Test data management:

Why Does Test Data Still Slow Us Down?

As the North America Leader for Banking and Capital Markets Testing at Capgemini, I often hear this question: “Why does test data still slow us down?” In an era of Agile and AI-driven delivery, the answer shouldn’t be complexity, but it often is. Test Data Management (TDM) is evolving rapidly, and the rise of synthetic data and Gen AI is rewriting the rules. The question now is: will organizations treat test data as a strategic asset or remain stuck in old habits?

What the World Quality Report 2025 reveals about TDM

The Data Quality chapter in the 17th edition of the World Quality Report explores how organizations are transforming TDM to meet modern demands. Gen AI has stepped into the spotlight, enabling synthetic data creation and accelerating provisioning. Adoption is growing, 95% of respondents use Gen AI for test data generation in some capacity, and 35% of organizations now generate more than a quarter of their test data synthetically, up from 24% last year.

Yet challenges persist: fragmented ownership, low tooling maturity, and compliance pressures keep many from scaling success. Only 10% report full lifecycle integration of Gen AI, and just 34% treat TDM as a strategic initiative. The gap between innovation and implementation remains wide.

Our perspective: closing the gap between potential and practice

When contributing to this chapter, I focused on the disconnect between potential and practice. What surprised me most was that 50% of organizations lack centralized ownership of test data, leaving governance fragmented and efficiency compromised. Even with Gen AI breakthroughs, many teams still rely on basic scripts and manual validation.

Synthetic data is clearly the future, it improves accuracy, supports compliance, and reduces reliance on sensitive production data. But without strategic alignment and advanced tooling, adoption remains shallow. The most effective organizations are consolidating capabilities into single platforms, embedding compliance controls, and expanding automation beyond generation to validation and tuning.

What’s next for TDM

Survey data reveals that synthetic data adoption is steadily increasing, with organizations now generating an average of 25% of their test data synthetically, up from 24% last year. This growth reflects a clear trend toward leveraging synthetic data for improved accuracy, compliance, and scalability.

While full lifecycle integration of Gen AI remains rare at 10%, the widespread use of AI-driven tools for test data generation (95%) signals strong momentum. These numbers indicate that synthetic data and Gen AI are no longer experimental, they are becoming essential components of modern Test Data Management. We expect this upward trajectory to continue as organizations seek efficiency and regulatory alignment.

Turning bottlenecks into breakthroughs

If test data feels like a bottleneck, start small: automate provisioning, adopt synthetic data for one critical workflow, and measure the impact. These steps can transform TDM from a compliance burden into a strategic pillar of quality engineering.

Ready to learn more? Download the full World Quality Report and discover how organizations are redefining test data for the Agile era.