The 5 Most Important Metrics For Test Data Management

TDM metrics: what are they? What makes them important to you? To put it simply, TDM metrics serve as indicators of how well your Test Data Management process is working. Having a solid testing strategy requires TDM. Developing and delivering high-quality software on time requires a great testing strategy. Thus, TDM metrics play a crucial-but often overlooked-role in a well-rounded software quality strategy.

TDM Metrics: 5 You Should Be Aware Of

Let's review five key characteristics of a useful test data set without further ado. When you track and improve these characteristics, you will be on your way to improving your TDM approach.

Data Literacy

Data literacy refers to the ability to understand, process, and analyze data. The term "data literacy" has a wide range of applications. Professionals dealing with data should possess this skill as well as consider it a desirable quality in said data.

When it comes to the trait of the data, perhaps a better term would be data readability, but the point remains the same. Test data should be able to be understood, processed, and analyzed by different computer systems than those that produced them. When it comes to data obtention techniques that rely on real data being copied from production servers, this is especially important.

Data Security

It should come as no surprise that we have a no-brainer on our list. The fact that test data-as well as all kinds of data-should be secure should surprise no one. It's always been that way, but nowadays digital security is more important than ever. It is imperative that organizations take all measures necessary to ensure that sensitive data-especially personal information-will not be accessed by unauthorized actors, including testing professionals such as QA analysts and testers.

Data security isn't just morally right. Due to GDPR and similar legislation around the world, it is the legally required thing to do. In addition, it's a good idea. Despite privacy regulations, protecting user data is the right move since failing to do so hurts an organization in many ways, primarily by damaging its reputation.
So, approaches like data masking become vital when obtaining test data from production cloning.

Data Age

Test results become stale over time. It sounds strange, but it's true. How does it relate to you?

You might have certain tasks or procedures in your application that use time stamped data. It might be necessary for `those dates to be recent. If the test used data from a production snapshot three years ago, what would happen?

It's right: The tests targeting those procedures wouldn't work well, or they might produce inconsistent results. This is why monitoring data age is important. Test data aging techniques can be used to artificially alter the dates in test data based on how important it is for your testing strategy that the test data is "fresh."

Data Quality

The term "quality" is used here to cover a few different traits that useful test data should have. Test data, for example, must maintain integrity in order to be used in test cases. Basically, it means it adheres to the domain rules of the application under test as well as the constraints and rules of the database schema.

Imagine your application is a school management program. At least one of your domain rules requires that a student be enrolled in a course. A student who is not enrolled in any course is in violation.

What could’ve caused such an invalid state? If you’re dealing with “fake” test data, then the answer is a faulty synthetic data generation process. On the other hand, if you’re working with data cloned from production, something might’ve gone bad when performing a process such as data subsetting.

Regardless of the cause, what really matters is having mechanisms in place to enable you to detect and fix such inconsistencies in quality.

Automation

Automation is a metric/quality less related to your test data itself and more to your whole TDM approach. How widespread is the use of automation throughout your organization? Are you having data compliance processes built into your DevOps strategy?

More importantly, how automated is the tracking of your data literacy, data security, data age, and data quality metrics?

It’s essential to automate the process of test data profiling and verification. Only with automated alerts and monitoring will you be able to really improve the traits above and take your TDM approach to the next level.

Give Your Tests Some Love by Providing Them Great Data

Today we’ve covered yet another TDM-related topic. Namely, TDM metrics. We’ve argued that since software testing is crucial for achieving high-quality software and great test data is essential for a healthy testing strategy, what follows is that anything you do to improve your

TDM approach is an important component of your overall software quality strategy. TDM metrics have been a mostly overlooked piece of the software quality puzzle. In this post, we’ve set out to change that scenario by offering a list of five TDM metrics that, if tracked and improved upon, have the ability to make your testing strategy more efficient and sound.