6 May, 2021 | James Briers - CTO & Co-Founder at IDS
As technology – and our own understanding of data management – improves, it is easy to try to run before you can walk, but we need to often revisit the basics and understand that a solid foundation is necessary to reap the rewards of test data management, so I’m going to outline some simple strategies you can use to improve your results.
These three simple steps can be combined to de-risk your projects and increase their likelihood of completion on time and on budget.
Preparing your Test Data:
Whilst it is of paramount importance not to test on production data IE live, real data, it is important that your testing data represents an accurate replica of real data.
Creating and preparing large volumes of this representative data can take a long time and is seen to be a difficult task.
I am happy to say that thanks to software innovations such as iData – which you can download the brochure for here – this doesn’t have to be the case.
Automate, Automate, Automate:
Most data management initiatives – the well-thought out ones at least – will take into account the three critical components of People, Process & Technology. Your Test Data Management strategy should also look at these three components and constantly ask which parts can be automated.
Data Refreshes, for instance, are an area of low-hanging fruit for your automation efforts. I don’t know why anyone would still be doing this in 2022 and beyond when tools can automate this for you.
Obfuscate, Obfuscate, Obfuscate:
Data Obfuscation or “Masking” refers to the hiding your original data behind a modified version of it, so as to protect certain elements such as privacy.
This is an increasingly important element of data management thanks to more mainstream privacy laws such as the General Data Protection Regulation. As a result of this legislation new data records such as IP addresses can count as Personally Identifiable Information (PII) and therefore should be hidden if recorded in case of breaches during testing or otherwise.
Ideally, those organizations with a more mature approach to test data management should have tools that are able to help with the generation of synthetic data, preparing it for use and obfuscation capabilities for use during the testing process.