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Leveraging Automated Data Validation to Reduce Software Development Timelines and Enhance Test Coverage

By industrializing data validation, QA organizations can accelerate time-to-market and improve the quality and quantity of data analyzed, thus boosting credibility with end users and advancing business transformation initiatives. Our enterprise data testing framework overcomes the impediments of traditional test automation solutions and provides the ability to test end-to-end data. This solution is intuitive and custom-izable based on data testing needs. This white paper illustrates how our new dataTestPro solution can be applied across industries to enhance the data validation process. It also elucidates how this solution significantly reduced manual efforts, and improved QA effectiveness, when deployed at a leading insurance organization. Homogeneity in test data is a thing of the past. In fact, heterogeneity of data is now the norm across all industries. A decade ago, a data pool of 10 million records was considered to be large. Today, the volume of data stored by enterprises is often in the range of petabyte or even exabyte. Quality teams across enterprises expend significant effort comparing and validating data across multitier databases, legacy systems, data warehouses, etc. This activity is error-prone, cumbersome and often results in defect leakage due to heterogeneous, complex and voluminous data. It also requires testers with advanced database skills. Whenever time-consuming manual data validation impacts project schedules, testing efforts are squeezed to get the project back on schedule. Where each and every bit of data is of paramount importance, this is not an acceptable solution since it puts the business at significant risk. Automating data validation is an optimal solution to address these challenges. In our view, this requires quality assurance (QA) organizations to industrialize the data validation process. Therefore, we have created an enterprise solution that streamlines the data validation process and assists in the identification of errors that typically emerge across the IT landscape.