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37 This synthesis describes and discusses data management and governance practices at departments of transportation (DOTs) (including those of states, the District of Columbia, and Puerto Rico) and local transportation agencies [metropolitan planning organizations (MPOs) and municipalities]. 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 agencies. 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. For local agencies, 19 agencies responded to the Phase 1 survey, and 11 agencies responded to the follow-up survey. ConClusions The conclusions of this study in four areas (data governance, data warehousing and cloud computing, data integration and sharing, and data quality) are presented here. Data Governance ⢠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 and sharing. In most cases, DOTs have data stewards and data coordinators for managing individual data sets and coordinating data management within a business area (e.g., asset management, safety). What is lacking, in most cases, is a data governance council/board for policy making and coordination at the enterprise level. ⢠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. Currently, data stewards, coordinators, and custodians hold various positions in their business areas, such as planners, engineers, geographic information system (GIS) specialists, and information technology specialists. ⢠Data governance is more mature in DOTs than local agencies. However, this conclusion should be viewed with caution because (1) a small sample of local agencies responded to the survey, and (2) some local agencies commented that their agencies are users, rather than owners, of data. Data Warehousing and Cloud Computing ⢠There is an association between the presence of designated data stewards and the use of data warehousing systems. Data sets that have data stewards tend to reside in data warehouses or marts. Conversely, data sets that do not have designated stewards tend to reside in disparate files and databases. ⢠Transportation agencies have a wealth of archived data. For DOTs, a clear majority of respondents (70% or more) routinely archive pavement inventory and condition data, roadway inventory, traffic monitoring data, highway performance monitoring system data, project construction data, crash data, and bridge inventory and condition data. ⢠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. chapter six ConClusions anD Future researCh
38 ⢠The use of cloud computing services is expected to grow in the future, but most DOTs and local agencies are uncertain about the magnitude of this growth in their agencies. Data integration and sharing ⢠Transportation agencies use multiple location referencing method (LRMs), indicating that the use of incompatible LRMs remains an impediment to increased data integration in these agencies. However, geographic coordinates (e.g., longitude-latitude or state plane coordinates) and route mile points are commonly used LRMs by both DOTs and local agencies. ⢠An area prime for reducing the duplication of data within DOTs is the creation of digital as-builts from three-dimensional models used in design and construction. However, the integration of these as-builts into legacy data management systems is challenging, in part because of the inherent limitations of legacy systems (e.g., some legacy systems do not use georeferenced data) and broader institutional issues (e.g., data owned/managed by different parts of the agency). ⢠Most DOT survey respondents indicated that the following strategies have a major effect on improving data sharing and access: (1) increased use of web-based data storage and access, and (2) improved database management systems. For local agencies, the reduced use of hardware and software that require a specialized data format also appears to have a major effect. Data Quality ⢠This study addressed seven dimensions of data quality: accuracy, completeness, timeliness, rele- vancy, 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 dimension least evaluated by DOTs. ⢠Slightly more than half of the DOT respondents indicated that 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. Future researCh This study identified the following gaps in current knowledge that are suggested for future research: ⢠Develop a data management and governance guidebook and training materials for transportation agencies. ⢠Conduct case studies to assess and quantify the magnitude and complexity of data managed by state DOTs and identify resources for managing these data effectively. ⢠Identify and measure the benefits, costs, and risks (e.g., data security risks) of adopting cloud computing services for transportation agencies (considering both state-owned and commercially owned data centers/clouds). ⢠Develop methods and metrics for evaluating data quality considering multiple quality dimensions (e.g., accuracy, completeness, timeliness, relevancy, consistency, accessibility, and access security). ⢠Develop guidance for integrating data sets that are typically not integrated but would be beneficial to integrate with roadway inventory. Examples of these data sets include environmental data, travel modeling data, pavement work history data, and project construction data. ⢠Investigate the potential of georeferenced digital as-builts and roadway data as a framework for integrating data within transportation agencies. ⢠Develop methods and case studies for mining archived data at transportation agencies to dis- cover hidden patterns and learn from past experiences.
