Test Data Life Cycle

In the previous posts, I explained about the various concepts surrounding Test data creation and maintenance, namely Data Subset, Data Masking, Test Data Ageing, Test Data Refresh, Data Archive and Gold Copy.  In this post, I will focus on the life cycle of Test Data.

So what is meant by a life cycle.  Life Cycle is the various stages that a product/service/artifact goes through before attaining its end of life.  So a Test Data Cycle explains the various stages through which the test data goes through in order to reach its end of life or alternatively start a recurring life cycle.

So similar to a test life cycle or a software development life cycle, Test Data goes through the following phases.  They are

Requirement Gathering & Analysis

This is pretty straightforward.  In this phase, the test data requirements pertaining to the test requirements are gathered.  They are categorized into various heads

• Pain Areas
• Data Sources
• Data Security/Masking
• Data Volume requirements
• Data Archival requirements
• Test Data Refresh considerations
• Gold Copy considerations

This phase is typically carried out in the form of a TDM assessment or Test Data Assessment.  Since that topic requires separate attention, I will dedicate a blog post to it.

Planning & Design

Data Masking in TDM

In my previous posts, I explained about the Challenges in Production Cloning.  One of the major challenges in the Production Cloning approach is Data Security.  This post will focus on the solution for Data Security, Data Masking.

As already explained, Data Masking is the process of masking the sensitive fields from the complete data set.    The whole objective of data masking is to ensure that no sensitive data is leaked into non-production regions like the Dev and Testing regions.

What are the sensitive fields that needs to be masked?  That basically depends on the project needs.  But some of the generic fields that need to be masked are:

• Personal information like First names, Last Names, Email IDs, DOB, Phone & Fax numbers, SSN Numbers, National Insurance Numbers, Other national unique identifiers.
• In Banking, Financial Services & Insurance industry - Bank Balances, Account numbers, Credit card numbers, Policy numbers, etc.
• In Healthcare industry - PHI attributes like Medical record numbers, Member IDs, etc.

This list is by no means exhaustive, but will give a fair idea of how many fields are sensitive in nature that needs to be handled with care.  Any lapse in masking any of these fields might have a big impact on the Organization as a whole.

Challenges in Data Masking