1. University of Nevada, Reno Data-Driven Organization Governance 1 Governing a data-driven organization (4/24/2014)  Define governance within organizations.  Understand the general activities of governance.  Define relative levels of governance “maturity”.  Identify key aspects of data governance  Discuss how an organization governs data. 1

2. Data-driven decision making is relative  Organizations are in various stages of using data to support organizational decision making. Spans from standard accounting reports to competing on analytics.  Organizations vary in data availability. Internal silos to integrated data. Internal only to integrated external and internal.  Organizations vary in data understanding. We have data??? to Data is an asset.  Organizations vary in analytical capability. 2

3. What does it mean “to govern”?  gov·ern v. gov·erned, gov·ern·ing, gov·erns To make and administer public policy and affairs To exercise sovereign authority To control the speed or magnitude To regulate To control actions or behavior To keep under control; to restrain To exercise a deciding or determining influence To exercise political authority To have or exercise a determining influence 3

4. 4 Corporate Governance IT Governance Data Governance

5. University of Nevada, Reno Data-Driven Organization Governance 5 Governance Activities  What are the key activities in corporate governance?  What are the key activities in IT governance? 5

6. Governance Artifacts 6 Policy Policy defines “what” the organization must do or not do. They are the principles/rules of an organization. Standards A standard sets a requirement and/or creates a baseline Guidelines A guideline describes a preferred approach with practical directions. What do we need policies about? What is the goal of a given policy? What level of standards are necessary? How detailed should a standard be? What guidelines would be helpful? Drives

7. 7 IT Governance Data Internal, external, structured, unstructured, purchased, free Process Ongoing work Project Short term goals People Staff skills Technology DBMS, multimedia Applications ERP, app software Facilities Network, servers

8. University of Nevada, Reno Data-Driven Organization Governance 8 IT Governance Frameworks  Information Technology Infrastructure Library (ITIL)  Control Objectives for Information and Related Technology (COBIT).  Commonalities: Align IT objectives with business goals. Provide list of general processes that must be accomplished by IT organizations. Provide metrics to evaluate efficacy of processes. Use maturity models to evaluate relative formality of processes. 8

9. University of Nevada, Reno Data-Driven Organization Governance 9 Evaluating governance based on maturity level  “Maturity” evaluates the degree of formality and optimization of a policy/standard/guideline structure.  A maturity model: Defines high and low levels of maturity. Categorizes degree of maturity based on key characteristics. Describes steps that move an organization from one level of maturity to another. 9

10. Why use a maturity model? 10 Maturity Model Assessment Describe the overall environment. Evaluate current state of capabilities. Consistently compare evaluations over time. Compare organization to other organizations. Improvement Define and create a path for progression. Direct the potential next steps. Target priorities and resources necessary for action.

11. Capability Maturity Model Integration (Carnegie Mellon Software Engineering Institute) 11

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13. What are the key aspects of data governance?  Protect and Control: Improve data quality. Protect and safeguard data. Assign ownership. Understand the value of data and impact of loss. Control change.  Make Useful : Define data across the organization. Integrate data from a variety of different sources. Ensure data availability. Enhance data accessibility.  Adapt and Change: Encourage data use. Facilitate ongoing data evolution and acquisition. 13

14. 14 Data Governance InitialManagedDefinedQuantitative Management Optimized Protect and Control Manually maintain trusted sources. Inconsistent encryption and usage standards. Undefined ownership. Individual projects with formal security standards. ETL processes managed by project. Definition of organization security standards. Definition of ETL processes. KPIs are set and tracked. Security is centralized service Monitor risks across apps and dept structures. KPIs are compared against others. Proactive inquiry into potential threats. Experimentation Make Useful Inconsistent silo structures. No overall data definition. Data definition for internal data. Some integration. Data definition & integration across apps. KPIs are set and tracked. Eliminate bad usage. Research usage outside of org. Adapt best practices. Adapt and Change No formal usage policy. Data identified for future purchase by chance/luck. Chaotic. Tacit agreement on ongoing use of data for decision making. Individual project management. Explicit agreement on use of data. Data identification and acquisition processes. KPIs are set and tracked. Understand trends in data usage. Encourage innovation and experimental usage of data.

15. How to govern data?  Governance artifacts: Policies, standards, guidelines.  IT is all about the people. Organizational structure issues: Who is responsible for data governance? Who will establish the policies, standards, guidelines? Who will encourage the policies, standards, guidelines? Who will enforce the policies, standards, guidelines? How should data be managed across the enterprise? Should “protect and control”, “make useful”, “adapt and change” activities be handled separately? 15

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