Leveraging Data Governance to Drive Institutional Effectiveness

1. Leveraging Data Governance to Drive Institutional Effectiveness Kathleen Shaw, MBA Vice Provost for Planning & Decision Support AIR Forum 2017 June 1, 2017

2. Today’s Objectives • National context: data governance within higher education • Data governance at VCU • Addressing challenges and barriers • Using data governance to address institution-wide data problems

3. National Context

4. BENEFITS RISKS Operational uncertainty Failures of security, privacy and audit and compliance Resource constraints Effective Data Governance Official vs. ad hoc definitions Clear responsibilities Structured and unstructured data Capacity for analytics Competitive advantage Source: Educause Center for Policy and Research, The Compelling Case for Data Governance, March 19, 2015.

5. Effective data management is critical Increasing internal demands for data to inform decision-making Growing data complexity Need for analyses and predictive modeling, not just data reporting User audience continues to expand, as do range of information demands Institutional data increasingly recognized as enterprise asset that can confer competitive advantage Improved service delivery Enhanced brand image Contribute to identifying and realizing cost savings Inform new program opportunities

6. Effective data management is critical (cont’d.) Continued growth in regulatory compliance requirements Changes occurring with growing frequency – state/federal/accrediting bodies Reporting requirements becoming more complex and challenging, e.g., IPEDS HR Ever-expanding need to make the case for higher education Legislatures and regulatory bodies Boards of visitors Parents and families of prospective and current students Current and potential donors Employers Media

7. Multiple DG Models across Higher Ed

8. Data Governance at VCU

9. VCU Data Challenges VERSIONS OF DATA “TRUTH” PROLIFERATION OF SHADOW SYSTEMS MULTIPLE SYSTEMS OF RECORD LIMITED AVAILABILITY AND ACCESSIBILITY OF DATA DATA SILOS = DUPLICATED EFFORTS INCREASED SOFTWARE AND MAINTENANCE COSTS

10. VCU’s Approach to Information Management Data and Information Management Council (DIMC) formed with pan-university representation in fall 2013 Council charged by president and reports to BOV each quarter Co-chaired by VP for Planning & Decision Support and CIO Goals: Reduce risks and vulnerability around compliance, security, and privacy; Strengthen trust in institutional data by improving availability, reliability, accuracy and consistency; and Increase operational efficiencies through effective data administration and management practices that lower costs and improve data integrity Entered 3rd phase (implementation) in January 2017

11. DIMC Charge • Review and document university's existing data and information management practices and related business processes • Recommenduniversity-wide standards for data administration aspects of business processes, data definitions, data dictionaries • Adopt, communicate and oversee implementation of university-wide standards for data administration aspects of business processes and data access • Engage in priority setting and policy setting as related to the above

12. DIMC Structure: University-wide Representation Provides Creates Establishes Administrative Processes Data Stewards Usage Rules Access Rules Forms Data Trustees Data Issue Inventory Access and Roles

13. Completed DIMC Initiatives: Phases I and II Policy & Standards • Draft over-arching data governance policy out for public comment • Toolkit designed and deployed • Master data definition initiative underway Data Stewardship • Data stewardship framework complete • Data domain liaisons identified for areas with multiple data stewards • Data roles being embedded within new HR definitions Data Map • Institutional data map complete • Major business processes mapped • Web-based, drill-down format under development Communication & Outreach • Website redesign underway: components identified; draft FAQs; public/private access controls • Formal training (trustees/stewards) spring 2017

14. Provides common point of reference • Guides decisions about data access and use • Clarifies boundaries and interdependencies among various systems and units • Contributes to more effective information sharing • Provides framework to support strategic planning Example: Institutional Data Map

15. Current DIMC Direction: Phase III

16. Overcoming Obstacles

17. Addressing Barriers & Challenges • Gain senior leadership buy-in • Establish general principles of data governance • Transparency parameters and guidelines • Data stewardship framework, with acknowledged and managed responsibilities and authority • High level process for development, review and approval • Identify technology underpinnings (systems, applications and technical skills)

18. Addressing Barriers & Challenges (Cont’d.) • Demonstrate data overlaps and inconsistencies • Solicit and share key data pain points • Build interdisciplinary teams • Communicate broadly

19. Executive (Data Trustees) IT Subject Resource Experts System / Data Resource Experts It staff including system admins, application developers, data security and other data management roles in IT Resolving Conflicts Strategic Level Data and Information Management Council One person, plus alternate are representing each major business unit & representation from IT. Office of Planning and Decision Support Communication Office of Planning and Decision Support Provide subject matter expertise and advisory assistance at each level. Responsible for Data and Information Management administration including but not limited to scheduling meetings, documenting and publishing notes and materials, managing council’s website and all program related communication . Tactical Level Data Stewards Line of Business (Business Domain) Data Stewards Escalation Path IT Subject Resource Experts Operational Level Operational Data Managers and SMEs (Data Custodians, Producers, Users) These people are presently defining, producing and using data as part of their jobs. Issue / Conflict Resolution Path

20. Addressing Institution-wide Challenges

21. Issue Management Approach • Issue management submission (Google form) • Submitter contact info • Issue and SME classifications • Detailed issue description • History (if known) of prior attempts to address • Related policies and/or processes (where known) • Areas impacted by data issue • Internal • External • Risk assessment (following university ERM criteria) • DIMC review and scoring • Task force formation, work plan and results

22. Setting Issue Priorities Example

23. Data Problems: Current Areas of Focus

24. Final Considerations Data and information management is a program, not a project. There is no end date Success depends upon university-wide collaboration and continuous communication across all levels Need to demonstrate progress from the start to strengthen collaboration and increase willingness to support change Having a “big stick” truly helps to make the case for and sustain interest in data governance

25. Useful Resources • Educause Center for Analysis and Research (ECAR) • https://www.educause.edu/ecar/ecar-working-groups/data-and-analytics • Data Governance and Information Quality Conference 2017 • http://www.debtechint.com/dgiq2017/ • Dataversity webinars and articles • http://www.dataversity.net/

26. Thank you Kathleen Shaw, MBA Vice Provost Office of Planning and Decision Support kshaw5@vcu.edu