Driving Compliance and Competitive Positioning Through BI and Data Governance

1. Driving Compliance and Competitive Positioning Through BI and Data Governance Steven Zagoudis MetaGovernance® LLC

2. During This Presentation You Will Learn: • A primer on Data Governance • How to implement a compliance-driven BI solution • The role technology plays in Data Governance • How Data Governance and BI can lead to better competitive positioning

3. Implementing a Compliance-Driven BI Solution

4. Begin with Data Governance

5. What is Data Governance?

6. Garbage In – Garbage Out

7. It makes no sense to clean up the lake if you are still polluting the river

8. Data Governance Crosses the Entire Organizational Structure

9. Value Maturity A Simplified Maturity Model can Gauge Data Governance Progress • Four tier structure removes complexity • No time wasted on obsessive ‘grading’ • Focus on key business objectives, not the model • Move to a more refined model over time The four-tier Data Governance Maturity Model by Daedalean Associates

10. Random Acts of Data Quality • Typically companies are in very reactive mode • Isolated data quality efforts target specific problems • No consistent behavior • Efforts are stove-piped Isolated (Level 1) • Technology not used to facilitate resolution • Term ‘Data Governance’ not used The four-tier Data Governance Maturity Model by Daedalean Associates

11. Awareness (Level 2) Organizational Awareness Begins • Still reactive, but groundswell towards proactive • Organizations sense underlying data problems • Data ‘ownership’ becomes critical • Working groups or committees formed • Technology solutions still evasive • Term ‘Data Governance’ bantered about The four-tier Data Governance Maturity Model by Daedalean Associates

12. Integrated (Level 3) A Systematic Look Begins to Emerge • Metadata discussions become the norm • Business and technology partner on data issues • Data Governance groups officially formed • Proactive efforts to identify and resolve issues • Technology enabled disclosure control framework introduced • Ongoing metrics measure data health quality The four-tier Data Governance Maturity Model by Daedalean Associates

13. Optimizing (Level 4) Evolving to a Learning Organization • The metadata beast is tamed by process and technology • There is a mind shift around data and information • Lineage reporting and impact analysis are enabled • A shared vision exists on the need for quality • Technology-enabled disclosure control frameworks are prevalent • Data Governance drives process re-engineering The four-tier Data Governance Maturity Model by Daedalean Associates

14. Business and technology partner Data Governance drives process change Value Organizational Awareness Localized efforts Optimizing (Level 4) Integrated (Level 3) Awareness (Level 2) Isolated (Level 1) Maturity Make Advancements Along a Data Governance Maturity Model The four-tier Data Governance Maturity Model by Daedalean Associates

15. Basic Premise Behind Stage Theory • You can’t skip a stage • An accurate, objective assessment is critical • Senior management often has difficulty reconciling between ‘wishful state’ and reality • Beware of the ‘emperor with no clothing’ syndrome Each stage has a unique vocabulary and level of understanding that must be honored to solve the right problems for your organization

16. Form the Data Governance Function Within your Organization • Start with a small working group • Include primary business data owners • Include technology leads that understand data • Define an initial charter (don’t boil the ocean) • Pick a known data issue to resolve first • Address root cause of the issue, not symptom • Communicate to gain organizational support

17. Follow the flow of data to reveal Data Governance Stakeholders

18. Colors are an Effective Way to Illustrate Data Ownership Sales Forecast Market Customer Credit Pricing Collateral Risk Default Employee Valuation Budget General Ledger Fixed Assets Vendor

19. Implement a Disclosure Control Framework

20. Data Quality is No Longer an Option in Today’s Economic and Political Climate

21. Understand the Political Landscape Around Data Quality • Redundant data copies will get out of sync • Accountants and Controllers require extensive general controls to ensure data accuracy • Automation leads to the need for more controls, not less due to the elimination of manual checks • Companies must run their business and report business results from the same set of data • Enterprise Risk Management, Legal, Financial Reporting, Corporate Communications, etc. are becoming interested in data quality

22. Automated Data Controls Must Reconcile Multiple Systems to Ensure that they are in Sync in Aggregate

23. Process and Technology Merge to Produce Internal Data Controls

24. Identify Key Data Elements to Include in the Disclosure Control Framework • Sarbanes-Oxley Key Control Points • Elements of manual reconciliations • Financial measures in Form 10-K and associated disclosures • Key measures in Board reports • Key Risk metrics • Key Call Report measures • Key Profitability measures

25. Implement a Disclosure Control Framework

26. Data Quality Metrics are an Effective Communication Tool • Number of data issues by problem type • Number of senior-management reportable exceptions • Dollar impact of error by system • Number of adjustments by department • Trending of data quality by subject area • Data issues causing financial restatement • Exception and excessive values are ‘news’

27. Design for Data Integrity

28. Use a Business-Driven BI Methodology What is the average distribution cost and forecasted sales for each geographic region by product for each distribution channel for August 2008? How does the cost compare to August 2007?

29. Leverage Technology for Rapid Deployment and Flexibility • Use a methodology that enables Business Analysts and end-users to work side-by-side to document fundamental information needs • Define Business Models, not Data Models • Generate the schema from the Business Model • Load transactional-level data and generate aggregation and balance tables automatically • Automatically create and manage the exposure layer • Create source-system independence in the solution

30. Leverage Technology to Enforce Data Integrity • Use technology that manages slowly-changing dimensions and foreign keys automatically • Implement data integrity filters to identify data anomalies that exist in source system data • Achieve the correct balance in Dimensional vs. 3NF • Define as ‘star schema’ to facilitate understanding • Implement in 3NF to enforce data integrity • Expose as ‘star schema’’ for easy data access

31. Define Mart Reference Data Transaction Data Result Sets Implement a Metadata-Driven Solution Meta Data BI Staging Area Meta & Reference Data Repository OLAP Create Star Schema Generate Dimension Tables Analytics Data Loading Validation Data Marts Data Mart Creation Warehouse (Reporting Schema) Reporting Direct Access

32. Technology can provide Tremendous Efficiency Gains and Flexibility • Simplifying the business data modeling • Simplifying the database creation and data movement • Enforcing data integrity • Implementing automated data control frameworks • Simplifying maintenance and allowing rapid flexibility to ever-changing business needs • Enabling the project teams to focus on the business problem and not exclusively focusing on the IT problem

33. Leverage BI for Competitive Positioning

34. The Business Intelligence Hierarchy

35. “News” is differences that makes a difference.” Gregory Bateson “Noise” is meaningless data generated along with desired data…a loud , confused clamor or commotion. Webster’s dictionary “News Versus Noise” is Critical to BI Business Intelligence is the emergence of “news” from the “noise” to enable companies to identify and take deliberate action to improve cost effectiveness and revenue generation.

36. Data Governance and BI Drive Process Optimization

37. In Summary… • Most Data Warehouse efforts fail due to data quality • Business will ‘walk away’ if they don’t trust the data • Designing with Data Governance in mind is the solution • Regulatory and competitive stakes are increasing • Disclosure control frameworks prove data accuracy • Technology can be a tremendous asset • Data Governance and BI follow predefined maturity models that must be recognized and followed

38. Thank you for participating Please remember to complete and return your evaluation form following this session.