Data Management and Governance Practices (2017)

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32 This chapter summarizes and discusses the findings of the surveys of local transportation agencies (MPOs and cities). It is important that these results be considered preliminary because of the small number of responding agencies. Similar to the discussion of the DOTs’ surveys (chapter three), this chapter includes the experiences and practices of local agencies in the following topics: • Data governance; • Data warehousing and cloud computing; • Data integration and sharing; and • Data quality assurance. Data Governance Of 19 local agencies that participated, only one reported having a data governance board; however, five respondents indicated their agencies have data coordinators, and two respondents indicated their agencies are in the process of designating data coordinators. The remaining 12 respondents indicated their agencies do not have data coordinators. Additional inputs from respondents who indicated their agencies have data coordinators indicated the coordinators are GIS staff, transporta- tion planners, or data management specialists. No respondents indicated their agency has a document describing its data governance model. The survey asked the respondents to identify data sets (from a list of 17) for which their agencies have designated stewards (Figure 26). These results indicate that data collected during the planning phase (particularly data about travel modeling and transportation improvement programs) are more likely to have designated data stewards at local transportation agencies than are data collected at later phases of the asset/project life cycle. Survey participants were asked to describe the extent of the effect of four factors on limiting progress toward implementing data governance in their agencies. As shown in Figure 27, lack of staffing and “other mission-related issues are more pressing” were commonly described as major factors, with eight and seven responses, respectively. These results are consistent with the results obtained from the DOTs’ surveys. Data WarehousinG anD clouD computinG Results of the survey question about the use of data warehouses or marts indicate that most local agencies do not use data warehouses or marts for managing their transportation data (Figure 28). However, the results also indicate that road inventory data, crash data, traffic monitoring data, travel modeling data, and TIPs are more likely than other data sets to be managed in data warehouses or marts. A related question was included in the survey to determine what data are archived at local agencies to retain historical information. Figure 29 shows that most responding agencies do not archive their transportation data. However, the results also show that travel modeling and TIPs data at local agencies are more likely to be archived than are other data sets. chapter five local transportation aGencies’ practices anD experiences

33 Project construction data HPMS Project design and materials data Inventory and condition data for other assets Bridge work history data Pavement work history data Others Real estate data Environmental impact and compliance data Bridge inventory and condition data procurement dataContracts/ Pavement inventory and condition data Traffic monitoring data Financial data Crash data Roadway inventory Transportation improvement programs data Travel modeling data No. of local agency respondents What data in your agency have designated stewards? 0 2 4 6 8 10 12 14 16 18 FIGURE 26 Designation of data stewards at local agencies. To what extent do the following factors limit progress on instituting data governance in your agency? 8 5 4 7* 2 4 4 3 1 2 2 1 Lack of staffing Lack of resources Hard to justify the cost and effort Other mission-related issues are more pressing No. of respondents Major factor Minor factor Not an issue *Indicates number of respondents FIGURE 27 Factors limiting progress toward implementing data governance in local agencies. What data in your agency are maintained in data warehouses or marts? Project construction data Project design and materials data Pavement work history data Others Real estate data Inventory and condition data for other assets Bridge work history data Financial data procurement dataContracts/ Environmental impact and compliance data HPMS Pavement inventory and condition data Bridge inventory and condition data Crash data Transportation improvement programs data Travel modeling data Traffic monitoring data Roadway inventory No. of local agency respondents 0 2 4 6 8 10 12 14 16 18 FIGURE 28 Use of data warehouses or marts at local agencies.

34 When asked about the volume of data they maintain, eight of nine respondents answered “unknown,” indicating that most local agencies do not have reliable estimates of the amount of data they maintain. Of nine agencies that responded to a question about the use of cloud computing services for managing data, five indicated that the services are not used by their agencies, one indicated that the answer is unknown, and three indicated that 1% to 10% of their data are stored and managed using cloud computing services. When asked to estimate the percentage of their transportation data stored and managed using cloud computing in the next 5 years, four respondents could not provide an estimate, two estimated the amount to be 1% to 10%, two estimated the amount to be zero, and one estimated the amount to be more than 50%. These results indicate that local agencies are uncertain how cloud computing would affect their data management practices in the future. Data inteGration anD sharinG The survey included questions to assess the level of data integration at local agencies across the 17 data sets discussed. In all, the respondents identified 37 pairs of data sets that are integrated in their agencies to serve various business needs (Figure C3 in Appendix C). Fifty percent of the respondents indicated that their agencies integrate the following data sets: • Roadway inventory data and traffic monitoring data; • Roadway inventory data and travel modeling data; and • Environmental impact and compliance data and project design and materials data. The respondents identified 80 pairs of data sets that would be beneficial for their agencies to integrate (Figure C4 in Appendix C). Fifty percent of local agency respondents indicated their agencies would benefit from integrating the following pairs of data sets: • Roadway inventory data and crash data; and • Roadway inventory data and travel modeling data. What data in your agency are archived systematically to retain historical information? Project construction data Inventory and condition data for other assets Bridge work history data Pavement work history data HPMS Others Project design and materials data Environmental impact and compliance data Bridge inventory and condition data Traffic monitoring data Real estate data Roadway inventory procurement dataContracts/ Pavement inventory and condition data Financial data Crash data Transportation improvement programs data Travel modeling data No. of local agency respondents 0 2 4 6 8 10 12 14 16 18 FIGURE 29 Use of data archiving to retain historical information at local agencies.

35 The survey examined the use of six LRMs by local agencies, including route mile point, route reference post, link-node, route street reference, multilevel LRS, and geographic coordinates. The results of eight responses related to this issue are shown in Figure 30. These results show that multiple LRMs are being used by local agencies in various data sets. However, local agencies use fewer LRMs than do DOTs. Geographic coordinates appear to be the most commonly used LRM at local agencies, followed by the route mile point and link-node methods. As discussed, the use of GPS in current data collection technologies may have contributed to the increasing use of geographic coordinates as an LRM. The survey further shows that the route street reference, route reference point, and multilevel LRS are not used in the surveyed local agencies. A similar conclusion can be made about the use of these LRMs for individual data sets. To evaluate agency data sharing methods with external stakeholders, including public and private entities, five options were provided to the respondents to select from: online open access, online preauthorized access, upon request (e.g., data sent by e-mail or a file sharing service), not shared outside the agency, or data shared through other methods. Some agencies indicated more than one sharing method for some data sets. The online open access and “upon request” methods are used by more than half of the responding agencies (Figure 31). In addition, online open access and “upon request” methods are most commonly used when the results are divided by individual data sets. However, for all data sets, at least one respondent indicated that the agency does not share these data sets with users outside the agency. Most agencies that indicated other sharing methods pointed out that the data sets are not owned by their agencies. Respondents were asked to identify strategies that would improve or have improved data sharing and access at their agencies. As shown in Figure 32, four respondents indicated that two of these What location referencing methods are used in your agency? 0 1 2 3 4 5 6 7 8 Geographic coordinates Other N o. o f a ge nc ie s Route mile point Link–node FIGURE 30 Use of LRMs at local agencies. How does your agency share data with outside users (public and private entities)? 0 1 2 3 4 5 6 7 8 9 Online (open access) Upon request Not shared outside agency Other N o. o f a ge nc ie s Online (preauthorized access) FIGURE 31 Data sharing methods with external users at local agencies.

36 strategies have a major effect on improving data sharing and access: (1) improved database manage- ment systems, and (2) reduced use of hardware and software that require specialized data format. A respondent commented that improved framework to centralized data has a major effect on data sharing and access. Responding to a question about data management tools most useful for accessing and sharing data within agencies, some local agencies identified the following: • Relational databases, such as MS Access, and spreadsheets; • GIS tools; and • Data marts. Data Quality As shown in Table 8, most respondents indicated that all of these data quality elements are evaluated in at least some data areas within their agencies. These results suggest that accuracy, completeness, and relevancy are the most commonly evaluated data quality element by local agencies. On incorporating feedback from data users, only one agency (of eight that responded to this question) indicated that it has mechanisms in place for incorporating feedback from data users in the data collection process. That agency’s respondent further explained that the process is implemented through meetings and comments dropped in suggestion boxes. What strategies would improve (or have improved) data sharing and access within your agency? 4 4 4 [VALUE]* 2 4 3 4 2 1 1 1 1 1 1 Reduced use of hardware and software that require specialized data format Improved database management systems Increased use of web-based data storage and access Improved metadata No. of respondents Major effect Minor factor No effect Not applicable *Indicates number of respondents FIGURE 32 Strategies for improving data sharing and access at local agencies. To What Extent Are Data Quality Elements Evaluated in Your Agency? Accuracy Completeness Timeliness Relevancy Consistency Accessibility Access Security Evaluated in all or most areas 6 4 3 4 4 4 3 Evaluated in some areas 3 3 6 4 2 3 4 Evaluated in a few areas 1 2 1 1 4 2 2 Not evaluated 1 1 1 1 1 2 2 TAbLE 8 DATA QuALITy ELEMENTS EvALuATED AT LOCAL AGENCIES (number of agencies)