Historically, Test Data Management (TDM) has been providing a set of tools to fulfill the needs of engineers in testing and software development—necessary but often resource-intensive and costly. The focus traditionally has been on minimizing expenses rather than exploring how TDM could contribute to growth. Traditional methods involve manual data management, lengthy provisioning cycles, and balancing the creation of realistic test data with privacy and security concerns. As a result, this set of disjointed tools is managed based on their costs. What if there was another approach that leveraged a test data practice for growth?
It is no hyperbole when we say we live in the most technically aided period of human history. Software developers and engineering teams today offload so much of their work to various systems and software that they are able to spend their time focusing on the most complex challenges. The nasty ones that lead to the pile of gold at the end of the rainbow. If you’ve read this far and haven’t burst into laughter or rolled your eyes off the table, you may not know many developers. We can catch you up—this isn’t what is happening.
Something well north of 50% of an engineer’s time working with test data is before it becomes available in their lower-level environment. Test data management may have evolved to provide better masking or more advanced generation, but it still hasn’t solved the core issue within the developer lifecycle.
This article just begins to explore this challenge. We need to start by reframing an organization's test data practice from a cost center to a value center. What follows are a few areas where we’ve seen a modern approach to antiquated test data management tooling that drives real value across the business.
It goes without saying speed to market is a factor that makes or breaks a line of business. The ability to deliver new features and products quickly can make the difference between leading the market and falling behind. By leveraging automation and a more unified integration into CI/CD pipelines, organizations can significantly reduce the time required to provide high-quality test data. Ultimately enabling shorter development and testing cycles, allowing companies to bring products to market faster and directly contribute to revenue growth. Companies that accelerate their time-to-market can achieve higher profitability and market share.
The testing and experimentation Venn diagram is something we will have to explore later, but we could go so far as to say the ability to experiment successfully (resulting in the release of a new feature/product/etc.) is directly correlated to the speed of provisioning and quality—or how production-like it is—of synthetic data they are using. Organizations that can explore new ideas, test novel features, and iterate quickly without needing access to production data will drive more value over the long term.
Look for approaches to test data that favor efficient and in-sync test data provisioning vs. those that work from outdated copies of production data.
We’ve heard from technical leaders that there is often a tension between enabling speed to delivery while also reducing compliance risk. This tension is compounded in a test data practice as developers want to be as close to production data as possible. So compromises are made, risk profiles updated, and everyone leaves feeling they got something.
Those compromises look like copies of production databases managed by as few people as possible, who are left to fulfill a constant stream of test data needs. You can see why TDM has been stuck providing tools. However, with a different approach, access to production data is done through a transparent and compliant provisioning engine that stays in sync with production while providing engineers with the necessary test data. This approach mitigates the risk of costly fines and opens up new market opportunities. Where organizations can confidently expand into new regions and industries by ensuring that all test data complies with global standards, capturing new customers and revenue streams.
Shifting from a traditional TDM approach to a modern, automated one does more than just streamline processes—it significantly reduces operational costs. By automating test data provisioning, organizations can eliminate much of the manual effort and reduce the need for extensive infrastructure. For instance, by automating the provisioning and de-provisioning of test data environments, companies can avoid the expenses of maintaining unused data storage. This efficiency can reduce storage costs by up to 30%. This newfound efficiency not only lowers expenses but also frees up resources that can be redirected toward strategic growth initiatives. With less time spent on managing test data, teams can focus on innovation, accelerating time-to-market, and driving revenue growth.
This modern approach transcends traditional methods by automating data provisioning and enabling a unified, integrated practice that accelerates development cycles and drives faster delivery. With the integration of automation and AI, businesses can generate high-quality, compliant data in a way that empowers teams to experiment and innovate confidently. This AI-driven, interconnected approach to test data not only shortens time to market but also ensures sustained success in an increasingly competitive environment.
Transforming Test Data Management from a cost center into a revenue-generating asset is now a reality. By modernizing your approach, TDM becomes a powerful driver of innovation, compliance, and growth. This shift not only reduces costs but also accelerates time to market, fueling your competitive edge and opening new avenues for profitability.
Don’t just manage your test data—leverage it to propel your business forward. Discover how Synthesized can unlock the full potential of your test data practice.
