Test data management requires skills like understanding business, user requirements, strategic view to data provisioning mechanisms – synthetic, production extraction, subsetting, masking, etc, understanding the technical architecture, components, data flows, underlying database schemas – all play out to be crucial role in the test data management.
Often test teams lack access to data for their testing needs. DB teams often provision the data in non-automated way, which forces the testing teams to push the testing to later cycles/sprints. This results in unearthing defects late in the life cycle, thereby increasing the risk of such digital transformation initiatives. The lack of emphasis on quality of data often derails such exercises.
Not all engagements can afford synthetic data generation mechanism. Quick turnaround time requires to adopt production data extraction, subsetting and masking approaches. Often the lack of understanding the relevance to compliance can lead to sensitive being used in testing exercises, potentially leading to huge penalties associated with non-compliance.