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Management and Use of Data for Transportation Performance Management: Guide for Practitioners Appendix A: Capabilities Checklists
Management and Use of Data for Transportation Performance Management: Guide for Practitioners Introduction â¢ Foundation â¢ Reporting â¢ Insight â¢ Cases 135 Step 1: Specify & Define Data Determine what types of data are needed, how data will be used within TPM business processes, and based on this â specify attributes, scope, level of spatial granularity, and frequency of updates. Basic ï¯ The business need for data has been identified and documentation of this need is available for future reference. ï¯ An inventory of existing agency data sources has been compiled. ï¯ Managers of the units responsible for data collection can describe the primary users and uses of that data. ï¯ Data requirements to meet internal and external performance reporting requirements are defined and documented â including attributes, scope, and granularity. ï¯ Location referencing methods for performance data are established to enable linkages with other agency data sets. ï¯ Update frequencies for new data are defined and documented. ï¯ Authoritative data sources have been designated for performance measure calculations. Advancing ï¯ Discussions about data requirements arenât constrained by the status quo â they reflect what is important to know about transportation performance in order to improve. ï¯ Data needs are identified to support the entire TPM cycle (beyond performance reporting) including root cause analysis, identification and prioritization of improvements, and evaluation of impacts. ï¯ Minimum data quality standards are established considering timeliness, accuracy, completeness, consistency, and accessibility. ï¯ Data requirements are defined collaboratively across business units â including GIS and information technology. ï¯ Data communities of interest (or equivalent) have been established to identify data improvements to support different business needs. Gaps & Improvement Ideas
Management and Use of Data for Transportation Performance Management: Guide for Practitioners Introduction â¢ Foundation â¢ Reporting â¢ Insight â¢ Cases 136 Step 2: Obtain Data Acquire the data needed to support the entire TPM process including data needed to calculate performance measures, understand trends, set realistic targets, and improve performance. Basic ï¯ Data collection procedures and protocols are defined and documented. ï¯ Data collection and processing workflows are mapped to clearly assigned responsibilities and deadlines. ï¯ Existing agency data sources are reviewed prior to collection of new data. ï¯ Available external (public and private) data sources are reviewed prior to collection of new data. ï¯ Quality management procedures are defined and documented â including training and certification for data collection personnel. ï¯ Requirements are in place that ensure new data collection adheres to agency location referencing standards. ï¯ Impacts of changes to existing data collection methods are assessed to minimize loss of consistent trend data and disruption to existing reports. ï¯ Data sources are assessed to understand usage restrictions that may limit value. Advancing ï¯ The full cost of new data acquisition is estimatedâconsidering initial collection, ongoing updates, and supporting staff and technology infrastructure. ï¯ Funding for regular data updates (beyond the initial collection) is planned and committed. ï¯ There is regular communication with partner agencies to identify opportunities for collaboration on data collection. ï¯ Periodic scans are conducted to identify ways to improve data quality and collection efficiency. ï¯ Agency guidance and/or coordination protocols have been established to assist business units wishing to purchase commercial data sources. ï¯ Specialists with appropriate expertise (in-house or contractors) evaluate use of emerging private data sources. ï¯ Data requirements are defined with consideration of opportunities to create valuable information through integration of multiple data sources. Gaps & Improvement Ideas
Management and Use of Data for Transportation Performance Management: Guide for Practitioners Introduction â¢ Foundation â¢ Reporting â¢ Insight â¢ Cases 137 Step 3: Store & Manage Data Determine where and how to store the data, how much data to keep, how data can be integrated across repositories, and which best practices should be implemented for QA and documentation. Basic ï¯ Data needed for TPM is stored in databases that are managed and regularly backed-up to provide protection from unauthorized access and corruption. ï¯ Back-ups are tested on a regular, established cycle (e.g. monthly). ï¯ Quality control procedures are in place to flag records that do not meet established validation criteria. ï¯ Data dictionary information (metadata) is maintained and stored in a standardized fashion. ï¯ Annual data snapshots are created for coordinated reporting across data programs. Advancing ï¯ Hardware and software requirements for data storage, updating, integration and access are understood. ï¯ Central data repositories have been established to integrate data from multiple sources and provide source data for reporting and analysis. ï¯ Cloud and hosted storage options are considered for larger and more complex data sets. ï¯ Data retention policies and archiving protocols have been updated to reflect lower storage costs and analysis of TPM business data needs. ï¯ A range of data storage options are available to support databases with high transaction volumes and memory-intensive calculations as well as archived data retained for future use. ï¯ Standards have been adopted to enable combining data from different sources. ï¯ Data from multiple sources are fused to assemble a more complete and accurate data set than would be possible from any single source. ï¯ Where appropriate, edge computing techniques are usedâ involving data processing at the source (e.g. at the site of the field sensor) rather than within a centralized repository. Gaps & Improvement Ideas
Management and Use of Data for Transportation Performance Management: Guide for Practitioners Introduction â¢ Foundation â¢ Reporting â¢ Insight â¢ Cases 138 Step 4: Share Data Share transportation performance data across business units within an agency, across agencies, or with the general public. Basic ï¯ Employees are aware of key performance data sources within the agency. ï¯ There are clear agency policies in place that data should be shared unless the need to protect it is demonstrated. ï¯ There are protocols defined for how to share data to meet different needs that consider use of state and federal open data portals and hosted or cloud solutions. ï¯ Open data portals are used to share data. ï¯ Data explanations are provided in âplain Englishâ to help users understand meaning, sources and limitations. Advancing ï¯ Data governance and stewardship structures have been established to facilitate communication about data sharing and identify opportunities for synergies across business units for collaborating or combining data sources. ï¯ Data sharing agreements are used (internal to an agency and between and agency and its partners) that specify what data will be shared, when and how â and establish a clear understanding of data limitations and expectations for use. ï¯ Data are shared in formats that are designed to meet the needs of different users which may include standard reports, data feeds, and dashboards. ï¯ Data with sensitive elements are sanitized for public distribution. ï¯ Data contracts and sharing agreements are reviewed to ensure that agency flexibility is retained. Gaps & Improvement Ideas
Management and Use of Data for Transportation Performance Management: Guide for Practitioners Introduction â¢ Foundation â¢ Reporting â¢ Insight â¢ Cases 139 Step 5: Analyze & Use Data Use analysis tools to convert data into information, identify intended uses and users for these tools, designate one or more individuals to develop specialized experience with these tools, and use these tools to support decision making. Basic ï¯ Analysts are aware of and taking advantage of existing commercial off-the-shelf, open source and publicly available tools for analysis, visualization, forecasting and scenario analysis. ï¯ Analysts are trained in use of data analysis and visualization tools. ï¯ Private sector or university contractors are used to provide data analysis services as alternatives to standing up analysis capabilities in-house. ï¯ Data are available that are sufficiently accurate to meet analysis requirements. ï¯ Visualization and analysis tools are used to explore and discover data anomalies and limitations. ï¯ Data preparation and analysis tasks are well-defined and planned to ensure sufficient calendar time and staff resources. ï¯ Analysts are able to identify trends and causal factors. ï¯ Data element meanings, data transformations and analysis assumptions are documented. Advancing ï¯ Predictive models for key transportation performance measures are validated based on multiple cycles of application. ï¯ Targets are established based on predictive analysis relating revenues and programmed work to performance results. ï¯ Data mining is conducted to support âback-castingââwhich involves starting with a future vision and analyzing current and historical data to estimate changes required to move from the current situation to the future vision. ï¯ Cooperative arrangements across agencies have been established to transform data into information (e.g. the state DOT performs analysis of travel time reliability, computes measures for each facility and provides the data for use by MPOs and local agencies.) ï¯ Predictive analytics and machine learning techniques are applied for predicting asset failure probabilities and other performance measures. Gaps & Improvement Ideas
Management and Use of Data for Transportation Performance Management: Guide for Practitioners Introduction â¢ Foundation â¢ Reporting â¢ Insight â¢ Cases 140 Step 6: Present & Communicate Data Interpret performance results, develop effective ways of communicating narratives based on the data, create visualizations in a variety of formats, identify needs for data improvement or augmentation. Basic ï¯ Managers and analysts meet to review and interpret performance results. ï¯ Story lines for performance results are developed, reviewed and communicated. ï¯ Training is offered to internal staff to build skills in data presentation and communication. ï¯ Staff have capabilities to present data in a variety of formats tailored to the needs of different audiences including heat maps, thematic maps, timelines, and other infographics. ï¯ A combination of narrative and graphical presentation is used to communicate performance information. Advancing ï¯ Feedback from data consumers is sought and used to improve communication of information to different target audiences. ï¯ Individuals with expertise in data visualization and communication are available to support development of performance data products. ï¯ Social media is used to communicate key results or draw people to more detailed communication products. ï¯ Specialized visualization and analysis environments have been developed â e.g. virtual reality simulators. Gaps & Improvement Ideas