The back-end data processes of an application are a reflection of the consumer’s behavior and expectation. The back-end system should perform like an impatient consumer who expects on-demand and in-the-moment information. Likewise, the TDM systems should provision qualitative test data in minutes.
Since TDM is the battleground to identify defects at the granular level, it is mushrooming into an industry of its own. Off late, there have been many consulting firms offering solutions to test data only. The TDM market is pacing to grow at a CAGR of 12.7%.
Now that we are discussing the process, let’s understand the key drivers that make it an essential practice for every organization.
Importance of Test Data Management
As we know, the testing stage helps teams report, track, fix, and re-test product defects until they reach the expected quality standards. Test data management is detrimental to the software development process because:
- Effective test data management significantly reduces a product’s time to market. Automated testing accelerates the process function and makes it more efficient.
- By testing the software and quality for errors earlier, teams can avoid costs that exponentially increase with unfixed bugs.
Here is a quick run-through of the process of TDM that you should know.
TDM: The Process
During the primary planning stage, a testing team can plan and prepare a list of tests. Not only this, but they can also identify the data requirement of each test and work on the documentation accordingly.
A life cycle defines the various stages a product or service has to go through before it reaches its end. A test data cycle explains the different phases the test data has to go through to start a recurring life cycle.
Creating & Fabricating Data
This is the first step when it comes to actual data generation. Also known as test data fabrication, creating on-demand fake data using specific solutions helps imitate the production guidelines. The process should easily re-generate the synthetic data. This helps the test teams experiment with large volumes and various data. At this point, you must choose the right TDM platform. If the foundation goes wrong, the entire TDM goes wrong.
For example, K2view offers TDM expertise to generate real-time rule-based and masked synthetic test data. This automatically ensures data integrity across all environments.
Furthermore, it lets the managers monitor incoming requests, perform scheduling, and track execution status. While we are at it, the highlight of their data product platform is its ability to extract all data for a particular business entity from multiple source systems.
The data product platform is famous for its micro-DB approach, wherein all business partners (customers’) data is stored in one database. The fabric overall maintains millions of such micro-databases.
Analysis & Learning
Software testing teams then gather and consolidate the needed data requirements. During this stage, they decide on data access, backup, storage, etc. Always make sure there is a straightforward selection criterion for the data that will eventually be included in testing.
Business entities need to cover several testing scenarios. They also need to understand the data volume they need, its sources, and frequent data-refreshing requirements.
To do this, you can use an automated data catalogue that catalogwith the inventory and classify the test data assets simultaneously. It also maps out information supply chains. These steps ensure that your test data stays relevant for the testing teams.
After creation and analysis, here are the further steps:-
As test data management relies on production data, finding and obfuscating sensitive data is crucial. This includes tokens, passwords, and personal & financial information. Different strategies like data anonymization and masking anonymization leak.
Also referred to as data slicing, this practice allows teams to obtain only a portion of data during production cloning. This makes sense, as you only need a fraction of the available data for testing. Data subsetting in such a scenario helps reduce the infrastructure and storage costs of production cloning.
Data extraction for testing is a complex and time-consuming process as it targets multiple source systems. A proper TDM solution simplifies and automates this process as it integrates with the production data systems and eventually extracts the test data per predefined rules. This is why a sound TDM system must be easy to sync, adaptable and can roll back the test data on demand.
Provision & Versioning of the Test Data
Provisioning is making test data available for the target development and test environments. It includes moving fabricated and masked data from several source systems into the target environment. This may also include techniques such as legacy backup, cloning, snap shooting, and others.
Versioning of the test data repositories ensures accurate iteration of the tests, followed by control over the granular changes to the data.
Validate Test Data
This phase validates that the test data deployed on non-production systems are secure and compliant with Information privacy regulations. This further attests that the test data is in no capacity violating the confidentiality of the users.
Lastly, a successful TDM practice involves troubleshooting issues on the go while responding to addition and updating queries.
In this post, we discussed the steps of TDM processing in detail. From the learnings, it is clear that enterprises should embrace automation and prepare for a more user-governed and agile web 3.0.
They should rewrite their strategies and ensure qualitative, accurate, and speedier test data provisioning. After all, that’s the first step to an error-free application and excellent customer experience.