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Webinar Data Governance 2.0 For The Data Economy Challenges (Part 1)

This webinar discusses the challenges of data governance in the data economy and provides recommendations for an agile approach to data governance. It explores the five domains of data governance and the importance of information/data quality, data security and privacy, master data management, standardization and metadata management, and data life-cycle management. It also addresses the need for companies to change their data governance objectives to address new challenges.

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Webinar Data Governance 2.0 For The Data Economy Challenges (Part 1)

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  1. WebinarData Governance 2.0 For The Data Economy Challenges (Part 1) Henry Peyret, Principal Analyst August 6, 2013. Call in at 10:55 a.m. Eastern time

  2. Renew data governance: agenda Today’s data governance challenges Tomorrow’s data governance characteristics Recommendations

  3. The five domains of data governance Data life-cycle management Standardization and metadata management Information/data quality Master data management Data security and privacy

  4. Companies struggle with implementation Base: 634 business intelligence users and planners; Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012

  5. The data economy ecosystem Source: May 8, 2013, “Introducing Adaptive Intelligence” Forrester report

  6. Data governance objectives should change to address new challenges (cont.) Information/data quality Data security and privacy Master data management Standardization and metadata management Data life-cycle management

  7. Data governance objectives should change to address new challenges (cont.) Information/data quality Data security and privacy Master data management Standardization and metadata management Data life-cycle management

  8. Data governance objectives should change to address new challenges (cont.) Information/data quality Data security and privacy Master data management Standardization and metadata management Data life-cycle management

  9. Data governance objectives should change to address new challenges (cont.) Information/data quality Data security and privacy Master data management Standardization and metadata management Data life-cycle management

  10. Data governance objectives should change to address new challenges (cont.) Information/data quality Data security and privacy Master data management Standardization and metadata management Data life-cycle management

  11. Data governance objectives should change to address new challenges (cont.) Source: July, 18 2013, “The Transformation Of Data Governance” report

  12. Renew data governance: agenda • Today’s data governance challenges • Data governance is rigid, difficult to justify, and the business does not see the value. • Data governance along BI or MDM projects without holistic view • Data governance is judged as inefficient. Tomorrow’s data governance characteristics Recommendations

  13. Data governance 2.0 definition An agile approach to data governance focused on just enough controls for managing risk, which enables broader and more insightful use of data required by the evolving needs of an expanding business ecosystem

  14. Data governance 2.0 definition (cont.) An agile approach to data governance focused on just enough controls for managing risk, which enables broader and more insightful use of data required by the evolving needs of an expanding business ecosystem

  15. Data governance 2.0 definition (cont.) An agile approach to data governance focused on just enough controls for managing risk, which enables broader and more insightful use of data required by the evolving needs of an expanding business ecosystem

  16. Data governance 2.0 definition (cont.) An agile approach to data governance focused on just enough controls for managing risk, which enables broader and more insightful use of data required by the evolving needs of an expanding business ecosystem.

  17. Data governance 1.0 versus 2.0 characteristics

  18. Renew data governance: agenda Today’s data governance challenges • Tomorrow’s data governance characteristics • Become more operational . . . . • Allow day-to-day business decisions. • Focusing more on data usage risks mitigation • Become a learning machine. Recommendations

  19. Recommendations Watch for “mad marketers” as there has been “mad traders.” Think to put a controller on “next-best-actions” business platforms. Enhancing ethical rules and how to turn them in operations and if possible in platforms Learn from errors and trials: debrief the mistakes and learn.

  20. Henry Peyret +33 68482.9551 hpeyret@forrester.com Twitter: @hpeyret

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