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Test2008 Resurrecting The Prodigal Son Data Quality (


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Test2008 Resurrecting The Prodigal Son Data Quality (

  1. 1. Resurrecting the Prodigal Son - Data Quality “ Rise from Ashes: Battle of Data Quality Testing”
  2. 2. Speakers <ul><li>Bhoomika Goyal </li></ul><ul><ul><li>Working @ Microsoft for over an year </li></ul></ul><ul><ul><li>Engineer from Mumbai </li></ul></ul><ul><ul><li>Loves playing Chess, Solving Puzzles and Reading </li></ul></ul><ul><li>Raj </li></ul><ul><li> </li></ul><ul><ul><li>W orking @ Microsoft Business Intelligence COE </li></ul></ul><ul><ul><li>5.5 + years of Testing experience </li></ul></ul><ul><ul><li>Loves watching movies, reading suspense thrillers & playing cricket </li></ul></ul><ul><ul><li>Passion - Testing ( ) </li></ul></ul>
  3. 3. Horror Story <ul><li> Loss: $ 125 million </li></ul><ul><li> Reason: Discrepancy between the two </li></ul><ul><li> measures (rocket thrusts to newtons) </li></ul><ul><li>NASA Mars Climate Orbiter spacecraft LOST </li></ul>
  4. 4. Bad, Bad, Bad Data Quality Erroneous Mailing hit $611 billion for US businesses in 2002
  5. 5. DQ is not my problem? Think Again !!!!!
  6. 6. DQ Hot Candidates Data Movement Migrations Backups Restore Import Export Data Warehousing Business Intelligence OLTP OLAP CRM ERP
  7. 7. DQ Ishikawa Diagram Bad Decisions (Loss $ & Customers) DQ Reqmts not documented Lack of white box testing Data is dynamic CRM & ERPs Implementations Mergers / Take Over
  8. 8. Data Quality DQ is an indicator that tells about the health of the DATA
  9. 9. GOOD Data Quality DQ is good if data is fit to use for decision making
  10. 10. Data Quality Testing <ul><ul><li>Involves validating , monitoring & reporting various attributes of Data </li></ul></ul><ul><ul><li>like </li></ul></ul><ul><ul><li>accuracy , validity , timeliness etc </li></ul></ul>
  11. 11. DQ Checks Row Counts Consistency Referential Integrity Redundancy Usability Completeness Domain Integrity Timeliness Accuracy Validity
  12. 12. Row Count Check
  13. 13. Completeness Check
  14. 14. Among Voters seen Dead People US General Election: 4,755 deceased people voted
  15. 15. Consistency Check
  16. 16. A One-House, $400 Million Bubble Goes Pop $1,21, 000 overvalued at $ 400 million Govt. Expected $8 million as Tax Revenue
  17. 17. Accuracy Check
  18. 18. Validity Check
  19. 19. CD Mail Fraud <ul><li>Man received 22,260 CDs at discounted price by making each address different enough </li></ul> David Loshin 123 Main Street Any town, NY 11787 David Loshin 123 Main Street, Near Wal-Mart Any town, NY 11787
  20. 20. Redundancy Check
  21. 21. Referential Integrity Check
  22. 22. Domain Integrity Check
  23. 23. Timeliness
  24. 24. How do we test DQ? DQ Rule Engine Metadata Results Create Procedure RowCount (SrcTbl, TgtTbl) Begin Declare SRC, TGT Integer Select SRC = Count(*) from SrcTbl Select TGT = Count(*) from TgtTbl) If SRC = TGT Then Return “PASS” Else Return SRC – TGT End If End Metadata Results Row Count Logic Duplicate Logic Create Procedure Duplicate(Tbl) Begin Declare Dup Integer Select Dup = Count of Select * from Tbl GroupBy <<ColumnList>> Having count(*) > 1 If Dup = 0 Then Return “PASS” Else Return Dup End If End End Rule Tbl1 Tbl2 RC Emp Emp RI Emp Dept DC HR HR Rule Result Comment RC Pass - RI Fail 10 DC Pass -
  25. 25. You can’t improve what you can’t measure Threshold Time 5 % 10 % 100 % Data Quality Red: BAD DQ Yellow: Watch it Green: Good DQ
  26. 26. DQ Testing is your friend !!! <ul><li>High Data (Test) Coverage </li></ul><ul><li>Automation (Manual Effort Reduction) </li></ul><ul><li>High confidence about your data </li></ul><ul><li>Accurate Decisions </li></ul>
  27. 27. References <ul><li> </li></ul><ul><li>,295582,sid91_gci1251808,00.html </li></ul><ul><li> </li></ul>
  28. 28. <ul><li>Thank you. </li></ul><ul><li>[email_address] </li></ul><ul><li>[email_address] </li></ul>