Test data management can be a real headache when you’re working to keep your QA team using accurate, secure, and usable data. Come on—without the right tools in the workflow, managing test data feels more like juggling swords of fire. Enter test data management (TDM) tools to the rescue. These tools enable you to create, mask, manage, and provision data to your test environments without breaking things—or breaking a sweat.
Looking to streamline your QA process? Here are five of the top test data management tools of today. Each includes a little something different in their offering, so you’re sure to get the right fit for your team.
1. K2view
K2view is a standalone, all-in-one solution that can easily manage complex enterprise environments. It is that kind of tool that seems like it was made by individuals that understand the day-to-day challenge of QA and test teams. What makes it so appealing is how much freedom and autonomy it grants to the testers. If your QA testers are fed up with having to ask the developers for even the simplest data request, the self-service nature of K2view will be a breath of fresh air.
With K2view, testers can subset massive production datasets into smaller, usable chunks. They can also version those datasets, roll them back to a previous state, reserve test data to avoid conflicts, and even apply data aging to simulate long-term user behavior.
One of the highlights of the tool is its clever data masking engine. It does not only work on structured data such as databases—there’s also support for unstructured data, e.g., log data and documents. K2view scans your data for personally identifiable information (PII) automatically and has more than 200 different masking functions in its arsenal to use while preserving the data relationships. With this, your test environments are secure as well as realistic simultaneously, which isn’t a simple thing to do.
2. Informatica Test Data Management
Informatica’s test data management tool is essentially the Swiss Army knife of the TDM. It’s an old established tool that has earned a good reputation for working well on enterprise-scale data. In the case of Informatica, the thing that I really like about it is that it allows you to extract production-type data and clean it up—so you’re not exposing sensitive data in the test.
It has data masking, subsetting, and synthetic data capabilities. Which is a fancy way of explaining that it can assist you in safeguarding personal information, reducing your data to what you need in the first place, and even creating fake data when there isn’t any.
The good news? It works very well in conjunction with other systems—no matter whether you’re working in a cloud-based environment or more traditional on-prem setup. It’s certainly a good fit for bigger organizations or anyone already using other Informatica tools.
3. Delphix
Delphix is the top pick for those speed- and agility-focused teams. It’s a contemporary test data management system designed for the DevOps generation. Therefore, in the event that your team carries out continuous testing or you work in accordance with agile development phases, Delphix will fit in perfectly.
Delphix’s uniqueness comes in the speed in which it delivers production-like data in a masked format to test environments. Being in a position to refresh your test data in a matter of minutes rather than hours or days makes a huge difference when releasing the software as fast as possible but confidently.
It also handles data masking very well and has a self-service model for developers and testers, especially for data science use cases. Which means you do not necessarily need to wait on the data folks to get the data you need—you can get it yourself. And because it’s cloud-native, it works well whether you’re working in AWS or Azure or some other cloud.
4. IBM InfoSphere Optim
Depending on your organization being knee-deep in IBM technologies, InfoSphere Optim might not only be your first stop. It’s designed for complicated enterprise structures and can handle test data for databases, applications, and even mainframes.
IBM Optim does a good job of giving you only the right amount of data you need for your tests, rather than bringing in the whole production environment. It also masks data on the fly, meaning that personally identifiable information (PII) never reaches the wrong hands.
One of its more useful aspects is that it maintains the inter-relationships between data sets even after masking. Sounds jargon-y, but it’s a real thing when you have to test apps that use linked databases—such as those used in finance or healthcare. It’s not the simplest to install, but when it’s up and running it’s a real workhorse for TDM work.
5. GenRocket
Occasionally you don’t want to pull data from production altogether—especially when the data might be sensitive or doesn’t even include the edge cases that you’re attempting to test. That’s when GenRocket comes in. It’s all about synthetic data generation and does so very, very well.
GenRocket enables you to produce enormous amounts of test data that resemble and perform as close to real data as possible without ever taking it directly from your production systems. What that does for you is enable you to craft test cases that would be difficult—or even impossible—to recreate using real data.
It’s perfect for performance tests, systems that handle a high volume of transactions, or even for testing that occasional edge case that you only see when you’re not in production. You can format the data to your liking and even create it on the fly for the specific test case. It’ll take a bit to get your data models up in the beginning but after that you have a tremendous amount of control.
So, Which Should You Choose?
It all comes down to the size of your team, your test needs, and the complexity of your data landscape. If you’re a large organization with deep pockets and complex systems to unscramble, Informatica or IBM InfoSphere Optim are good choices. If you value speed and agility first and foremost, you might prefer Delphix. K2View injects a new approach through virtualization. If you need good fake data in a hurry, you’ll love GenRocket.
At the end of the day, data management for tests isn’t merely about keeping things organized—it’s about letting your team test better, faster, and more securely. Getting the right tool in place can significantly lower bottlenecks in your tests and keep the release pipeline running smoothly. Have a favorite TDM tool of your own? Or struggling to find the right fit? It’s about what’s important to your team.
