Oct 12, 2021
Synthetic Data Generation – a boon for non-production environment
Posted by Vijayanathan Naganathan
You might wonder, why clients are not giving in production data to be copied on to non-production environment for all non-production activities like enhancements, testing, training, etc – that seems the easiest thing on earth. Actually, clients dread copying data from production into non-production environment, as migrating copies of data from production to non-production is quite expensive and can potentially lead to non-compliance with regulatory standards and hence impact business reputation.
Data sensitivity, compliance and regulatory requirements, mandate production data to be obfuscated for use in non-production environment. This results in about 20% of SDLC effort and associated cost going down the drain by searching, securing and administering test data manually. From our own experience and from what we observe in the market, data administration turns out to become a bottleneck in the SDLC, leading to client dissatisfaction due to increasing costs, time and poor quality. In addition to this, in today’s scenario of considerably high rate of offshoring, migrating data from on-shore to off-shore (or) allowing offshore based team to access the production data onsite is ruled out due to the statutory data regulations that need to be complied with. Non-compliance in this space leads to loss of customer trust and ultimately business loss.
So, what is the best optimal way out? Today’s clients are looking for synthetic test data generation solutions, for provisioning the apt data at the apt time to the desired environments for rigorous testing, in a pay-as-you-go-mode. Synthetic data generation helps in improving the data quality, which in turn improves the application quality early in SDLC, helping the high quality application to be taken to the market earlier at reduced IT spend. This provides a great lever to outsourcing companies to get data synthetically created at offshore in the quickest possible time, while still ensuring regulatory compliance.
Putting a synthetic data generation tool in place alone is not sufficient to see the desired results. Well, enterprises need have a well-orchestrated engine in place comprising of synthetic data generation tool, data management processes and aptly skilled team on the ground for data provisioning and management exercise. Project teams are advised to build/leverage a centralized data administering service, that will not only eliminate data dependencies but also exponentiate the data value to business.
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