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Summary Data-driven processes and technological advances have led to a steady increase in the amount and complexity of data collected and managed by state departments of transportation (DOTs) and local transportation agencies, such as municipalities and metropolitan planning organizations (MPOs). Examples of these data include asset inventory and condition data, usage data from traffic counts, roadway design and construction data, and financial data. These data reside in attribute databases, geospatial databases, computer-aided design (CAD) files, three-dimensional models, multimedia files (e.g., image, video), and other forms. Increasingly, transportation agencies are viewing these data as assets that should be managed systematically and effectively, as physical infrastructure assets are managed. Although data provide opportunities to facilitate decision making at transportation agencies, there are challenges involved in managing large and diverse data that serve multiple business needs. These challenges are manifested in various aspects of data management, such as data quality assur- ance, integration, and access. This synthesis provides information on current practices in data gover- nance, data warehousing and cloud computing, data integration and sharing, and data quality assurance. This information can be used by transportation agencies to learn about and ultimately advance the current state of the practice in transportation data management and governance. The information provided in this synthesis was gathered through a review of the literature, a two-phase online survey, and follow-up interviews with a sample of four agencies. All 52 DOTs (50 states, District of Columbia, and Puerto Rico) were invited to participate in the surveys. The surveys also were distributed to municipalities and MPOs through the National Association of City Transportation Officials (NACTO) and the Association of Metropolitan Planning Organizations (AMPO). Forty-three DOTs responded to the Phase 1 survey, and 34 DOTs responded to the follow-up survey, representing response rates of 83% and 65%, respectively. Of local agencies, 19 responded to the Phase 1 survey and 11 responded to the follow-up survey. The surveys were conducted through NCHRP in cooperation with AASHTO. AASHTO provided an e-mail distribution list to members of the Standing Committee on Planning (SCOP) and members of SCOPâs Data Subcommittee. In the data governance area, the study found that a pyramid-shaped data governance structure is commonly used in the literature. This structure consists of (1) an upper-level council or committee providing oversight and strategic direction, (2) enterprise data stewards providing coordination across business units, and (3) stewards accountable for the quality and use of individual information tech- nology. Data stewards, coordinators, and custodians hold various positions in their business areas, such as planners, engineers, and geographic information system (GIS) specialists. Interviews conducted as part of this study with a sample of transportation agencies indicated that key motivations and early benefits of implementing data governance include (1) improved accountability to produce high quality and reliable data (sources of truth), (2) ensuring that the data are accessible and integrated using a common linear referencing system, and (3) engaging business areas within transportation agencies in their data, rather than viewing data as strictly an information technology (IT) issue. Currently, a bottom-up approach for data management appears to be taking place. A more top-down data governance approach could help recognize and leverage the value of data generated and/or stored in various agency silos and could spur increased data integration. In most cases, DOTs have data Data management anD governance PracticeS
2 stewards and data coordinators for managing individual data sets and coordinating data management across multiple data sets within a business area (e.g., asset management, safety). However, most agencies indicated they do not have a data governance council or board (responsible for policy making and coordination at the enterprise level) and do not have a document that describes their data governance model and serves as a guide. Most survey respondents described the following as major factors in limiting progress toward implementing data governance: (1) lack of staffing, (2) other mission-related issues are more pressing, and (3) lack of resources. With respect to data warehousing and cloud computing, the study found that most DOTs store and manage data collected during the operation and monitoring phases of roadway systems (e.g., roadway inventory, condition, and performance) in data warehouses or marts. Conversely, data collected at the early phases of the asset/project life cycle are more likely to reside in disparate files and data- bases. Although there is a general agreement in the literature that transportation agencies collect and manage large amounts of data, most DOTs and local agencies do not have reliable estimates of the amount of data they maintain. The use of cloud computing services for storing and managing data is expected to grow; however, most DOTs and local agencies are uncertain about the magnitude of this growth in their agencies. Transportation agencies are using multiple linear and geographic referencing methods in their data sets, indicating that incompatibility among these methods remains an impediment to increased data integration within these agencies. The use of a common referencing system that unifies these methods can potentially facilitate data integration within transportation agencies. Most survey respondents indicated that the following strategies have major effects on improving data sharing and access: (1) increased use of web-based data storage and access, (2) improved data- base management systems, and (3) reduced use of hardware and software that require specialized (e.g., proprietary) data formats. The study addressed seven data quality dimensions: accuracy, completeness, timeliness, relevancy, consistency, accessibility, and access security. Most survey respondents indicated that these data quality dimensions are evaluated in at least some data areas in their agencies. For DOTs, timeliness, accuracy, and access security are most commonly evaluated. Conversely, consistency is the data quality dimen- sion least evaluated by DOTs. Slightly more than half of the DOT respondents indicated their agencies have mechanisms in place for incorporating feedback from data users into the data collection process. These feedback mechanisms include ad hoc meetings, surveys, steering committees, web forms, and direct e-mails. Finally, this study identified several areas of future research, including development of a data management and governance guidebook and training materials; identifying the benefits, costs, and risks (e.g., security risks) of adopting cloud computing services for transportation agencies; develop- ment of methods and metrics for evaluating data quality considering multiple quality dimensions; development of guidance and framework for integrating data within transportation agencies; case studies to assess the magnitude and complexity of data managed by transportation agencies; and development of methods and case studies for mining archived data at these agencies.
