When Elon Musk stressed how self-driving cars would dominate the market in years to come in Dubai during the World Government Summit, it indeed took the audience in amusement.
However, he also emphasized a prolonged time for this technology to disrupt manual driving completely. This was enough to express the predominant requirement of executing extensive software testing techniques and integrating the correct test data for perpetual testing.
He spearheaded the autonomous driving phenomenon by announcing the official entry of Tesla into the United Arab Emirates Market with its Model X and Model S crossover vehicles. The market for test data management has transitioned to an advanced collection of strategies, primarily driven by the improved locus on application uptime, enhanced time-to-market, and reasonable costs.
There are no second guesses when we say Test Data Management is swiftly culminating adjacent to the other IT enterprises like cloud or DevOps. Additionally, Test Data Management is no longer considered only as a back-office function. TDM has evolved as a vital business accelerator for security, cost-effectiveness, and performance agility.
What makes Test Data Management Important?
Test Data Management helps software quality teams command and examine files, data, strategies, and processes for the whole software testing lifecycle. The anticipated growth for Test Data Management in 2021 is 12.7% CAGR.
It requires preparation, collection & storage, devising, and executing the software testing practices properly.
The primary purpose of test data management is to build, maintain and support the source codes of the software for testing purposes. It also assists developers in separating test and production data to support the established software’s version, track errors, and different methodologies that work in software testing.
Additionally, Test data management also serves to regulate and optimize the significance of the data involved in software testing to organize and institute the resources required during software testing. The data generated and assembled is beneficial for software testing thus, test data management fulfills the requirements of the entire testing lifecycle also. It assists in making the software effective during the result submission as it minimizes the efforts and time used for processing the data.
For improved results, the following Test Data Management checklist must be taken into consideration.
- Ensure the general test data component.
- Organize the systematic flow of test data.
- Create reports and dashboards for metric development.
- Consider what to prioritize and allot data accordingly.
- Constitute the different test data versions and their processing.
Challenges surrounding the Test Data Projects
Speed, Security, Quality, and price for test development projects are the frequent obstruction points towards faster and secured projects of test data. SDLC (Software development lifecycle) causes these obstructions during shifting data. Following are the most prevalent challenges faced by organizations during test data management.
Test environment provisioning is a slow, hand-operated, and high-touch process.
IT Organizations who depend upon the request-fulfill principle for solving the organizational queries exercise a reasonable amount of time and work to build test data copies. This process might take days or weeks to get resolved and update provision data, due to which developers often find themselves queuing after other requests.
However, the time and efforts required to transition to a different test management system are in direct sync with the number of representatives working in the process. Organizations, usually running with more than five personnel to establish and provide data, make the process prolonged and stretch the time invested in application delivery.
Data masking appends friction to release cycles.
Data breaches and security threats have shot up in recent years, making it a glaring challenge that impacts the application development process. Especially the applications that process sensitive information like credit card numbers or patient lists are more vulnerable to data theft. It also escalates the cost associated with data protection and management, though businesses are constantly stressing the enhancement of data reusability, yet the requirements are not touching the threshold.
Therefore, Data Masking secures the system against any data infringements to secure the regulatory compliances. Additionally, test data management teams are getting their hands on advanced technologies to retrieve data quickly and securely. Data Virtualization is amongst those advanced technologies where access to virtual data is comparatively quick, and neither uses physical infrastructure costs.
Storage costs are also at a constant upscale.
IT teams build multiple, concurrent copies of the test data, which takes ample storage space and is technically an ineffective utilization of storage. To optimize the storage capacity and meet the collective requirements, operation teams must organize test data availability across different groups and release accounts. If not appropriately optimized, development teams have to contest against shared and inadequate environments, which implicates the essential application projects, queuing after one another.
Following are some of the most beneficial exercises to simplify and interpret the testing procedure.
- Comprehend the test data
- Acquire multiple data subsets from different sources.
- Automate both the actual and anticipated results.
- Protect the delicate data
- Revive test data for practical testing.
The Modern Approach to Test Data Management
Formulation of Test Data Management through Data platforms can empower businesses to modify the operation and optimize the consumption of data. Counterfeiting the eminent challenge of data silos, the modern approach has assisted IT teams in masking and delivering data with 100x capacity and utilizing only ten times less space.
What is the result? With less utilization of infrastructure, IT operation teams can compile longer projects in reduced time.
- Accelerated release cycles and time-to-market: Three and a half days to stimulate an environment vs. Ten minutes through self-service.
- Assured data security and regulatory compliance- data privacy during non-production.
- Improved quality statements with minimal costs- 15% vs. 0% data-related defects.