1. BLM Data Quality

2. Purpose - after this course you will be able to… Describe why Data Quality matters Describe why Data Quality matters Define what is data quality Define what is data quality Show how Data Quality fits into the Data Life Cycle Show how Data Quality fits into the Data Life Cycle Explain the measures of data quality Explain the measures of data quality Describe the general data quality process Describe the general data quality process Demonstrate a knowledge of how to measure data quality Demonstrate a knowledge of how to measure data quality

3. Data Quality Data: A representation of facts, which when put into context becomes information that is used to draw a conclusion or make a decision. Data: A representation of facts, which when put into context becomes information that is used to draw a conclusion or make a decision. The only people who DO NOT need to worry about data quality are those who neither create nor use data. The only people who DO NOT need to worry about data quality are those who neither create nor use data.

4. Data Life Cycle - Evaluate and QA/QC The Evaluate Phase of the BLM Data Life Cycle is where numerous data evaluation factors are addressed The Evaluate Phase of the BLM Data Life Cycle is where numerous data evaluation factors are addressed The QA/QC location in the middle shows that data quality should be addressed throughout the entire life cycle The QA/QC location in the middle shows that data quality should be addressed throughout the entire life cycle QA/QC PLAN ACCESS ARCHIVE ACQUIRE EVALUATE MAINTAIN

5. What is Data Quality? BLM has defined data quality as “fitness for the intended use” BLM has defined data quality as “fitness for the intended use” Data quality may be considered as the sum of all data characteristics that determine how useful the data is in performing specific business processes.

6. Data Quality Dimensions Validity Validity Non-Duplication Non-Duplication Completeness Completeness Relationship Validity Relationship Validity Consistency Consistency Concurrency Concurrency

7. Data Quality Dimensions Timeliness Timeliness Accurate (to reality) Accurate (to reality) Accurate (to surrogate source) Accurate (to surrogate source) Precision Precision Derivation Integrity Derivation Integrity

8. Data Quality Dimensions Completeness (Features) Completeness (Features) Positional Accuracy Positional Accuracy Logical Consistency Logical Consistency Attribute Accuracy Attribute Accuracy Geometric Accuracy (Raster) Geometric Accuracy (Raster) Radiometric Accuracy (Raster) Radiometric Accuracy (Raster)

16. Data Quality Dimensions Geospatial Data quality measures can include the previous list as well as additional quality measuresGeospatial Data quality measures can include the previous list as well as additional quality measures Errors may be propagated from one dataset to the next and need to be measured and trackedErrors may be propagated from one dataset to the next and need to be measured and tracked

17. Data Quality Management Business Process Plan Measure Evaluate Report Use the table to define what combination of quality dimensions to apply. Plan who does what and when. Use the tools and methods selected in the planning step to acquire, compile, and summarize (quantitatively or qualitatively) the results for each Apply professional judgment to form an aggregate conclusion or report specific quantifiable measurements. Insert result into metadata file and report result to data maintenance function. Report quality measurement to the “Plan” process.

18. Beware of First Impressions Error Rates

19. An Error Rate is the number of times an error is made divided by the total number of entries An Error Rate is the number of times an error is made divided by the total number of entries (ER = # of Errors/Number of Entries) (ER = # of Errors/Number of Entries) HOWEVER, the trick with establishing what is an acceptable error rate and how you do quality control to prevent it is in determining what you are measuring against HOWEVER, the trick with establishing what is an acceptable error rate and how you do quality control to prevent it is in determining what you are measuring against

23. Points to Remember Determine the relative importance of the Fields you are entering (compared to other fields you are entering)Determine the relative importance of the Fields you are entering (compared to other fields you are entering) Adjust any quality control factors (# in sample, for instance) to ensure that accuracy level is properly accounted forAdjust any quality control factors (# in sample, for instance) to ensure that accuracy level is properly accounted for Target training and review to those fields with the highest accuracy level requirementTarget training and review to those fields with the highest accuracy level requirement Do not assume overall quality based on entries alone; ensure that the relative importance of certain entries are factored inDo not assume overall quality based on entries alone; ensure that the relative importance of certain entries are factored in Anyone can lie (or at least mislead) with statisticsAnyone can lie (or at least mislead) with statistics

24. Addressing Data Quality Data Quality Plans should be developed during project planning Data Quality Plans should be developed during project planning During all data acquisitions, regardless of method; collecting, buying, sharing, converting legacy data During all data acquisitions, regardless of method; collecting, buying, sharing, converting legacy data Review and analysis of existing Data Sets and Applications Review and analysis of existing Data Sets and Applications Whenever data are accessed and used Whenever data are accessed and used

25. Data Quality Support Data Quality Staff are available at the National Operations Center (NOC)- Branch of Resource Data in the Division of Resource Services Data Quality Staff are available at the National Operations Center (NOC)- Branch of Resource Data in the Division of Resource Services Data Quality Tools are available through the NOC Staff Data Quality Tools are available through the NOC Staff https://blmspace.blm.doi.net/wo/wodm/Pages/ https://blmspace.blm.doi.net/wo/wodm/Pages/ HomePage.aspx HomePage.aspx

26. Purpose - After this course you will be able to… Describe why Data Quality matters Describe why Data Quality matters Define Data Quality Define Data Quality Show how Data Quality fits into the Data Life Cycle Show how Data Quality fits into the Data Life Cycle Explain the Dimensions of data quality Explain the Dimensions of data quality Describe the general data quality process Describe the general data quality process Demonstrate a knowledge of how to measure data quality Demonstrate a knowledge of how to measure data quality

27. Summary Data Quality is the responsibility of every BLM employee who collects, manages, or uses data in their decision making processes. Data Quality is the responsibility of every BLM employee who collects, manages, or uses data in their decision making processes.