The Challenge

The scale and complexity of enterprise technology systems is skyrocketing. To ensure that applications can be released quickly and flawlessly, today’s developers rely on high volumes of test data. Without this data, enterprises risk releasing buggy software that can disrupt mission-critical processes and create a cascade of failures across a business unit.

For the insurance firm, a lack of an end-to-end TDM framework was impacting their ability to release and update new application features. Legacy testing procedures, outdated provisioning protocols, and an over dependance on manual processes caused significant delays in data provisioning, stretching project teams thin with data acquisition and delivery tasks. Even when the data was able to be delivered quickly, inadequate masking in non-production environments created a high risk of compliance breaches.

To fix their data provisioning operations, the company wanted a solution provider who specialized in establishing end-to-end TDM frameworks. And since they’d shifted to an agile delivery model with accelerated dev cycles and testing requirements, they needed a quick turnaround in their TDM implementation. To alleviate the rising workloads on project teams, the firm was also looking to automate test data provisioning, while eliminating the risk of data breaches and complying with data security and privacy regulations.

The Solution

To start with, we established a four-stage delivery strategy. The first step involved evaluating the client’s use cases and application landscape to better understand their data challenges and princesses. Based on this, we then developed a POC from the selected tools, and prepared the infrastructure, databases, and networks necessary to operationalize the TDM solution across application clusters. The next step involved abstracting and virtualizing the data sources to compress their storage requirements. At the same time, we also refined legacy PII parameters, and released a masked version of their virtualized golden copy to the pre-production environment.

To help the development and IQE teams autonomously control the data refresh cycles within their production environments, we created a self-service portal and an accelerated protocol for data refresh frequency and clean up. Finally, we demoed and deployed TDM applications on multiple clusters using a phased approach, and kickstarted the synthetic test data fabrication process.