IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.

1. Data Governance Challenges at BP IRM Data Governance Europe Conference London, February 2009 Chris Bradley Ken Dunn

3. 1. What is Data Governance?

13. 2. BP Roles and Approaches

15. Our global presence

18. Establish Local Accountabilities Local Information Director Local Specification Owners [local data] Data Steward(s) Data Quality Steward(s) Collaborating Specification Owners [Data common across many localities] + Collaborating Information Director(s) + IT & Business Implementation re-using common data

19. Principles, Asset Types and Governance Master Data MI/BI Data Transaction Unstructured Information Asset Types Unique definitions Recognised ownership Life-Cycle Management Information Principles Information Director Consumer Business Owner Steward Information Governance Business & Technical Accessible repositories

21. 3. BP Challenges & Case Studies

27. 4. Making Data Governance Happen

28. Model-Driven Data Governance Repository & Model-Driven Multiple Audiences: Multiple Levels of “Data” Objects: 3NF Subject Area Business Entity Logical Entity Physical Table Implemented Table / DDL Is Mapped To Is Mapped To Is Mapped To Is Mapped To

29. Establish a Corporate Repository

31. Measure Data Management Maturity Level 1 - Initial Level 2 - Repeatable Data Principles Delivering broad Quality & Re-use Ideal, Obtaining Optimal Value from Data As-Is To-Be As-Is As-Is As-Is To-Be To-Be To-Be Aspiration Obtaining Limited Benefits Operating in “Fire Fighting” Mode Undesirable Level 4 - Managed Level 5 - Optimised Level 3 - Defined Data Ownership Model does not exist. Data Owners, if any, evolve on their own during project rollouts (i.e. self appointed data owners). Data Ownership Model does not exist. Owners commissioned in the short-term for specific projects & initiatives. Ownership tends to be in form of “Data Teams” or “Super Users” that manage “all” data. Defined Data Ownership Model exists. Ownership Model is loosely applied to key data entities. Data Ownership Model is implemented for the key data entities. Governance process regularly reviews this model and its application, updating and improving as needed. Data Ownership Model has been extended such that the majority of data entities are now governed in a consistent manner. Data definitions unknown and/or inconsistent across the business(s). Key data defined in the short-term for specific projects & initiatives. Definitions are not leveraged from project to project and change often. Key data definitions exist to those who know where to look. Multiple sets of definitions exist because no rationalization/standardization occurs. Single set of data definitions exist for the key data entities. Definitions are published to a central location that is accessible to other programs, projects and users in secure manner. Data definitions extended beyond just “key” data entities. Common data definitions used throughout the businesses & functions. Data repository(s) does not exist. Disparate set of data repositories exist as a result of specific projects & initiatives. Little or no synch/communication across these tools. Multiple data repositories that synchronize and/or communicate via bespoke interfaces. A single integrated data repository houses the “record of reference” (single version of the truth). Other systems access the RoR from the central integrated repository. Central data repository is optimized via standard data collection & distribution mechanisms. Data accessible to other programs, projects and users in secure manner. Complete lack of procedures or controls for key data operations of create, read, update & delete. No warehouse and/or archiving processes in place. Short term procedures or controls for key data operations of create, read, update & delete. Ltd warehouse & archiving driven only by space constraints. Limited procedures or controls for key data operations of create, read, update & delete. Warehouse/archiving defined only for key data entities. Defined & consistent set of procedures & ctrls for key data operations of create, read, update & delete. Key data is proactively monitored so that arch’ing/warehousing occurs at optimal times. Defined & consistent set of procedures & ctrls extend beyond just key data. End-to-end automated “create to archive/warehouse” processes optimize the life-cycle mgmt. of all data. Recognized Ownership Unique Definitions Accessible Repositories Lifecycle Management

32. Maturity @ your company Data Governance Visibility Technology Trigger Peak of inflated expectations Trough of disillusionment Slope of enlightenment Plateau of productivity Typical Gartner “hype cycle” Avoid the abyss via investment in “sustain” activities Current position Beware this is not “fire & forget”

35. Questions? Chris Bradley Business Consulting Manager [email_address] +44 7501 224230 Ken Dunn Head of Information Architecture [email_address] +1 630 836 7805 Contact details