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Tom Kunz

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Tom Kunz

  1. 1. Data Quality APAC Congress 2011 In Pursuit of Data Quality: When the Business Demands Results <ul><li>Tom Kunz </li></ul><ul><li>Data Manager, Downstream, Shell </li></ul>Finance Operations - Data Use this area for cover image (height 6.5cm, width 8cm)
  2. 2. Today’s Agenda <ul><li>  </li></ul><ul><li>Who is Shell? </li></ul><ul><li>Can a professional data organization exist in a big company? </li></ul><ul><li>Can a practical data governance structure really be created? </li></ul><ul><li>How can metadata accelerate data quality improvement? </li></ul><ul><li>Why would I want to use six sigma and lean techniques to solve data quality issues? </li></ul><ul><li>Does knowing the cost of poor data quality really help? </li></ul><ul><li>What are the key takeaways? </li></ul>In pursuit of Data Quality:  When the Business demands results
  3. 3. Business Overview Some Data About Shell <ul><li>Who is Shell? </li></ul><ul><li>1.0 </li></ul>
  4. 4. Business Overview UPGRADER PLANT ON AND OFFSHORE OIL AND GAS REFINERY GAS TO LIQUIDS PLANT BIOFUELS PLANT CHEMICAL PLANT LNG LIQUEFACTION PLANT LNG REGASIFICATION TERMINAL WIND TURBINES POWER STATION <ul><li>CHEMICAL PRODUCTS </li></ul><ul><li>USED FOR: </li></ul><ul><ul><li>Plastics </li></ul></ul><ul><ul><li>Coatings </li></ul></ul><ul><ul><li>Detergents </li></ul></ul><ul><li>REFINED OIL PRODUCTS </li></ul><ul><ul><li>(Bio) Fuels </li></ul></ul><ul><ul><li>Lubricants </li></ul></ul><ul><ul><li>Bitumen </li></ul></ul><ul><ul><li>Liquefied petroleum gas </li></ul></ul><ul><li>GAS AND ELECTRICITY </li></ul><ul><ul><li>Industrial use </li></ul></ul><ul><ul><li>Domestic use </li></ul></ul>
  5. 5. FACTS AND FIGURES – SHELL PERFORMANCE IN 2009 Source: 2009 Annual Report 2010 data available March 15th
  6. 6. The problem The solution The new opportunities <ul><li>Can a professional data organization exist in a big company? </li></ul><ul><li>2.0 </li></ul>
  7. 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 &quot;unexplained assumptions&quot; 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. 8. The Problem Fr a gm ent a tio n Here a touch… There a touch… Everywhere a touch, touch…
  9. 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. 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. 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
  12. 12. The Impossible Dream The Long and Winding Road I’m A Believer <ul><li>Can a practical data governance structure really be created? </li></ul><ul><li>3.0 </li></ul>
  13. 13. The Impossible Dream <ul><li>Where everything just works….. </li></ul>Footer: Title may be placed here or disclaimer if required. May sit up to two lines in depth. <ul><li>Business understands master data </li></ul><ul><li>Business takes ownership for data quality </li></ul><ul><li>Process designers are valued </li></ul><ul><li>Continuous improvement is a mindset </li></ul><ul><li>Results are more important than politics </li></ul><ul><li>E2E process is understood </li></ul><ul><li>Data gatherers know what to do </li></ul><ul><li>Data processes are managed </li></ul><ul><li>Feedback is welcomed </li></ul>
  14. 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
  15. 15. I’m a Believer Data Value Owner Data Gatherer Process Manager Data Operations <ul><li>Business understands master data and its processes </li></ul><ul><li>Business takes ownership for data quality </li></ul><ul><li>Process designers are valued </li></ul><ul><li>Continuous improvement is a mindset </li></ul><ul><li>Results are more important than politics </li></ul><ul><li>E2E process is understood </li></ul><ul><li>Data gatherers know what to do </li></ul><ul><li>Data processes are managed </li></ul><ul><li>Feedback is welcomed </li></ul>
  16. 16. What it is How we used it What we learned <ul><li>How can metadata accelerate data quality improvement? </li></ul><ul><li>4.0 </li></ul>
  17. 17. Metadata: What it is <ul><li>Data about Data </li></ul><ul><ul><ul><li>Describes the contents of the information </li></ul></ul></ul><ul><ul><ul><li>Provides documentation or information about a specific piece of information </li></ul></ul></ul><ul><ul><ul><li>Include elements and attributes such as a name, size or type </li></ul></ul></ul><ul><ul><ul><li>Can represent the location or ownership of the file </li></ul></ul></ul><ul><ul><ul><li>Any other information that needs to be noted about the data </li></ul></ul></ul><ul><ul><ul><li>Can be information about frequency or volume of updates </li></ul></ul></ul>
  18. 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. 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:
  20. 20. Danger: Low Hanging Fruit! Structuring for success Delivering the goods <ul><li>Why would I want to use six sigma and lean techniques to solve data quality issues? </li></ul><ul><li>5.0 </li></ul>
  21. 21. Danger: Low Hanging Fruit! What happens when you pick it and it just grows back?
  22. 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. 23. Delivering the Goods Reducing Costs Increasing speed Improving quality
  24. 24. Everybody has a model What works for us When it just doesn’t matter…much <ul><li>Does knowing the cost of poor data quality really help? </li></ul><ul><li>6.0 </li></ul>
  25. 25. Everybody has a model
  26. 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
  27. 27. When COPDQ just doesn’t matter…. much <ul><li>Business is energized </li></ul><ul><li>Resources are available </li></ul><ul><li>Hot spots are known </li></ul>
  28. 28. <ul><li>What are the key takeaways? </li></ul><ul><li>7.0 </li></ul>In Pursuit of Data Quality: When the business demands results
  29. 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
  30. 30. <ul><li>Q & A </li></ul>

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