IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.
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
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
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”
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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
Notas do Editor
Slide Update: This slide is reviewed on an annual basis. Next update – April 2009. Speaker’s Notes: The information on this slide is taken from the Sustainability Report World Map featured on bp.com and will not be updated until the next Sustainability Report is published in April 2009. This slide is part of a set of seven slides that show where BP operates around the world. A simple option would be to use this slide only. However if you would prefer to link to more detail on a specific region then click on the Region buttons whilst in slide show. If you wish to incorporate these slides as part of your own slide pack you will need to adjust the links to your new slide numbers. To do this : 1)Right click on the button ( showing the region name) 2) Select Action Settings option 3) Go to Mouse Click 4) Select the Hyperlink option 5) Choose Slide option from the drop down menu 6) Preview and select the correct slide.
Most companies have silos of information. Why? People are busy and information sharing is seen as an “extra effort” WIIFM: There is no incentive to share information To remove those silos and encourage information sharing, remember: Reward drives behaviour. Make info sharing a carrot, not a stick Make it EASY for people Listen!! What do they want? Why aren’t they sharing now? Start Small, “Pick your Battles” wisely, and Communicate Find the ONE key pain that will have visibility Small, incremental, initial successes go a long towards long-term buy-in. i.e. “Don’t boil the ocean” Communicate successes back to the users Make the users an active part of the process Remember, we’re in the Blog era now! Users don’t want to be passive readers, but active participants. Allow users to update their own information (with the appropriate security and lifecycle controls in place) Make it easy Integrate governance into their daily workflow Automation and integration are key, for example: automatic updates to the metadata repository upon data model check-in. email notification when data definitions have changed