In all kinds of industries, speed has always been a critical component. This is even more true as applications are now at the center. More and more companies are now software companies, who hope to be outstanding on the market by making apps that they deliver to the clients, partners different.
For instance, GE is a company that made an investment of 1 billion dollars in a software center, known as GE Digital in order to differentiate itself as a platform and an application company. also, Walt Disney Parks and Resorts, which made the Magic Band enabled by RFID technology to serve customers in an all-in-one entry ticket, by which customers can utilize their mobile phones and online banking apps instead of visiting the local branch directly.
Because of this evolving trend, there is an increasing demand for data, which is both the output and the oil for apps. However, because applications generate more and more data, it is more and more challenging and expensive to manage. For example, with every terabyte of data growth in production, there are ten terabytes used for developing, testing as well as other non-production use cases. Taking that situation into mind, we know that we should have a test data management to handle that massive data proliferation throughout storage and servers.
The necessity of test data management
In terms of testing software applications, almost everyone has their own data issue. For example, software development teams do not have enough approach to the test data they require, which is asking for a more effective test data management solution.
In the past, application teams had to produce data for development and testing in a siloed and unstructured style. Because the volume of app projects has grown up, a lot of big IT companies have captured the chance to improve economies of scale through centralization. After taking a look at large efficiency gains from the result of such opportunities, many IT teams have widened the scope of test data management by using data generation tools, data masking to manipulate production and so on.
The challenges of software development
Because the new software development methods are rising, quicker and more iterative release cycles have led to a lot of challenges. For example, which is also the most notable one, creating a test environment with the proper type of test data is a slow and manual process. A lot of companies now still keep using a request-fulfil model, in which ticket requests are often delayed or not achieved fully. Duplicating a dataset can take a long time, even several weeks, and often come with a lot of effort needed by teams. Every component in the software delivery pipeline has been automated, it is rather influencing the speed at which application projects can improve, apart from data distribution.
Another problem is errors. Currently, application economy also has to spend billions of dollars every year to deal with bugs. The reason may be that software development teams are not able to approach to the right test data. Size, time and type of test data are all important design elements that are all usually stuck in the expense of saving compute, storage and time.
Another difficulty when we mention test data management is data security. With the increasing number of data breaches, data masking is now more adopted, but not without adding friction to app development. The processes of data masking can take a lot of time.
Last but not least, test data proliferation and storage costs are keeping on rising every day. But in spite of the efforts to improve data reusability, the needs of app teams are still not deserved.
Now on the market, the number of test data management tools and software is still expanding. Nevertheless, there is still a high demand and the productivity benefits from those solutions are not still untapped.
An evolving best practice
High-quality test data management teams are seeking for new technologies in order to generate the right test data in a secure and convenient manner without influencing the bank. In spite of the fact that there is no single tool coming with all of test data management, one thing is certainly true: at least one factor is missing.
As usual, the missing piece here is data visualization. Unlike physical data, virtual data can be quickly provided within minutes, which makes it easier to distribute test data to app teams. Also, unlike storage replication methods, data virtualization is all integrated in order to perform software testing without delays.
A conventional model may take days or even weeks to carry out while self-service data can be accessed within a few minutes.
What is more, virtual data also refers to powerful data control features. It can be quickly reset and distributed to test code changes in isolation. This may result in massive software quality advancements in order to better customer experience.
Currently, advanced IT companies are making test data management better with integrated data masking and distribution. Administrators can both deliver virtual data to app teams in a short time and execute repeatable masking algorithms to streamline the handoff between processes.
Finally, virtual data, which can be a duplication of any data saved in a relational database or file system will share common data blocks throughout duplications. As a result, this will take account of 1 per tenth the space of physical data. For companies which are developing quickly, the capability of flatlining infrastructure support expenses can have a big impact on both the bottom line and top line because those savings will be made investment again into a more strategic initiatives regarding development.
Better test data management is the quicker delivery of applications as well as the quicker achievement of business purposes. Companies that are not able to invest in test data management will lead themselves at risks of falling behind other rivals. In conclusion, test data management would be your triumph instead of your downfall.