Applications sit at the heart of today’s enterprise businesses, and just about every application brings about new challenges and more complexity to development teams. Give up these three outdated practices to deliver high-quality software faster to market.
Applications sit at the heart of today’s enterprise businesses. While agile methodologies, shift left, and continuous integration and continuous deployment (CI/CD) have allowed to automate and accelerate development, enterprise teams are still constrained by the speed and quality of moving data to testing environments.
AppDev teams are constantly looking for ways to improve test data management (TDM) within the software development lifecycle (SDLC) workflow. Rather than wasting hours on fixing bugs related to data and waiting for production-quality test data, they're more interested in spending their time to develop new features and release those features more frequently.
In order to achieve both, enterprise software teams need production-quality data in their SDLC workflow to detect and prevent data-related defects earlier. However, you typically find teams reproducing their production data as a one-time exercise for test environments, which is not only time-consuming but requires a substantial amount of infrastructure work. And over time, data becomes stale and leads to defects later in the pipeline, lowering the quality of work and further extending the release cycle. You simply can’t build dependable and high-performance applications using bad data.
What has been adopted as the status quo in software testing is no longer a viable IT or business solution. Here are three outdated practices that prevent software teams from delivering high-quality software faster to market:
Full copies of production for test environments require longer provisioning times and more storage. When you’re dealing with multiple data sources in testing, both of these issues are amplified. As a result, developers and QA teams use stale data which further augments the time it takes to discover defects and to fix those defects into the later stages of the SDLC.
Sharing production-quality data environments among teams result in conflict. Oftentimes, teams are having to share a single dev or QA environment because there isn’t enough storage or there’s high cost associated to support full copies. But when new features and fixes have to be delivered within days or even hours, this tempo can’t be supported with a single instance of a test data environment.
Subsetting data to accelerate data delivery. While subsetting data facilitates copying and moving full, production-sized datasets to lower tier environments earlier in the SDLC workflow, it fails to adequately embody real-world data conditions. Relying on subsets prevents QA teams from identifying edge cases and outliers as well as dramatically delays the discovery of defects, which leads to longer cycle times to fix bugs.
What’s the big takeaway? Enterprise software teams need production-quality data to detect and prevent data-related defects earlier in their SDLC workflow. If you’re looking to acquire new tools to improve your software testing efforts, consider a DataOps platform that supports teams in the following ways:
Virtualize and mask data from heterogeneous data sources
Enable distribution of lightweight virtual copies to different stakeholders via self-service access
Have the ability to integrate into the SDLC workflows with API automation, so that TDM teams can quickly manipulate test data and operationalize the rollout of test data to dev and QA teams
Today, every company is a data company, and that means modern software development requires realistic test data to be delivered with speed and security. A foundational change in how data is accessed, managed, and secured across the enterprise will be critical in modernizing your data strategy to drive enterprise-wide transformation.
Download our Test Data Management whitepaper to access the full checklists and hear from some of the world’s largest enterprise companies on how they eliminate data-related defects earlier in the SDLC workflow.