Data Governance A Worthwhile Organizational Change

1. Data Governance A Worthwhile Organizational Change Fabrice Forsans Senior Director - Enterprise Data Management Digital River, Inc. January 21, 2010

2. Agenda • Introductions • Digital River, Inc. • Data Governance • Binary versus Ternary Organization • Example: Data Governance Matrix Organization • The “Chief Data Officer” • Key Data Management Programs • Implementing DG: • Digital River’s SAP implementation • Digital River’s Meta Data Registry • Wrap Up • Q & A

3. Introductions

4. Attendees • Which area do you see yourself in? Business / IT / Data

5. Presenter • Since 1/1/2008, responsible for Enterprise Data Management at Digital River, Inc., Eden Prairie, MN • Education: • Engineering diploma, Aerodynamics & Thermodynamics; ENSMA, Poitiers, France, 1989. • MBA, Finance & Economics, NYU’s Stern School of Business, New York, 1995 • U.S. Professional background: • Financial Management: Utility, Insurance (IT and Procurement), Professional Services • Data Management consulting (PwC Advisory, New York and Minneapolis) • Personal background: • In US since 1993, MN since 2004 • Three children (11, 7 and 5) • Dual French/American citizenship

6. Digital River, Inc.

7. Meet Digital River 2007 Highlights Revenues $349 million Net Income $71 million Revenues $394 million Net Income $64 million NASDAQ: DRIV Founded in 1994 2008 Highlights Global e-commerce expertise People + process + technology Managing over $3 billion in annual online sales

8. Unmatched global experience and reach 23 languages supported • Minneapolis • Aliso Viejo • Chicago • Lincoln • Pittsburgh • Portland • Provo • San Diego • Cologne • London • Luxembourg • São Paulo • Shanghai • Shannon • Stockholm • Taipei • Tokyo Over 100 localized payment methods 20 transaction currencies 185 display currencies (ISO) Global footprint

9. The on-demand technology advantage The Daily Stats • 20 million pages • 30 million emails • 175,000 orders • 5 terabytes of digital content • 20,000 physical shipments • 2 second page loads 99.997% uptime Managed to < 40% utilization 30 APIs + 300 integrations PCI level 1 8 global data centers Dublin Stockholm (2) Eden Prairie Cologne (2) Minneapolis Aliso Viejo

10. Helping companies succeed in e-commerce Software Consumer Electronics Gaming Retail Distribution

11. Data Governance

12. Plenty of Definitions out there… • Wikipedia (Categories: Information technology governance | Data management): • Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization's data across the business enterprise […]. These goals are realized by the implementation of Data governance programs, or initiatives. • SearchDataManagement.com: • Data governance (DG) refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures.

13. Plenty of Definitions out there… • Jill Dyché(“Data Governance in 3-D”; 8/28/2008): • Data governance is the decision-making process that prioritizes investments, allocates resources, and measures results to ensure that data is managed and deployed to support business needs. • Trillium Software (Home >> Products >> Data Profiling >> Data Governance): • Data governance is about creating an overall business strategy to manage data assets—a sustained effort to monitor and improve data throughout your enterprise • Robert S. Seiner (“What does it mean to ‘govern data’?”, The Data Administration Newsletter; 7/1/2009) provides eight (!) definitions for “govern”, and three principles for each…for a total of 24 statements, all starting with “Governing data means…” (http://www.tdan.com/view-articles/10867).

14. Putting it all together: Data Governance has all the characteristics of any strategic governance process People Strategy Process Technology Assets Programs Management Plan Governing body Business needs support Decision-making Data Governance attributes Procedures Data Governance means treating data as a strategic area within the enterprise (e.g. Sales, Finance, HR, Sourcing, etc…)

15. Governance analogies • Data Governance need not be invented from scratch:

16. Why so hard to deploy…? • Cultural barriers and a lack of senior-level sponsorship: • In 2006, Gartner predicted that less than 10% of organizations will succeed at their first attempts at data governance • Lack of Data Management ownership: • Concept of enterprise data governance is new to many organizations and key components of data management are not well established • Lack of Data Management knowledge: • Data quality (including profiling analysis), master data management and meta data management skills are still hard to find (not systems knowledge, but underlying programs) • Fear of required organizational structure changes: • Assigning Data Governance responsibility to an independent senior “data governor” (not IT-dependent) requires significant changes to existing work flows and policies

17. Binary versus Ternary Organization

18. The Data Management wheel • Embracing DM means fundamentally changing the organizational structure of a company: DM Bus IT DM deployment IT Bus Binary model: No Data Mgmt IT and Business frictions Ternary model: Data Mgmt No IT and Business frictions • The DM “wheel” is owned by the Data Stewards • Data Stewards interface with Business and ITStewards to carry out Data Management activities

19. Example: Data Governance Matrix Organization

20. Example of an Enterprise Data Management Organization CDO* DM Council/ Steering Committee Senior DM Executive * Chief Data Officer ** Data Management Area: typically determined using a Data Consumption Matrix (regularly updated) *** Data Stewards can either belong to the EDMO, remain in their respective DMA, or both. Program Managers DQ MDM MDR LDM . . . DMA** 1 DMA** 2 Data Stewards *** DMA** 3 DMA** 4

21. The Chief Data Officer

22. The CDO must be able to act Strategically • Data cannot be governed independently • Continuous conflict of interests between Technology and Data Management • Data not managed as a strategic asset • Difficulty to enforce quality rules across all enterprise • High cost, low returns • Data becomes silo-driven (like IT…) • Responsibility without authority Data Mgmt. IT / MIS Data Governance + IT Governance CIO / VP Technology CDO / VP Data Mgmt. Process Mgmt Data Mgmt Data Mgmt. Manager / Director • Data governed as an independent asset • Centralized authority over data programs • Cost reductions from uniform DM processes • Improved control over compliance and financial risks • Clear accountability for all aspects of data • Data scalable across the enterprise, and over time (growth, acquisitions…) • Data Management no longer dependent on IT strategy

23. Key Data Management Programs

24. Data Quality Program (DQP) • Objective: • Centralize the management of quality rules for all enterprise data elements • Roles & responsibilities: • DQP Manager: responsible for the deployment of the DQP, and ongoing management of rules. Must be involved in all POC (Point of Capture) data flows. • Business Stewards: own the determination of rules. Engage their Data Stewards when an update/new rule is required. • IT Stewards: build and maintain the interfaces between data consuming systems and DQP application (i.e. Trillium) • Data Stewards: handle the implementation and regular review of their assigned rules (monthly data quality meetings, rules sign off, Data Quality policy enforcement, etc…)

25. Master Data Management Program (MDP) • Objective: • Centralize all aspects of enterprise master data management, from quality, storage to syndication (requires DQP be deployed) • Roles & responsibilities: • MDP Manager: responsible for ensuring all enterprise master data is available, accurate, and unique. • Business Stewards: own the quality determination of master data, including the de-duplication matching logic. • IT Stewards: build and maintain syndication process between the MDM application and the consuming systems. Note: IT Stewards DO NOT modify/update master data. • Data Stewards: ensure master data is accurate for their assigned DMA(s); enforce MDM policy, and are the only resources allowed to modify master data content.

26. Meta Data Management Program (MTDP) • Objective: • Centralize all aspects of enterprise meta data management, through the creation and ownership of the corporate MDR (Meta Data Registry) • Roles & responsibilities: • MTDP Manager: ensures all meta data is properly defined and available to both human and machines. • Business Stewards: provide and is accountable for the content of the MDR. • IT Stewards: provide support for the MDR, and help establish required interfaces between the MDR and consuming applications. • Data Stewards: enforce the MTDP policy; support the MTDP Manager in maintaining the MDR.

27. Implementing DG: DR’s SAP implementation

28. DR’s initial DQP deployment: • DQP supported by Trillium Software System® applications: • TS Quality supports the DQP for the SAP implementation • All data from e-commerce systems extracted and sent to Trillium before SAP load • TS Discovery output provides core framework for the Business Rules Determination workshops • All quality rules, including data transformation, are managed by the SAP Data Steward (separation of data vs. process) • SAP-specific Business rules can be updated/changed easily, without any ETL modification. Identification Management Data Impact assessment IT Bus. Monitoring Clarification & remediation

29. DR’s first DQP deployment using Trillium - Continued Ancillary systems ETL SAP REPORT SAP XI/PI SAP ECC 6 BI eComm SAP BW eComm TRILLIUM ETL drop zone SAP MDM eComm TSS ® • Structure • Extract • Transform • Load • Content • Quality Rules • Governance • Certification • Process • Integration • Productivity • Controls • Reporting • Accuracy • Flexibility • Scalability

30. DR Cleansing Data Process I II III IV V Locate Data Set Profile Data Set Conduct DQ Workshops DQ Business Rules Signoff Apply Business Rules (“Cleanse”) DQ Report Business Rules

31. The Business Rule Book • All Business Rules in Trillium are recorded in the Business Rule Book. • Each rule is approved and signed off by a Business Steward:

32. Applying Business Rules with Trillium

33. Sample Data Quality Report • Measures the level of data quality = rate of compliance with business rules (Trillium output) • Data Quality is measured monthly, after updates in Business Rules from previous report • Data Stewards responsible for acting on DQ Dashboard metrics

34. Implementing DG: DR’s Meta Data Registry

35. Meta Data Management Program (MTDP) • Meta Data Management is a key Data Governance process • Several frameworks available. DR uses ISO 11179. • The Meta Data Registry (MDR) is main tool supporting the MTDP. • To my knowledge, only one company offers ISO 11179-compliant MDR application, Data Foundations’ OneData MDR (www.datafoundations.com) • MDR stores and provides company information about: • Data semantics • Data description • Data accountability • Data location • Data mapping and relationship

36. DR MDR home page

37. MDR display: data element level

38. Wrap Up • Data Governance is a strategic and permanent investment • Data Governance dramatically reduces operational costs, mainly for the information-delivery processes • Data Governance helps reduce various risk exposures (financial, regulatory, market and strategic) • Data Governance requires an organizational change, from the Business/IT model, to the Business/Data/IT model • Data Governance requires a top “Data Governor”, and a dedicated Data Governance team • Data Governance exists through corporate Data Management programs

39. Thank you! Any questions?