BLM Data Quality

1. BLM Data Quality

2. Purpose- after this course you will be able to… • describe why Data Quality matters • define what is data quality • show how Data Quality fits into the Data Life Cycle • explain the measures of data quality • describe the general data quality process • 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. • The only people who DO NOTneed 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 QA/QC location in the middle shows that data quality should be addressed throughout the entire life cycle PLAN ARCHIVE ACQUIRE QA/QC EVALUATE MAINTAIN ACCESS

5. What is Data Quality? • 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 Measures • Conformity to standards • Completeness • Currency • Consistency • Accuracy • Relevance • Complaints or any other Known/ Suspected Problems

12. Data Quality Measures • Geospatial 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 tracked

13. Error Rates Beware of First Impressions

14. Error Rates • 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) • 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

18. Points to Remember • 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 for • Target 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 in • Anyone can lie (or at least mislead) with statistics

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

20. 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 Tools are available through the NOC Staff • https://blmspace.blm.doi.net/wo/wodm/Pages/HomePage.aspx

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