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Dallas Data Brewery Meetup #2: Data Quality Perception


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Brief, introductiory slides for second Dallas Data Brewery meetup. Topic: Data Quality Perception.

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Dallas Data Brewery Meetup #2: Data Quality Perception

  1. 1. Data QualityPerceptiondata breweryDallas Data Brewery, June 2013
  2. 2. Topic■ What is "high quality data"?■ What are data quality expectations?you, people or businesses you know have■ Business issues and data qualityHow to you deal with it?■ What happens when you ignore it?
  3. 3. What is data quality ?
  4. 4. Dimensions■ completeness – data provided■ accuracy – reflecting real world■ credibility – regarded as true■ timeliness – up-to-date■ consistency – matching facts across datasets■ integrity – valid references between datasets... and there are more
  5. 5. Fallacies■ “good data are error-free and valid”■ “improving quality means cleansing”■ “it is IT problem”■ “it can be fixed”
  6. 6. Short Story:CompletenessOpen Public Procurements
  7. 7. from this...
  8. 8. ... to this:
  9. 9. 0%25%50%75%100%2005-32005-52005-72005-92005-112006-12006-32006-52006-72006-92006-112007-12007-32007-52007-72007-92007-112008-12008-32008-52008-72008-92008-112009-12009-32009-52009-72009-92009-112010-12010-32010-52010-72010-9betterhave it allnoneQuality measurecompleteness: 55%how many % of the field is filled andsuccessfully processed?
  10. 10. type 1 type 2+
  11. 11. how many % of the field is filledand successfully processed?0%25%50%75%100%2005-32005-52005-72005-92005-102005-122006-32006-52006-72006-92006-112007-12007-32007-52007-72007-92007-102007-122008-32008-52008-72008-92008-112009-12009-32009-52009-72009-92009-112010-12010-32010-52010-72010-9Quality measurecompleteness: 88%betterhave it allnone
  12. 12. What does that mean:“high quality data?”?
  13. 13. 85% ?
  14. 14. Conclusion
  15. 15. appropriate for givenpurpose
  16. 16. Data Project■ define data quality requirements■ measure during development■ provide data quality report
  17. 17. More topics■ Data quality measurementindicators, probes■ Data quality managementroles, processes, impact■ Data cleansing
  18. 18. Thank ■ 