- A professional data organization can exist within a large company like Shell by managing data as a process across the organization and aligning roles and responsibilities. - Metadata can accelerate data quality improvement by providing information about the contents, location, and attributes of data that can help identify issues and opportunities to reduce errors. - Applying techniques from Six Sigma and Lean can help solve data quality issues by structuring improvement efforts, prioritizing projects, and quantifying the costs and risks of poor quality data to motivate necessary changes.

5. FACTS AND FIGURES – SHELL PERFORMANCE IN 2009 Source: 2009 Annual Report 2010 data available March 15th

7. The Problem: Does Data Quality Matter? Missing aircraft info could pose security threat NEW YORK (AP) — The Federal Aviation Administration 's aircraft registry is missing key information on who owns one-third of the 357,000 private and commercial planes in the U.S. — a gap the agency fears could be exploited by terrorists and drug trafficker s. While he served abroad, his credit was under siege Federal Reserve plays major role in fate of 2006 market Homeland Security contributed bad data to military intelligence database Greenspan is probably one of the most-intuitive economists because he concluded the Fed had bad data . Mr. Baur said that those operating the database had misinterpreted their mandate and that what was intended as an antiterrorist database became, in some respects, a catch-all for leads on possible disruptions and threats against military installations in the United States, including protests against the military presence in Iraq. Report: Low oil spill estimates rested on "unexplained assumptions" Bad data? Infection Prevention groups reject federal Healthcare Associated Infections report. 'An outdated and incomplete picture of HAIs' Faced with a critical federal report on the lack of progress against healthcare associated infections, the nation's leading infection prevention groups find themselves in the thankless position of having to challenge the methodology of the report without appearing to be in denial about HAIs. A 2005 survey by the U.S. Public Interest Research Group found 79% of credit reports contained errors , and 25% contained enough mistakes to prevent the individual from obtaining credit. Once the credit system accepts bad data, it can be next to impossible to clear. The reports authors say they cannot tell if the low estimates actually slowed the response to the oil spill, but say they likely undermined public confidence in BP and the federal response team, regardless.

8. The Problem Fr a gm ent a tio n Here a touch… There a touch… Everywhere a touch, touch…

9. The Solution: Manage Data as a Process in Finance P0 Data risk management Data quality assurance Meta-data management Data lifecycle management Audit and reporting Controls & compliance Assess, quantify and maximize the business value of enterprise data assets across the value chain (including suppliers, partners, customers) Capture, use, maintain, archive and delete data Define, measure, improve, and certify the quality (accuracy, validity, completeness, timeliness) of data Identify, assess, avoid, accept, mitigate, or transfer out risks Identify and establish control requirements for data and ensure compliance (including privacy, security, regulatory aspects) Measure and monitor data quality, risks, and efficacy of governance Capture, use, maintain semantic definitions for business terms and data models Create Value

10. The Solution: A process-based data management organization Data Manager Data Manager Data Manager Business Facing Data Manager Process Manager Process Manager Process Manager Process Manager “ Certification” Upstream Downstream Projects & Technology Finance, HR, Corporate, legal Businesses Process owners Accounts Aligned by Data Process Assets & Projects Organisation & People Real Estate Contracts Convenience Retail Products B2B Customers Card Customers Retail Site Customers Facilities and Equipment Materials and Services Vendors Procurement Contracts Lubes Products Etc… Data Competency Framework Data Teams

11. New Opportunities A Third-quartile data Top-quartile data Migrate: De-fragment and migrate data activities into a single team of dedicated data professionals Operate & measure: Operate and measure end-to-end data process performance: KPIs, controls, quality standards. Improve: Continuously improve data quality by addressing processes, tools, capabilities, quality standards 1 2 3 B

14. The Long and Winding Road Business Sponsored Go where the need is Keep the scope narrow Slippery Slopes Compromise Go slow at times … and then start again

18. Metadata: How we use it Fields with a significant number of updates in a given period Identification of fields not used in the design, but actually have data in them Fields critical to the success of a particular process but not covered by a current data quality standard

19. Metadata: What we are learning… Frequency and number of updates to each field in the customer master Fields with data in them, but not used in the design Fields included in the data quality compliance standards Discover fields that are candidates for mass upload tools Reduce effort by no longer populating unused fields Identify which fields are not in data quality standards that should be Data about Data:

21. Danger: Low Hanging Fruit! What happens when you pick it and it just grows back?

22. Structuring for Success Operations Improvement Logs Business Pain Points Prioritization of Improvement Projects Project Charter Project Charter Project Charter Operations Business Black Belt Coaching Develop Greenbelts Develop Greenbelts

23. Delivering the Goods Reducing Costs Increasing speed Improving quality

25. Everybody has a model

26. What works for us – FMEA (Failure Mode Effect Analysis) Cost of providing the data PLUS Cost of compliance to the standard VS. Cost of non-compliance to the standard (Requires RISK BASED analysis

29. Takeaways Footer: Title may be placed here or disclaimer if required. May sit up to two lines in depth. May appear on Title pg. In pursuit of Data Quality: When the Business demands results