Data Management and Governance Practices (2017)

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3 Background Data-driven processes and technological advances have led to a steady increase in the amount and complexity of data collected and maintained 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. Data reside in attribute databases, geo- spatial 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 to be managed systematically and effectively, in a manner similar to how physical infrastructure assets are managed (Spy Pond Partners, LLC and Iteris, Inc. 2015a). A rule of thumb often used in the private sector is that the volume of corporate data doubles every 18 months (Bhansali 2013), indicating the ever-increasing volume of data in today’s world. 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 assurance, integration, and access. The literature suggests that these challenges are more widespread in manag- ing data across, rather than within, organizational units at transportation agencies (Spy Pond Partners, LLC and Iteris, Inc. 2015a). Data maintained by a specific unit within the agency often need to be shared with multiple users and integrated with multiple data sets. For example, traffic monitoring data are used for conducting safety analyses, developing transportation improvement programs, designing pavement, and developing asset management plans. As a result, it is important that traffic data be inte- grated with multiple data sets to serve multiple business needs (e.g., integration of traffic volume data with pavement condition data to develop pavement management plans). However, currently these data often reside in a collection of modern and legacy databases that are difficult to integrate (Cambridge Systematics, Inc. et al. 2010). These difficulties in integrating disparate data can lead to collecting data that already exist within other parts of the agency. An area prime for reducing the duplication of data 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 remains a challenge. This synthesis provides information on current practices in data governance, quality assurance, integration and sharing, and warehousing at transportation agencies. This information can be used by transportation agencies to learn about, and ultimately advance, the current state of the practice in data management and governance. SyntheSiS Scope and MethodS The information provided in this synthesis was gathered through a review of the literature, a survey of state DOTs and local transportation agencies, and follow-up interviews with a sample of four agencies. The survey was conducted in two phases, as follows: • Phase 1—This phase of the survey focused on practices pertaining to: – Agencywide data governance and management. – Data warehousing, stewardship, and archival for 17 data areas that are inclusive of most data sets maintained by transportations agencies. chapter one introduction

4 • Phase 2—This follow-up survey focused on detailed practices pertaining to: – The extent to which data quality is evaluated by transportation agencies. – Data integration and sharing, including data sets that are integrated or would be beneficial to integrate, consistency of location referencing systems within agency, strategies for improving data sharing and access, and methods for sharing data with outside users (public or private entities). – Use of data warehousing and cloud computing for storing and maintaining data. 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 SCOP’s Data Subcommittee. All 52 DOTs (50 states, District of Columbia, and Puerto Rico) were invited to participate in the surveys. The survey questionnaires also were distributed to municipali- ties and MPOs through the National Association of City Transportation Officials (NACTO) and the Association of Metropolitan Planning Organizations (AMPO). The survey methodology consisted of the following steps: • Step 1: NCHRP sent an initial invitation to SCOP members, NACTO, and AMPO through e-mail. • Step 2: Approximately 2 weeks later, e-mail reminders were sent to invitees of state DOTs that had not responded to the initial invitation. • Step 3: Approximately 2 weeks later, follow-up phone calls were made to invitees of state DOTs that had not responded to the second invitation. • Step 4: NCHRP staff sent follow-up e-mail messages and made phone calls periodically to invitees to encourage participation. Forty-three DOTs (83%) responded to the Phase 1 survey, and 34 DOTs (65%) responded to the follow-up survey. In accordance with NCHRP guidelines regarding survey response rates, data obtained from the Phase 1 survey are expressed as a percentage of the responses received, whereas data obtained from Phase 2 survey are expressed as the number of responses received. For local agencies, 19 agencies responded to the Phase 1 survey and 11 agencies responded to the follow-up survey. Fig- ures 1 and 2 show maps of DOTs and local agencies that responded to the survey. Participated in survey Did not participate FIGURE 1 Map of DOTs that participated in the survey.

5 Three state DOTs (Alaska Department of Transportation and Public Facilities, Nebraska Depart- ment of Roads, and Iowa DOT) and one local agency (Chicago Metropolitan Agency for Planning) were interviewed regarding their data governance experiences and practices. The results of these interviews are presented throughout this report. terMinology Key terms used in the survey instrument and in this report are defined as follows: • Access security: Ability to restrict access to data to maintain security. • Accessibility: Ability of authorized users to access the data. • Accuracy: Closeness between a data value and the real-world value that it represents. • Cloud computing: Date are stored and managed on remote computers “in the cloud.” These computers are owned and operated by others and connect to users’ computers by means of the Internet. • Completeness: Absence of missing values in the data set. • Consistency: Degree to which the data item is presented in the same format across agency. • Data governance board/council/steering committee: Group that institutes policies and oversees activities regarding data governance throughout the organization. Data governance is defined as “the execution and enforcement of authority over the management of data assets and the performance of data functions” (Cambridge Systematics, Inc. et al. 2010). • Data coordinator: Individual or committee that coordinates the organization, sharing, access, and use of multiple data sets within a business area (e.g., asset management, safety). • Data warehouse/mart: A data warehouse is a unified repository of current and historical data obtained from multiple sources. A data mart is a scaled-down version of a data warehouse. • Data steward: Individual who is accountable for assuring the quality of a specific data set, ensuring compliance with data rules and regulations, defining metadata, and relaying the appro- priate use of the data. • Data custodians: Cross-functional group of individuals, vendors, and data managers who are responsible for day-to-day execution of the governance rules and data management activities. FIGURE 2 Map of local agencies that participated in the survey. Planning organization Municipality

6 • Data archiving: The process of moving electronic data that are no longer actively used to a separate storage device for long-term retention (Spy Pond Partners, LLC and Iteris, Inc. 2015b). • Enterprise data stewards: Group of individuals who facilitate cross-subject area and cross- business unit priorities, projects, and agreement, and act as champions of data governance within their program areas. • Geographic coordinates: Geospatial coordinates, such as latitude and longitude, or state plane coordinates. • Linear referencing systems (LRS): Location systems that define a known starting point and reference locations of objects at a linear distance from that point (Olsen et al. 2013). • Location referencing method (LRM): A mechanism for finding and stating the location of an unknown point by referencing it to a known point (Adams et al. 2001). • Multilevel linear referencing system: Includes multiple linear referencing methods and trans- formation mechanism to a common one (Pierce et al. 2013). • Relevancy: Data are applicable and useful for the task at hand. • Route mile point: Distance from the beginning of the route. • Route reference post: Distance and direction from a physical mile marker posted on the route. • Route street reference: Distance and direction on one street from its intersection with another street. • Timeliness: How up-to-date the data are with respect to the task at hand. Other terminology in this synthesis and in the literature review should be interpreted in context. The meanings generally will be clear from the definitions provided, the discussions presented, or through examples. report organization This synthesis of practice is organized into six chapters: • Chapter one—Introduction. The chapter introduces the synthesis by providing background information and summarizing the scope and organization of the synthesis report. • Chapter two—Review of Literature on Transportation Data. The findings from the literature are summarized and presented. The chapter describes categories of data collected and generated at various phases of the transportation project/asset life cycle. It also includes a discussion of transportation data assembled by state DOTs to meet reporting and compliance requirements. • Chapter three—Review of Literature on Data Management and Governance. The chapter pro- vides a review of the literature on data governance, data warehousing and integration, and data quality. • Chapter four—State Departments of Transportation Practices and Experiences. The chapter summarizes and discusses the findings of the surveys of state DOTs. • Chapter five—Local Transportation Agencies’ Practices and Experiences. The chapter summa- rizes and discusses the findings of the surveys of local transportation agencies. • Chapter six—Conclusions and Future Research. The synthesis concludes with key observations and findings and suggestions for future research and outreach to advance the data maintenance state of practice within state DOTs and local transportation agencies. • Appendices—Appendix A appears only in the web version of this report. It provides the question- naire that was distributed electronically to the participants along with a summary of responses. Appendix B provides a list of respondents. Appendix C summarizes responses to data integration questions.