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1 Sullivan and Stickel TRANSPORTATION AGENCY DATA GOVERNANCE PROGRAMS GETTING STARTED TOWARD SUSTAINABILITY 0 0 Jill Sullivan (Corresponding Author) Alaska Department of Transportation and Public Facilities P.O. Box 00 Juneau, AK 0 0 (fax) jill.sullivan@alaska.gov Jack R. Stickel Alaska Department of Transportation and Public Facilities P.O. Box 00 Juneau, AK 0 0 (fax) jack.stickel@alaska.gov November, 0 0 Text words, plus 0 words for Figure and 0 words for Tables =, Words

2 Sullivan and Stickel 0 0 ABSTRACT Transportation agencies deal with a multitude of issues covering increased streams of data, new program requirements, information technology changes, refocusing agency strategic goals, and constrained budgets. MAP- (Moving Ahead for Progress in the st Century Act) establishes a performance, outcome-based approach for transportation programs. A data governance program can improve an agency s capability to manage their transportation data programs effectively and to address these data challenges. Starting and sustaining a data governance program is a daunting task, in part because the concepts are hard to explain. There is typically a strategic event or series of events that trigger an agency to start a data governance program, e.g. developing a transportation asset management program, incorporating an enterprise geographic information system, or evolving information service needs. Starting data governance programs requires a common vision, documented data business work flows, defined roles and responsibilities, and transition progress tracking. This paper highlights effective fundamental steps that can aide in establishing and sustaining a strong data governance framework. This paper provides the context for data governance within an overall data management program and includes a business need for data governance, targeted areas and initial steps to consider, current initiatives in transportation, and finally, and an overview of how the Alaska Department of Transportation and Public Facilities initiated a data governance program in response to a transportation asset management information system, the transition from a legacy mainframe transportation database to a geospatial linear referenced based system, and the new MAP- data requirements.

3 Sullivan and Stickel INTRODUCTION Transportation agencies deal with a multitude of issues covering increased streams of data, new program requirements, information technology changes, refocusing agency strategic goals, and constrained budgets. Furthermore, MAP- (Moving Ahead for Progress in the st Century Act) establishes a performance, outcome-based approach for safety and asset management putting even more pressure on agencies to provide more and better data necessary to meet reporting requirements. A data governance program can improve an agency s capability to manage their transportation data programs to address evolving data requirements. A strong data governance program covering the data life-cycle can: Document the data management processes and goals; Align data management programs with the agency s strategic goals and mission; and Develop a long-range data management structure. Data governance can ensure efficient expenditure of resources for data acquisition and information management by identifying risks, addressing gaps, and eliminating overlaps. Moreover, applying data governance principles can better support the agency s transportation strategic goals for planning, programming, design, and operations. These principles can promote collaboration, consistency, and efficiency in data collection, acquisition, organization, and dissemination. Data governance is actively being addressed in the transportation asset management (TAM) and highway safety areas as transportation agencies address the MAP- requirements and the increasing requests for more quality data. Simultaneously, many transportation agencies are also involved in developing new strategies for geographic information system and information technology services. Hence a need for improved data management is essential. Getting started with a transportation data governance program, either for a targeted business area such as highway safety or an enterprise wide solution, can be an intimidating task. This paper provides key steps and best practices that have been found helpful in assisting transportation agencies in getting started with data governance and moving to a sustainable program. This paper also provides an example of how data governance began in the Alaska Department of Transportation and Public Facilities (DOT&PF) Transportation Information Group (TIG) as a grass roots approach in responding to a legacy transportation database transition, department-wide challenges in establishing a TAM Information System (TAMIS), and the MAP- data requirements. DEFINITIONS While often used interchangeably, the terms data management, data governance, and data stewardship are often used interchangeably. NCHRP Report, Target-Setting Methods and Data Management to Support Performance-Based Resource Allocation by Transportation Agencies, defines data management as the development, execution, and oversight of architectures, policies, practices, and procedures to manage the information lifecycle needs of

4 Sullivan and Stickel an enterprise.as it pertains to data collection, storage, security, data inventory, analysis, quality control, reporting, and visualization (). Further, NCHRP Report defines data governance as the execution and enforcement of authority over the management of data assets and the performance of data functions and data stewardship as the formalization of accountability for the management of data resources. The functions of data governance and data stewardship are typically part of an overall data management program. ISSUES AND OUTCOMES Common Data Governance Issues Ken O Connor, an independent data consultant, provides an excellent summary of data governance issues from an information technology (IT) point of view (): Quality of informational data is not as high as desired; Quality of data entered by front-end staff is not as high as desired; No culture of data as an asset or resource ; No clear ownership of data; Business management doesn t understand what data quality means; No enterprise-wide data quality measurement of data content; No Service Level Agreements (SLAs) defined for the required data quality level; Accessibility of data is poor; Data migration and Extract, Transform, Load (ETL) projects are metadata driven; No master repository of business rules; No ownership of cross business unit business rules; No enterprise-wide data dictionary; Disconnected islands of data, e.g. silos ; and No enterprise-wide data model. Furthermore business units collecting and maintaining a data set may not have a clear picture of exactly how it s used and for what purpose, thus leading to a disconnect between what managers need for their decision making. The Data Governance Society s web poll provides the following insight into how the Society s members see the top barriers to establishing a data governance program (): Buy-in and support (upward and horizontally) % Operational structure -% Change management % Defining measures 0% Return on investment - 0% Talent acquisition 0% Technology % Key performance indicators - % Other <%

5 Sullivan and Stickel From a user point of view, over 0 percent of the data governance implementation barriers are institutional rather than technical. These figures are not too surprising; the challenges and gaps are certainly real in Alaska DOT&PF. A data governance program can address many of these institutional issues. Outcomes of a Strong Data Governance Program A good data governance program can improve transportation data programs. NCHRP Report 0-, Task 00, Transportation Data Self-Assessment Guide, identifies benefits that can be derived from a good data governance program (): Broadened perspective of the strategic and operational importance of data; Better, data-driven support decision-making, project delivery, and problem solving; Improved alignment between data resources/quality and data user requirements; Greater usability and sharing of data; Added credibility for data and associated analysis/applications; Better balance between long-term/strategic and short-term/tactical improvements; Enhanced organizational capability to quickly respond to threats and opportunities; Creating a framework and culture for continuously improving data management; Improved quality, maintenance, and sustainability of data; Clearer definition of data investment and spending priorities; Added prevention of future data problems and risk mitigation; Promotion of better and more efficient data usage; and Creation of opportunities for cost savings and better allocation of limited data resources. The follow-on NCHRP 0- will provide guidance on conducting data self-assessments (). DATA GOVERNANCE FRAMEWORK Current Initiatives Many states and some Federal agencies are involved in data governance tailored to specific business programs. DOTs including Alaska (), Caltrans, Colorado, Florida, Minnesota, Oregon, and Virginia have begun or have developed data business plan governance structures. The United States Department of Transportation (U.S DOT) has developed a data business plan governance structure to improve coordination among real-time data capture programs and serve as a prototype for other U.S. DOT offices (). The FMCSA Data Management Support Services has used data governance to analyze their current data management environment as their agency moves from a program-based approach to an enterprise approach (). Highway Safety The FHWA Office of Safety conducted a capabilities assessment for each State on collecting, managing, and using roadway safety data as part of the Roadway Safety Data Partnership (RDSP) (, 0). The assessment examined data management and governance from the perspective of people, policies, and technology. Assessment questions cover: Executive and leadership support

6 Sullivan and Stickel Data governance board Traffic Records Coordinating Committee (TRCC) Safety data strategic plan Stewardship and data champions Information technology tools and involvement Traffic records improvement program Data business plan to support strategic safety data Database business rules Standards and communication protocols The FHWA Office of Safety is advancing data governance for state safety programs. TRB will sponsor sessions at the 0 annual meeting and a peer exchange later in March 0 to document the following: Safety data governance issues that are common to transportation agencies; Prioritized transportation agency safety data governance issues for future research; An understanding of the motivations for agency safety data governance and crossfunctional data collaboration; An understanding on how different agencies approach safety data governance to ensure that safety data are accurate, authoritative, actionable, accessible, and affordable; and Approaches for improving a transportation agency s safety programs through a data governance program. Transportation Asset Management Many state DOTs are developing TAM plans to meet MAP- requirements and to improve their asset management practices. Data management and data governance are essential elements of a transportation asset management plan (TAMP). AASHTO states that a mature asset management program is fueled by consistent, high quality, integrated data and processes for transforming this data into information that influences decision making at tactical, operational, and strategic levels (). The biggest challenge faced by agencies to improve and integrate their data to support asset management is the lack of effective data governance. AASHTO s TAM Guide A Focus on Implementation, Chapter, provides guidance on the importance of strong data governance and its role in the overall data management framework. GETTING STARTED Fundamental Steps While there are differences in each agency s strategic goals, information requirements, organizational structure, and information technology capabilities, there are common steps that can be used to start and sustain a data governance program (): Accountability assign roles, responsibilities, and accountability at each level of the organization.

7 Sullivan and Stickel Business Processes model and document the business processes so that the data systems, stakeholders, and data processes are well understood. Communication and Education raise the level of awareness through communication and education on the value of data and data governance so there is a common focus. Core Team Leaders establish a well-qualified core team to develop the data governance structure and convey the data governance principles to the agency. Data Governance Strategy formalize the data governance strategy in a comprehensive document that addresses policies, guidelines, and standards. Data Program Alignment align the governance structure with the strategic goals and link these to data collection, reporting, and analysis. Documentation prepare metadata, business processes, and common definitions for tracking data quality and bridging sematic differences. Information Technology involve the information technology staff at each maturity level of the data governance process. Maturity Models establish a self-evaluation for current status, desired status, a gap analysis, and an action plan on achieving the desired maturity level. Organizational Culture establish a common focus so that everyone is considered a data steward. Priorities establish data governance priorities through a combination of strategic evaluation, capability maturity analysis, and formal self- assessment. Risk Assessment establish a formal data program risk assessment process from a lifecycle perspective that includes policies, standards, data repositories, and calculation processes. Stove Pipes address the institutional and data silos that are a detriment to the organization. Subject Matter Experts identify subject matter experts and assign them as data stewards and/or data owners. The Data Governance Institute (DGI) provides a wealth of information on data governance which can be used to start a data governance program (). The DGI data governance framework lays out a ten step process covering people and organizational bodies, rules of engagement, and processes for addressing how the data is used, when, and by whom? The DGI addresses each step, starting at the highest level with developing a mission and vision. This step is not to be taken lightly as it sets the stage for data stakeholders and what to expect. The ten step framework can address fundamental questions such as who should be involved, what does data governance mean, what does it do and how much do we need? Jill Dyche s + model identifies primary and secondary success factors (Figure ) to be considered when developing a data governance program (). These success factors are particularly relevant to transportation agencies. All data governance programs should strive toward the primary success factors. The criticality of the secondary factors will vary across transportation agencies and business units within an agency. From an Alaska perspective, three success factors stand out: Importance of having senior management support for data governance;

8 Sullivan and Stickel Value in involving data providers and consumers to help define data costs, benefits, risks, and priorities; and Potential for increasing data governance success by integrating into other ongoing initiatives like asset management. Primary Success Factors Establish guiding principles for data collection priorities, ownership/stewardship, data quality, level of service, business rules, and data architecture (models, meta-data, and data dictionaries) Identify the key data management roles needed in the organization for data architecture, metadata managers, data owners, and data stewards Assign roles, responsibilities, and accountability for data management decisions Establish a core data governance working group, composed of both business and information technology members, to design a realistic data governance tailored to the organizational structure Design a data governance structure that identifies decision workflows and work hand-off points Secondary Success Factors Develop and apply data governance principles to the workflow Establish executive sponsorship to hold business units accountable Automate business processes, if practical, for maintaining data models, tracking business rules, monitoring system performance, and managing quality assurance Develop a data governance council composed of business and IT members that are wellinformed on the agency s strategic objectives Ensure that work centers whose data is not widely used adhere to the data governance principles TABLE Primary and Secondary Success Factors () Targeting Key Areas If you are unable to jumpstart a data governance framework you can still establish good data management principles that will pave the way for a future data governance structure. Improving data quality is the outward result of better data management practices. The Health Information Management Systems Society provides six data quality areas that can yield immediate results (): Data definitions Data lineage Data accuracy Data consistency Data accessibility / availability Data security Data lineage relates to that data s origins and how it s transformed, and intermingled over time. Lineage is particularly important for geographic information system road network changes and for other transportation data sets where data is often transformed, e.g. traffic data. Other areas that should be targeted when making improvements in the data management area include (). Standards for defining o Data definitions o Taxonomies o Master reference data o Enterprise data model

9 Sullivan and Stickel 0 Processes for Managing o Data definitions o Data quality o Data change management o Data access Organizational Responsibilities o Data governance oversight o Roles and responsibilities o Planning and prioritization o Training o Data stewards o Data custodians Technologies for managing o Data dictionaries o Master data management tools o Data access and discovery tools o Data manipulation Master Data Management (MDM) is a functional area that needs particular attention. MDM provides a common vocabulary and frame of reference for business data that is accessed by multiple work centers within an agency. MDM data sets are commonly referred to as a master data set or the gold copy. The Gartner Group defines master data as the consistent and uniform set of identifiers and extended attributes that describe the core entities of the enterprise and are used across multiple business processes (). Key areas in a transportation agency where MDM data sets are essential include the geographic information system, the linear reference system, highway safety, traffic, roadway inventory, and asset management. Knowing that the reference data set exists, how to access it, who maintains it, and what update processes exist are all important questions. Establishing the MDM policies and procedures is one area can lead to investment in data governance. Once the MDM data sets are established, a methodology is needed to provide information about the master data sets. A metadata registry can be used to manage and share metadata and provide a common understanding of the meaning and quality of the data (). Typical registries include naming conventions, definitions, attributes, and classification. The International Organization for Standardization (ISO) / International Electrotechnical Commission (IEC) is an example of a formal metadata registry (). The Alaska DOT&PF is currently developing both a data and information systems registry as part of its TAM program. This will have many benefits, but overall it provides a repository of this information so that department personnel can access and review registered data from a single source. A Comprehensive Data Governance Structure Data Management (DAMA) International is a professional organization for advancing information and data management knowledge. DAMA s publications provide a substantive resource for data management functions, terminology, and best practices from an information technology perspective. These publications can be very useful in developing a data governance

10 Sullivan and Stickel structure across business areas. The DAMA Guide to the Data Management Book of Knowledge provides a comprehensive process model for data management functions and an excellent toolkit in getting started with a data governance program. The data management functions are (): ) Data governance ) Data architecture management ) Data development ) Database operations management ) Data security management ) Reference & master data management ) Data warehousing & business intelligence management ) Document and content management ) Metadata management 0) Data quality management Figure shows the DAMA Data Management Framework for structuring these ten data management functions (). Data Governance is at the center of the framework with each of the nine data management segments building the base for data governance. Each data management functional area provides types of activities that potentially could take place. This approach highlights the opportunity to establish solid data management practices and then implement data governance over the data management framework. The DAMA Data Dictionary of Data Management provides a common data management vocabulary to be used across the enterprise. This is particularly useful for agencies with multiple business units that may have diverse data definitions or no definitions (0).

11 Sullivan and Stickel 0 FIGURE DAMA Data Management Framework () Implementing DAMA Data Management Framework The State of Colorado s Office of Information Technology (OIT) has embraced the DAMA Data Management Framework (). The OIT is the central authority over all information technology systems and resources in Colorado, thus permitting them to drive the data management practices. Their program is broken into five goals, each with specific strategic objectives to achieve a goal (Table ). While the Colorado initiative is designed at the state government level most of the strategies are applicable to a transportation agency. The Colorado Data Strategy document details their data management program emphasizing data governance as the core function that must be an ongoing program, and one of continuous improvement (). An agency developing a data governance program can follow Colorado s example through a strategic vision and a data business planning document.

12 Sullivan and Stickel 0 Goals Strategic Objectives to Achieve Goal Create a data management organization Establish a sustainable data Develop enterprise data polices and standards management and governance program Create a data stewards council and formalize data stewardship activities and processes Create an information sharing culture Create an inventory of all data standards and definitions for all data elements Establish data governance polices, processes, and standards to manage the flow of data from capture to user Document all data sets and Identify authoritative data sources for all data types implement enterprise data Create and implement an enterprise data dictionary and taxonomy standard Implement master data management technology to ensure quality, reliability, and integrity of data Provide data quality audits as part of the ongoing monitoring of data quality Define the secure technical data exchange architecture Develop policies and processes for data exchange Create an integrated data Adopt enterprise data exchange standards sharing environment to provide a single, accurate, Provide tools to allow people with the right authorizations to easily access the data consistent source of data Provide for open source application development for data visualization and information sharing Train internal and external stakeholders/users to use the system(s) Adopt and enforce data standards through the planning and development process Optimize data and information Consolidate and central data resources where possible assets to reduce costs Leverage data, tools, and infrastructure across the enterprise Develop solutions that serve multi-work center business needs to facilitate collaboration and partnership Develop an enterprise data security classification policy Ensure privacy, security, and Classify enterprise data in accordance with the data security classification policy compliancy Develop an enterprise privacy policy Implement robust, sophisticated access and authentication technology and processes to ensure privacy and security of data TABLE Colorado s Goals and Strategic Objectives () A STATE DOT DATA GOVERNANCE EXPERIENCE The DAMA Data Management Framework provides a broad solution to establishing a data governance program in a transportation agency. The remainder of this paper takes a look at what the Alaska DOT&PF s Transportation Information Group has done to implement data governance with a view toward the DAMA Framework. Three key events helped drive the data governance process: () a legacy transportation mainframe database migration to a geospatial linear referenced based system, () a department-wide TAM information system and data governance program, and () the new MAP- data requirements. The Alaska DOT&PF adopted TAM to improve asset management practices. TAM leaders have formed an executive and steering level committees focused on developing a department-wide data governance structure. They have also formed working group committees

13 Sullivan and Stickel that focus on various business areas of the department, e.g. data integration, communications, planning and programming, and technical areas like highway safety and pavement management. The TAM data integration team supports the overall structure of TAM by researching, advising and providing information for technical data matters such as data integration, standards, policies, system compatibility and workflows. A significant decision by this team was to adopt a common linear reference system (LRS) as the enterprise location reference tool for all assets. That common LRS is housed within the Alaska DOT&PF TIG s geospatial linear referenced based system, the Spatially Integrated Roadway Information System (SIRIS). The TIG is in the final stages of transitioning from a linear reference based mainframe database, the Highway Analysis System (HAS), to the SIRIS environment. When completed, SIRIS will consist of three separate components: the Roadway Data System (RDS), Traffic, and Crash. RDS is the spatial and LRS foundation for SIRIS. It contains the road centerline/lrs network, background features, jurisdictional boundaries, and common roadway inventory features and attributes. As the names imply, the traffic and crash components will focus on the management, analysis, and reporting of traffic and crash data. The three separate SIRIS components will integrate by location through a common LRS network and method (). Important to note is the data governance did not exist within the department prior to the TAM and the SIRIS data governance endeavors. Developing a Data Governance Plan SIRIS data governance was not entirely appreciated until the RDS was more fully developed and customers were requesting data sets and maps of the new road network. What quickly became paramount was the need for a framework to improve the data quality, gain control over the critical data elements and promote efficient data usage. The Data Governance Plan establishes this framework and provides an opportunity to sustain data governance over time and have it institutionalized as part of the culture. The transition team initially defined data governance and how it could support the needs through scenarios. If the data managers did not understand what data governance was and how it could benefit them then there was no point in moving forward. Scenarios allowed the transition team to envision various customer requests for data and how those might be addressed through a data governance approach. Table lists the goals that evolved from the scenarios (). In general, the transition team wanted to ensure that a small GIS team of five employees could meet the ever growing demands of federal, state and custom reporting needs without jeopardizing the quality of the data and without hindering data improvements.

14 Sullivan and Stickel Number Goal Goal Description To define what SIRIS significant data means, and to identify the significant data elements that will be included in the RDS, crash and traffic database, and communicate these data elements to potential users in DOT&PF. To define a process to add new critical data elements or to modify existing critical data elements in any SIRIS component database, such that: Critical data elements are not duplicated within SIRIS; and Data element additions and updates are done so to address user needs (either TIG user needs or the needs of other DOT&PF groups), while balancing the effort and resources required to manage and maintain any data element. 0 Goal To maintain high levels of data quality through: Clearly identifying each data element s accessibility and communicating this to potential users; Promoting data integration and interoperability through data management and partnerships Striving for current, accurate, consistent, and uniform data through a strong data stewardship program; Improving data timeliness through data stewardship, information technology solutions, and cooperative stakeholder efforts. Goal To maintain the quality and integrity of the SIRIS critical data elements by: Assigning a data steward for each data element and communicating these to potential users; Describing the data steward roles and responsibilities; Overseeing the data stewardship process to maintain the overall SIRIS data quality; and, Performing regular cycles of data collection to ensure data is current and accurate. TABLE Alaska DOT SIRIS Data Governance Goals Defining Critical Data An essential piece to the SIRIS data governance plan was to define SIRIS critical data and to provide guidance to SIRIS operators and users about what comprises critical data. To attempt to govern all data recorded by DOT&PF business units would be an enormous and overwhelming task. Therefore, a certain level of data needed to be defined as critical and governed through an agreed upon process. In order to determine SIRIS critical data, the transition team developed four categories of data shown in Figure (). Category, Development Data, is not considered critical data. This includes data that is not ready for circulation to others in an official capacity, nor is there intent for the data to be accessed by other systems or users directly, e.g. internal databases, spreadsheet or data files that individuals or groups use for working projects.

15 Sullivan and Stickel CATEGORY Development Data (e.g. internal spreadsheets, notes, local files) Data stored in user s local files CATEGORY SIRIS Published/End User Data (e.g. ready for user or system consumption) SIRIS Production Data (e.g. data still being edited/reviewed) CATEGORY External to SIRIS Published Data (e.g. ready for user or system consumption) SIRIS CRITICAL DATA SETS 0 0 FIGURE Categories of Data SIRIS comprises Category : Production Data and Published/End User Data. Both are considered critical SIRIS data sets. Category, External to SIRIS Published Data, are also viewed as critical SIRIS data sets if they are data sets that SIRIS components rely upon such as those related to crash, traffic or RDS. Category, End User Reports, are not considered critical because they are created for a specific use and are not queried by other systems. Data Governance Structure and Roles To accomplish the SIRIS data governance goals the transition team developed a two-tiered governance structure: CATEGORY End User Reports (e.g. user generated reports or calculations) Data stored on local user computers or networks Data Governance Board. This will consist of high level TIG managers to assist in resolving disagreements, prioritizing staff time, and offering insight from a broader DOT&PF perspective. Data Management Team(s). This will consist of TIG data managers and a TAM representative that will make data governance decisions and manage the data governance process. Initially, there will be one Data Management Team for all SIRIS components. However, this may grow to more than one team if there ever becomes clear separation of actions around groups of data elements. The key role of the Data Management Team is to (): evaluate existing and proposed data sets using the Data Value Assessment Form and respond to requesters

16 Sullivan and Stickel develop a data model and data integration plan for each approved data set implement data management solutions develop and operate a catalog of significant data sets. The data model and data integration plan will define the format and data definitions needed to enable data integration, interoperability and accessibility. This also includes identifying data owners, stewards and custodians; a key objective of the TIG data governance. TIG cannot be the stewards and/or custodians for all the critical data as its entirely impractical and would ultimately lead to stale data sets. Data Governance Principles Data principles are the basis of actions taken by the Data Governance Board, Data Management Team(s), Data Stewards, Data Custodians and Data Owners. They are the founding principles that will guide the data governance structure toward building and maintaining a quality data set that customers want to use and that data stewards will want to always contribute to and support. Below is a list of SIRIS data governance principles (). Principle - All SIRIS critical data shall be described by Metadata. Principle - All SIRIS critical data shall have a defined Data Steward. Principle - SIRIS critical data shall not be duplicated. Principle - SIRIS data governance shall seek a balance between all that is possible and what is practicable. Principle - The owners and/or experts of a data set shall be the Data Steward. Data stewards are pinnacle to the entire process as they are the ones responsible for the accuracy of the data. They must also commit to the up-keep of the critical data or the intent of the SIRIS data governance fail. Data Governance Workflow Another important piece to data governance is the workflow, i.e. to define the technical and policy related activities to be performed to execute the data governance. The SIRIS data governance workflow outlines a series of steps to take when considering new data sets and to implement a strategy for governing each data set. Register and Catalog Existing and New Data Sets The first step is to register and catalog existing and new data sets. The registry is simply a catalog of each data set and its metadata. Fortunately the TAM data integration team is creating an information system and data registry that serves a similar purpose for all department critical data sets. They will use the SIRIS data elements as it first entry. Sharing the data registry will reduce any duplicative efforts between the TAM efforts and SIRIS data governance. Evaluate & Score Existing and New Data Sets The transition team created a Data Value Assessment Form to evaluate and score new data sets. This step will allow the transition team to distinguish the critical data elements and document

17 Sullivan and Stickel sufficient descriptions of each one. The Data Value Assessment Form includes a list of questions about the proposed data request. If the Data Management Team determines that a proposed data set is critical they then determine where on the data management solutions spectrum the data set falls. This step is essential as it determines where the data will be stored and how the data set will integrate with SIRIS components. Figure shows an illustration of the data management solutions spectrum (). If a data set were to fall in the Registered in Catalog Only, it will be stored external to SIRIS components but registered in the SIRIS catalog to enable management and access. The further to the right of the spectrum the data set falls the more closely it integrates with SIRIS FIGURE Data Management Solutions Spectrum Data Management Team Review The Data Management Team will review and notify the individual or group that initiated the request with feedback and a formal response. If the data set is not selected as a critical SIRIS data set, an explanation will be provided. If the requestor wishes to pursue the discussion further, the Data Governance Board will review and respond. If the data set is selected (i.e. considered critical to SIRIS), the Team will provide feedback and suggestions to the requestor along with further instruction on next steps. It is not the intent of the Data Management Team to turn down requestor s or do all the work if the request is approved. The overall intent is ensure improvements to the data are ongoing and to always maintain focus on developing quality data sets. This is also an education process for those requesting the data. Often times a requestor is unaware of the level of effort it takes to just add a data layer or to just revise a current layer. It s the Data Management Team s job to educate the requester that if they want quality data then these are the questions (Data Value Assessment Form) that must be answered in order to ensure a quality data set. If the team can t approve it then the requestor has more knowledge about what it takes to provide and maintain quality data. Also as time goes by users start to think of the SIRIS data set as an asset or resource that they can truly value and support. Implementing SIRIS Data Governance The transition team is on target to complete the HAS to SIRIS Transition by end of 0. This aligns well with the data governance implementation. The Data Governance Board and Data Management Team kicked off the plan An overview with the Director of Program Development

18 Sullivan and Stickel followed to get executive endorsement of the process. The transition team expects that the process may need fine-tuning as TIG implements the SIRIS data governance. Similar to Colorado s statement, the data governance must be an ongoing program, and one of continuous improvement (). It is important to note that while the TAM Executive and Steering Committees are developing a data governance structure for all department data and information systems, the SIRIS data governance will aptly support the goals of the TAM data governance. Several HAS to SIRIS transition team members reside on the TAM Data Integration Team. This helps to ensure that TIG s progess toward data governance will meet the goals of the TAM data governance plans and that it won t overlap or entail mistaken authority that the SIRIS data governance is not authorized to perform. Derived Benefits To date, SIRIS data governance has reduced the burden on the small GIS team and better positions them to support the DOT&PF TAM data governance efforts. Prior to data governance the GIS staff were overburdened with requests, not knowing how to prioritize them into their already busy schedule, and were left feeling pressured to be the data stewards of all the SIRIS data. By having a process in place that identifies the critical data sets, assigns roles and responsibilities and requires data managers to fill out a data valuation assessment questionnaire, the data governance has alleviated much of that burden. This new structure also positions TIG as a leader in the data governance making it more efficient for the DOT&PF TAM teams to adopt and incorporate similar goals and guiding principles. SIRIS data governance is also better structured to handle the 0 statewide data collection of over,000 centerline miles. Having a process in place has significantly improved integration efforts with pavement management and bridge design, who also need similar and timely data collection. With a single data collection effort the DOT&PF saves costs and resources and is able to build a more timley and quality road centerline network that supports all LRS needs within the department (i.e., TAMIS). CONCLUSIONS A strong data governance structure has a multitude of benefits. Getting started with data governance, however, can be an overwhelming task due to the many steps needed to develop and sustain. Fortunately, many professionals in the field of data management have created guides to help transportation agencies succeed, e.g. DAMA International, Jill Dyche, Data Governance Institute, Data Governance Society, and Ken O Connor. There are excellent ongoing initiatives in FHWA, TRB and AASHTO that provide valuable approaches. As states prepare for the MAP- performance requirements, data programs can potentially be integrated with the transportation asset management and safety governance structures. Sharing state experiences provides an insight on what works and what doesn t. The

19 Sullivan and Stickel 0 State of Colorado adopted the DAMA model to implement a data strategy that is directly tied to the overall goal of improving state services. The Alaska DOT&PF s was more of a grassroots approach starting with user scenario s to help generate ideas of what to include in a data governance plan. The result created a plan that could sustain a quality data set overtime, meet the growing TAM requirements and generate a data set that will be viewed as a valuable resource. Thru research and determination to improve an agencies data management practices, a transportation agency can quickly come up to speed on the fundamentals needed to support a data governance program. Even if data governance is not attainable in the immediate future, at the very least agencies can establish good data management principles that will prepare them for the full implementation.

20 Sullivan and Stickel REFERENCES. Cambridge Systematics, Inc.. Target-Setting Methods and Data Management to Support Performance-Based Resource Allocation by Transportation Agencies, NCHRP Report. Transportation Research Board of the National Academies, 00.. O Connor, K., Process for Assessing Status of Common Enterprise-Wide Data Governance Issues, Accessed November, 0.. Data Governance Society. Accessed July 0, 0.. Secrest, C., K. Schneweis, and G. Yarbrough. Transportation Data Self-Assessment Guide. NCHRP Report 0-, Task 00. Transportation Research Board of the National Academies, 0.. Implementing Transportation Data Program Self-Assessment, NCHRP 0- (Active), Being prepared for the Transportation Research Board of the National Academies, Washington, D.C. Accessed November, 0. Cambridge Systematics, Inc., Data Governance, Standards, and Knowledge Management. Prepared for the Alaska Department of Transportation and Public Facilities, Juneau, 00.. Vandervalk, A., D. Snyder, and J. K. Hajek. U.S. DOT Roadway Transportation Data Business Plan (Phase). Publication FHWA-JPO--0. FHWA, U.S. Department of Transportation, 0.. Melvin, P., Data Management To-Be Summary Analysis & Description (draft). Prepared for the Federal Motor Carrier Safety Administration, February, 0.. Vanasse Hangen Brustlin, Inc. (VHB), United States Roadway Safety Data Capabilities Assessment. Prepared for Federal Highway Administration Office of Safety, July, 0. Accessed November, FHWA, Roadway Safety Data Partnership, Accessed November, 0.. AASHTO, Transportation Asset Management Guide A Focus on Implementation. Publication TAMGFI-, American Association of State Highway and Transportation Officials, 0.. Stickel, J, and A. Vandervalk. Data Business Plans and Governance Programs Aligning Transportation Data to Agency Strategic Objectives. Submitted for Transportation Research Board publication, Nov 0.. The Data Governance Institute. Accessed November, 0.. Dyche, J., BeyeNetwork. Data Governance Next Practices: The + Model, Accessed November, 0.. Healthcare Information and Management Systems Society. Clinical & Business Intelligence: Data Management A Foundation for Analytics: Data Governance, April 0. Accessed November, 0.. White, A., D. Newman, D. Logan, and J. Radcliffe. Mastering Master Data Management, Gartner, Inc. January, 00. Accessed November, 0.

21 Sullivan and Stickel 0. Bargmeyer, B.E and D.W. Gillman, Metadata Standards and Metadata Registries: An Overview, Bureau of Transportation Statistics. Accessed November, 0.. International Organization for Standardization/International Electrotechnical Commission, ISO/IEC, Information Technology Metadata registries (MDR), Accessed November, 0.. The DAMA Guide to The Data Management Book of Knowledge (DAMA-DMBOK Guide), First Edition, Technics Publications, LLC, Bradley Beach, NJ., The DAMA Dictionary of Data Management Book, nd Edition, Technics Publications, LLC, Bradley Beach, NJ., 0.. State of Colorado Office of Information Technology. State of Colorado Data Strategy 00, Accessed November, 0.. Oliver, R. D. Roadway Data Services Overview. Alaska Department of Transportation and Public Facilities, 0.. Athey Creek Consultants. SIRIS Data Governance Plan. Prepared for the Alaska Department of Transportation and Public Facilities, 0.