test data management strategy<\/a>.<\/span><\/p>\nAccording to a report published on <\/span>Markets and Markets<\/span><\/i><\/b>, \u201c<\/span>The test data management market is expected to grow from USD 584.6 Million in 2017 to USD 1,060.9 Million by 2022, at a Compound Annual Growth Rate (CAGR) of 12.7%<\/span><\/i><\/b>.<\/span>\u201d<\/span><\/p>\nThe factors driving the <\/span>t<\/span>est <\/span>d<\/span>ata <\/span>m<\/span>anagement include <\/span>increased quality of test conditions and growing need for leveraging data integration tools, optimized storage and processing costs, better technical support to black-box testing teams<\/span>.<\/span><\/p>\nThe increased adoption of <\/span>test data management<\/span> solutions and services by several industry verticals, such as, IT, Telecom, BFSI, healthcare and life sciences, government, and retail have boosted the <\/span>need for<\/span> test data management.<\/span><\/p>\nT<\/span>est Data Management plays a key role in ensuring quality roll out of applications at the right time. As a service\/practice, it has matured in most organizations. The predominant activities include sub-setting, masking, data refresh and synthetic creation standardized through Test Data Management, Extract Transform Load (ETL) or Database (DB) tools.<\/span><\/p>\nIn the prevalent agile environment, it is imperative to strategize the test data management efforts.<\/span><\/p>\nTest Data Management practices in an Agile environment that improve business value <\/strong><\/h4>\nWhile there are frequent sprints in an agile methodology, the need for an appropriate test data is high and managing this test data yields better results. <\/span><\/p>\nThe best practices to be followed for handling test data management in an agile environment include \u2013 <\/span><\/p>\n\n- Identifying the appropriate data to test<\/span><\/b> \u2013 The data that needs to be tested should be identified appropriately. To do so, one needs to have an end-to-end exposure to the overall business process.<\/span><\/li>\n<\/ul>\n
\n- Creat<\/span><\/b>ing<\/span><\/b> a replica of Production data<\/span><\/b> \u2013 <\/span>As<\/span> the data in the production environment is the best candidate for usage, it is<\/span> advisable to create a replica of it. This will <\/span>result in having<\/span> realistic databases<\/span> that will act as a baseline for all the test data repositories in the management framework.<\/span><\/li>\n
- Keeping the data safe and secure \u2013 <\/span><\/b>In the process of simulating the real data, it is also important to keep the simulated data safe and secure. This will help create a secure repository in the test data management framework and can be reused without any malicious attacks.<\/span><\/li>\n
- Refreshing the data after every regression cycle \u2013 <\/span><\/b>Keeping the data in sync with the latest built features is important in creating an effective test data management solution. This help<\/span>s<\/span> maximize your test efficiency.<\/span><\/li>\n
- Automate as many test cases as possible \u2013 <\/span><\/b>It is always recommended to automate as many test cases as possible, paving way for agility and less prone to errors.<\/span><\/li>\n<\/ul>\n
By proper implementation of test data management solutions, it becomes very promising for enterprises to increase their business value while <\/span>assuring customer experience.<\/span><\/p>\nHowever, in the era of digitalization and ever-changing customer needs, every methodology is prone to challenges. It is imperative for businesses to stay focused and take steps to overcome any challenges that come their way.<\/span><\/p>\n