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ETIS09 - Data Quality: Common Problems & Checks - Presentation

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ETIS09 - Data Quality: Common Problems & Checks - Presentation

  1. 1. Data Management & Warehousing Data Quality:Common Problems & Checks Date: 24 April 2009 Location: Zagreb, Croatia David M. Walker davidw@datamgmt.com +44 (0) 7050 028 911 - http://www.datamgmt.com
  2. 2. Agenda•! Introduction•! Common Problems•! Automated Checking•! Profiling Checks•! Conclusions24 April 2009 © 2009 Data Management & Warehousing Page 2
  3. 3. Introduction•! Data Quality problems are a SOURCE SYSTEM issue and an ETL issue –! They just manifest themselves in the data warehouse•! Prevention is better than cure –! Fixing the source system or the ETL is ALWAYS cheaper and more effective than cleaning the data in the ETL or in the Data Warehouse itself•! Data Quality is a continuous process –! It is never finished and always needs to be monitored24 April 2009 © 2009 Data Management & Warehousing Page 3
  4. 4. The Impact of Poor Data Quality•! Devalues the data warehouse –! Discourages people from trusting or using the system and therefore curtailing the life of the data warehouse•! Highlights failings in the source system and/or the business process –! Businesses would rather fix at any cost in the data warehouse and pretend that there isn’t a source system problem24 April 2009 © 2009 Data Management & Warehousing Page 4
  5. 5. Common Problems•! 11 types of problem that account for the most common problems•! They usually reflect poor design and/or implementation of systems•! Most can be fixed or monitored and managed to limit the impact24 April 2009 © 2009 Data Management & Warehousing Page 5
  6. 6. Referential Issues•! Keys that are not unique –! Systems that do not enforce the unique primary key or flat files or spreadsheets –! Also generated by ETL that creates a surrogate key incorrectly•! Referential Integrity Failures –! Where referentially integrity is not enforced values in the child table are created that are not in the parent table24 April 2009 © 2009 Data Management & Warehousing Page 6
  7. 7. Data Type Issues•! Format Errors –! Typically in Date/Time type fields –! 02/04/2009 2nd April (UK) or 4th Feb (US)•! Inappropriate Data Types –! Storing Dates in Character Strings 20090624 as YYYYMMDD format string –! But what about 20090230?24 April 2009 © 2009 Data Management & Warehousing Page 7
  8. 8. Data Model Issues•! De-normalised tables –! Commonly created for performance reasons –! Inherently duplicates data –! Often gets out of sync•! Data/Column Retirement –! Upgrade to system retires a column –! ETL continues to use the old column•! Poor Table/Column Naming –! Don’t assume that a column does what it says –! Don’t assume that a column is still being used for it’s original purpose24 April 2009 © 2009 Data Management & Warehousing Page 8
  9. 9. Data Content Issues•! Null Values –! Systems that have many optional fields will often have missing values –! Null values allow rows to be silently omitted from queries•! Inappropriate Values –! Databases allow special characters and/or leading/trailing white space –! “DataspaceQuality” != “DatatabQuality”24 April 2009 © 2009 Data Management & Warehousing Page 9
  10. 10. Data Feed Issues•! Missing Data –! Where a stream of files are loaded by ETL if one is dropped it can go un-noticed –! Common with CDR type loads in Telcos•! Late Data –! A short term data quality issue –! Leaves users believing there is a problem –! Produces inconsistent reporting over time24 April 2009 © 2009 Data Management & Warehousing Page 10
  11. 11. Automated Checking•! Regularly run checks•! Broad Coverage across systems –! 100s and 1000s not 10s of queries –! Run in a low priority loop in the background•! Used against: –! Sources –! Data Staging –! Data Warehouse•! No Product Required –! We often implement this as a controlling shell script and lots of small scripts, one for each check24 April 2009 © 2009 Data Management & Warehousing Page 11
  12. 12. Trending•! Absolute Trending –! Track an expected value over time •! e.g. Returned Mail is usually less than 500 items per day •! If the value is <500 status is green, 501 to 1000 amber and >1000 red•! Statistical Process Control (SPC) Trending –! Track an expected value where the value changes over time •! e.g. Telco CDRs – expect more as the company grows •! Don’t want to be continuously changing the threshold •! Compare current load to historical means •! If current load within 2 Standard Deviations – Green, 3 Standard Deviations – Amber, 4 or more Standard Deviations - Red24 April 2009 © 2009 Data Management & Warehousing Page 12
  13. 13. Flow Control•! Flow Control –! ETL manipulates data •! Joins, De-duplicates, Filters, Aggregates, etc –! Use the formula: Source Count - Filtered Count – DeDup Count – Target Count = 0•! Trusted Source –! Compare the result with a third system –! e.g. Does the Count of Switch CDRs = Count of those processed by the billing system Count of those processed in the DWH24 April 2009 © 2009 Data Management & Warehousing Page 13
  14. 14. Business Rules Based•! Specific rules to match known business rules –! Account holders > 18 (Sys Date – DoB) –! Account holders < 115 –! Credit Card numbers are 16 digits long –! Number of accounts without a status•! Result should yield Zero24 April 2009 © 2009 Data Management & Warehousing Page 14
  15. 15. Automated Checking - Ops•! Managed by exception –! Red given priority –! Amber are always followed up•! Massive number of checks –! 100’s are good –! 1000’s are better•! Presentation –! Alerts, RAG, Graphical, Numerical, etc.24 April 2009 © 2009 Data Management & Warehousing Page 15
  16. 16. Data Profiling Checks•! Run manually because they need to be interpreted by a human•! Leads to new business rules being added to the automated checks•! Can be done with simple reporting tools or commercial data profiling tools24 April 2009 © 2009 Data Management & Warehousing Page 16
  17. 17. Frequency Outliers•! Count discreet values in a table and check items with many more or less than normal –! e.g. DoB 01-01-01 many times more common than any other value indicates source default and something that needs work –! e.g. Count of SMS messages significantly lower on a given day may equate to a genuine system failure and therefore not a DQ problem24 April 2009 © 2009 Data Management & Warehousing Page 17
  18. 18. Maximum & Minimum•! Determine what a valid range for any value should be –! e.g. age between 18 and 115 –! Immediately finds individual data quality issues that can be resolved –! Allows an analyst to create new business rules to prevent future problems24 April 2009 © 2009 Data Management & Warehousing Page 18
  19. 19. Sequential Keys•! If a system has a sequential key: Max Value – Min Value – Count = 0•! If this is true – is it too perfect for an operational system and therefore test data•! If this is false – what has caused the gaps, are the deletions intentional?24 April 2009 © 2009 Data Management & Warehousing Page 19
  20. 20. Data Types•! Validation of mis-used data types before loading –! e.g. Dates in Character fields –! Format: YYYYMMDD –! Check MM between 01 and 12 –! Check DD between 01 and 31 –! Check MMDD does not include 0230, 0231 –! etc.24 April 2009 © 2009 Data Management & Warehousing Page 20
  21. 21. Skewed Pattern Profiling•! Looking for specific patterns in data –! e.g. UK National Insurance Numbers (?) have the format AA 99 99 99 A –! Pattern match all values looking for exceptions•! Number Lengths are a special case –! e.g Credit Card Numbers are 16 digits long24 April 2009 © 2009 Data Management & Warehousing Page 21
  22. 22. Content Checking•! Content Checking is the manual review of character strings•! Needs a good understanding of the nature of the data•! Often determines the need to do analysis of other types24 April 2009 © 2009 Data Management & Warehousing Page 22
  23. 23. Nulls & White Space•! Nulls –! Fields that have large proportion of nulls are usually not useful –! Also common is default status of null (e.g. Account us either closed or null)•! White Space –! Not Null fields with a single space –! Tab instead of space –! Leading/Trailing white space –! Double White Space: “DavidSpaceSpaceWalker”24 April 2009 © 2009 Data Management & Warehousing Page 23
  24. 24. Punctuation & Control Chars•! Punctuation –! CSV files that are not properly quoted perform field shifts –! Address lines with extra commas•! Control Characters –! Data fields that contain ASCII character codes 0 to 31 and 127 to 159 are often ‘invisible’ when viewed in queries but cause failures –! Also be aware of ‘code-page’ specifics24 April 2009 © 2009 Data Management & Warehousing Page 24
  25. 25. Problem Management Matrix24 April 2009 © 2009 Data Management & Warehousing Page 25
  26. 26. Continuous DQ Process24 April 2009 © 2009 Data Management & Warehousing Page 26
  27. 27. Quality is FREE …… as long as you are prepared to INVEST HEAVILY in itPhilip Crosby 1980Especially true ofData Quality24 April 2009 © 2009 Data Management & Warehousing Page 27
  28. 28. Data Management & Warehousing Data Quality:Common Problems & Checks Thank You David M. Walker davidw@datamgmt.com +44 (0) 7050 028 911 - http://www.datamgmt.com

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