Part two in this series identified some key risk factors associated with a data migration, which underpin the shocking migration failure rates identified in part one. Let’s now consider common reasons why these risks factor are not successfully mitigated. …

Today, more than 50% of organisations are using full-size copies of production data in database development and testing [1], while 54% of test teams still depend on production data copies [2]. This use of low-variety production data undermines test coverage, along with the software quality that depends on it. Too…

Part one in this article series summarized the shockingly high failure rates for migration projects, identifying data migration as a key area of project failure. This article will now consider the types of risks that should be mitigated when planning a data migration. The risks fall into three broad categories…

Today, the majority of enterprises are engaged in ongoing system migrations — and most of those projects will either fail completely, overrun on time, or exceed their budget. Too often, key failings during data migrations combine with insufficient testing and design. This leads to compliance risks and costly bugs, which…

The increased demand for new software, faster delivery and better quality calls for greater automation across every stage of the software delivery lifecycle (SDLC). In fact, 46% believe that increasing the level of automation is the most important aspect when working towards making testing and development more efficient [1]. However…

Today, there is a greater-than-ever need for parallelisation in testing and development. “Agile” and iterative delivery practices hinge on teams working in parallel, as otherwise iterations become mini-waterfalls as one team waits for another to finish. DevOps and CI/CD likewise rely on parallel processes, while part of the speed gains…

In 2022, (test) data availability, quality, and compliance risks remain a major headache for software development. Parallel teams, pipelines, and frameworks remain dependent on outdated Test Data Management (TDM) practices, particularly those focused on copying unwieldy data sets periodically to non-production. …

The demand for ever more complex, varied, and larger data has created a situation where QA can no longer keep up. Burdened by outdated Test Data Management (TDM) practices and technical debt, agile teams are looking for new ways to overcome test data bottlenecks. Modern TDM tools and techniques are…

A glance at industry research from recent years shows that test data remains one of the major bottlenecks to fix in DevOps and CI/CD: The most recent Continuous Testing Report found that the average test team spends a massive 44% of their time finding, waiting for, or making test data.[i] …