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Guide for Data Management II-29 The success factors described in Volume I, Section 4.8 will be referenced in each subsection. The Guide is intended to provide more tools and details to assist agencies in implementing and applying the success factors to achieve successful data management. 2.1 Establishing the Need for Data Management/Governance The need and urgency for data management improvements are not always shared across all levels of an agency. In some cases, a senior manager within the agency identifies the need, and in other cases, individuals at lower levels recognize the value of improved data management. Nev- ertheless, a clear case must be established to secure resources and commitment to proceed with a data management improvement strategy. This section is designed to assist agencies in making that case. The first section covers the relationship between data management and performance measure- ment in a transportation agency. The second section documents definitions and advantages of data governance techniques. The third section presents a data management maturity model and the final section provides a tool for assessing how well an agency is performing in data manage- ment and governance. The two key success factors related to establishing the need for data governance are the following: Demonstrate the Return on Investment (ROI) to the organization regarding the use of data management and data governance in order to gain buy-in from executives and decision-makers. Demonstrate with specific examples how the use of data governance can meet the goals and targets most important to executives. ROI can be determined in many ways and on many levels within an organization. For instance, in a Highway Safety Improvement Program (HSIP) ROI can be determined in the following ways: (1) from the perspective of the HSIP Statewide Coordinator, an investment in more resources (e.g., people, technology, tools) may lead to the ROI of an improved HSIP strategic plan; (2) for traffic and safety engineers, an investment in Global Positioning System (GPS) field inventory projects may lead to the ROI of improved crash locations; and (3) for the Highway Safety Planning Agency, an investment in electronic data collection may lead to the ROI of improved quality of crash records. ROI also can be realized across business functional areas within an agency or across agency boundaries. In the highway crash safety example, ROI can be realized in the following ways: (1) for law enforcement personnel, an investment in electronic crash data collection and sub- mittal may lead to the ROI of reduced time to complete the accident investigation and review; (2) for maintenance and operations personnel, an investment in digital imaging capabilities may lead to a ROI of quicker and less costly asset management inventory and reduced cost to prepare HSIP projects for the traffic and safety engineers; and (3) for the executive manage- ment, investment in an enterprise Geographic Information System (GIS) deployment may lead to the ROI for improved tradeoff analysis on project selection by visualizing the crash his- tory, traffic, and pavement condition. A data governance framework, implemented on an enterprise level, supports ROI by pro- viding a means of monitoring and tracking progress of various business programs for execu- tives as well as data stewards, stakeholders, and users of the source data. Data governance provides methods, tools, and processes for the following: Traceability--aligning data programs with the agency's business needs. Establishing data area communities of interest and working groups that examine needs in common areas and on a regular basis is essential.

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II-30 Guide for Target-Setting and Data Management Performance Measures--should be reflective of the business needs identified in the trace- ability exercise. Risk Assessment--requires the agency to assess (1) how much data is needed, (2) how accurate should the data be, (3) what should the refresh rate of the data be, and (4) who should have access to the data as well as many other questions which help to assess the risks associated with a particular data program. Value of Data Programs--needs to be demonstrated to users and those who authorize investments in the data programs. This can be done effectively through the use of visuali- zation tools, use of enterprise GIS systems, collecting data once and using it for many pur- poses, and demonstrated improvements in business operations through the use of quality, accurate, timely, and easily accessible data and information. Knowledge Management--must become part of the data governance framework in order to ensure that lessons learned and experiences pertaining to business operations within the organization are not lost. This will help to increase the ROI for time and resources com- mitted to support of data programs. Formalize a Data Business Plan for the agency or department which identifies how each employee's job is linked to the agency's mission and goals, thereby clarifying the importance of their role in the overall success of the department/office. Corporation X uses a committee composed of the Finance Department, the Capital Committee, and the Performance Measure- ment Group to monitor the data collection procedures and data revisions, as well as to set data standards and operating definitions. A Data Management program strengthens support for performance management in a transportation agency through the use of a Data Business Plan. Relationship of Data Management and Stewardship to Performance Measurement and Target Setting in a Transportation Agency Each transportation agency is faced with many challenges and needs regarding the availability of data and information to support business operations. The needs described were identified by Mn/DOT in July 2008, in preparation for the development of a data business plan for that agency. They pertain to the ability of the data programs to support performance measures, target setting, and prioritization of resources in Mn/DOT. Many of these needs are relevant to transportation agencies across the nation and include the following: More transparency and accountability, More efficient ways to locate and take advantage of available data and information, Better methods to look at and integrate data from multiple sources, Processes and systems that reduce redundancy and promote consistency in data results, More timely and real-time data and information, and More department-wide spatial data tools. One of the ways to address these and other data-related needs is through the establishment of a structured data management program and data governance framework. Data management and data governance can help the agency to prioritize the most critical data needs and identify the resources available to address those needs in a timely manner. Institutional challenges may include: centralized policy-making and decentralized execution of those policies; limited appreciation by decision-makers of the role of data systems in support- ing business operations; and lack of formal policies and standards which guide the collection,

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Guide for Data Management II-31 processing, and use of data within the organization. It is particularly critical to have standard- ized policies and procedures for management of data and information when that information is the foundation of performance measurement and target setting programs for an agency. A data management program is used to coordinate the establishment and enforcement of data policies and standards for the organization. Challenges to establishing a Data Management program may be both institutional and technical in nature. However, implementing Stewardship and Governance in the organization supports the overall role of Data Management. Definitions and Benefits of Data Management, Stewardship, and Governance Data management is defined as the development, execution, and oversight of architectures, policies, practices, and procedures to manage the information lifecycle needs of an enterprise in an effective manner as it pertains to data collection, storage, security, data inventory, analysis, quality control, reporting, and visualization. Data governance is defined as the execution and enforcement of authority over the manage- ment of data assets and the performance of data functions. The management of data assets for an organization or state DOT is usually accomplished through a data governance board or coun- cil. This role is critical in successfully managing data programs that meet business needs and in supporting a comprehensive data business plan for the organization. More information on data governance is included in Volume I, Section 4.3. Data stewardship is defined as the formalization of accountability for the management of data resources. Data stewardship is a role performed by individuals within an organization known as data stewards. A data program in this report refers to specific data systems that support a business area of the organization. The "program" usually includes the functions of data collection, analysis, and reporting. In the case of a DOT, some examples of these programs include traffic, roadway inven- tory, safety, and pavement data. The definitions and examples are covered in more detail in Volume I, Section 4.2. A strong Data Management program improves data quality and limits potential risks to the agency regarding loss of critical data and information. Data Management A Data Management program is used to do the following: Strengthen the ability of data programs to support core business functions of the agency, Improve data quality throughout the organization, Protect data as an asset of the agency, and Limit risks associated with loss of data and information. Data Governance The benefits of using data governance can be demonstrated from three different perspectives within the agency--policy, practical and technical.

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II-32 Guide for Target-Setting and Data Management From a policy standpoint, data governance promotes the understanding of data as a valuable asset to the organization and encourages the management of data from both a technical and business perspective. On a practical level, the use of a data governance model provides for access to data standards, policies, and procedures on an enterprise basis. It provides a central focus for identifying and establishing rules for the collection, storage, and use of data in the organization. From the technical perspective, use of data governance results in reducing the need to main- tain duplicate data systems, improves data quality, and provides new opportunities to imple- ment better tools for managing and integrating data. Incorporating some form of data management and governance within the organizational structure of the agency can benefit every transportation agency because their business operations rely on quality data programs for decision-making. In support of data quality control, Corporation X's dedicated performance measurement group "owns" the data that is gathered by the hardware, software, and processes. In this way it controls the quality of the data so that it is neither too dirty (which would render it useless) nor too pure (which would result in an exorbitant cost). Data Management Maturity Model A maturity model is a framework describing aspects of the development of an organization with respect to a certain process. It is a helpful tool to assess where an organization stands with respect to implementing certain processes. A maturity model also can be used to benchmark for comparison or assist an agency in understanding common concepts related to an issue or process. A typical maturity model identifies levels and characteristics of those levels. The model can be used to assess an agency's status and assist in identifying next steps to achieve success towards an ultimate goal state. A Data Management Maturity Model is used to assess how the roles of people, technology, and institutional arrangements help the agency to advance from a state that is un-governed to a governed state. A maturity model was developed here to document levels of maturity related to the develop- ment and application of data. The desired end state is the establishment and maintenance of a data governance system that supports performance measurement and target setting within a transportation agency environment. The criteria (people/processes, technology/tools, and insti- tutional/governance) are the following: People/processes--This refers to the willingness, understanding, and commitment of people within the agency to embrace data management. It also refers to processes that may be in place to assure employees understand and appreciate the value of data management. Technology/tools--This refers to the use of tools and techniques designed to assist agencies in collecting, integrating, analyzing, and reporting data. More details are provided in Volume I, Sections 3.4 and 3.5. Institutional/governance--Refers to the institutional structure within an agency to ensure consistent management of data programs. More detail can be found in Volume I, Sections 3.2 and 3.3.

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Guide for Data Management II-33 The levels are somewhat generic in nature and are described as: 0--Ad Hoc; 1--Aware; 2-- Planning; 3--Defined; 4--Managed; 5--Integrated; and 6--Continuously Improving. Table 2.1 documents the levels of maturity within the categories. It is assumed that the model will be used to assess the overall status of data management within the entire agency; however, it also can be used to assess the status within a unit of the agency. To assist agencies in determining where they are in the process, the following characteristics are provided related to each level. People 0. Management and staff across the agency do not recognize a specific need for a data manage- ment program to support performance management. 1. Some personnel in the agency are aware of the need for a formal data management program and/or processes to support performance management but are not involved in developing such a program. 2. Some personnel in the information technology (or similar) office of the agency currently par- ticipate in the development and implementation of a data management program for the agency. 3. Work teams have been identified in several offices across the agency to participate in the development and implementation of a data management program. 4. Staff across the agency are aware of the data management program and use the program rou- tinely for the collection and use of data within the agency. 5. Staff across the agency are actively involved in recommending changes for data management policies, standards, and procedures, as business needs change and new performance manage- ment goals are identified. 6. People in the agency are fully engaged in continuous improvement related to data manage- ment and performance measures. Technology/Tools 0. The agency does not have any information technology tools in place to support data management. 1. The agency has delegated the responsibility to a specific office, such as Information Tech- nology, to determine what IT tools are needed to support data management across the agency. 2. The agency has implemented some information technology tools, including GIS, data mod- els, data repositories, data dictionaries, etc., to support data management in certain offices of the agency. 3/4. The agency uses information technology tools on a widespread basis, including such appli- cations as an enterprise data warehouse, GIS systems which integrate business data from var- ious offices, and dashboards and scorecards delivered through a web-enabled interface for access agency-wide. The agency uses Service Oriented Architecture (SOA) and Open Data- base Connectivity (ODBC) in the development of new applications to support future inte- gration of applications. 5. The agency uses a Knowledge Management system throughout the agency to support its data management program. 5. Performance management tools, such as dashboards and scorecards, are used in every office of the agency to monitor the progress of agency programs in meeting the agency mission and goals. 6. Performance measures and targets are adjusted as needed and displayed on the agency dash- board, or similar mechanism, to maintain peak program performance across the agency. 6. The use of technology and BI tools in the agency improves the overall management of pro- grams in the agency, in accordance with the strategic mission, goals, and targets.

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II-34 Guide for Target-Setting and Data Management Table 2.1. Data management maturity model matrix. 6--Continuous Level 0--Ad Hoc 1--Aware 2--Planning 3--Defined 4--Managed 5--Integrated Improvement Technology/ No tools in place. Planning for tools to Planning for tools to Implemented some Widespread Integrated, Ongoing assessment Tools support data support data tools to support data implementation of widespread of new technology to management in some management across management but not tools to support data implementation of support and improve offices. the agency or for a widespread across the management but not tools to support data data management and specific office. agency. integrated. management and performance performance measurement. measurement. People/ Not aware of need Aware of need for Aware of need for Aware of need for Aware of need for Aware of need for The agency is able to Awareness for improved data improved data improved data improved data improved data improved data develop performance management to management to support management to management to management to management to measures and predict support performance support performance support performance support performance support performance outcomes for performance measurement processes. measurement measurement measurement measurement programs based on measurement No action has been processes. processes. processes. processes. success with other processes. taken. Some steps have been Some steps have Improvements are Technology and programs. made within the been made within the under way to improve institutional processes agency to improve agency to improve both technology and are in place to support technology or both technology and institutional settings data management for institutional setting to institutional settings to support data performance support data to support data management across measures. management in at management in more the agency. least one office. than one office. Institutional/ No data The agency is Some level of data Data Business Data Business Plan Fully operational data Data governance Governance governance in discussing needs/plans program assessment Planning underway, developed with data governance structure structure fully place. for data governance. and formulation of including assessment complete in place. supports data roles for data development of and data governance management activities managers is underway governance models structure defined. across the agency. in one or more offices for multiple offices in of the agency. the agency.

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Guide for Data Management II-35 Institutional/Governance 0. The agency is not aware of the need for an institutional arrangement or organizational struc- ture to support data governance. 0. The agency does not have strong executive level support for data governance. 0. The agency does not have a Data Business Plan in place to support management of core data programs. 0. The agency does not have defined roles, such as data stewards, stakeholders, business owners (of data), and communities of interest, to support a data governance framework. 1. Agency senior management recognizes the need for a Data Business Plan to manage critical data programs; however, a plan has not yet been developed. 2. The agency is developing a Data Business Plan to support management of strategic data programs. 3. A limited number of offices in the agency have implemented a Data Business Plan to manage the core data programs for their area. 4. The agency has strong executive and senior management support for data governance. 5. An enterprise Data Business Plan has been developed to support management of core data programs across the agency. 5. The agency Data Business Plan has been incorporated into the overall agency strategic plan. 5. Data champions have been identified in each business area of the agency. 5. Communities of interest, which are comprised of internal and external users and stakeholders for core data programs, have been defined. 5. A data governance council or data governance board exists at the agency to direct the data management activities of the agency. 5. The agency has developed and published a Data Governance manual or handbook which identifies the roles and responsibilities of staff in the agency to support data governance operations. 6. The agency has developed a data catalog with data definitions, standards, policies, and pro- cedures for the collection and use of data in the organization. The catalog is available on an enterprise basis through an electronic system such as a Knowledge Management system. Application of the Transportation Data Governance Model assumes that an agency recog- nizes the need to embrace and apply data management and governance concepts. The first suc- cess factor listed in earlier sections states that an agency should "Demonstrate the ROI to the organization regarding the use of data management and data governance in order to gain buy- in from executives and decision-makers. Demonstrate with specific examples how the use of data governance can meet the goals and targets most important to executives." This can be done by citing examples of other agencies that have certain accomplished levels of maturity with respect to the model. Examples can be found throughout the case studies and examples cited in Section 4. Planning for Data Management There are several ways to achieve success with respect to data management and governance to support performance measures programs. One approach is to develop a Data Business Plan. Whether an agency formally refers to their process improvement as a data business plan or not, the following common steps should be taken: 1. Establish goals for data improvement process; 2. Assess data programs; 3. Establish governance programs; 4. Ensure proper use of technology/tools; and 5. Link data management to performance measures and target-setting.