If you’re choosing a Test Data Management (TDM) solution today, you aren't just buying a test data tool. You’re deciding how your company will protect sensitive data in non-production environments, how fast your teams can ship software, and whether you’ll end up with a unified platform, or a patchwork of incompatible tools.
Most enterprises are currently stuck in a risky middle ground. According to our 2025 State of Test Data Management survey, 93% of organizations admit they are only "mostly compliant" with data privacy regulations in testing, meaning PII often remains exposed in lower environments. Furthermore, 90% of organizations are still using legacy TDM platforms introduced over 15 years ago, which they plan to replace.
This article outlines how to evaluate a TDM vendor. While the focus is TDM, a complete evaluation must also address enterprise data masking and synthetic data generation. In a modern TDM architecture, these 3 modules are inseparable.
Before reviewing feature lists, clarify the problems you’re hiring a vendor to fix. Across recent analyst reports and buyer's guides, 5 pain points consistently emerge:
Wait times that kill velocity
Manual provisioning – relying on SQL scripts and ticket queues – can take days or weeks. In a DevOps world, this slows down release cycles.
Compliance risks in lower environment
Teams often copy production data to test environments and plan to mask it later, leaving PII exposed.
Broken referential integrity
Masking or subsetting data at the table level often breaks the relationships between applications (e.g., CRM, billing, and core banking). When integrity breaks, tests fail for the wrong reasons.
Fragmented tooling
Using one tool for subsetting, another for masking, and a third for synthetic data, creates integration debt and inconsistent governance.
The high cost of low software quality
Poor test data leads to bugs in production. According to Capers Jones, 85% of defects are introduced during coding, and fixing them in the release stage costs 640x more than fixing them early.1
Do not evaluate these as separate tools. A mature test data management strategy requires a single platform that delivers 3 integrated pillars:
Test data management (core) extracts, subsets, versions, and delivers data on demand to testers and developers
Data masking discovers sensitive data (PII/PHI) and protects it across every environment (static) or at the point of access (dynamic)
Synthetic data generation creates realistic, production-like data from scratch when real data is unavailable or insufficient
If, for example, a vendor scopes themselves as just masking or just synthetic data – without a strong TDM foundation – you risk creating data silos.
Features may change, but your architecture is more or less permanent. Ensure your vendor satisfies these 3 architectural requirements.
You can’t manage or mask data you can’t reach. Your vendor must connect natively to your entire ecosystem, including:
Legacy systems: Mainframes, DB2, VSAM, IMS
Relational databases: Oracle, SQL Server, Postgres
Cloud platforms: Snowflake, Databricks, Azure, AWS
NoSQL: MongoDB, Cassandra, Couchbase
SaaS applications: Salesforce, Workday, ServiceNow
Unstructured data: JSON, XML, PDF documents, and images
This is the most critical differentiator. Traditional tools manage data by tables. Modern platforms manage test data by business entities (Customer, Employee, Order, Device).
An entity-based test data management approach organizes data by business entities, where the test data for a specific business entity (e.g., an individual customer) is organized as an integrated unit. This allows the platform to extract, mask, and provision a single customer's data across dozens of systems while maintaining perfect referential integrity. It also hides this complexity away from users of the TDM tool.
If a vendor relies on table-based joins, they will struggle to maintain integrity across complex, multi-system environments and introduce significant complexities into the process.
Enterprises deal with massive data volumes. A single-node architecture will hit a performance wall. Look for a vendor that uses distributed, parallel processing to mask and subset billions of rows within tight maintenance windows.
Use the following checklist to cut through marketing fluff and verify the vendor's capabilities.
You can’t mask what you can’t see. A robust solution must automatically discover PII and classify sensitive data via:
PII classification profiles data based on Regex and LLM.
Data catalog tracks where your PII data lies with respect to specific regulations (CPRA, HIPAA, PCI, GDPR, and DORA European regulations).
Basic redaction is not enough. You need a library of masking functions that preserve data format and utility. These functions must be customizable code-free.
Static data masking permanently de-identifies data for lower environments.
Dynamic data masking anonymizes data in real-time based on user roles.
Format preservation ensures a masked credit card number still passes validation checks (LUN check) so applications don't break.
Data subsetting reduces infrastructure costs and speeds up testing. Look for two types:
Representative subsetting creates a statistically valid "mini production" (e.g., 5% of data) that preserves data distribution.
Parameter-based subsetting extracts specific scenarios, such as "all customers in California with an invoice balance over $5,000".
Real data isn't always the answer. You need a toolbox of generation methods:
Rule-based generation fabricates entity data based on business rules (no production data needed).
GenAI-based generation trains models on masked production data to generate "lookalike" synthetic entities that preserve statistical properties.
Entity cloning duplicates existing entities to create volume for performance testing.
Test data provisioning should be instant, not a ticket-based, lengthy process. Development and QA teams must be provided access to a self-service portal to:
Search for specific business entities (e.g., "Find me a customer with a suspended account") in any higher environment
Provision masked entity data to their sandbox in minutes.
Reserve data to prevent conflicts with other testers.
Version and rollback datasets for regression testing.
TDM must be part of your pipeline. Ensure the vendor offers a rich API layer to trigger data provisioning, masking, and tear-down tasks directly from tools like Jenkins, Azure DevOps, or GitHub Actions.
If you hear these phrases during a demo, proceed with caution:
We’ll do the masking, but first you need to find the PII.
Uh oh: This forces you to manually maintain rules across hundreds of systems. Discovery and masking must be integrated.
We ensure integrity within the database.
Beware: You need integrity across databases. If they can't explain how a masked Customer ID in Salesforce stays consistent with the mainframe, they can’t support end-to-end testing.
You need to write scripts for that.
Why it’s suspect: Heavy reliance on SQL scripts or custom code (e.g., for test data aging) creates a brittle environment that only specialists can maintain. Look for out-of-the-box functionality coupled with low-code/no-code configuration.
We only do synthetic data / virtualization.
Heads up: Point solutions leave gaps. Synthetic-only tools struggle with complex system relationships. Data virtualization tools often lack masking depth and access to less common data sources.
Make your RFP or PoC count by asking the following questions, regarding:
Architecture
Do you organize data by business entity or by table? Show me how you maintain referential integrity for a single customer across five different systems.
Discovery
Do you use AI/LLMs for classification? Show me how you identify sensitive data in unstructured files or notes fields.
Coverage
Show me how you integrate to my data landscape that includes mainframe IMS, Workday, and MongoDB.
Agility
Show me the workflow for a developer or tester to request and receive a fresh, masked dataset. How long does it take?
Deployment
How do you deploy on the cloud while controling costs? How do you handle data that lives partly on-prem (mainframe) and partly in the cloud (Snowflake)?
Pricing
How do costs scale with increased data volume, and increased usage?
The right TDM solution transforms testing from a bottleneck into a competitive advantage - letting you catch defects early, stay compliant, and release your software faster.
When evaluating vendors, look past the feature list to the architecture. An entity-based approach – one that understands your data in a business context, rather than in terms of rows and columns – is the only way to achieve TDM, masking, and synthetic data generation at enterprise scale, at the same time.
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