When it comes to non-production environments for your application development or training, it’s incredibly important to make sure you have mechanisms in place to support them, not just to make the lives of your development, testing and training teams easier, but to improve the quality of any applications you roll out, and of the business as a whole. 

You might think that issues in managing test environments, while important, only actually affect the test and can be ironed out before software moves into live environments, but this really isn’t the case. By not accurately simulating production scenarios in your test environments, you can miss finding errors and bugs that will go on to manifest in production, where the cost of fixing can increase tenfold. This is where automated test data management systems come in.

By supporting your development, testing and training teams with a process which delivers representative data, you’re reducing preparation times and presenting the opportunity for testing to reduce the margins of error in test and live environments by using data which is aligned with production. No matter how strong, experienced and thorough your team are, mistakes do happen – it’s just a part of life, by automating your test data management, you can help significantly reduce this margin of error and increase the ability to prevent issues from occurring for development, testers, and for many different branches of the business.

There are several different areas which can be negatively impacted by not having a mechanism in place to support non-production environments with data.

Development and Testing

Robust test data management systems can have a massive impact on your testing and development teams. By automating data processes, you will reduce testing activity times by up to 30% and lift a huge burden from the shoulders of your test team who can then focus on other important parts of their role. 

In fact, manual testing currently increases the development and testing efforts, increasing testing time by up to 75%, as your team must create the test data for any testing cycle.

Using non-representative data will result in a reduction in the accuracy of test executions, as you will be testing based on assumptions and not based on real scenarios. Hand-crafted test data is built on the opinions of the team creating the data, so you can’t be sure it is truly comprehensive. Automating the process for masking, obfuscating or generating test data removes human bias to provide you with more repeatable, accurate and representative datasets overall.

Data Security

Traditional manual data management, while opening you up to inaccuracies and poor data coverage or quality, can also actually constitute a breach of data security, regulations, or open you up to further security issues when it comes to sensitive user data. If you’re testing applications using production data, as many testing teams do, you may be introducing regulatory breaches and risks to your organisation. If you are testing with live data without robust processes in place to protect sensitive data you may be exposing this data to unauthorised entities.

One alternative to this is manual obfuscation, however, this is also open to human error. Manual obfuscation can potentially miss sensitive data fields and once more expose you to breach of regulations. In a world of GDPR and other regulations, an automated Test Data Management solution gives you the chance to further your data and quality assurance against different test conditions without falling foul of data regulations and suffering any adverse repercussions.

If you have concerns around managing test data or want to find out more about how we can help you improve your testing processes, please get in touch with the team at ids.


Your Data Quality Primer: Everything you need to know

Welcome to your Data Quality Primer from the iData Quality Academy – in this useful guide you’ll find everything you need to know to improve your understanding of data quality, assorted into useful categories for you. You don’t have to read through it in order, you can jump right to the section you’re interested in!

Your Data Quality Primer: Everything you need to know

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