The yardstick of customer experience lies with the finesse in QA. That’s exactly why product developers from SMEs to giant corporations are implementing Test Data Management (TDM) in their data science practice. Since TDM creates, manages, and delivers qualitative data to testing teams, it is directly responsible for streamlining the workloads for the QA teams. If 2021 sets the narrative for data fabrics, expect similar anticipation around TDM from 2022 to 2027. Here’s a detailed run-through of the key trends to follow.
Not Confined to Software Testers
First things first, TDM is not confined to QA teams anymore. Stakeholders, other than software testers, are increasingly participating in building a comprehensive TDM practice. So far there’s a significant contribution from developers, data scientists, and others. This trend of cross-functional engagement is expected to rise in upcoming quarters. As per a survey from Delphix, QA teams account only for 50% of the total time in TDM teams. Project teams devote 16% of their working hours while IT operations stand at 10%.
Emphasis on Data Masking
There’s an increased emphasis on online security & privacy protocols along with total compliance with regional regulations. This is one of the reasons why data masking is a top test data management trend this year. Experts use this technique to make user data used for testing protocols and also protect the user’s information at the same time. There are several data privacy laws like CPRA and GDPR which now demand anonymous test data to cut back on the damage caused in case of a data breach.
For organizations, masking is an effective tool to provide qualitative data feeds for testing without exposure and thus any risk of theft. Off-late, many tools have begun to use ‘masking’ as an integral practice in TDM. K2View, for example, is using its micro-DB-enabled fabric to ensure seamless testing. Their test data management platform allows you to pull out any data related to business entities from the related production system. It also synthesized any missing data when needed. This is because in-flight data masking allows a business entity to protect its sensitive data before it is deployed to a test environment for testing protocols. It allows companies to support the in-flight masking of both structured and unstructured data. These tools also help maintain the referential integrity of masked data across various data stores and applications.
Implementing Intelligent Data Generation
The upcoming trends in TDM also involve using synthetic data generation tools. Because this data is stimulated artificially it does not contain any confidential information. In addition, it also makes the test data more compliant with evolving industry standards.
Synthetic data generation tools also help you generate test data in different formats depending on the structure data provided initially. The data generated can either be random, pathwise, or goal-oriented.
Test data generation tools have also improved during the transformation journey from Agile and DevOps to intelligent automated solutions. These allow you to identify set patterns in the production data and automatically produce synthetic data while keeping the same structure. Synthetic data generation tools also keep the referential integrity of the generated data intact. The latest tools are intelligent, modular, and communicate through external APIs so that they can easily integrate with both automation frameworks and DevOps.
Building Prefabricated Data Sets
Always make sure that the test data you inherit is sustainable. This means that even though there is burnt data, you may require new test data in the same pattern for future testing purposes.
An effective TDM solution is to re-use test data as and when required. Experts have proven that a good approach is to create prefabricated test data suites based on stable domains in the data structure and format. The primary benefit of this strategy is the time that it saves for identifying domain-based errors and test data creation in the system.
Modern TDM trends use virtual assistants for user interaction so that they can select data from prefabricated test data sets according to their requirements. By using this approach experts can now cut down on data provisioning delays and focus on performing tasks that they are good at. However, a downside of prefabricated data is that it may not always expose all the TDM problems which is why it is always advised to use a combination of internal and prefabricated data sets.
The TDM market is growing at a CAGR of 11.6% and could value USD 1752 M by 2027. This year, the market will be bifurcated into retail & agriculture, BFSI, healthcare, IT, telecom, education, and other sectors depending on the end-user. The IT and telecom sector alone is expected to bloom at the highest CAGR in the coming time.
We can expect retailers to use data management software for understanding their demographic data like user preferences, favorites, shopping habits, and purchase history.
This will directly allow them to plan and design their market growth and promo strategy according to the user demands. In the coming time, more and more companies will depend on data management software for earning profits and this will directly increase the demand for data management tools.
Primarily, the global test data management market can be divided into North America, Asia Pacific, Europe, and the rest of the world. Industry experts predict that North America will lead in the coming years when it comes to market share and market domination.
This is because this region is adopting emerging technologies (cloud platforms, big data) rapidly. As a result, investors look forward to investing in companies based out of North America as they find most TDM solution vendors based here.
Data is bound to grow and so do the consumer expectations about in-the-moment insights. Therefore, not only developers and analysts but testers too have a greater responsibility of producing the best possible outcome. And TDM is a great channel to leap.