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

Chapter: Appendix C - Data Management Assessment Elements

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Suggested Citation:"Appendix C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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 C - Data Management Assessment Elements." 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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62 A P P E N D I X C Data Management Assessment Elements Element 1: Data Strategy and Governance 1.1 Strategy and Direcon Leadership commitment and strategic planning to maximize value of data to meet agency goals Level 1 Agency-Wide: Data collecon and management is performed by individual business units with lile or no agency-wide direcon or coordinaon. Data improvements are not systemacally or regularly idenfied—they are implemented reacvely or opportuniscally. Program Specific: Data improvements are not systemacally or regularly idenfied—they are implemented on a reacve or opportunisc basis. Level 2 Agency-Wide: Efforts to implement agency-wide data governance or assess agency-wide data needs are being discussed or planned. Data improvement needs are idenfied and communicated to management informally and efficiently. Program Specific: Data improvement needs are idenfied and communicated to management informally. Level 3 Agency-Wide: Execuve leadership has communicated the expectaon that business units and IT funcons should collaborate on idenfying and implemenng data improvements of agency-wide benefit. Data business plans or equivalent planning tools have been prepared to idenfy short and longer term data collecon and management strategies that align with business objecves. Data improvement needs have been systemacally reviewed, assessed, and documented. Program Specific: Data business plans or equivalent planning tools have been prepared to idenfy short and longer term data collecon and management strategies that align with business objecves. Data improvement needs have been systemacally reviewed, assessed and documented. Level 4 Agency-Wide: Agency leadership regularly communicates and demonstrates acve support for data improvements that will lead to improved agency effecveness and efficiency. Agency leadership acvely works to facilitate collaboraon across business units on data improvements and maintain strong partnerships between IT and bu siness unit managers. Data business plans or equivalent planning tools are regularly updated. A regular process of data needs assessment is in place and is used to drive budgeng decisions.

Data Management Assessment Elements 63 Element Description This sub-element looks at the extent to which the agency leadership or data program manager has demonstrated a commitment to managing data as a strategic asset—through establishment of data governance structures, communications, and planning activities to ensure alignment between data investments and business needs. Support for the AASHTO Data Principles The following boxes show how the data management assessment elements tie to the AASHTO data principles. The first element is broad based and therefore covers all of the AASHTO data principles. Subsequent elements focus on specific aspects of data management and therefore are applicable to selected AASHTO data principles.  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, decisions about what data to collect and how to manage it are made in a highly decentralized fashion. As agencies mature, investments in data are made in a more deliberate and coordinated fashion. Agencies and data program managers can better answer questions such as “are we collecting the right data?” and “are we managing our data effec- tively?” Agencies can better identify where relatively unproductive or lower value data invest- ments can be discontinued or diverted to higher value data investments. Relevant Improvement Actions  Data Governance Bodies  Data Governance and Stewardship Policies  Data Business Plans 1.1 Strategy and Direcon Program Specific: Data business plans or equivalent planning tools are regularly updated. A regular process of data needs assessment is in place and is used to drive budgeng decisions. Level 5 Agency-Wide and Program Specific Data governance and planning acvies are connually refined to focus on key risks and opportunies and eliminate acvies without demonstrated payoff. Data governance and planning acvies have a high probability of connuing through changes in execuve leadership.

64 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element 1: Data Strategy and Governance 1.2 Roles and Accountability Clear roles, accountability, and decision-making authority for data quality, value. and appropriate use Level 1 Agency-Wide and Program Specific: Accountability for the quality, value, and appropriate use of data has not been clearly established. Level 2 Agency-Wide: One or more individuals have been idenfied to lead agency-wide data governance acvies. A business lead or point person has been designated for each major data set or applicaon but the responsibilies of the role haven't been spelled out. Program Specific: A business lead or point person has been designated for each major data set or applicaon but the responsibilies of their role haven't been spelled out. Level 3 Agency-Wide: An agency-wide data governance body has been established with representaon from IT and business funcons and has defined its charter. Objecves and performance metrics for data governance and stewardship have been defined and documented. Role(s) have been designated to idenfy points of accountability for data quality, value, and appropriate use—for priority data programs or data subject categories. Decision-making authority has been defined for collecon/acquision of new data, disconnuaon of current data collecon, and significant changes to the content of exisng data. Capabilies and skills for data management are included in staff posion descripons, agency recruing, and staff development efforts. Program Specific: Role(s) have been designated to idenfy points of accountability for data quality, value, and appropriate use—for priority data programs or data subject categories. Decision-making authority has been defined for collecon/acquision of new data, disconnuaon of current data collecon, and significant changes to the content of exisng data. Capabilies and skills for data management are included in staff posion descripons, agency recruing, and staff development efforts. Level 4 Agency-Wide: An agency-wide data governance body is acve and achieving results recognized as valuable. The agency is successfully idenfying and resolving situaons where individual business unit interests are in conflict with agency-wide interests related to data collecon and management Staff with responsibility for data stewardship and management have sufficient me and training to carry out these responsibilies. Staff with responsibility for data stewardship and management play an acve role in defining data improvements and periodically produce reports of progress to their managers. Program Specific: Staff with responsibility for data stewardship and management have sufficient me and training to carry out these responsibilies. data improvements and periodically produce reports of progress to their managers. Staff with responsibility for data stewardship and management play an acve role in defining

Data Management Assessment Elements 65 Element Description This sub-element assesses the extent to which roles and accountability for data stewardship have been agreed-upon, defined, documented, and assigned to individuals. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, there is a lack of clarity about who “owns” data and who is account- able for making sure that data meets business needs. Responsibilities have not yet been defined for ensuring that different business units coordinate on data collection and data management activities to maximize efficiencies. As agencies move up the maturity scale, roles and responsi- bilities are more formalized. Managers ensure that staff are assigned to data stewardship and management roles and are sufficiently trained and provided with resources. Formalizing and documenting roles and accountability for data (1) creates a consistent and sustainable frame- work for proper data management, (2) reduces the agency’s dependence on “heroic efforts” to take care of what needs to be done, and (3) helps to ensure that staff are proactive about provid- ing the right data efficiently, with the right quality, and in the right form. Relevant Improvement Actions  Data Governance Bodies  Data Governance and Stewardship Policies  Data Business Plans  Data Management Roles & Responsibilities 1.2 Roles and Accountability Level 5 Agency-Wide: A charter for agency-wide data governance body is reviewed periodically and updated based on experience. Stewardship roles are periodically reviewed and refined to reflect new or changing data requirements and implementaon of new data systems. Staff with responsibility for data stewardship and management are coordinang with their peers in the agency and with external data partners to deliver best value for resources invested. Data management-related metrics are rounely considered in employee performance reviews. Program Specific: Stewardship roles are periodically reviewed and refined to reflect new or changing data requirements and implementaon of new data systems. Staff with responsibility for data stewardship and management are coordinang with their peers in the agency and with external data partners to deliver best value for resources invested. Data management-related metrics are rounely considered in employee performance reviews.

66 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element 1: Data Strategy and Governance 1.3 Policies and Procedures Adop on of principles, policies, and business processes for managing data as a strategic agency asset Level 1 No formal policies and procedures have been defined. Level 2 Execuve leadership has endorsed basic data principles. Level 3 The scope of agency-wide data governance has been established. Data classificaons have been defined based on agency-wide importance or need for cross- business unit integraon. A limited set of data management policies have been adopted for priority data categories. The agency has a documented procedure and decision-making process for requesng and evaluang new data collecon or acquision requests. Level 4 A comprehensive set of data management policies has been adopted based on collaboraon across the agency, including IT, business units, and records management. Processes are in place to monitor and enforce compliance with policies. The agency has a documented and implemented procedure for requesng and evaluang new data collecon or acquision requests (i.e., the documented procedure is rounely followed). Level 5 Policies are regularly reviewed and updated based on factors such as awareness/reach within the agency, effecveness, and cost burden. Element Description This sub-element looks at the extent to which the agency has established clear policies and procedures about how data is to be managed as a corporate asset. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, there are no written and adopted policies and procedures related to data governance and management. As agencies move up the maturity scale, policies and pro- cedures are drafted, adopted, and implemented throughout the agency. The policies and proce- dures help standardize how an agency treats data. If implemented well, policies and procedures result in higher quality data, more effective use of data, and clear decision-making processes around data. Relevant Improvement Actions  Data Governance and Stewardship Policies

Data Management Assessment Elements 67 Element 1: Data Strategy and Governance 1.4 Data Asset Inventory and Value Tracking of agency data assets and their value added Level 1 Agency-Wide: There are no inventories of available data sets. There is limited awareness of how data sets are used and what value is being provided. Program Specific: There is limited awareness of how data sets are used and what value is being provided. Level 2 Agency-Wide: Some business units maintain lists of available data sets but there is no consistent, agency-wide data inventory. There is general awareness of how different data sets are used and what value is being provided, but no records are kept on this. Program Specific: There is general awareness of how different data sets are used and what value is being provided, but no records are kept on this. Level 3 Agency-Wide: Data sets of agency-wide importance have been idenfied and documented with basic elements, including business contact, technical contact, locaon, and descripon. Primary users and uses of each data set have been idenfied and documented Data collecon or acquision costs are tracked. Program Specific: Primary users and uses of each data set have been idenfied and documented. Data collecon or acquision costs are tracked. Level 4 Agency-Wide: A consistent agency-wide inventory of data sets is maintained and kept current as new data sets come on line. Data inventory informaon is used to idenfy duplicave data sets that can be eliminated or consolidated. Managers use informaon about data storage and management costs to evaluate opportunies for improved efficiencies. Program Specific: Managers use informaon about data storage and management costs to evaluate opportunies for improved efficiencies. Level 5 Agency-Wide and Program Specific: There is a good understanding of the value provided by each data set with respect to agency efficiency and effecveness. Data collecon and management methods are regularly evaluated and improved. Element Description This sub-element looks at the extent to which the agency or data program manager has docu- mented the data, its uses, and its value to the agency. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable

68 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Benefits of Moving Up the Maturity Scale At lower levels of maturity, information about data and its uses resides in the heads of a few staff members—nothing is written down. As agencies move up the maturity scale, they con- sistently document their data sets and track how data sets are used. This provides the basis for articulating the value of different types of data to the agency and weighing data collection and maintenance costs against value added. It also enables agencies to identify areas of duplication and opportunities for consolidation. Relevant Improvement Actions  Data Catalogs and Dictionaries  Data Value Mapping Element 1: Data Strategy and Governance 1.5 Relaonships with Data Customers Connecons between data producers and users Level 1 There are no proacve outreach acvies to understand data user needs. Level 2 Informal, limited outreach to other business units has been conducted to idenfy how they might use available data sets. Level 3 Meengs have been held with current or potenal new users for our data to understand their needs. This informaon has been taken into account in developing plans for improvements. Level 4 Input from data customers is routinely solicited, collected, and considered through various online and in person forums (e.g., Communies of Interest). Level 5 There are formal, wri­en agreements that document what data will be provided to customers, when, and how. A process is in place to periodically re-negoate these agreements. Element Description This sub-element looks at the extent to which data program managers have established chan- nels of communication with data users. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, data program managers do not actively communicate with data users to understand how they use data and obtain feedback on data quality. As agencies move up the maturity scale, data program managers reach out to data users and act on feedback received to make improvements. In some situations, service level agreements can be negotiated to for- malize what data is provided, at what frequency, and in what form. Strengthening relationships between data providers and data customers helps agencies avoid situations in which data is being produced but not used as intended. A functioning feedback loop between data providers and customers helps data providers focus on data improvements that add value.

Data Management Assessment Elements 69 Relevant Improvement Actions  Data Communities of Interest Element 1: Data Strategy and Governance 1.6 Data Management Sustainability Con nuity of data management knowledge and exper se through staff transi ons Level 1 Agency-Wide and Program Specific: Risks and needs associated with data management knowledge and core competencies are not well understood. Level 2 Agency-Wide and Program Specific: There is some understanding of risks associated with rerement of key individuals with specialized knowledge of data systems—but these risks have not been systemacally idenfied. Level 3 Agency-Wide and Program Specific: Risks associated with potenal loss of key individuals with specialized knowledge of data systems have been systemacally idenfied. Strategies have been developed to migate these risks. Core competencies for data management are included in staff posion descripons, agency recruing, and staff development efforts. Level 4 Agency-Wide and Program Specific: There is a standard process in place to ensure connuity in data management pracces through staff transions. Staffing requirements for data management acvies are understood and planned for. Processes are in place to ensure that work commitments are in line with available staff resources. Level 5 Agency-Wide and Program Specific: People with specialized knowledge about agency data sets have been idenfied and there are succession plans and mentoring strategies in place to pass on this knowledge to others. There is a funconing process to bring on new skills and capabilies as needed to meet changing technologies and data management methods. Element Description This sub-element assesses the extent to which the agency can sustain data management func- tions through staff transitions. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, the agency is not aware of risks associated with departures of staff with specialized knowledge and skills related to particular data sets or data management practices. As agencies move up the maturity scale, these risks are systematically identified and mitigation actions are in place—including succession plans and mentoring strategies. A proactive approach to ensuring data management sustainability reduces risks of disruption to data access or reporting activities. It also provides for an orderly and efficient transition of responsibilities.

70 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Relevant Improvement Actions  Succession Planning and Management  Core Competency Definition Element 2: Data Life Cycle Management 2.1 Data Updang Well-defined and coordinated data update cycles Level 1 Agency-Wide and Program Specific: Data updang cycles and business rules for data updates have not been defined. Level 2 Agency-Wide and Program Specific: Updang cycles have been established but have not been documented. Level 3 Agency-Wide and Program Specific: Updang cycles have been documented. Business rules have been defined for how key data enes are added, updated, and deleted. Level 4 Agency-Wide and Program Specific: Updang cycles are being consistently followed. Business rules for data updang are embedded in and enforced by applicaons (where applicable). Level 5 Agency-Wide and Program Specific: Data updang methods are periodically reviewed to idenfy opportunies for improved efficiencies. Element Description This sub-element assesses the extent to which update methods and cycles have been defined and documented for key data sets. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, data updates are made ad hoc and users are not aware of data updating frequencies or methods. In addition, rules for adding and deleting key data entities (e.g., routes, projects, and employees) have not been developed. As agencies move up the matu- rity scale, they create and maintain business rules for how each major data set is to be updated. Where applicable, business rules are embedded into applications. For example, an HR system may include a wizard for adding a new employee that makes sure that all required data elements are entered. Defining rules for data updates is a critical step that affects the cost of data main- tenance and also the level of quality that will be provided. Formalizing rules for data updates provides clarity for both data users and data managers. Relevant Improvement Actions  Standard Operating Procedures

Data Management Assessment Elements 71 Element 2: Data Life Cycle Management 2.2 Data Access Control Well-defined policies and guidelines for managing access to data sets Level 1 Agency-Wide and Program Specific: There are no established policies for determining if access to data sets should be limited. Level 2 Agency-Wide and Program Specific: A process of defining what data is sensive and needs to be protected is underway. A process of defining what data can be shared outside the agency is underway. Level 3 Agency-Wide and Program Specific: Standard guidelines are available for idenfying and protecng sensive data. Criteria and processes have been defined for making data available to the public. Level 4 Agency-Wide and Program Specific: All core data sets have been classified based on guidelines for data protecon and access. Data owners/providers are complying with guidelines for data protecon and access. Level 5 Agency-Wide and Program Specific: Data access guidelines and procedures are well established and periodically reviewed and updated. Element Description This sub-element assesses the extent to which the agency manages access to data sets to protect sensitive information and maintain data integrity. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, the agency’s approach to managing access to data is ad hoc. As agencies move up the maturity scale, there is a standard method for classifying sensitive informa- tion and a formal process for defining access privileges as new data sets are brought on line. Stan- dardizing and formalizing data access control supports compliance with applicable information security regulations and prevents data corruption due to unauthorized or unmanaged changes. It also enables agencies to define and apply consistent criteria for what data is to be shared openly versus kept internal to the agency. Relevant Improvement Actions  Data Access Policies

72 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element 2: Data Life Cycle Management 2.3 Data Findability and Documenta on Availability of data catalogs and dic onaries that enable discovery and understanding of available agency data assets Level 1 Agency-Wide and Program Specific: Users rely on "word of mouth" to discover what data is available. There are variaons across data sets in terms of the level and type of available documentaon. Level 2 Agency-Wide and Program Specific: Efforts are under way to improve data findability and documentaon through adopon of common meta data standards, development of data set catalogs, or creaon of web pages with links to commonly requested data sets. Level 3 Agency-Wide: An agency-wide data catalog or meta data repository has been established to improve data findability for business users and is being populated. Standards and policies have been defined to ensure that a data diconary is available for each data set. Templates for describing data collecon, updang, and reporng processes have been developed and are starng to be used. Program Specific: Standards and policies have been defined to ensure that a data diconary is available for each data set. Templates for describing data collecon, updang, and reporng processes have been developed and are starng to be used. Level 4 Agency-Wide: Business users can access a list of available agency data sets to discover data of potenal value to meet their needs. Consistent documentaon is available describing data collecon, updang, and reporng cycles for most of the agency's core data sets. Data diconary informaon is available and current. Quality assurance processes are in place to ensure that data diconary informaon is complete and useful. Processes are in place to keep the data set lisng (or catalog) current when data sets are added or disconnued. Program Specific: Data diconary informaon is available and current. Quality assurance processes are in place to ensure that data diconary informaon is complete and useful. Processes are in place to keep the data set lisng (or catalog) current when data sets are added or disconnued. Level 5 Agency-Wide: Business users can search for availability of agency data on a parcular subject or enty type. The agency periodically evaluates opportunies to refine its approach to data documentaon based on user needs and new technologies. Documentaon of data sets is periodically improved based on feedback from users and research into best pracces. Program Specific: Documentaon of data sets is periodically improved based on feedback from users and research into best pracces.

Data Management Assessment Elements 73 Element Description This sub-element assesses the extent to which the agency ensures that potential data users can discover what data is available and understand the potential applicability of a data set for a given business need. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, data sets are discovered primarily by word of mouth. As agen- cies move up the maturity scale, standard information is maintained and made available about what each data set contains, including the meaning of each data element. Providing an easily accessible catalog of data sets (or sources) adds value to existing data by promoting its re-use and minimizes the chances that duplicate data will be collected. Documenting the source and derivation of data elements also reduces risks associated with data misuse. Relevant Improvement Actions  Data Catalogs and Dictionaries  Data Management Plans  Data Curation Profiles Element 2: Data Life Cycle Management 2.4 Data Backups and Archiving Guidelines and procedures for protecon and long-term preservaon of data assets Level 1 Agency-Wide: There may be important data sets managed using desktop applications within individual business units, but these have not been systemacally idenfied. Each business unit is responsible for ensuring that its data sets are backed up and periodically archived to enable future retrieval and use. Program Specific: Backups of data sets are made ad hoc. Level 2 Agency-Wide: Several of the agency's important data sets are managed using desktop applicaons (e.g., spreadsheets) but plans are in process to bring these into enterprise databases. Data owners receive informal (unwri­en) guidance regarding frequency and storage locaons for backups and archive copies. Program Specific: Backups of data sets are made regularly, but there are no wri­en procedures on backup frequency or storage locaons. Archive copies of data sets exist, but there are no wri­en procedures on how to create these and how to retrieve them.

74 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element Description This sub-element assesses the extent to which active data sets are backed up and inactive data sets are archived for future use as needed. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, backups and archiving are performed ad hoc. As agencies move up the maturity scale, the agency has developed and reliably follows guidance and procedures that specify what types of data will be centrally managed (e.g., stored in enterprise databases), how frequently backups will occur, where backups will be stored, and who is responsible for mak- ing and testing backups. In addition, there will be a well-defined process for identifying which inactive or historical data sets should be archived and what type of business user access to the archived information should be provided to meet business needs. Formalizing backup processes and verifying that they are working reduces the risk of data loss due to hardware failures and other sources of data corruption. Formalizing archiving processes (1) enables agencies to iden- tify where data sets can be retired to reduce data maintenance costs and (2) ensures that business user needs are considered when determining appropriate archive methods. 2.4 Data Backups and Archiving Level 3 Agency-Wide: Most of the agency's important data sets are managed within enterprise databases (e.g., Oracle, SQLServer) and regular backups are made. Wrien procedures on backup frequency and storage loca ons are available. Wrien procedures on data archiving and retrieval are available. Program Specific: Wrien procedures on backup frequency and storage loca ons are available. Wrien procedures on data archiving and retrieval are available. Level 4 Agency-Wide: All of the agency's important data sets are managed within enterprise databases (e.g., Oracle, SQLServer) and regular backups are made. and update them as appropriate to reflect user feedback or changing needs. Backup procedures are consistently followed. Archiving procedures are consistently followed. Backup procedures have been fully tested. Archiving procedures have been fully tested. Program Specific: Backup procedures are consistently followed. Archiving procedures are consistently followed. Backup procedures have been fully tested. Archiving procedures have been fully tested. Level 5 Agency-Wide and Program Specific: Data managers and stewards periodically review exis ng data backup and archiving procedures

Data Management Assessment Elements 75 Relevant Improvement Actions  Data Governance and Stewardship Policies  Data Retention Schedules and Archiving Element 2: Data Life Cycle Management 2.5 Data Change Management Processes to minimize unan cipated downstream effects of data changes. Level 1 Agency-Wide and Program Specific: There are no defined processes for analyzing how changes to the data structure in one system may affect reports or other dependent systems. Level 2 Agency-Wide and Program Specific: A standard data change management process is being developed. Level 3 Agency-Wide and Program Specific: A standard change management process has been defined for changes to data elements that may affect mulple systems. This involves consultaon and communicaon with affected data owners and users and propagaon of the changes across databases as needed. Change analysis and propagaon processes are mostly manual. Level 4 Agency-Wide: A meta data repository or other tool is available to conduct change impact analysis (i.e., idenficaon of which systems and database tables contain a parcular data element). Automated processes are in place to manage changes to code lists and propagate these changes to various business systems. Automated processes are in place to manage addions, deleons, and changes to master data enes. A change management process is in place and funconing as intended. Program Specific: A change management process is in place and funconing as intended. Level 5 Agency-Wide and Program Specific: A periodic review is conducted of the nature and extent of data changes to improve future data architecture and change management pracces. Element Description This sub-element assesses the extent to which procedures are in place to manage the process of making changes to data structures in one data set or system that may affect other systems or reports. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, changes to data structures, definitions, or unique identifiers are made as needed—without awareness of potential unintended consequences. Effects may be

76 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide discovered only when downstream applications or reports stop working as a result of the changes made. As agencies move up the maturity scale, an active approach is in place for anticipating downstream effects of changes, communicating with the data stewards of these downstream systems, and implementing changes in a controlled, automated, and coordinated manner. Such an approach focuses on management of “master data” that exists across multiple agency systems. Putting active and robust change management processes in place helps to prevent (1) business disruptions from broken reports or queries and (2) introduction of inconsistencies in data struc- tures and definitions across systems that hinder creating an integrated view of data. Relevant Improvement Actions  Data Change Management  Reference Data Management Element 2: Data Life Cycle Management 2.6 Data Delivery Delivery of data to users in various convenient, useful, and usable forms Level 1 Agency-Wide and Program Specific: Data reporng is accomplished in a decentralized fashion—individual data or applicaon owners separately plan and implement reporng capabilies. Program Specific: Limited standard reports are available, but there are no ad hoc query capabilies. New reports require soware development resources to implement. Level 2 Agency-Wide: The agency is exploring agency-wide needs and opportunies for improving access to integrated agency data in usable forms. Pilot iniaves may be underway. Ad hoc query tools are available but geared to a few individuals with specialized training. Program Specific: Ad hoc query tools are available but geared to a few individuals with specialized training. Level 3 Agency-Wide: The agency has implemented enterprise soluons for data access, reporng, visualizaon, and analysis (e.g., data warehouse, data marts, and dashboards). Agency employees have access to a common map-based interface that allows them to view and analyze various informaon (e.g., pavement condion, bridge condion, crashes, traffic counts, programmed projects, and completed projects). The agency provides access to both "live" data and "frozen" or "snapshot" data, depending on an assessment of business needs. The agency has developed a standard approach to accessing agency data from mobile devices. Program Specific: Reporng and query tools are available for general use within the agency and do not require specialized training. Business needs for access to both live data and frozen/snapshot data have been idenfied.

Data Management Assessment Elements 77 Element Description This sub-element assesses the extent to which data is delivered to end users in convenient forms suited to best meet business needs. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, data is collected or acquired without careful consideration of the wide range of potential uses and the types of delivery formats that would best serve these uses. As agen- cies move up the maturity scale, the agency implements tools and processes to ensure that data is delivered in usable forms. This may involve various techniques including data integration and trans- formation (e.g., to combine traffic and pavement data or to aggregate financial transactions into meaningful categories), development of exception reports, use of GIS portals and business intel- ligence platforms, and creation of open data feeds. Emphasizing data delivery promotes data use and re-use, thereby producing more value from data investments, and increases agency efficiency by reducing the need for time-consuming data manipulation and custom report development. Relevant Improvement Actions  Data Delivery Platforms Level 5 Agency-Wide: The agency has implemented a flexible architecture for reporng and mapping that enables easy addion of new data sources and enhanced analysis capabilies in response to newly idenfied requirements. The agency rounely improves data access and usability based on feedback from users and monitoring of the latest technology developments. Program Specific: Data is shared outside of the agency via a statewide or naonal GIS portal or clearinghouse Access to data is provided through a service or applicaon programming interface (API). Level 4 Agency-Wide: The agency has the experse and tools in house to develop data marts that allow employees to "slice and dice" data sets, perform ad hoc queries, and produce reports at the desired level of summarizaon. The agency has the experse and tools in house to combine data sets based on different road secons (e.g., 10th mile secons for pavement informaon and 2-mile secons for AADT). Agency employees can easily visualize trend informaon for asset condion, capital and maintenance expenditures, traffic, crash rates, and other important agency performance indicators. Agency field staff can access informaon about assets, projects, or work orders on mobile devices. The agency has sufficient network connecvity and bandwidth to enable remote data access from field offices. Program Specific: Data is made available through various formats and plaŽorms (e.g., GIS portal, mobile devices, and dashboards) to meet idenfied business requirements. 2.6 Data Delivery

78 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element 3: Data Architecture and Integration 3.1 Locaon Referencing Common locaon referencing methods across agency data sets Level 1 Agency-Wide: The agency does not have a single common LRS. Data sets, including locaon elements, cannot be spaally integrated with other agency data sets. Program Specific: Data sets, including locaon elements, cannot be spaally integrated with other agency data sets. Level 2 Agency-Wide: The agency is working toward establishing a single common LRS. Representaon of locaon informaon is being standardized. Program Specific: Representaon of locaon informaon is being standardized. Level 3 Agency-Wide: The agency has developed a single common LRS. Quality standards for the LRS have been established with input from various business units. The agency has defined a process for propagang changes in the LRS to various agency data sets. New data sets that include locaon elements are collected using the agency's LRS. Program Specific: New data sets that include locaon elements are collected using the agency's common LRS. Level 4 Agency-Wide: The agency’s LRS is used for all agency data sets that include locaon. The agency’s LRS meets established quality standards. Methods are in place and funconing to propagate changes in locaon referencing resulng from road network changes to business data sets. Methods are in place and funconing to translate between coordinate-based locaon referencing (e.g., latude/longitude) and linear referencing (e.g., route-milepoint). Program Specific: Methods are in place and funconing to propagate changes in locaon referencing resulng from road network changes to business data sets. Methods are in place and funconing to translate between coordinate-based locaon referencing (e.g., latude/longitude) and linear referencing (e.g., route-milepoint). Level 5 Agency-Wide: The agency has a standard architecture for linking agency GIS and LRS data to business data systems. Methods for propagang changes in locaon referencing resulng from road network changes are automated. Data owners/managers work closely with agency GIS staff and proacvely work to improve their data sets' consistency with agency-wide standards. Program Specific: Methods for propagang changes in locaon referencing resulng from road network changes are automated. Data owners/managers work closely with agency GIS staff and actively work to improve data sets' consistency with agency-wide standards.

Data Management Assessment Elements 79 Element Description This sub-element assesses the extent to which the agency has standardized methods for loca- tion referencing, including linear referencing for its road-related data sets. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, different data sets use different methods for location referencing and standards for location referencing have not been established. This results in an inability to map information reliably and integrate different data that have a spatial component. As agencies move up the maturity scale, location referencing standards are developed and adopted, existing data sets are transformed as needed to use the standard referencing methods, and the standards applied for new data sets are collected or acquired. In addition, a process is in place to propagate changes in linear referencing to various data sets as road changes occur or as errors are corrected. Standardization and management of location referencing enables agencies to visualize and integrate data efficiently. Relevant Improvement Actions  Common Geospatial Referencing  Data Change Management Element 3: Data Architecture and Integration 3.2 Geospaal Data Management Standardized approach to collecon and management of geospaal data Level 1 The agency does not provide enterprise-wide planning and support for management and integraon of geospaal data. Management of geospaal data is not integrated with other agency data management and IT funcons. Level 2 The agency has designated responsibilies for enterprise-wide planning and support for managing geospaal data. The agency manages a collecon of spaal data sets and makes them available for internal use. Level 3 The agency has wrien policies and standards defining how geospaal data is to be collected, stored, managed, shared, and integrated with non-spaal data aributes. The agency considers spaal data in their IT strategic plan (or equivalent) that idenfies investment needs and priories for hardware, so€ware, and data. The agency has idenfied data enes that should have standard locaon referencing. Level 4 The agency has a well-understood and funconing process for collecng, adding, and updang geospaal data sets. The agency has a standard approach to assigning spaal locaon to key data enes (e.g., construcon projects and assets.) Training and support is provided to ensure adherence to adopted policies and standards for geospaal data collecon and management and to build skills in spaal data analysis. Level 5 Spaal data collecon, management, and visualizaon requirements are fully integrated within the agency's IT and data management planning and operaonal funcons. The agency periodically reevaluates and updates its approach to geospaal data management to reflect changes in technology, data availability and cost, and user requirements.

80 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element Description This sub-element assesses the extent to which the agency has a standard approach to collect- ing, managing, and integrating spatial data. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, various methods may be used across the agency for collecting and managing spatial data. Hardware, software and services related to GIS are not standardized and are not well coordinated with “mainstream” agency functions for data management, reporting, integration, or application development. As agencies move up the maturity scale, the agency views spatial data management and reporting/mapping as integral to its overall data manage- ment and delivery function. Standard methods, processes, and tools are provided to ensure that GIS data is integrated with other agency business data. Training and support are made available to agency staff to ensure that they can make effective use of available data. Building a consistent agency approach for managing spatial data (1) promotes efficiency in use of hardware, software, and staff expertise; (2) standardizes and streamlines data integration processes, thereby reduc- ing the need for time-consuming, repetitive tasks; and (3) ensures that various data is spatially enabled to provide business value. Relevant Improvement Actions  Data Stewardship and Governance Policies  Data Delivery Platforms  Common Geospatial Referencing Element 3: Data Architecture and Integration 3.3 Data Consistency and Integra on Standards and prac ces to ensure use of consistent coding and common links so that different data sets can be combined to meet business in forma on needs Level 1 Agency-Wide: There have been no formal efforts to plan for data integra on/linkage across business applica ons outside of the context of individual applica on development projects. Lists of values for coded fields are defined for each applica on and there are no policies requiring consistency of code lists across applica ons. Program Specific: Data sets have not been reviewed to determine consistency with applicable agency or industry standards.

Data Management Assessment Elements 81 3.3 Data Consistency and Integra on Level 5 Agency-Wide: The agency has a process in place to assess opportuni es for re-use of exis ng data sources as new applica ons come on line or new data acquisi on efforts are considered. The agency maintains an agency-wide data model and uses this model to minimize data duplica on and inconsistencies as new data and systems come on line. The agency has developed a "to be" data and system architecture and uses this architecture to guide system addi on, replacement, consolida ons, and updates. Program Specific: Opportuni es to improve data integra on and consistency with other agency data sets are reviewed annually. Level 2 Agency-Wide: Efforts are underway to iden fy key integra on points across data sets. Efforts are underway to iden fy data duplica on and inconsistencies across sources. Some code lists are standardized across applica ons (e.g., city/county codes and organiza onal unit codes). Program Specific: Cross-reference lists have been developed to allow for data to be used in conjunc on with other data sets (e.g., state versus federal project ID). Level 3 Agency-Wide: The agency has iden fied and defined fundamental master data en es (e.g., projects, roadway segments, bridges, and employees) present in mul ple business applica ons and has mapped which physical systems contain informa on related to these data en es. The agency has established a list of key link fields (e.g., route ID and project ID) and have standardized these across systems to integrate different data sets to provide answers to business ques ons of interest. The agency has iden fied single authorita ve source systems for key data elements of agency- wide interest. The agency has iden fied common code lists and maintains these lists in a central loca on. Program Specific: Data sets/applica ons adhere to agency standard link fields that have been established to facilitate cross-system integra on. Standard code lists are used within data sets/applica ons if they are available (e.g., city/county codes and organiza onal unit codes). Level 4 Agency-Wide: The agency has procedures in place to ensure that externally procured data sets and applica ons adhere to established data standards and can be linked to exis ng data. The agency has one or more skilled individuals with responsibility for data architecture and integra on across systems. Program Specific: Procedures are in place to ensure that externally procured data sets and applica ons adhere to established data standards.

82 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element Description This sub-element assesses the extent to which the agency manages database creation and application development processes to minimize duplication and ensure integration. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, each new database and application development effort is imple- mented in isolation. Any efforts to ensure linkage with existing data is the result of individual development team initiatives—as opposed to a standard agency process. As agencies move up the maturity scale, they seek to ensure that different data sets can be linked. They manage the database and application development process to include an architectural review function that enforces standards and uses common code lists and services. This approach minimizes data duplication and facilitates data integration. It also increases efficiency of data maintenance requirements by consolidating code lists and other data tables. Relevant Improvement Actions  Data Architecture Practices and Roles  Reference Data Management  Master Data Management Element 3: Data Architecture and Integration 3.4 Temporal Data Management Standardiza on of date- me data elements to enable trend analysis and integra on across data sets collected and updated on varying cycles Level 1 Agency-Wide and Program Specific: No standards or guidelines are in place regarding date- and me-related data elements (e.g., naming of fiscal versus calendar year, dis nguishing data collec on dates, data loading or update dates, and data effec ve dates). There is no defined strategy for integra ng data sets to provide a consistent "point-in- me" view of integrated informa on. Level 2 Agency-Wide and Program Specific: Naming conven ons and common prac ces are in use regarding date- and me-related data elements, but no wrien guidelines exist. There is some understanding of user needs for trend analysis and crea ng snapshot views of data for analysis and repor ng, but these needs have not been explo red systema cally or comprehensively. There is experience with integra ng data to create a snapshot-in- me view, but no repeatable procedures for this have been defined.

Data Management Assessment Elements 83 Element Description This sub-element assesses the extent to which requirements for standardizing temporal data elements are considered so as to ensure that data representing different periods can be combined as needed to support analysis. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, treatment of temporal data elements is not standardized—each new database and application development effort determines its own formats and requirements. As agencies move up the maturity scale, they consider business requirements for time-based queries and trend analysis. Based on these business requirements, they establish standards for tempo- ral data elements (e.g., always use month and year to convert between calendar and fiscal year; always distinguish between data update date and the effective date of an observation.) In addition they establish processes to create snapshots of data sets to represent point-in-time conditions as needed for specific business purposes (e.g., safety analysis). Analogous to standardization of spa- tial referencing, a standard approach to temporal referencing ensures that different data sets can be integrated to provide business value. For example, both “when” and “where” are key questions for understanding cause-and-effect relationships among system performance, crashes and fatali- ties, asset condition, construction project completion, weather events, and land development. Relevant Improvement Actions  Data Architecture Practices and Roles  Standardized Approach to Temporal Data 3.4 Temporal Data Management Level 3 Agency-Wide and Program Specific: There are documented guidelines for ensuring consistency in use of date- and me-related data elements across data sets and applicaons. Data user requirements for trend analysis, snapshots, and other uses of temporal informaon have been documented. There are documented procedures or models for integrang across data sets to create a snapshot-in-me view. Level 4 Agency-Wide: There is consistency across the agency's major data sets in use of date- and me-related data elements across data sets and applicaons. Data user requirements for trend analysis, snapshots, and other uses of temporal informaon can be met without major changes to data structures or substanal new development effort. Program Specific: Data user requirements for trend analysis, snapshots, and other uses of temporal informaon can be met without major changes to data structures or substanal new development effort. Level 5 Agency-Wide and Program Specific: Data user requirements for trend analysis, snapshots, and other uses of temporal informaon can be met through largely automated processes.

84 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element 4: Data Collaboration 4.1 Internal Agency Collabora on Collabora on across agency business units to leverage opportuni es for efficiencies in data collec on and management Level 1 Agency-Wide: Most data collecon efforts in the agency are independent—there has been lile or no effort to coordinate across business units. The agency does not have informaon about the extent of data duplicaon. Program Specific: There have been no efforts to coordinate data collecon or management acvies with other business units. Level 2 Agency-Wide: The agency has assessed the extent to which there is duplicaon across data sets within the agency. Opportunies for coordinang data collecon and management across business units (e.g., safety and asset management) are periodically discussed, but limited progress has been made. Program Specific: Opportunies for coordinang data collecon and/or management acvies with other business units have been discussed, but no acon has been taken. Level 3 Agency-Wide: The agency has implemented a data collecon effort involving coordinaon of more than one business unit (e.g., use of video imagery from pavement data collecon to extract data on other assets). The agency has defined metrics to track improvements in data collecon and storage efficiency. Program Specific: A specific opportunity for coordinated data collecon has been idenfied and is being pursued. Level 4 Agency-Wide: Agency business data owners are encouraged and incenvized to share their data with a broader audience within the agency (where appropriate). Agency business data owners are encouraged and incenvized to plan new data collecon iniaves in partnerships with other business units where informaon needs of mulple units can be simultaneously addressed. The agency monitors progress of efforts to reduce data duplication. Program Specific: Data collecon is rounely coordinated with one or more other business units. Level 5 Agency-Wide: The agency periodically reviews its data collecon programs to idenfy opportunies to leverage new technologies and externally available data sources. The agency regularly seeks opportunies to minimize or reduce redundancy in data collecon, storage, and processing. Program Specific: New internal agency partnerships on data collecon and management are acvely sought to achieve economies of scale and make best use of limited staff and budget. Element Description This sub-element assesses the extent to which there is collaboration and coordination across different organizational units on data collection and management.

Data Management Assessment Elements 85 Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, data collection and acquisition efforts are planned and executed independently to meet the needs of different business units. Each business unit views the data they collect as “their own” and doesn’t consider the possible value of sharing the data with others in the agency. As agencies move up the maturity scale, data collection efforts are coordi- nated across business units and data is shared. Data partnerships are encouraged and incentiv- ized. New data collection technologies are pursued that can provide multiple types of data at once (e.g., videologs and LiDAR). In addition, business units work closely with the agency’s IT group to take advantage of enterprise reporting and other data access platforms. A collaborative approach to data collection and management reduces duplicative efforts and prevents prolifera- tion of multiple overlapping and inconsistent data sets. Relevant Improvement Actions  Multi-Purpose Data Collection  Data Outsourcing  Data Business Plans  Data Governance Bodies Element 4: Data Collaboration 4.2 External Agency Collabora on Partnerships with external en es to share data and avoid duplica on Level 1 Agency-Wide: Individual business units obtain and use publicly available data from external enties as needs and opportunies arise. The agency has acquired single "point-in-me" data sets from external enes. Program Specific: Publicly available data from external enes is obtained and used as needs and opportunies arise. Level 2 Agency-Wide: The agency is exploring partnerships with other public- and private-sector organizaons to share data on an ongoing basis. Program Specific: Partnerships with other public- and private-sector organizaons are being explored to share data on an ongoing basis. Level 3 Agency-Wide: The agency has data-sharing agreements with external enes. The agency provides "self-serve" access to data sets of value to external users. Program Specific: Data-sharing agreements are in place with external enes. "Self-serve" access is provided to data sets of value to external users.

86 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Element Description This sub-element assesses the extent to which the agency seeks out externally available data and develops data-sharing arrangements and partnerships with external public- and private- sector entities. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, staff in different business units may seek out and acquire data sets from external entities on a one-time basis as needs arise. External requests for agency data sets are considered on a case-by-case basis. As agencies move up the maturity scale, data-sharing agreements are developed as appropriate to make best use of both internal and external data resources. Rather than making or fulfilling a series of one-time, special data requests, regular processes are set up to share data on an ongoing basis. The agency provides self-serve access to key data sets for which there are frequent requests. An active approach to external data collabo- ration saves the agency staff time in fulfilling data requests and provides opportunities for the agency to gain access to a richer pool of data at a lower cost than would be required if it were to collect and manage the data in house. Relevant Improvement Actions  Data Clearinghouses/Open Data Platforms  Data-Sharing Agreements  Data Partnerships 4.2 External Agency Collabora on Level 4 Agency-Wide: The agency has sustained partnerships with external en es involving regular update cycles. Program Specific: Data-sharing agreements with external en es have been sustained over me (2+ years) and through mul ple data update cycles. Level 5 Agency-Wide: The agency rou nely seeks new opportuni es for data partnerships with external en es. They have designated staff liaison responsibili es for managing these external partnerships. Program Specific: New opportuni es for data partnerships with external en es are ac vely sought. Staff liaison responsibili es for managing these external partnerships have been designated.

Data Management Assessment Elements 87 Element 5: Data Quality 5.1 Data Quality Measurement and Repor ng Metrics and repor ng to ensure user understanding of current data quality Level 1 Agency-Wide: There are no agency-wide ac vi es related to data quality measurement and repor ng. Program Specific: Data quality metrics have not been iden fied. Level 2 Agency-Wide: The agency is exploring establishment of common data quality metrics for shared data elements. Program Specific: Metrics for data quality are being defined. Level 3 Agency-Wide: The agency has defined common data quality metrics across data programs to integrate data (e.g., loca onal accuracy). Program Specific: Metrics and standards for accuracy, including loca on accuracy, are defined and documented. Metrics and standards for meliness and currency are defined and documented. Metrics and standards for completeness, including coverage or required en es/areas and inclusion of required a€ributes, have been defined and documented. Level 4 Agency-Wide: The agency has implemented data quality standards, verifica on techniques, and reports for common data elements. Program Specific: Processes are in place to measure and document the level of accuracy, currency, and completeness of data sets. Informa on about data accuracy, currency, and completeness is rou nely shared with users. Where data is based on sampling, informa on about confidence levels is made available to data users. Level 5 Agency-Wide: The agency iden fies new areas where common data quality metrics across data programs would be beneficial. Program Specific: Data quality measurement processes, metrics, and measurement techniques are reviewed periodically and refined as needed. Element Description This sub-element assesses the extent to which data quality metrics have been defined and used to inform users about the level of currency, accuracy, coverage, and completeness of a given data set. Data reliability is considered to be related to accuracy and is not distinguished here as a sepa- rate characteristic. Data integrity, consistency, and confidentiality are other important aspects of data quality considered as part of Assessment Elements 2 and 3. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable

88 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Benefits of Moving Up the Maturity Scale At lower levels of maturity, there is a lack of awareness about the quality of different agency data sets beyond anecdotal information. As agencies move up the maturity scale, they measure and report on data quality using metrics reflecting key characteristics of concern to potential users. The agency provides standard definitions of different data quality metrics and models for how to measure data quality to facilitate application within different data program areas and enable data users to become familiar with a consistent set of measures. Providing data users with data quality metrics can help users to determine whether a data set is sufficiently accurate to meet their needs and help to address lack of trust in data that users may have as a result of seeing a single example of an erroneous data value. Finally, it can provide a basis for initiating data qual- ity improvement efforts and tracking their progress. Data quality measurement can be costly, so it is important to focus on a few essential measures and take advantage of quality metrics that can be automatically generated (e.g., adherence to validation rules). Relevant Improvement Actions  Data Quality Measurement and Improvement Element 5: Data Quality Pracces for improving quality of exisng data and ensuring quality of newly acquired data Level 1 Agency-Wide: Data quality is assessed and improved in the context of individual data programs. No agency-wide support is provided. Program Specific: Data quality is addressed ad hoc in response to reported issues. There is no standard approach in place for quality assurance for new data collecon and acquision. Level 2 Agency-Wide: Limited technical assistance is available for data program managers or business units on fundamental data quality concepts and pracces. Program Specific: There have been efforts to work with data users to discuss and define data quality requirements. Standard pracces are being defined to ensure the quality of data collected or acquired. Level 3 Agency-Wide: The agency has established guidelines for determining spaal accuracy requirements and appropriate collecon methods for new data collecon efforts. The agency incorporates pracces supporng data quality within the standard so­ware development process, including definion and documentaon of business rules for data validaon and use of standard lists of values. Program Specific: Standard, documented data quality assurance and improvement processes are defined. Business rules for assessing data validity have been defined. Specific guidance and procedures for data collecon and processing is rounely provided to ensure consistency. A formal process for data cerficaon and acceptance has been defined—including provision for correcng or re-collecng data when it does not meet minimum requirements for accuracy. 5.2 Data Quality Assurance and Improvement

Data Management Assessment Elements 89 Element Description This sub-element assesses the extent to which the agency pursues a systematic and proactive approach to data quality assurance and improvement. Support for the AASHTO Data Principles  Valuable  Available  Reliable  Authorized  Clear  Efficient  Accountable Benefits of Moving Up the Maturity Scale At lower levels of maturity, data quality is addressed as issues are reported. Staff responsible for initiating new data collection efforts do not have any standard agency guidance to follow for inclusion of data quality assurance practices in the effort. As agencies move up the maturity scale, data quality is addressed actively, using multiple techniques. These include use of standard quality control and quality assurance processes for new data collection, development and appli- cation of data validation business rules, use of automated data cleansing processes to identify potentially erroneous data values, and establishment of efficient error reporting and correction processes. Data quality improvement efforts need to be tailored to specific data types and col- lection methods. Appropriate application of data quality improvement techniques is important to ensure that data can be used as intended and can be used to produce reliable information that is valuable for decision making. Relevant Improvement Actions  Data Quality Measurement and Improvement Level 4 Agency-Wide: The agency provides standard tools for gathering and tracking response to user feedback on data quality issues. The agency has deployed data profiling and cleansing tools and uses these tools to idenfy (and, where possible) correct data quality issues. Program Specific: Program Specific: Standard, documented data quality assurance processes are rounely followed. Data collecon personnel are trained and cerfied based on demonstrated understanding of standard pracces. Business rules for data validity are built into data entry and collecon applicaons. Level 5 Agency-Wide: The agency provides tools for specificaon, maintenance, and management of business rules. Data quality assurance processes are regularly assessed and improved. Data collecon and acquision pracces are regularly reviewed to idenfy lessons learned and areas for improvement. Automated error reporng tools are available for data users. Data validaon and cleansing tools are used to idenfy and address missing or invalid values. 5.2 Data Quality Assurance and Improvement

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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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