GIS Data Quality

1. GIS Data Quality Producing better data quality through robust business processes BrightStar TRAINING Kim Ollivier

2. Schedule Day One Suggested breaks for the following times: Start: 9:00 Session 1 ( 90 min) Morning tea: 10:30 to 10:45 Session 2 ( 105 min) Lunch: 12:30 to 1:30 Session 3 ( 90 min) Afternoon tea: 3:00 to 3:15 Session 4 ( 105 min) Finish: 5:00 Each session will have an exercise or interactive discussion

3. Today • Introduction • What causes poor quality • Lunch • Assessing Quality processes • GIS upgrade project examples

4. Tomorrow • Metadata • Designing rules • Lunch • Data warehouse and ETL • Feature maintenance

5. Overview • Introduce yourself • Your goals for this course? • Build a data quality system • Avoid the worst traps • Be able to describe a project scope • Budget, timeline, priorities

6. Sections of course based on With permission from the author ISBN 978-0-09771400-2

7. What is Data Quality? “If they are fit for their intended uses in operations, decision making and planning.” “If they correctly represent the real-world construct to which they refer.”

8. Spatial Accuracy

10. Statistical Accuracy False Positives False negatives Completeness Score = Relevant Relevant + Missing Accuracy Score = Relevant - Errors Relevant Overall Score = Relevant - Errors Relevant + Missing

11. Completeness • LINZ Bulk Data Extract • metadata\meta.html

12. Data Profiling • Find out what is there • Assess the risks • Understand data challenges early • Have an enterprise view of all data

13. Profile Metrics • Integrity • Consistency • Completeness, Density • Validity • Timeliness • Accessibility • Uniqueness

14. Security • Confidentiality • Possession • Integrity • Authenticity • Availability • Utility

15. Consistency • Discrepancies between attributes • Exceptions in a cluster • Spatial discrepancies

18. A GIS Data Quality System Assess Data Quality Assessment Data Profiling Improve Recognise Prevent Data Cleaning Monitoring Data Integration Interfaces Ensuring Quality of Data Conversion and Consolidation Building Data Quality Metadata Warehouse Monitor Recurrent Data Quality Assessment

19. Course examples • LINZ coordinate upgrade 1998-2003 • NSCC services upgrade 2008 • Valuation roll structure and matching • ETL of utilites from SDE to Autocad • Address location issues NAR, DRA Documents and examples on memory stick

20. Exercise 1:Nominate your database Select a representative example dataset for later discussion • You may be responsible for • Or, you have to integrate • Or, you have to load it • Or, you supply it to others Morning Tea

21. Assessing Quality • Project steps • Required roles • Defining the objectives • Designing rules • Scorecard and Metadata • Frequency of assessment • Common mistakes

22. Processes causing data decay Processes bringing data from outside Initial Data Conversion Changes not captured System Upgrades System Consolidations New Data Uses Manual Data Entry Loss of Expertise Batch Feeds Process Automation Real-Time Interfaces Processes Affecting Data Quality Database    Processes changing data from within Data processing Data cleaning Data purging

23. Outside: Initial Data Conversion • Define data mapping • Extract, Transform, Load (ETL) • Drown in Data Problems • Find Scapegoat 

24. Outside: System Consolidation • Often from mergers (Auckland?) • Unplanned, unreasonable timeframes • Head-on two car wreck • Square pegs into round holes • Winner – loser merging (50% wrong)

25. Outside: Manual Data Entry • High error rate • Complex and poor entry forms • Users find ways around checks • Forcing non blanks does not work

26. Outside: Batch Feeds • Large volumes mean lots of errors • Source system subject to changes • Errors accumulate • Especially dangerous if triggers activated

27. Outside: Real-Time Interfaces • Data between db’s in synchronisation • Data in small packets out of context • Too fast to validate • Rejection loses record, so accepted • Faster or better but not both!

28. Decay: Changes Not Captured • Object changes are unnoticed by computers • Retroactive changes may not be propagated

29. Decay: System Upgrades • The data is assumed to comply with the new requirements • Upgrades are tested against what the data is supposed to be, not what is actually there • Once upgrades are implemented everything goes haywire

30. Decay: New Data Uses • “Fitness to the purpose of use” may not apply • Acceptable error rates may now be an issue • Value granularity, map scale • Data retention policy

31. Decay: Loss of Expertise • Meaning of codes may change over time that only “experts” know • Experts know when data looks wrong • Retirees rehired to work systems • Auckland address points were entered on corners and the rest guessed, later used as exact.

32. Decay: Process Automation • Web 2.0 bots automate form filling • Transactions are generated without ever being checked by people • Customers given automated access are more sensitive to errors in their own data

33. Within: Data Processing • Changes in the programs • Programs may not keep up with changes in data collection • Processing may be done at the wrong time

34. Special GIS Data Issues • Coordinate data not usually readable • Data models CAD v GIS • Fuzzy matching is not Boolean (near) • Atomic objects harder to define • Features have 2,3,4,5 dimensions • Projection systems are not exact • Topology requires special operators

35. Within: Data Purging • Highly risky for data quality • Relevant data may be purged • Erroneous data may fit criteria • It may not work the next year

36. Within: Data Cleaning • En masse processes may add errors • Cleaning processes may have bugs • Incomplete information about data

37. Assessing Data Quality • Data profiling • Interview users • Examine data model • Data Gazing

38. Data Gazing • Count the records • Just open the sources and scroll • Sort and look at the ends • Run some simple frequency reports • See if the field names make sense • What is missing that should be there Lunch

39. Data Cleaning • There are always lots of errors • It is too much to inspect all by hand • Data experts are rare and too busy • It does not fix process errors • You may make it worse

40. Automated Cleaning • The only practical method • Needs sophisticated pattern analysis • Allow for backtracking • Data quality rules are interdependent

41. Common Mistakes • Inadequate Staffing of Data Quality Teams • Hoping That Data Will Get Better by Itself • Lack of Data Quality Assessment • Narrow Focus • Bad Metadata • Ignoring Data Quality During Data Conversions • Winner-Loser Approach in Data Consolidation • Inadequate Monitoring of Data Interfaces • Forgetting About Data Decay • Poor Organization of Data Quality Metadata

42. Metadata • Data model • Business rules, relations, state • Subclasses (lookup tables) • GIS Metadata (NZGLS or ISO) XML • Readme.txt Includes everything known about the data

43. Data Exchange • Batch or interactive • ETL (Extract Transform Load) • Replication • Time differences in data

44. GIS in Business Processes • Integrates many different sources • Spatial patterns are revealed • Display thousands of records simultaneously with direct access • Location now seen as important

45. Scorecard DQ Score Score Summary Score Decompositions Intermediate Error Reports Atomic Level Data Quality Information

46. Case Study • Outline a GIS data quality system • Measles Chart • Prioritise • Interview • Build up a scorecard Afternoon Tea

47. Assessment Exercise • Split into pairs • Interview one person about their dataset • Collect basic information • Devise a strategy for a profile • Rotate pair with another • Interview other person • Verbal reports to class

48. Major Upgrade Projects • LINZ Coordinate upgrade • NSCC Coordinate upgrade

49. References • Data Quality Assessment – Arkady Maydanchik