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Top Test Data Management Use Cases

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Unlock faster, safer, and compliant test processes

Choosing the right use case for test data management tools is often a challenge for organizations. Our deck helps guide that decision-making process by presenting a range of practical, real-world applications. It’s designed to help your team align technology with organizational needs faster.

Each use case in the deck includes hard industry data on the costs of poor test data management, along with the strategic importance of solving those pain points. From compliance risks to inefficiencies in development cycles, this resource paints a clear picture of what’s at stake as well as how Syntho can help you overcome specific challenges with powerful, tailored test data solutions. Here’s a preview of what you’ll get out of this document:

  • Real-world application guidance to help stakeholders quickly identify the most relevant use case for their organization
  • Quantified industry challenges, with data on costs, risks, and inefficiencies linked to poor test data practices
  • Strategic context explaining why each use case is critical and how organizations can prioritize them

$5m

Companies spend $5 million annually on bugs fixes due to poor testing

30%

Of QA team’s time is wasted managing invalid test data

40%

Higher cost when testing manually

73%

Of compliance breaches in 2024 stemmed from inadequate software testing plans

Why Test Data Management matters more than ever

Managing test data isn’t just a technical task — it’s a strategic advantage. Using real production data introduces delays, risks, and compliance challenges. This is where synthetic test data and smart data masking step in:

  • Accelerate development with instantly available, realistic test data
  • Reduce costs by eliminating manual data provisioning
  • Ensure compliance by removing sensitive information at the source
  • Boost coverage with scalable, on-demand test data scenarios

Our whitepaper dives into real-life implementations from companies solving these

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Quantified insights on the cost of poor test data

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