2022 is a landmark year for data management. While the world is more digital-savvy (thanks to COVID), the subsequent rise in consumption of services is inevitable. More products mean more testing and thus better test data management processes.
As we all know, the testing phase in SDLC tracks, fixes, and re-tests the product until it reaches a set quality standard. It reduces the time needed to market a product by automating the testing processes. This allows you to function more efficiently while saving time.
Moreover, the software development processes cost you exponentially more if these errors and defects are not fixed in time. To avoid deterioration of the software quality, testing must be done in advance to fix any errors.
While we are at it, the test data management market is predicted to grow at a 12.7% CAGR. We handpick the top factors that could accelerate the adoption of advanced TDM.
1: Rise in IoT and Big Data
According to the United Nations, approximately 64.2 zettabytes of data were created in 2020. This shows a staggering 314% increase when compared to 2015. This growing data along with technological advancements by the IoT industry has played a big role in driving stimulation and the TDM market as a whole. We can credit this exponential growth to the data that is now passively collected from daily user interactions with smart digital services and products that include credit cards, smartphones, and even social media.
The demand and adoption of stimulation and TDM solutions are likely to increase in the coming future because of the direct increase in demand for several end-use industries. But, at the same time, a few factors like lack of expertise and a high initial investment amount may slightly hamper the growth of the market in the coming period from 2021 to 2027.
2: Mandatory Data Regulations
Thanks to the GDPR, all personal data is now confidential and safe. This means that all data must be protected against any unlawful or unauthorized proceedings or damage caused by accidents.
Enterprises can also invite unwanted heavy fines in case of a data breach. These fees can be avoided if you ensure minimum risk of the breach along with effective decision-making processes and high-quality data streams. These are a few of the reasons that have made TDM very popular these days.
While testing data, apply data masking techniques to protect the personal information of your users and simultaneously make it usable for testing purposes. Data privacy laws (both GDPR and CPRA) always demand the test data be anonymous as this minimizes the damage done in the event of a data breach.
3: Emphasis Upon Data and Its Continuous Delivery
Continuous delivery directly depends upon provisioning relevant, accurate, and high-quality data that includes testing coverage, automating protocols, and continuous testing. By producing high-quality data you can make it easier to spot errors and defects early on in the development process. This gives a cheap fix and also makes sure that there are fewer bugs in the product lifecycle. Always remember that in case QA and testing fail because of poor quality data, your end-product is bound to fail as well.
An immediate effect of bad-quality data is customer complaints on social media which can go viral at any given moment. This also means that these users can easily shift to a different service provider or brand and can also take their family, friends, and loyal followers along with them. Contrary to this data with advanced security, good hygiene and seamless data management will provide your users with enhanced customer experience (CX) which eventually leads to customer satisfaction, loyalty, higher revenue, and an improved brand reputation.
Now, provisioning high-quality data into continuous integration and delivery pipelines is an important trend to note. If you check new-age TDM platforms, all of them emphasize qualitative provisioning. For example, IBM and K2view TDM solutions feed test data subsets with referential integrity from multiple production sources. They do so through user-defined rules.
This empowers testing teams to consume less time for retrieving and preparing test data, thereby achieving the QA cycle more efficiently. Ultimately, the solution software allows developers and testers to shift the testing process to the left and also relieves the pain of providing compliant test data and synthesizing any data on demand.
4: Increase in the Adoption of Automated Testing
The testing community has shown great interest in autonomous testing and this is likely to grow at an early rate in the coming years. According to the Omdia Report 2021, more and more organizations will continue to adopt autonomous testing this year. Around 90% of the companies who participated in the study will have successfully independently tested by 2024.
Applications will roll out faster thanks to these protocols. This will eventually provide customer service and testing processes well in time. QA engineers using automated testing tools still have to rely on developing automated test scenarios.
This is why organizations must be able to create test cases more quickly and easily, execute them in time, and eventually be able to analyze test data without human intervention. This is what autonomous testing consists of and this protocol will be required by every organization in the future.
Autonomous testing platforms work by using ML and AI to become contextually aware and then detect defects and errors automatically within a created test application. These test scenarios can be further executed with minimal or zero human intervention.
We discussed the key factors that are leading the industry’s transition towards advanced QA and how TDM is detrimental to the same. That being said, there are other factors such as increasing preference for CX, investments, and continuous upskilling. What other factors do you believe are contributing?