1. Data Quality Class 3

2. Goals Dimensions of Data Quality Data Extraction, Transformation, and Loading Data Cleansing Project

3. Dimensions of Data Quality Poor data quality is similar to obscenity- –It seems as if there are no real ways to measure it, but you know it when you see it! In reality, data quality can be measured –The frame of refernce for measurement is different Dimensions of Data Quality

4. Dimensions of Data Quality 2 Data Models Data Values Data Presentation Data Policy

5. Example: Sales Database

6. Data Quality of Data Models Clarity of definition Comprehensiveness Flexibility Robustness

7. Data Quality of Data Models 2 Essentialness Attribute granularity Precision of domains Homogeneity

8. Data Quality of Data Models 3 Naturalness Identifiability Obtainability Relevance

9. Data Quality of Data Models 4 Simplicity Semantic Consistency Structural Consistency

10. Data Quality of Data Values Accuracy Null values Completeness Consistency Currency

11. Accuracy Agreement with establsihed sources Database of record Other corroborative sources

12. Null Values Null vs. Missing –Unavailable –Not appliable –No value –Not classified –Truly null

13. Completeness Mandatory attributes require values Optional attributes may hold values (when and how?) Inapplicable attributes may not have a value (also when and how?) Completeness constraints

14. Consistency Are values in one set consistent with values in another set? Consistency relations between attributes in the same table Consistency assertions across acolumns Consistency relationships between tables

15. Currency/Timeliness What data is current? How is it kept up-to-date? Time expectations for accessibility to data

16. Data Quality of Data Presentation Appropriateness Correct Interpretation Flexibility Format Precision

17. Data Quality of Data Presentation 2 Portability Representation Consistency Representation of Null Values

18. Data Quality of Data Policy Access Metadata Privacy Fault-tolerance Security