Developing Test Data Management Strategies for True DevOps Agility


Testing applications in the IT infrastructure can be considered one of the most essential processes to ensure the overall success of the system. However, testing is also one of the most labor, time and cost-intensive activities while also acting as a bottleneck for most development efforts. When considering a DevOps cycle, testing is often kept on the back foot for the same reason. Application testing is often carried out on larger scales and in later stages. This only aggravates the cost inefficiencies in the cycle.

With technological advancements, testing applications can be simplified to a great extent and in many cases, automated as well. However, test data and its management can still bring in inefficiencies if not handled properly. Test environments rely heavily on the data for accurate and exhaustive testing, which is why having the right strategy to manage test data is a necessity for DevOps agility.

An efficient test data management strategy considers the following factors in consideration:

  • Reduction in time to acquire test data
  • Reduction in complexities to provision test data
  • Reduction in errors with test data

There are other factors like security and compliance to be considered but they can be handled with the right systems in place and do not contribute to agility directly. So let us look at the right strategic planning with test data management to achieve DevOps agility for faster development.

Categorical Optimization of Test Data Golden Copy

Enterprises host huge amounts of production data which is often copied as is for testing. However, not all the data is relevant to testing at any given point or phase of development. Test data requirements can vary significantly based on the development initiatives being executed.

These can be broadly categorized into:

  • Migration and/or modernization of IT infrastructure
  • Product development based on market needs and/or new offerings
  • Integration of new systems like analytics, machine learning, IoT, etc.

These development cycles are executed with procedural planning well in advance. Thus, test data management can be categorically optimized within the cycles based on test cases and requirements. This can be used to optimize golden copy based on the overall scope of the project, which can be further extended to (and based upon) governing policies, profiling and sub-setting scripts, and synthetic data generation. This form of golden copy creation of test data can reduce time and complexities in test data acquisition while paving the way for test automation. As a bonus, you can also reduce data storage and footprint costs without compromising testing.

Automation of Data Virtualization and Synthetic Data Generation

One of the most inefficient processes in application testing is acquiring the right data sub-sets in the required quantity. Manually scripting to fetch the test data and creating data to meet the requirements are processes that are time-consuming and error-prone, respectively. This is where one of the key ingredients of test automation comes into play.

On-demand test data provisioning can be sped up significantly with the automation of profiling and synthetic data generation. SLK has developed test automation and test data management systems that can provision data within hours compared to weeks; more than a 90% reduction in overall time to provision test data. A significant part of this time is spent masking sensitive data for security and compliance purposes, which could otherwise be brought down to less than two hours.

Download our whitepaper to discover more about how Test Data Management means faster delivery of apps and more opportunity for innovation.

AI for Overall Test Data Management

AI-enabled systems bring huge advantages to a business environment. While the application of AI and machine learning has already proven its worth in customer-facing functions, they can provide deeper strategic advantages in development and test automation. AI-enabled testing can significantly accelerate DevOps cycles through test data management, test data clean-up, test automation, and testing analytics.

SLK is a premier organization that has helped its clients establish efficient and agile IT infrastructure for their businesses. Our in-house AI technologies have been customized to meet the unique demands of our client’s businesses and have delivered exceptional results. We work closely with our customers to understand their business goals to design and develop the right solutions for them.

Test data management is a complex system that requires rigorous technical understanding and strategic business alignment to deliver efficiency. Inefficient test data management strategies can slow down DevOps cycles by months, if not years. In a competitive landscape, innovating quickly and catering to market needs is the only way to compete. Strategy is just the first step towards growth, but then the saying goes, well begun is half done!l

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