In the era of technology, tools have become an important factor in various approaches to test data management. On one hand, we have the traditional approach, i.e ETL Extract, Transform and Load. The approach is planned to execute test data of software or products in a set of format.
On the other hand, there is a holistic testing approach, which brings in the face of technology. It is fast, more effective and customer-centric that ETL testing. Although, HTDM does bring with it the essence of ETL testing but with a wave of technology. This framework combines the efforts of the test team and tools to deliver results like:
Proper data evaluation.
Compliance with the company standards.
Maintaining the track and health of data.
The adaption to Agile methodology has paced the speed of delivery of services. On the other hand, big companies consumer large amount of test data. Being a manual approach, ETL fails to manage the same properly. The chances are probable that data might be lost during the migration process or testing environment might loose its balance.
Holistic test data management is here to provide a broader aspect of test data experiences. The stages holistic approach goes through are:
Decoding the needs and risks of test data
The first stage lays the structure of what we call raw data. The raw data is then optimised as per the needs of the testing team with the help of automation tools. Agile methodology has made it mandatory to keep information updated every 2-3 weeks. After capturing the required data, it is assessed for the risks and threats posed if mistakenly used in some other way.
The invasion of ETL
To be honest information extraction already happened in the first stage of a holistic approach. While transformation is of any concern, it happens along the way within the tools before going for provision. The last stage will fulfil the goal of the test environment.
Implementing Fit-for-use data
A structured data is ready to be distributed to it required a place, but only after passing it through the test of data validation. Where the information is tested for its compliance capabilities. The later moves the process to mining, where data is divided into testing and training material.
In order to avoid any repercussions, the maximum part of the data is used for training purposes. Only a smaller portion is dedicated to testing. When all this is done, it is time to again delegate each data to specific test cases. Test data bookings are made to reserve certain data for key models in order to avoid any mishaps.
The victory of modern approach
All these stages might seem time consuming and tedious. But the best part of holistic method is that it is none of the above. Forming a structure around ETL, it adapts to agile methodology which brings automation into the picture. Imagine of all the manual paper work and engineering task you will be saved from when dealing with holistic testing.