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Data to Support Transportation Agency Business Needs: A Self-Assessment Guide (2015)

Chapter: Appendix D - Data Improvement Catalog

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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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Suggested Citation:"Appendix D - Data Improvement Catalog." National Academies of Sciences, Engineering, and Medicine. 2015. Data to Support Transportation Agency Business Needs: A Self-Assessment Guide. Washington, DC: The National Academies Press. doi: 10.17226/23463.
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90 A P P E N D I X D Data Improvement Catalog

Data Improvement Catalog 91 This appendix provides two resources for iden fying poten al data improvements. The first resource is a checklist of data improvement ideas, organized by type of improvement (e.g., informa on technology, data collec on, and data governance). This checklist includes ideas that can address gaps from the data value assessment (availability, quality, or usability) or the data management assessment. The second resource is a longer list of improvements to data management prac ces, organized by the four data management assessment elements. This resource provides example applica ons of different improvement types, as well as selected references that can provide addi onal informa on. 1. Checklist of Data Improvement Ideas by Category Informa on Technology q Implement new or upgraded source system for data q Sunset exis ng data source system q Implement new or improved data access solu on (e.g., GIS portal, business intelligence tool) q Deploy mobile solu on for data collec on and/or data access q Implement new or upgraded data integra on solu on (e.g., data warehouse, Extract-Transform- Load (ETL), enterprise service bus, and master data management) q Implement an enterprise meta data repository q Implement data profiling/data cleansing tools Data Presenta on and Analysis q Create new or improved data summaries and reports q Create new or improved data visualiza ons q Make data available via GIS q Develop new or improved data mining or analysis methods q Integrate data sources for improved insight Data Inves ga on and Documenta on q Conduct a data needs study for one or more business areas q Conduct a data risk assessment (e.g., iden fy data in non-enterprise systems and data sets with personally iden fiable informa on) q Conduct data value mapping (map how data is used within business processes) q Conduct data produc on mapping (map how data is produced and transformed) q Ra onalize/prune exis ng data (drop data elements and tables that are redundant or not ac vely used or needed) q Develop or improve data collec on manuals and associated training materials q Establish a Data Community of Interest q Create or update data catalogs and data dic onaries—establish processes to keep these updated as data sets change q Develop data quality metrics q Formalize procedures for data quality assessment and repor ng

92 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide q Develop and document data validaon business rules q Idenfy business requirements for historical data q Enhance communicaon strategies to increase awareness of agency data products and services q Create standard new staff orientaon briefing on available data and how to use it Data Collecon/Processing/Quality Improvement q Iniate new data collecon or acquision effort q Disconnue current data collecon effort q Change spaal coverage and/or frequency of exisng data collecon effort q Change exisng data collecon method (e.g., outsourcing, new technology, different equipment, and new sampling approach) q Implement new/improved data quality assurance/quality control procedure q Coordinate or combine exisng data collecon efforts across business areas or across the enterprise q Harvest data from exisng processes or sources (e.g., asset extracon from as-builts) q Change data elements, level of granularity, or data structure for exisng data set q Add/improve spaal referencing for exisng data sets q Negoate data-sharing agreement q Establish Service Level Agreement between data provider and user q Convert or migrate exisng data q Clean up exisng data Data Governance/Policy/Procedure/Standards q Establish data governance body or modify/strengthen charter for exisng body q Develop and adopt data principles and supporng policies q Develop a strategic data business plan for the organizaon or a parcular business funcon q Define and designate data stewardship roles and responsibilies q Idenfy categories of data to be managed as a corporate asset q Classify data based on sensivity—including designaon of data that can be shared externally q Designate a source system of record for shared agency data elements q Implement a reference (code list) data management strategy q Implement a master data management strategy q Develop a data change management process q Develop agency data standards (e.g., for locaon data and project idenfiers) q Develop an agency business data glossary q Document standard “design pa‹erns” for managing access to historical data (e.g., snapshot creaon) q Review and revise data retenon policies q Create standard methods for managing data access q Develop “standard operang procedures” for data collecon, updang, and quality assurance Data Management Staffing and Responsibilies q Integrate data management core competencies into posion descripons q Conduct staff training for specialized data management tasks q Create new staff posions (e.g., data architect, business analyst, and data manager) q Idenfy staff with crical data knowledge and begin knowledge transfer and succession planning

Data Improvement Catalog 93 2. Data Management Improvement Strategies, Examples and Resources (organized by assessment element) ELEMENT 1: Data Strategy and Governance Improvements Agencies can draw on various techniques for strengthening data governance and data strategic planning: • Data Governance Bodies—formal organizaonal structures to oversee data management policies, projects, iniaves, and investments • Data Governance and Stewardship Policies—adopng principles, policies, and business processes for managing data as a strategic agency asset • Data Business Plans—developing plans that idenfy the data required to meet agency business needs and develop strategies for ongoing management of data • Data Management Roles and Responsibilies—defining and documenng data management responsibilies for data management; building these into employee posion descripons • Data Value Mapping—diagramming how data is used within agency business processes to understand and document the value such data add • Data Communies of Interest—bringing together data users and producers to provide an ongoing forum for idenfying and priorizing data improvement needs and strategies based on mulple perspecves Examples Alaska Department of Transportaon and Public Facilies (DTPF) In Alaska, a comprehensive data business planning effort produced an inventory of data programs, applicaons, and systems; an enhanced understanding of the relaonships between data, applicaons, and business needs; and a framework for implemenng data governance. As part of Alaska DTPF’s data business planning activies, they have made extensive use of Unified Modeling Language (UML) diagrams to define use cases and illustrate how data is produced and consumed. For more informaon: Jack Sckel, or Jill Sullivan, Alaska DTPF, Jack.Sckel@alaska.gov or Jill.Sullivan@alaska.gov California Department of Transportaon (Caltrans) Caltrans has established a Transportaon System Data Governance Board made up of eleven division and three district office representaves. The Board’s mission is to ensure that Caltrans creates and maintains reliable transportaon system data accessible to the department and its partners. The Board is responsible for all aspects of data ownership, standards se”ng, collaboraon, reporng, and other acvies. Caltrans has also developed a Transportaon System Data Business Plan. The plan idenfied key issues facing the department, a data governance approach for resolving issues, and an organizaonal framework to iniate and manage the plan. The plan includes

94 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide • A governance approach to use data as an enterprise asset, including mission goals and success measures • Defini ons of governance roles and responsibili es • The development of core business processes for successful governance implementa on and detailed flow charts outlining steps, roles, and responsibili es for governance ac vi es (e.g., priori zing governance issues, monitoring progress on tasks and projects, and establishing and implemen ng new policies) • Descrip ons of data products and 12 corporate datasets • Processes for doing data quality assessments and the results of applying the processes to several core datasets • Assessment criteria for evalua ng data processes and corporate data and the results of applying the criteria to three high-priority datasets • Recommenda ons for an enterprise data architecture, including aƒributes and benefits of meta data and an outline for data catalogs • An implementa on plan and schedule For more informa on: Coco Briseno, California DOT, coco.briseno@dot.ca.gov hƒp://dot.ca.gov/hq/tsip/data_library/data_governance/CTS_DataBusinessPlan_8_29_11.pdf State of Colorado Data Management Program The State of Colorado has established a strong data management program in the Governor’s Office of Informa on Technology. The program uses data and informa on as enterprise assets and establishes standards and processes to support more flexible government services. In 2009, Colorado created the na on’s first state chief data officer posi on and began developing enterprise data models, enterprise architecture meta data management, and data quality management. The state’s data management program requires a strong program of informa on sharing to serve ci zens more effec vely, improve efficiency and effec veness, and inform policy making. For more informa on: hƒp://www.colorado.gov/cs/Satellite/OIT- Main/CBON/1251575408707 Colorado Department of Transporta on (CDOT) Colorado DOT data management ac vi es build on what was ini ated at the state level in the Colorado State Office of Informa on Technology. Consistent with state policy, CDOT established a Knowledge Management Governance Oversight Commiƒee to support a knowledge management governance framework within the department. The vision of the Commiƒee is to implement policies, procedures, and standards to be used in managing informa on, data, and content within CDOT to support the department’s mission and goals. The scope of the Commiƒee is to provide recommenda ons to the Informa on Technology Management Team for implementa on of standards, procedures, and work products for the enterprise as defined above, in coordina on with implementa on teams. Each year the Commiƒee publishes a report of accomplishments

Data Improvement Catalog 95 Responsibilies of the Commiee are to • Develop a strategy and process for implemenng knowledge management governance throughout the organizaon, to include the following: - Guidance on priories for implemenng governance on enterprise informaon, data, and content - Priorizaon of governance tasks - Creaon and tasking of implementaon teams - Guidance on developing a governance manual documenng the framework - Developing a plan for communicang the iniave to the organizaon - Idenfying a process for change management and training to support the governance framework and iniaves. • Develop and recommend a detailed governance framework to - Define roles and responsibilies in governance (e.g., board/council, steward, custodian, and stakeholder) - Define goals/objecves pertaining to the creaon, retenon, distribuon, and use of informaon, data, and content - Idenfy the value, business use, and priority of informaon, data, and content - Define the requirements for developing a knowledge catalog for the department In 2011, CDOT completed a Performance Data Business Plan to support enhanced data management, performance reporng, and decision making within the agency. The report recommended performance measures for fatalies, bridge condions, pavement condions, roadside condions, snow and ice control, roadway congeson, on-me construcon, on-budget construcon, and strategic acon item implementaon. In addion, the plan addressed data management methods, best pracces, and recommendaons for data governance. The work produced a data inventory, a data catalog, and a sample data governance work team charter. It also recommended measures for assessing data quality. For more informaon: William Johnson, Colorado DOT, will.johnson@state.co.us Michigan Department of Transporta on (MDOT) MDOT has established a Department Data Governance Council. The Council’s charter includes a data management vision, mission, and core principles. The Council meets at least monthly, and IT resources have been assigned to assist the Council. Council responsibilies include • Supporting the creaon of data life cycle documentaon • Establishing and maintaining data management principles • Developing, maintaining, and ensuring adherence to data management best pracces, standards, funcons, and data use and re-use guidelines • Advocating project-level standards • Providing direcon to IT teams

96 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide • Coordinang informaon sharing • Moderang issues from data stewards • Sponsoring the knowledge base • Verifying adherence to standard data concepts and definions defined by the department and the statewide DTMB • Empowering funconal areas to audit and enforce data integrity in compliance with the Data Management Policy • Recommending data stewardship resources and resource levels • Reporng progress to execuves and monitoring industry and corporate trends The Council has adopted a three-er model for implemenng data governance in the department. MDOT has defined a data manager role with the current focus on key data categories—including capital program data, GIS, and asset data. The data manager works with the agency Chief Data Steward to implement data improvements. For more informaon: Ron Vibbert, Michigan DOT, VIBBERTR@michigan.gov State of Minnesota In Minnesota, execuves from eight cabinet-level agencies meet quarterly in a State Data Governance Advisory Council to discuss coordinaon opportunies and strategies for • Increasing efficiencies in data management • Using data sets across state agencies • Minimizing and managing breaches in data security • Establishing an overall statewide data architecture At the me of this wring, a survey of state agencies was planned for 2015 to assess data management maturity levels. For more informaon: Minnesota IT, Jon Eichten, Jon.eichten@state.mn.us Minnesota Department of Transportaon (MnDOT) Data business planning was undertaken to recommend strategies and acons to address priority data gaps and needs in the areas of safety, infrastructure preservaon, and mobility; strengthen data governance; and validate and provide strategic direcon for GIS. In 2011, MnDOT established a Governance Council to implement acons to strengthen data governance. Among these actions were the establishment of business data domains and the definion of stewardship roles and responsibilies. N ine data domains have been idenfied that cover all basic data uses of the department: • Human resources data • Financial data • Planning, programming, and project data • Infrastructure data • Business stakeholder and customer data

Data Improvement Catalog 97 • Spaal data • Regulatory data • Recorded events data • Supporng assets data Within the domains, 120 subject area stewards have been idenfied and are undergoing training to clarify stewardship expectaons, role, and responsibilies. Data domain stewards meet monthly. A representave from the statewide IT group also a ends. To date, MnDOT domain stewards have focused on • Scoping IT projects in the context of idenfied data principles to minimize redundancies and foster discussion of how a project in one area may have broader effects on other areas or data systems in the department • Idenfying enterprise and authoritave sources of data and clarifying ownership responsibilies • Discussing data retenon needs and policies • Reviewing data access policies • Idenfying data-sharing opportunies within and external to the department and developing service level agreements to establish expectaons. For more informaon: Mark Nelson, Minnesota DOT, mark.b.nelson@state.mn.us US DOT FHWA The FHWA Office of Operaons Management developed Roadway Transport Data Business Plan Phase I and Phase II reports to improve coordination and communicaons and strengthen data governance across USDOT offices involved with roadway travel mobility data. Recommendaons to improve coordinaon and communicaons focused on idenfying gaps and redundancies in roadway travel mobility data programs and devising “rules of engagement” regarding collaboraon of the data funcons for roadway travel mobility data. The Data Business Plan recommends the establishment of a Mobility Data Coordinaon Group to (1) coordinate data issues affecng roadway mobility data and (2) work on cross-cu™ng data management issues related to data quality, p rivacy, and security. The USDOT Mobility Data Coordinaon Group would serve as an umbrella structure for smaller working groups who would meet to coordinate on specific data issues (e.g., travel data, modal data, or climate data). The plan also recommended the creaon of an internal Community of Interest that would • Coordinate on cross-cu™ng issues that affect data from all of the working groups • Represent those who use, access, integrate, or benefit from improved coordinaon and data management. The plan includes specific informaon on the roles and respons ibilies of the Mobility Data Coordinaon Group, the working groups, and the internal and external communies of interest.

98 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide -hp://ntl.bts.gov/lib/48000/48500/48531/6E33210B.pdf Virginia Department of Transportaon (VDOT) VDOT’s System Opera‚ons Directorate created a data business plan that provided a framework for mee‚ng the Department’s data needs related to maintenance, traffic engineering, and traffic opera‚ons. The plan development process involved vision development sessions with key stakeholder groups, analysis of exis‚ng data sets, and iden‚fica‚on of key gaps in data. The plan recommended a stewardship model to ensure ongoing management and improvement of the agency’s data resources. One of the strategies recommended in the data business plan was forma‚on of Data Communi‚es of Interest (COIs). A data COI includes staff from different un its with the department to collaborate on developing recommenda‚ons and guidelines about data needs. Five different COIs were established—for work planning and tracking data, financial data, bridge data, traffic and safety asset data, and ITS asset data. For further informa‚on: Bob Boothe, VDOT—Bob.Boothe@vdot.virginia.gov Virginia Transportaon Research Council (VTRC)/Virginia DOT (VDOT) VTRC conducted a business process modeling project for VDOT to describe how several planning and programming ac‚vi‚es could be integrated. Underlying reasons for doing the process modeling were to ensure that • Resources for construc‚on projects are used effec‚vely • Employees know where projects are in their construc‚on life cycles and how projects may have been changed • The ‚me of agency employees is used effec‚vely • The employees are working together to complete transporta‚on projects in a reasonable ‚me The process modeling effort included a step for documen‚ng who generates what informa‚on, products, and services; for whom; how; and for what reasons. The process encouraged the development of integrated systems across func‚onal areas and business ac‚vi‚es. For more informa‚on: hp://www.virginiadot.org/vtrc/main/online_reports/pdf/05- cr15.pdf Washington State Department of Transportaon (WSDOT) Data Governance. In January 2015, WSDOT issued a Secretary’s Execu‚ve Order crea‚ng a high-level Enterprise Informa‚on Governance Group (EIGG). The EIGG serves as the policy-se¡ng body for the department on data and informa‚on management issues and is responsible for establishing direc‚on and se¡ng policy that facilitates management of data and informa‚on in alignment with eight iden‚fied data and informa‚on principles. The Execu‚ve Order directs the EIGG to For addi‚onal informa‚on: hp://ntl.bts.gov/lib/48000/48500/48531/6E33210B.pdf

Data Improvement Catalog 99 • Review exisng data and informaon policies and periodically prepare reports summarizing the effecveness of current pracces while implemenng work plans to address gaps, inconsistencies, and any conflicng or unclear direcon • Develop policies that promote efficient and strategic use of data and informaon resources for all aspects of data collecon, storage, management, findability, and access • Idenfy roles and responsibilies for enforcement, accountability, and authority that support conformance with the data and informaon principles • Provide execuves with annual reports on accomplishments, improvements resulng from policy changes, and policy issues under consideraon. The Execuve Order further directs all employees to make effic ient and strategic use of data and informaon and directs WSDOT regions, execuves, directors, and employees to align their pracces with the data and informaon principles and policies. WSDOT has adopted the following data and informaon principles: 1. Data and informaon are crical to effecve business decision making at WSDOT and shall be maintained in a manner appropriate to meet business needs. 2. Data and informaon are strategic, long-term assets owned by WSDOT, not by individual business units. They are findable, retrievable, and shared. 3. Data and informaon shall be collected once, stored once, and used mulple mes. 4. Data and informaon that is not used shall not be collected or stored. 5. Data and informaon that is used by mulple applicaons or shared across business units shall be defined and managed from an enterprise perspecve and fit for various applicaons. 6. Data and informaon investments will consider business priorities, program impacts, and tradeoffs. 7. Data and informaon shall be managed to provide availability, security, and integrity—they shall be safe from harm and accessible by those who need them. 8. Data and informaon governance, costs, and stewardship processes will be transparent For more informaon: Leni Oman, WSDOT, Omanl@wsdot.wa.gov Data Value Mapping. WSDOT conducted a data value mapping exercise for their Highway Safety Project Programming Process. A diagram was produced that shows the data collecon, supporng data, data processing, and information needed to support all of the safety project acvies associated with the planning process, preliminary programming, design, construcon, maintenance and traffic operaons, and monitoring, reporng performance, and asset management. For more informaon: Ida van Schalkwyk, WSDOT, VanSchI@wsdot.wa.gov Data Business Planning. WSDOT conducted research and developed a state Freight Data System to address user needs for data on the economic impact of freight, system bo˜lenecks, and supply chains. As part of the research, the department • Completed an inventory of current freight data sources and compiled a database • Surveyed other state DOTs to learn about how freight data is being used, needs for freight data, approaches for addressing data needs, and a˜empted soluons

100 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide • Conducted workshops around the state to determine freight data user needs • Iden fied data gaps, redundancies, inaccuracies, and weaknesses in current data collec on. Research results revealed the absence of links between different data sources and gaps in the availability of needed data. Combining data sources addi onally raised concerns about the quality and consistency of fused data. The research recommended development of a maintainable, systema c, and coordinated data collec on framework. The framework will have • A new Freight Data Librarian/Educator to lead the effort and interact with state freight clientele, develop data partnerships, and serve as the freight data source for the state and • A Freight Database Manager who will develop a freight data warehouse and provide technical support. Other recommenda ons called for addi onal ongoing origin and des na on surveys and studies of carriers at the statewide, urban area, and county road levels. For more informa on: h„p://www.wsdot.wa.gov/research/reports/fullreports/690.1pdf Resources The Data Management Associa on Data Management Body of Knowledge h„p://www.dama.org/content/body-knowledge Na onal Associa on of State Chief Informa on Officers Data G overnance Ar cles: h„p://www.nascio.org/publica ons/documents/NASCIO-DataGovernance-Part1.pdf h„p://www.nascio.org/publica ons/documents/nascio-datagovernancep i.pdf h„p://www.nascio.org/publica ons/documents/nascio-datagovernancep ii.pdf Data Governance Ins tute www.DataGovernance.com Object Management Group—Business Process Modeling h„p://www.bpmn.org/ Object Management Group—Unified Modeling Language h„p://www.uml.org/ The Open Group Architecture Framework h„ps://www.opengroup.org/togaf/

Data Improvement Catalog 101 ELEMENT 2: Data Life Cycle Management Improvements Data life cycle management prac ces include • Standard Operang Procedures—for data collec on, upda ng, loading, backups, and archiving • Data Change Management—data change impact analysis and governance processes to minimize unintended consequences of changes to data structures or codes • Data Catalogs and Diconaries—documen ng data tables and columns in a standard manner; providing catalogs of agency data sets that facilitate understanding of and access to available data • Data Curaon Profiles—a standard method for documen ng “the story” of a research data set—describing its origin and role in a research project • Data Management Plans—plans that describe how data sets are to be managed throughout their life cycle, covering formats, documenta on, storage, access, and re-use • Data Retenon Schedules and Archiving—processes for determining how long different data sets will be kept, and strategies for archiving data that need to be retained, but are not in ac ve use • Data Access Policies—classifica on of data sets for controlling access to sensi ve or confiden al data; establishment of policies for data access • Data Delivery Pla orms—implementa on of data query and repor ng tools to facilitate delivery of data to users in various convenient, useful, and usable forms Examples Cornell University—Data Curaon Scien sts at Cornell University researched methods for increasing University and public access to demographic data. The research was designed to investigate the idea of using an external data repository that could offer web APIs similar to those being used at the University and that the public could use to access data. The scien sts acquire demographic data from the US Census and various other sources and add value by processing, analyzing, and distribu ng the data on their project website so as to make the data more accessible and easier to use. The research included an inventory of data sources (including how they are acquired, what they contain, and their size, format, and meta data), an analysis of aggrega on and analysis needs, an assessment of mapping requirements, and recommenda ons for data access tools. The research was designed to improve current methods and strengthen accessibility for internal and external demographic data users. For more informa on: hŒp://docs.lib.purdue.edu/cgi/viewcontent.cgi?ar cle=1026&context=dcp

102 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Michigan DOT (MDOT) Change Management. MDOT is using a commercial informa on management pla orm to build a meta data repository. When fully populated, this repository will enable change impact analysis by iden fying data tables that contain a specific data element— or an element derived from another element—that is being changed. For more informa on: Ron Vibbert, Michigan DOT, VIBBERTR@michigan.gov Data Delivery. MDOT publishes the Michigan Traffic Crash Report as an interac ve website that summarizes historical and annual crash trends and characteris cs. The website was the winner of the 2014 “Best Traffic Records Web Page” award presented by the Associa on of Transporta on Safety Informa on Profess ionals (ATSIP). For more informa on: www.michigantrafficcrashfacts.org Minnesota Department of Transportaon (MnDOT) Data Catalog. MnDOT implemented a Data Business Catalog applica on. Designated data stewards throughout the department iden ty and document data items within their designated domain areas. Data terms, along with per nent informa on or meta data, are published in the Business Data Catalog and made available to staff. The meta data elements for each data item include the approved term name, term defini on, and source of record, data classifica on, and responsible data steward. The Business Data Catalog helps prevent data redundancy and iden fies opportunities to leverage investment in informa on technology. For more informa on: John Solberg, Minnesota DOT, john.solberg@state.mn.us Data Access Policies. MnDOT has established an online guide and process for reques ng informa on consistent with the Minnesota Government Data Prac ces Act. The Guide outlines who has the right to access public data, how to make a request, how the department will respond, and how long it will take to get requested informa on. The policy includes a copy request form, along with data prac ces contacts. The Guide also addresses requests for crea ng new data or providing data in a specific form as well as copy costs. For more informa on: hšp://www.dot.state.mn.us/informa on/dataprac ces/index.html Retenon Schedules. MnDOT has established a records reten on schedule organized by data domain and subject area. The reten on schedule has been added to the Business Data Catalog so that users can search for records and achieve more repor ng flexibility. Staff can get the complete MnDOT records reten on schedule or generate reports only for records assigned to a par cular data domain or subject area or filtered by other criteria. Addi onal enhancements to boost usability include • Crea on of pages for publishing separate informa on and updates for data terms and records

Data Improvement Catalog 103 • Replacement of stac reports with more interacve, flexible, user-driven reports in the agency's crystal reports web portal For more informaon: Charles Stech, Minnesota DOT, Charles.stech@state.mn.us Oregon Department of Administrave Services The Oregon Department of Administraon Services (DAS) has adopted a formal policy to ensure that the state’s informaon assets are idenfied, properly classified, and protected throughout their life cycles. The policy provides that all state agency informaon will be classified and managed based on its confidenality, sensivity, value, and availability requirements, consistent with the Oregon Public Records Law. The four sensivity levels are • Level 1—“Published.” This includes low-sensivity informaon that is not protected from disclosure and will not jeopardize the privacy or security of agency employees, clients, and partners. This includes information regularly made available to the public. • Level 2—“Limited.” This includes sensive informaon that may not be protected from public disclosure but, if made easily or readily available, could jeopardize the security or privacy of employees, clients, and partners. Examples might include audit reports and risk management planning documents. • Level 3 –“Restricted.” This includes sensive informaon intended for limited business use. The informaon in this category typically may only be accessed and used by authorized internal pares in the performance of their dues. External pares must be under contractual obligaon of confidenality. Security threats at this level include changes to or destrucon of data, unauthorized disclosure, and violaon of privacy pracces. Unauthorized access and use could result in financial loss or idenfy the˜. • Level 4—“Crical.” This includes informaon that is extremely sensive and intended for use only by “named” individuals. This informaon is generally exempt from public disclosure because it may cause major damage or injury to named individual(s), employees, clients, or partners or cause damage to the agency. The policy includes labeling and handling convenons for limited or restricted crical informaon and outlines data disposal guidelines. For more informaon: hšp://www.oregon.gov/DAS/OP/docs/policy/state/107-004- 050.pdf Texas Department of Transportaon (TxDOT) TxDOT has established a formal policy that assigns responsibilies for maintaining roadway informaon. The policy defines how the Transportaon Planning and Programming (TPP) Division shares the responsibilies of roadway data maintenance with all district offices and the Construcon Division. The manual includes electronic links to definions for all of the terms included in the policy and links to district personnel responsible for maintaining data.

104 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide For more informaon: hp://onlinemanuals.txdot.gov/txdotmanuals/trm/data_maintenance_responsibility.htm Utah Department of Transportaon (UDOT) UDOT has gained naonal aenon with the implementaon of t he UDOT GIS Access to the Transportaon Enterprise (UGATE) and UPlan projects. UGATE is a spaally enabled data warehouse; UPlan provides access to a wide variety of geographic informaon in the department—in map, tabular, and straight-line diagram forms. The system was designed to provide a flexible, scalable plaŠorm for data shar ing to promote effecve decision making throughout the department. For addional informaon: hp://www.gis.Žwa.dot.gov/documents/Cloud_Technologies_for_GIS.htm#utah hp://environment.Žwa.dot.gov/integ/case_utah.asp hp://giscinc.com/category/case-studies/ Virginia Department of Transportaon (VDOT) VDOT’s Pavement Management Team within the agency’s Maintenance Division has developed a Standard Operang Procedures (SOP) document that describes the standard process to be followed for collecon, processing, loading, analyzing, and reporng of pavement condion data. This SOP defines specific responsibilies for the Pavement Management Team and Informaon Technology Division staff to ensure a clear understanding of roles and dependencies. For more informaon: Tanveer Chowdhury, VDOT: Tanveer.Chowdhury@vdot.virginia.gov Washington State Department of Transportaon (WSDOT) Data Catalog. WSDOT created an online “DOTS” (Data Or Term Search) applicaon designed to connect knowledge workers with data and informaon and promote a common data vocabulary within the agency. It provides informaon on what data is available, what the data means (meta data), where the data is housed, and who is responsible for managing the data. The applicaon integrates the work of business data stewards, subject maer experts, knowledge workers, applicaon developers, librarians, and technical stewards. The applicaon allows users to search for data resources. In addion, they can subscribe to informaon on changes in business concepts and aach support documentaon or URL references. For more informaon: Andy Evere, EveretA@wsdot.wa.gov Data Delivery. The WSDOT GeoPortal is an applicaon that allows users to view the agency’s spaal data via a web browser. Types of data in the GeoPortal include

Data Improvement Catalog 105 check boxes to choose from base maps and data layers. The GeoPortal allows users to • Measure distance or areas • Share maps via a URL link • View various city, district, and legislave boundaries • Select from various imagery, topographical, aerial, and other base maps • Locate mileposts, geographic coordinates, and street addresses For more informaon: h­p://www.wsdot.wa.gov/mapsdata/tools/geoportal_ext.htm Resources The Data Management Associaon Data Management Body of Knowledge h­p://www.dama.org/content/body-knowledge FHWA Asset Management Data Collecon for Supporng Decision Processes h­p://www.‹wa.dot.gov/asset/dataintegraon/if08018/assetmgmt_web.pdf Council on Library and Informaon Resources—Data Curaon h­p://www.clir.org/iniaves-partnerships/data-curaon Data Curaon Profiles Toolkit h­p://datacuraonprofiles.org/ Data Management Plan Guidance h­p://www.dcc.ac.uk/resources/how- guides/develop-data-plan and h­ps://purr.purdue.edu/dmp funconal classificaon, interchange drawings, city limits, and state routes. Users can

106 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide ELEMENT 3: Data Architecture and Integra on Improvements The following strategies can be pursued to establish standard data structures and management approaches to enable improved integraon across different data sources: • Common Geospa al Referencing—development, adopon, and ongoing maintenance of standard methods for measuring and referencing spaal locaons, including locaons along linear networks (e.g., based on highway route and distance from known reference point). • Standardized Approach to Temporal Data—establishment of standard aributes and common definions to describe temporal aspects of data sets and allow disparate data sets with a temporal dimension to be integrated. • Reference Data Management—ensuring of consistency of standard code lists across applicaons. • Master Data Management—ensuring that the organizaon maintains a “single version of the truth” with respect to core data enes (e.g., projects and roadways) through centralized management of master data and use of synchronizaon or replicaon services. • Data Architecture Prac ces and Roles—maintenance of an integrated agency- wide view of business data enes and their relaonships; establishment of review processes for new databases to ensure consistency and appropriate linkages with exisng data enes. • Business Glossaries—development of agreed-upon definions of data elements to facilitate integraon. • Data Integra on Tools—use of Extract-Transform-Load (ETL) and other data integraon tools to formalize data mappings and automate transformaons. Examples Idaho Transporta on Department (ITD) ITD has established procedures for maintaining data in their LRS. The department implemented a commercial product for synchronizing locaon informaon across separate systems in place for managing bridge, safety, and traffic data. The system allowed the agency to reduce high mainframe maintenance costs, automate event locaon stability, and eliminate “data integraon by memo.” Implementaon involved resolving issues related to the management and maintenance of both LRS and event temporal data. To maintain integrity, the department needed to create new standards and data maintenance rules to resolve issues related to temporal queries and temporal event conversion and provide capabilies to correct temporal mistakes. For more informaon: Brian Emmen, GIS Manager, brian.emmen@itd.idaho.gov Michigan DOT (MDOT) Michigan’s statewide Geographic Framework is a collaborave, integrated topological model made up of points, lines, and polygraphs all spaally related to one another. There are 21 feature classes (e.g., transportaon, boundaries, hydrography, and points of interest) that are topologically maintained with role-based stewardship. There are 160 plus data elements and 1.2 million line segments. The state’s enre roadway network is fully linearly referenced, with addional layers for cies, villages, townships, school

Data Improvement Catalog 107 districts, legislave boundaries, unincorporated places, census tracks and block groups, and adjusted census urban areas. The “master” LRS data is made possible by collaborave contrib uons under a shared services model where all agencies contribute to the overall budget and can benefit from results. The system is web-based in an Oracle Spaal Topology Model. The system has an established migraon process and there are built-in work flow and audit/approval processes. For more informaon: Ron Vibbert, Michigan DOT, VIBBERTR@michigan.gov New York State Department of Transporta on (NYSDOT) NYSDOT created a data warehouse and implemented new enterprise-wide business intelligence tools to comply with the reporng requirements associated with the American Recovery and Reinvestment Act (ARRA). The agency focused on idenfying and aggregang data from 13 exisng systems into a data warehouse to meet reporng needs. Efforts were also iniated to develop tools for gathering informaon from local governments and other partners who do not have access to agency source systems so that they could enter data directly into the warehouse. New enterprise business intelligence visualizaon tools were developed to use warehouse data for creang new data views, dashboards, and ways of consuming the informaon. For more informaon: h—p://www.ctg.albany.edu/publicaons/journals/dgo_2010_recoveryact/dgo_2010_rec overyact.pdf Ohio Department of Transporta on (ODOT) ODOT iniated a major research effort to develop a customized, executable, strategic enterprise architecture design for the departments. The research recommended the design of an enterprise architecture, consisng of • Business architecture—which defines the funconal structure of ODOT in terms of its business processes and organizaon and its associated business informaon needs • Applicaons architecture, which delineates the capabilies of specific applicaons used to support ODOT’s business funcons and how these various applicaons work together or integrate to support ODOT’s enterprise-wide informaon requirements • Data architecture, which establishes data standards for all of ODOT’s systems to support integraon and informaon sharing between these systems • Technical architecture, which describes the technical infrastructure and sožware technologies, which are shared services and not applicaon specific, and other specific hardware and operang system-level sožware technolog ies required to support the various business applicaons. For more informaon: www.dot.state.oh.us/Divisions/Planning/SPR/Research/reportsandplans/Reports/2014/ Administraon/134756_FR.pdf

108 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Oregon DOT Oregon DOT has draed a statewide Road Centerline Data Standard to • Ensure the compability of datasets within the same framework feature set and between other framework feature sets and themes • Help agencies responsible for creang, maintaining, and distribung road centerline data sets by reducing the costs of data sharing, data development, and data maintenance • Ensure that road centerline aribuon (including geometry) is as current, accurate, and complete as possible by relying on local road authories The standard describes the essenal elements and data structure necessary to adequately describe, produce, and use road centerline data produced in Oregon. This includes a core set of geospaal informaon and geometry for an accurate and current representaon of the state’s roadway network. The data environment for the standard is a vector model, composed of points and linking logical relaonships between those points. The exchange medium for road centerline data files is the ESRI shapefile, which is a public domain data structure relang points, lines, and feature aribuon (including shape geometry). This exchange medium is supported by all known GIS soware suites in use in Oregon. The standard includes specificaons for data characteriscs, graphic data elements, aributes, resoluon and accuracy, and other geospaal data requirements. The standard also includes a data diconary and glossary of terms. For more informaon: hp://www.oregon.gov/ODOT/TD/TDATA/gis/docs/TFIT/T- FIT_20061117_TransStandard_Dra_5_0.pdf Resources US Federal Highway Administration—ARNOLD Reference Manual: www.šwa.dot.gov/policyinformaon/hpms/documents/arnold_reference_manual_201 4.pdf FHWA Data Integraon Primer: hp://www.šwa.dot.gov/asset/dataintegraon/if10019/dip00.cfm The Data Warehousing Instute: www.infor@tdwi.org Open Methodology MDM Page: hp://mike2.openmethodology.org/wiki/Master_Data_Management_So luon_Offering

Data Improvement Catalog 109 ELEMENT 4: Data Collaboraon Improvements Strategies for establishing and suppor ng data partnerships include • Mul-Purpose Data Collecon—adop ng principles, policies, and business processes for managing data as a strategic agency asset • Data Clearinghouses/Open Data Plaorms—plaorms that enable mul ple par es to post data sets that others can discover and use; may include open data API access that enables data to be integrated into apps • Data Partnerships—interagency ini a ves to collabora vely acquire, build, and maintain data sets of common interest • Data-Sharing Agreements– agreements between organiza ons that establish ground rules for data sharing, including restric ons on use • Data Outsourcing—Leveraging available private-sector data sources Examples Massachuses Department of Transportaon (MassDOT) MassDOT began an Open Data Ini a ve in 2009, making available some data feeds to the public, and encouraging app developers to use these data feeds to provide the traveling public with useful informa on. Current data feeds include transit schedules, real- me travel me on selected highways, planned highway construc on and maintenance projects, bicycle facili es, and current Registry of Motor Vehicles branch wait mes. Real- me travel data is provided through anonymous tracking of Bluetooth- enabled devices carried by motorists and their vehicles. For more informa on: h“ps://www.massdot.state.ma.us/DevelopersData.aspx Minneapolis-St. Paul (Twin Cies) Metropolitan Council The Metro Regional Centerline Collabora ve (MRCC) is a joint collabora ve project involving managers and GIS staff from the 7-county Minneapolis-St. Paul metropolitan area, the Metropolitan Emergency Services Board, and the Metropolitan Council regional planning agency. The goal of the project is to facilitate the crea on of an authorita ve roadway centerline database that is locally sourced and maintained and that can meet the business needs of par cipa ng agencies. Thus far business needs have been documented and a draš data model has been developed. The MRCC can be used for • Vehicle rou ng • Address geocoding • Next genera on 911 call rou ng and loca on valida on • Emergency services dispatching • LRS use • Cartographic representa on of road features For more informa on: h“p://www.metrogis.org/projects/centerlines-ini a ve.aspx

110 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide State of Utah Spaal data is being shared across agencies and with the public through a Utah state managed Automated Geographic Reference Center (AGRC) clearinghouse. The site includes various address, aerial photography, bioscience, demographic, economy, elevaon/terrain, energy, environmental, farming, health, history, planning, recreaon, transportaon, ulies, and water data layers. For more informaon: h p://gis.utah.gov/ Utah DOT (UDOT) UDOT iniated a comprehensive LiDAR data collecon effort in 2011 to capture informaon needed for asset management and other related business needs. The effort was intended to lower overall data collecon costs by gathering mulple types of informaon at the same me. Several different departments parcipated in funding the data collecon, which included pavement condion (roughness, distress, ru‡ng), roadway geometrics, and inventory for several different roadway assets including bridges, walls, signs, signals, barriers, power poles, striping, curb cuts, intersecons, drainage, shoulders, and ATMS devices. Wisconsin Department of Transporta on (WisDOT) WisDOT has developed a Wisconsin Informaon System for Local Roads (WISLR), an internet-accessible system that local government road authories can use to report local roadway informaon on lane/shoulder widths, surface type, surface year, road category and funconal classificaon, and pavement condion rangs t o WisDOT. The tool uses GIS technology to combine local road data with interacve mapping funconality. Users can display data in tabular formats, spreadsheets, or maps. Local governments are using the WISLR query, analysis, and spreadsheet tools to analyze, update, and edit their data. WISLR is improving the accuracy of roadway inventory and pavement condion data. For more informaon: h p://www.dot.state.wi.us/localgov/wislr/ Resources USDOT Planning for Operaons Data Collecon and Sharing Resources: h p://www.plan4operaons.dot.gov/data_coll.htm FHWA GIS-T Operang Agreements Page: h p://gis.–wa.dot.gov/gdc_agreements.asp FHWA Office of Safety—Noteworthy Pracces web page: h p://safety.–wa.dot.gov/rsdp/noteworthy_pracces.aspx FHWA Research Data Exchange: h ps://www.its-rde.net/home New York State GIS Data Clearinghouse: (h ps://gis.ny.gov/gisdata/inventories/member.cfm?OrganizaonID=539) Outsourced Probe Data Symposium Proceedings (January 2015) h p://www.ntc.umd.edu/sites/default/files/documents/Publicaons/Proceedings_1st_P robe_Data_Symposium.pdf TRB Special Report—How We Travel: A Sustainable Naonal Program for Travel Data (suggests a collaborave approach to building a Naonal Data Program): h p://onlinepubs.trb.org/onlinepubs/sr/sr304.pdf

Data Improvement Catalog 111 ELEMENT 5: Data Quality Improvements Data quality improvement strategies include • Metrics—establishing and reporng metrics for assessing and describing data quality. • Data Valida on Rules—establishing business rules for data validity (e.g., acceptable ranges or variaons from a prior observaon). • Data Cleansing—Idenficaon and correcon (or exclusion) of data records that do not meet established validity criteria. • Data Collec on Quality Management Processes—establishing roles, responsibilies, and processes to ensure quality data from field data collecon including training, equipment calibraon, personnel and equipment cerficaon, comparison against control secons, and independent verificaon and validaon. Examples FHWA Report: Quality Management for Pavement Condi on Data The February, 2013, FHWA Report, A Praccal Guide for Quality Management of Pavement Condion Data Collecon, provides guidance on the principles and pracces of data quality management as applied to pavement condion data collecon. The report includes suggesons for • Specifying the data collecon rang protocols to be used, including those related to tracking linear referencing locaons • Establishing quality standards and acceptance criteria • Idenfying responsibilies, including training and succession planning of personnel • Performing quality control acvies • Monitoring and tesng for acceptance • Taking mely and appropriate correcve acon • Performing quality management reporng • Developing a data quality management plan The Guide also presents case studies of pracces in use by several transportaon agencies including • Oklahoma DOT data quality standards • Louisiana DOTD standards for the quality of video image • Pennsylvania DOT data acceptance process and criteria • Nebraska’s Department of Roads data collecon quality control process that includes calibraon of the profiler’s laser sensors, accelerometers and DMI, control site tesng, real-me system checks, and me-series comparisons Addional informaon can be found at h’p://www.–wa.dot.gov/pavement/management/qm/data_qm_guide.pdf FHWA Traffic Monitoring Guide An updated FHWA Traffic Monitoring Guide released in 2013 outlines pracces for • Traffic volume monitoring • Vehicle classificaon data collecon

112 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide • Truck weighing and data collecon at truck weigh sites • Traffic monitoring data formats The guide includes integrated sample designs for traffic monitoring and discusses sampling techniques as well as methods and measures for managing variability, enhancing data quality, and developing esmaon procedures. For more informaon: hp://www.wa.dot.gov/policyinformaon/tmguide/ FHWA Crash Data Improvement Program (CDIP) Guide The purpose of the CDIP Guide is to “assist state crash database managers and other traffic safety professionals in idenfying, defining and measu ring the characteriscs of the data quality within the state crash database.” The foci of the Guide are the quality- related aributes of meliness, accuracy, completeness, consistency, integraon, and accessibility of crash data. The CDIP Guide provides a mechanism by which States can establish baseline measures and subsequent assessments related to the crash data quality characteriscs. For further informaon: hp://safety.wa.dot.gov/cdip/finalrpt04122010/finalrpt04122010.pdf NHTSA In 2011, NHTSA issued a report containing model measures for state traffic records systems. The measures covered six key data quality aributes: meliness, accuracy, completeness, uniformity, integraon, and accessibility—across the six core state traffic record data systems—crash, vehicle, driver, roadway, citaon/adjudicaon, and emergency medical services (EMS)/injury surveillance. Subsequently, NHTSA iniated a series of state Traffic Records Program Assessments where teams of experts would comprehensively review state data, applicaon, organizaonal, planning, coordinaon, and investment pracces related to traffic data collecon, management, and reporng. Recommendaons from these NHTSA team assessments addressed data quality-related topics including • Custodial responsibilies for crash data • Data meliness issues • Data accuracy issues, including a data quality control program with the following components: o Data quality metrics for meliness, accuracy, completeness, consistency, integraon, and accessibility o Data quality monitoring and reporng o Procedures for returning erroneous data and reports o Connuous auding of data quality o Periodic reviews by independent auditors o Training procedures o Feedback mechanisms for reporng performance • Final acceptance criteria for data submissions

Data Improvement Catalog 113 For more informaon: www.nrd.nhtsa.dot.gov/Pubs/811441.pdf hp://www.nhtsa.gov/Data/Traffic+Records The Inter-American Development Bank The Inter-American Development Bank, Department of Infrastructure and Environment, completed an Assessment of Transport Data Availability and Quality in Lan America to idenfy transport data availability, coverage, and quality within selected developing countries to determine where there are gaps in data needed to esmate greenhouse gas emissions. The assessment established a scale of 1-5 to rate quality and also evaluated • The availability of me-series data • Whether the data was subject to quality assurance protocols • The accessibility of the data • The enes responsible for data collecon • The cycle for collecng data For more informaon: www.iadb.org MDOT The Michigan DOT Intermodal Management System (IMS) business processes define data needs and accuracy, completeness, and meliness requirements. The system includes 54 categories of data assessed quarterly for quality and completeness. Quarterly data quality reports include informaon on data currency (update due versus actual), known flaws (e.g., missing data), and importance (e.g., use to meet reporng requirements). Data quality categories are assigned as follows on the reports: • Green—data is complete, correct and capable of supporng business processes • Yellow—Data is incomplete or incorrect and could pose problems supporng business processes • Red—Data is incomplete or incorrect and incapable of supporng business processes For more informaon: hp://mdotcf.state.mi.us/public/tms/idm.cfm Resources The Data Management Associaon: www.dama.org Internaonal Associaon for Informaon and Data Quality: hp://iaidq.org/ FHWA Data Quality White Paper (hp://ops.šwa.dot.gov/publicaons/šwahop08038/pdf/dataqual_whitepaper.pdf) FHWA Traffic Data Quality Measurement: hp://ntl.bts.gov/lib/jpodocs/repts_te/14058.htm FHWA Traffic Monitoring Guide—Compendium of Quality Control Criteria: hp://www.šwa.dot.gov/policyinformaon/tmguide/tmg_2013/compendium-data- quality.cfm FHWA Recommended Framework for a Bridge QA/QC Program: hp://www.šwa.dot.gov/bridge/nbis/nbisframework.cfm

Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TDC Transit Development Corporation TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S.DOT United States Department of Transportation

TRA N SPO RTATIO N RESEA RCH BO A RD 500 Fifth Street, N W W ashington, D C 20001 A D D RESS SERV ICE REQ U ESTED ISBN 978-0-309-37485-9 9 7 8 0 3 0 9 3 7 4 8 5 9 9 0 0 0 0 N O N -PR O FIT O R G . U .S. PO STA G E PA ID C O LU M B IA , M D PER M IT N O . 88 D ata to Support Transportation A gency Business N eeds: A Self-A ssessm ent G uide N CH RP Report 814 TRB

Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Get This Book
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 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide
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TRB’s National Cooperative Highway Research Program (NCHRP) Report 814: Data to Support Transportation Agency Business Needs: A Self-Assessment Guide provides methods to evaluate and improve the value of their data for decision making and their data-management practices.

NCHRP Web-Only Document 214: Transportation Agency Self-Assessment of Data to Support Business Needs: Final Research Report describes the research process and methods used to develop NCHRP Report 814.

The following documents supplement the project and are available online:

This supplemental information is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences, Engineering, and Medicine or the Transportation Research Board (collectively "TRB") be liable for any loss or damage caused by the installation or operation of this product. TRB makes no representation or warranty of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.

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