Table of Contents

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  1. Preface
  2. Introduction to Test Data Management
  3. Test Data Manager
  4. Projects
  5. Policies
  6. Data Discovery
  7. Creating a Data Subset
  8. Performing a Data Masking Operation
  9. Data Masking Techniques and Parameters
  10. Data Generation
  11. Data Generation Techniques and Parameters
  12. Working with Test Data Warehouse
  13. Analyzing Test Data with Data Coverage
  14. Plans and Workflows
  15. Monitor
  16. Reports
  17. ilmcmd
  18. tdwcmd
  19. tdwquery
  20. Appendix A: Data Type Reference
  21. Appendix B: Data Type Reference for Test Data Warehouse
  22. Appendix C: Data Type Reference for Hadoop
  23. Appendix D: Glossary

Data Coverage Analysis Overview

Data Coverage Analysis Overview

You can analyze the data in a
data set
to check whether you have a minimum amount of data for specific test cases.
Create a data coverage task to perform pairwise data analysis and to create a visual representation of the data coverage in a
data set
. You can assess the quality of test data by analyzing combinations of values in any two columns. You can change the combinations to ensure that you cover all valid combinations of values. You can improve the quality of the data and move data across categories to meet the minimum data threshold that you require.
For example, you need to test a banking application that offers credit cards to customers. You create a
data set
with tables that contain data related to the credit card types and the criteria for each. The data could include location and the minimum balance required for each type of card. The
data set
also contains tables with customer information. To understand whether you have sufficient data for the different test cases, you need to analyze the amount of data that you have in different categories. For example, you need to know if you have sufficient data for each type of card in each location.
When you analyze the data, you also see if there is more data than what you require for some locations. You can then update the data records across columns or data ranges to ensure that you have sufficient data density for test cases.