6 Steps to a Successful Test Data Management Strategy

Ian Tick

Ian Tick

Head of Content, K2View

A solid test data management strategy doesn’t just happen. To ensure high-quality test data is within your team's reach, on-demand, follow these 6 steps.

Table of Contents


Step 1: Provision Test Data by Business Entities
Step 2: Extract on the Fly
Step 3: Refresh and Sync
Step 4: Mask
Step 5: Synthesize
Step 6: Provision
One System for All Your Test Data Management Strategy Needs

Step 1: Provision Test Data by Business Entities 

An effective test data management strategy enables testing teams to systematically provision production-like, trusted data, for any test case, easily and on demand.

Provisioning test data by business entities simplifies and accelerates test data provisioning.

Instead of having to understand the complexities of source systems, databases, tables, and columns, testing teams should be able to simply define the business entities for which they need test data.

For example, it should be easy for a software testing team at a bank to provision test data for 300 customers that live in Florida, married with 2+ children, and that have a bank balance greater than $5,000. The testers would not have to know that the customer data is collected from, and provisioned into, 35 different systems.

Step 2: Extract on the Fly

The tester, or test data automation, should be capable of requesting the data needed to perform a given test, in flight, without any preparation.

When the required data is fragmented and dispersed across many different systems and data sources,  test data management tools – that can integrate with the production systems, and extract test data according to predefined rules – can come in really handy.

Step 3: Refresh and Sync

Testing is an iterative process. When bugs are discovered and fixed, continuous testing ensures quality.

A test data management strategy should provide for the means to quickly roll back the test data that was previously used – by the specific tester, for the specific use case – without impacting the test data currently being used for other tests.

Companies need a test data management system that is adaptable, easy to sync, and capable of refreshing data granularly for each component – while maintaining complete control.

Step 4: Mask

We can’t discuss a test data management strategy without mentioning data masking for privacy and security. When dealing with production data, the challenge is to ensure data privacy, while maintaining the data’s integrity and keeping it secure.

It’s critical to comply with privacy compliance regulations, and protect the data from breaches, with data masking tools.The ability to unify the test data from multiple sources, anonymize or de-anonymize it, as required, and secure it every step of the way, creates a simple and efficient process for meeting data compliance and security constraints.

Step 5: Synthesize

When test teams can’t extract a sufficient volume of test data from production, they need synthetic-data generation tools to create artificial data.

A good test data management strategy should include the means to produce synthetic test data, based on real production data, while maintaining relational integrity of the data across all systems.

Step 6: Provision

After acquiring the necessary test data, generating missing data, and masking it as required, it’s time to move it to the target test environments.

Test data management solutions should offer a fast and seamless path from multiple systems to multiple environments. Companies should be able to upload, adjust, and remove data scenarios and business entities at any stage throughout the process.

One System for All Your Test Data Management Strategy Needs

Enterprise test data management tools support each of test data management strategy steps described above, with their unique ability to:
1. Create dynamic testing environments, on-the-fly, as part of testing automation
2. Enable unstructured data, such as voice, images, documents, etc.
3. Use any environment both as source and target
4. Reverse time, and “fix” faulty test data
5. Support hybrid (on-premise/cloud) testing environments
6. Migrate test data between data centers and the cloud

In short, they empower and support developers, testers, and DevOps teams, to accelerate software delivery, while improving the quality of the end product. Set up a test drive and see for yourself.

Get your test data management strategy right here.