Validata Blog: Talk AI-powered Testing

Many organizations use multiple systems to support the Reconciliation process which makes it difficult to get a unified view of the balance sheet and requires costly manual interventions to solve exceptions. With multiple sources of reconciliation data, there’s no single version of the truth. The manual work required to compare and reconcile data in different systems increases the administrative costs, introduces the risk of errors and slows down exception management. Manual processes and fragmented, siloed departments are the enemy when it comes to increasing operational efficiency.

The shift towards Agile and Continuous Delivery requires higher levels of test automation, with the main challenge being the creation and maintenance of test environments.

A big discrepancy between test and production environments can be another factor that causes problem to test automation cycles. In most cases, the end-users probably use a different resolution, browser version, or an operating system version than the one you’ve run the tests against, resulting in environments being the weakest link in the process.

The demands in software business have reached a point where only pure innovative and agile practices that embrace digital transformation can bring benefits to organizations. However, most current software delivery practices aren’t aligned with the new standards and the long duration of delivery cycles affect the quality of the product and the time of release. Agile teams may have shortened development cycles but only DevOps practices can bring development and operation teams closer together and facilitate collaboration, in order to deliver a good quality software product faster.

Test Data Management (TDM) has always been a critical part of the application development and testing life cycle, however its importance or having the right data delivered to the right people, at the right time, for testing purposes is overlooked. Poor test case design and the inefficient provisioning of poor quality data means that test teams find themselves without the data required to fully test a system. Quality is compromised in favour of delivering software on time and within budget.