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Page 53
Suggested Citation:"Appendix A - Glossary." 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 A - Glossary." 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 A - Glossary." 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 A - Glossary." 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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Page 56

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53 This appendix provides a glossary of data management terminology used in this Guide and the accompanying tools. The following sources were consulted for definitions: • AIIM—Association for Information and Image Management Glossary: http://www.aiim.org /community/wiki/view/glossary • DAMA—the DAMA Dictionary of Data Management, 1st Edition, 2008 • IRMT—International Records Management Trust (IRMT) Glossary of Terms: http://www .irmt.org/documents/educ_training/term%20modules/IRMT%20TERM%20Glossary%20 of%20Terms.pdf • ANSI/NISO Z39.19—Guidelines for the Construction, Format, and Management of Mono- lingual Controlled Vocabularies (2005) ISBN: 1-880124-65-3 is p. 157–167: http://www.niso .org/apps/group_public/download.php/12591/z39-19-2005r2010.pdf • SAA—Society of American Archivists Glossary: http://www2.archivists.org/glossary • OMB Circular A-130: http://www.whitehouse.gov/omb/circulars_a130_a130trans4/ • W3C—W3C Data Catalog Vocabulary—http://www.w3.org/TR/vocab-dcat/#class—dataset Definitions derived from these sources are referenced accordingly. Where sources are not noted, definitions were developed from multiple sources. For many of these terms, definitions vary considerably across sources. These definitions are not intended to be authoritative beyond the scope of this Guide. Business Rule. A formally stated constraint governing the characteristics or behavior of an object or the relationship between objects (entities) used to control the complexity of the activities of an enterprise. (Source: DAMA) Example: the width of an Interstate lane is 12 feet. Change Management or Change Control. Processes in place to review, evaluate, and coordinate changes to data products, applications, and systems before they are implemented to minimize impacts to users and reduce any change–related errors. (Source: Adapted from DAMA) Data. Representation of observations, concepts or instructions in a formalized manner suitable for communication, interpretation or processing by humans or computers. (Source: adapted from AIIM) Examples: a crash record; pavement roughness. Data Accuracy. The degree to which data represents actual conditions as they existed at the time of measurement. Data Architecture. A master set of data models and design approaches identifying the strategic data requirements and the components of data management solutions, usually at an enterprise level. (Source: DAMA) Data Archiving. The process of moving data that is no longer actively used to a separate data storage device for long-term retention. A P P E N D I X A Glossary

54 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Data Business Plan. A document that establishes data collection and management strategies that align with business objectives. Data Catalog. A listing of available data resources (e.g., data sets, query tools, maps, and reports) including descriptive information on what is included and how to access, compiled to facilitate discovery and understanding of available data. Data Classifications. A set of categories used to distinguish those key characteristics of a given data resource (e.g., level of sensitivity or degree of importance) used to determine appropriate governance policies. Data Community of Interest. The data owner, data steward, data users and other stakeholders with an active interest and role in the data program. (Source: Adapted from DAMA) Data Completeness. The degree to which the data provides sufficient coverage and includes values for all required data elements. For example, a data set may be considered incomplete because it is missing coverage of some portion of the road network, or some time periods, or some classes of travelers. Data Currency. The extent to which the data represents current conditions. Data Customer. A person or organization whose satisfaction with data products and services can determine the overall effectiveness and success of the programs. (Source: Adapted from DAMA) Data Dictionary. A place where a limited set of “data about the data” or meta data is stored. This may include technical meta data (e.g., column names and formats) and/or business meta data (e.g., data definitions, business rules, and code values). (Source: Adapted from DAMA) Data Domain. A category that can be used to group related data types in order to define steward- ship roles. Examples of data domains include financial, human resources, and infrastructure. Data Entities. A classification of the types of objects found in the real world—persons, places, things, concepts, and events—of interest to the enterprise. (Source: DAMA) Data Governance. The accountability for the management of an organization’s data assets to achieve its business purposes and compliance with any relevant legislation, regulation, and business practice. Data Governance Body. A high-level data governance structure in the organization that typically includes senior managers. Responsibilities may include identifying priorities for data governance policies, projects, or system enhancements, and the authorization, implementa- tion, and enforcement of data governance policies and standards. (Source: Adapted from DAMA) Data Inventory. A compilation of information about an agency’s data programs or major data categories that may include details on data types, storage locations, collection and update cycles, responsibilities, uses, and other information useful for data program management. Data Timeliness. The extent to which data is available within a useful time frame. Data Management. The processes and activities in place to develop, implement, and enforce policies and practices for protecting and enhancing the efficiency, value, and effectiveness of data and information. (Source: Adapted from DAMA) Data Management Area. This term can be used to provide an alternative, equivalent term for “Data Program” where use of the term “Data Program” would be perceived as a reference to a “computer program” as opposed to a programmatic function in an organization.

Glossary 55 Data Mapping. An activity to determine how data and associated information products (e.g., reports) are produced and used. Data mapping can be done to determine the value of the data for particular business processes or to identify data gaps or redundancies. Data Owner(s). People or groups with decision-making authority for initiating or discontinu- ing the data program and who determine the content of what data is collected. Data Program. An organizational function with significant data management responsibilities that can include scoping, collecting, managing, and/or delivering a particular category or form of data. Sometimes this function resides in a single organizational unit; at other times it is split across business units and IT units. Examples of DOT data programs include GIS, Road Inven- tory, HPMS, Traffic Monitoring, Crash Records, and Construction Project Data. Data Quality. The degree to which data is accurate, complete, timely, and consistent with requirements and business rules and relevant for a given use. (Source: Adapted from DAMA) Data Quality Assurance. Processes to ensure that data meets specified requirements. Data Quality Control. Processes to detect defects in collected data and take appropriate action. Data Set. A collection of data made available for access or download in one or more formats. (Source: adapted from W3C) Examples: a state’s crash records for a single year; a database with roughness measures for pavement segments on the state highway system. Data Steward(s). People who are accountable for the quality, value, and appropriate use of the data. Data Stewardship. The formal, specifically assigned and entrusted accountability for business (as opposed to information technology) responsibilities ensuring effective control and use of data and information assets. Data Visualization. Techniques for graphical representation of trends, patterns, and other information. (Source: Adapted from DAMA) Data Warehouse. An integrated, centralized decision support data base and related software programs that can be used to collect, cleanse, transform, and store data from various sources to support business needs. (Source: Adapted from DAMA) Enterprise Data Architecture. An integrated collection of models and design approaches to align information, data, processes, projects, data systems/applications, and technology with the goals of the agency. (Source: Adapted from DAMA) Findability. The degree to which relevant information is easy to find when needed; findabil- ity is improved through application of meta data, taxonomies and other organizing tools, and search technologies. (Source: Adapted from AIIM) Geospatial Data. Data that includes location, specified with explicit geographic positioning information. Information Management. How an organization (e.g., a DT) efficiently plans, collects, cre- ates, organizes, uses, controls, stores, disseminates, and disposes of data and information and ensures that the value of that data and information is understood and fully exploited. Knowledge Management. An umbrella term for various techniques for building, using and sustaining the knowledge and experience of an organization’s employees. Life Cycle. The stages through which data or information passes, typically characterized as creation or collection, processing, dissemination, use, storage, and disposition. (Source: OMB Circular A-130)

56 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Linear Referencing System (LRS). A system for maintaining location information for events that occur along a linear network such as a road or rail line. It includes one or more methods for specifying the location of any point along the network based on distance from a known reference location (e.g., intersection-offset or county-relative milepoint). Master Data. Shared data about the core entities of an enterprise. In a private company, exam- ples of core entities are customers, products, and vendors; in a DOT, examples of master data entities are routes, projects, funding sources, and district or regional offices. Meta Data. Data describing context, content, and structure of documents and records and the management of such documents and records through time. Literally, data about data. (Source: Adapted from AIIM/ISO 15489) Records Management. The systematic and administrative control of records throughout their life cycle to ensure efficiency and economy in their creation, use, handling, control, mainte- nance, and disposition. Similar to document management, but focused on documents that have been designated as official records with an emphasis on legal, regulatory, and risk management concerns. (Source: Adapted from SAA) Sensitive Data. Data that is confidential, privileged, or proprietary that should be protected from unauthorized disclosure, loss, misuse, or corruption to avoid serious consequences to the organization that owns it.

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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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