business Needs: A Self-Assessment Guide

Each of the components within the TPM framework depends on reliable and consistent performance data:

  • Lack of attention to data quality can undermine the success of the entire TPM program and lead to loss of credibility for an agency.
  • Lack of attention to data accessibility can increase the time and effort needed for agency staff to compile and use performance data for monitoring, reporting, and responding to external information requests. It can also impact external perceptions about an agency’s degree of transparency and result in missed opportunities to support external collaboration on performance reporting.
  • Lack of attention to data standardization and integration can impact an agency’s ability to develop effective strategies to address multiple performance goals. It can also impact an agency’s ability to understand the likely impacts of programmed projects and other planned work activities on future performance.
  • Lack of attention to data collection efficiency can result in missed opportunities for improved resource utilization.
  • Lack of attention to data governance can make it difficult for an agency to achieve and sustain improvements to data quality, access, integration, and efficiency.

Most agencies already have some data management processes in place. Because of this, the suggested implementation steps listed in Table C-1 will vary by agency. As an agency’s data management practices become more mature, benefits will be realized in the form of higher quality data that is accessible and usable across an agency in support of TPM.

Table C-1: Data Management Implementation Steps
Source: Federal Highway Administration
Data Quality Data Accessibility Data Standardization and Integration Data Collection Efficiency Data Governance
1. Establish data quality requirements and metrics 1. Establish requirements for different audiences 1. Assess data against standards and requirements 1. Identify opportunities for data collaboration 1. Define roles and accountability
2. Create data validation rules 2. Enhance data access methods and tools 2. Create and implement a data integration plan 2. Implement governance structures and policies
3. Develop quality management processes

Clarifying Terminology

Table C-2 presents the definitions for the data management terms used in this Guidebook. A full list of common TPM terminology and definitions is included in Appendix C: Glossary.

Table C-2: Data Management: Defining Common TPM Terminology
Source: Federal Highway Administration
Common Terms Definition Example
Data Accessibility The ease with which agency staff and partners can obtain data needed for transportation performance management. One State DOT has three different traffic operations centers that monitor real-time travel conditions. However, there are no procedures or systems in place to consolidate data across the centers or summarize it in a useful form for reporting.
Data Availability The degree to which data needed for TPM exist at the right level of detail, with sufficient coverage to meet information needs. Lack of supply chain data may limit a freight planner’s ability to evaluate the effectiveness of alternative strategies for freight mobility improvement.
Data Change Management Processes to coordinate and communicate changes to data definitions, data structures and associated information systems. Change management processes are aimed at minimizing impacts to users and reducing change-related errors. A change to the definition of bridge elements requires evaluation to determine and plan for impacts on performance of inspections, calculation of bridge condition indices, identification of rehabilitation strategies, and data structures and software supporting bridge inspection and management processes.
Data Governance Establishment of decision rights and accountability with respect to data. For example, who is accountable for data quality and how decisions about sharing data, investing in new data, or improving existing data are made. A State DOT’s information governance body defined a set of data policies that emphasize data as a shared agency asset and designated data stewards with responsibility for each category of data.
Data Integration Combining data that reside in different locations to present a unified view. Data may be integrated into a single physical repository. Alternatively, data may be integrated “virtually” without creation of a new physical data repository. The DOT established a data warehouse to provide an integrated view of capital projects, including current status, assets, funding sources, and costs to date.
Data Quality The degree to which data are suitable for a given use, considering consistency with requirements and established business rules, accuracy, completeness, and currency or timeliness. Lack of timely crash data challenges a safety planner’s ability to address emerging safety issues.
Data Standardization Practices to ensure different data sets adhere to established standards–which may pertain to inclusion of certain attributes, the definition and meaning of data attributes, their specific format, measurement or quality specifications, allowable values, etc. Use of a standard linear referencing system (LRS) enables an agency to display data about traffic, crashes, and various highway features on the same map.
Data Validation Process that uses specified criteria to determine whether data are correct, complete and meaningful. Validation routines are run on pavement condition data to check for out-of-range condition measures and distresses that are not compatible with the recorded pavement type.
Source System of Record The designated authoritative source system for a given type of data. A single source system is designated to avoid a situation in which multiple versions of a data set are being updated independently and not kept in sync. The agency’s traffic monitoring system is the source system of record for annual average daily traffic (AADT) data.
Transportation Performance Management A strategic approach that uses system information to make investment and policy decisions to achieve performance goals. Determining what results are to be pursued and using information from past performance levels and forecasted conditions to guide investments.

Relationship to TPM Components

The ten TPM components are interconnected and often interdependent. Table C-3 summarizes how each of the nine other components relate to the data management component.

Table C-3: Data Management Relationship to TPM Components
Source: Federal Highway Administration
Component Summary Definition Relationship to Performance-Based Planning
01. Strategic Direction The establishment of an agency’s focus through well-defined goals/objectives and a set of aligned performance measures. Data management processes must be responsive to an agency’s business needs, as established by the strategic direction.
02. Target Setting The use of baseline data, information on possible strategies, resource constraints, and forecasting tools to collaboratively set targets. Target setting establishes data quality, access, and integration requirements to be addressed in data management processes.
03. Performance-Based Planning Use of a strategic direction to drive development and documentation of agency strategies and priorities in the long-range transportation plan and other plans. Performance-Based Planning establishes data quality, access and integration requirements to be addressed in data management processes. It relies on data managed from multiple internal and external sources, and therefore benefits from a coordinated data collection strategy.
04. Performance-Based Programming Allocation of resources to projects to achieve strategic goals, objectives and performance targets. Clear linkages established between investments made and their expected performance outputs and outcomes. Performance-Based Programming establishes data quality, access, and integration requirements to be addressed in data management processes. It relies on data managed from multiple internal and external sources, and therefore benefits from a coordinated data collection strategy.
05. Monitoring and Adjustment Processes to monitor and assess actions taken and outcomes achieved. Establishes a feedback loop to adjust programming, planning, and benchmarking/ decisions. Provides key insight into the efficacy of investments. Data management processes directly support Monitoring and Adjustment, which depends on availability of timely, accurate, and authoritative data.
06. Reporting and Communication Products, techniques, and processes to communicate performance information to different audiences for maximum impact. Data management processes ensure that data are produced in an efficient and reliable manner.
A. TPM Organization and Culture Institutionalization of a TPM culture within the organization, as evidenced by leadership support, employee buy-in, and embedded organizational structures and processes that support TPM. Strong data management functions depend on an organizational culture that values data-driven decision making and understands the commitment required to create and sustain quality data.
B. External Collaboration and Coordination Established processes to collaborate and coordinate with agency partners and stakeholders on planning/ visioning, target setting, programming, data sharing, and reporting. Sharing data with agency partners is a key element of External Collaboration. Data sharing strengthens transparency and accountability and maximizes use of available resources for data gathering and management across agencies.
D. Data Usability and Analysis Existence of useful and valuable data sets and analysis capabilities, provided in usable, convenient forms to support TPM. Sound data management practices ensure availability, quality and integrity of data for visualization, analysis and prediction.