Open data quality: a practical view          Sharon Dawes        www.ctg.albany.edu        Open Data Roundtable   St. Pete...
Open data philosophy• Make “high value” government data sets available  in structured, machine readable form• Provide easy...
However . . .• The value of open data lies in data use.• This value of depends on the users’ point of view   – Users are i...
Sidney Harris, 2012
Sources of data problems           Conventional wisdom               Provenance                PracticesConsequences of th...
Where do open data come from?Administrative systems                         Embedded in program operations              Go...
Case 1: Give me shelter
Case 2: Cadastral records
Case 3: Where does the money go?
data
datatechnology
datatechnology             management
datatechnology                policy             management
context               datatechnology                policy             management
Data quality = fitness for use• Matters most from the user’s point of  view• Depends on the user’s purpose• Four types of ...
Dimensions of data qualityAccessibility                Extent to which data is available, or easily and quickly retrievabl...
Metadata637 pages
Data quality “tools”• For open data providers      •   Appreciate data as an asset, a source of value      •   Adopt infor...
Summary•   A philosophy of openness•   A government-wide strategy for data access•   A focus on value generation through d...
Thank youwww.ctg.albany.edu
Sharon Dawes (CTG Albany) Open data quality: a practical view
Sharon Dawes (CTG Albany) Open data quality: a practical view
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Sharon Dawes (CTG Albany) Open data quality: a practical view

  1. 1. Open data quality: a practical view Sharon Dawes www.ctg.albany.edu Open Data Roundtable St. Petersburg, October 9, 2012
  2. 2. Open data philosophy• Make “high value” government data sets available in structured, machine readable form• Provide easy one-stop access to all open data sets from all departments• No fees or other requirements for access or use• Add value through applications, challenges, promotions, data sharing communities
  3. 3. However . . .• The value of open data lies in data use.• This value of depends on the users’ point of view – Users are individuals and organizations outside government who use the data directly or through applications created by developers• Value depends on the quality of the data for a given use by a given user.
  4. 4. Sidney Harris, 2012
  5. 5. Sources of data problems Conventional wisdom Provenance PracticesConsequences of the problems Underuse Misuse Non-use Shifting costs and responsibilities
  6. 6. Where do open data come from?Administrative systems Embedded in program operations Governed by specific policies and laws Gathered in particular contexts for certain internal purposes By people with different kinds and levels of knowledge and expertise
  7. 7. Case 1: Give me shelter
  8. 8. Case 2: Cadastral records
  9. 9. Case 3: Where does the money go?
  10. 10. data
  11. 11. datatechnology
  12. 12. datatechnology management
  13. 13. datatechnology policy management
  14. 14. context datatechnology policy management
  15. 15. Data quality = fitness for use• Matters most from the user’s point of view• Depends on the user’s purpose• Four types of quality: – Intrinsic – Contextual – Representational – Access-related• Usually involves trade offs – e.g., timeliness vs. completeness (Wang & Strong, 1996, Ballou & Pazer, 1995)
  16. 16. Dimensions of data qualityAccessibility Extent to which data is available, or easily and quickly retrievableAppropriate Amount of Data Extent to which the volume of data is appropriate for the task at handBelievability Extent to which data is regarded as true and credible Extent to which data is not missing and is of sufficient breadth and depth for theCompleteness task at handConcise Representation Extent to which data is compactly representedConsistent Representation Extent to which data is presented in the same formatEase of Manipulation Extent to which data is easy to manipulate and apply to different tasksFree-of-Error Extent to which data is correct and reliable Extent to which data is in appropriate languages, symbols, and units, and theInterpretability definitions are clearObjectivity Extent to which data is unbiased, unprejudiced, and impartialRelevancy Extent to which data is applicable and helpful for the task at handReputation Extent to which data is highly regarded in terms of its source or content Pipino, et al, 2002Security Extent to which access to data is restricted appropriately to maintain its securityTimeliness Extent to which the data is sufficiently up-to-date for the task at handUnderstandability Extent to which data is easily comprehendedValue-Added Extent to which data is beneficial and provides advantages from its use
  17. 17. Metadata637 pages
  18. 18. Data quality “tools”• For open data providers • Appreciate data as an asset, a source of value • Adopt information policies to preserve and enhance value • Create and maintain metadata to support unknown users • Adopt stewardship practices• For open data users • Be skeptical, ask questions • Understand the nature and context of the data • Use data sets with caution • Combine data sets with great caution• A good approach: create and support data communities
  19. 19. Summary• A philosophy of openness• A government-wide strategy for data access• A focus on value generation through data use• Realistic appreciation for data problems and limitations• Support for hard and soft data quality tools plus active user engagement strategies
  20. 20. Thank youwww.ctg.albany.edu

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