Failure to prioritize data in the app testing phase can lead to significant consequences for organizations across the board.
Organizations will forever remain stunted in reaching their application development goals—increasing reliability, performance, functionality, or all the above— in relation to their competitors without the continuous adoption of agile and ever-changing DevOps technologies. The transition to new DevOps tactics, such as continuous integration and delivery (CI/CD), is the new standard in meeting heightened market demands and customer expectations. However, proper implementation requires a critical revamp to application testing—an entirely different approach than what is used in traditional waterfall methodologies: code, compile, test, deploy, and, after months of work, release.
Although essential, organizations can’t solely focus on usability and user experience when implementing DevOps strategies and lose sight of strategic QA testing for data integrity—which subsequently helps inform the strategy of how data is retained. That said, a company’s testing is only as good as the data they’re bringing into the test environment. To develop the highest quality applications, developers must prioritize pulling in production data to ensure it addresses all fringe use cases and issues once it’s out of production and pushed live to users.
Realistic Data Transforms Application Testing
Utilizing current DevOp processes is no longer enough to test applications in isolated environments. Instead, teams must incorporate up-to-date data into the testing process; real-world scenarios must be replicated to validate their code and ensure that the software functions as expected under various conditions.
Using realistic data sets during testing also allows teams to validate application performance, security, and user experience more effectively, as well as allows teams to better address potential issues proactively, leading to faster bug detection, reduced development time, and an overall boost in application performance. In fact, 63% of developers saw significant improvement in their organization by implementing this practice, according to a 2023 Capgemini survey.
This is important regardless of industry—think eCommerce applications, where your website may perform flawlessly with a limited number of products and users. However, when real-world data with thousands of products and concurrent users is used, performance issues such as slow page loads and checkout failures emerge, highlighting the need for load testing and scalability assessments.
Alternatively, a financial software system tested in isolation may not encounter the complexities of real financial transactions and regulatory changes. Testing with up-to-date market data and real-world transaction volumes ensures the software’s accuracy and compliance with financial regulations—a scenario that can cause organizations major problems down the line.
Up-to-date Data Incorporation Methods and Supporting Tools are the North Star of QA Testing
To effectively incorporate data into the testing process, organizations must adopt process discovery, a practice that helps developers understand how users engage with applications and datasets. Process discovery provides insights into the specific steps taken in workflows, the frequency of interactions, and even potential shortcuts used by different user groups. Armed with this data, testing teams can focus on what needs to be tested more frequently and build comprehensive testing scripts that can be reused.
As organizations move towards agile development CI/CD—73% of them already having made the transition, according to a recent Stack Overflow report— they will also need to embrace automated testing as a foundational pillar of their testing strategy to increase overall efficiency and enable quicker time to market and more innovative development. Implementing automation speeds up testing cycles, reduces the burden on testing teams, and empowers developers to test during development—not just before production deployment.
Additionally, as the adaption of artificial intelligence (AI) continues to become necessary to remain competitive within nearly every practice and industry, its incorporation into the QA testing process is no exception. AI can play a pivotal role in predicting what should be tested as code changes, triggering pass/fail testing before manual intervention is required. However, to unlock AI’s potential in testing, organizations must have high-quality historical data to feed AI models continuously. This emphasizes the need for a robust data strategy, including data governance policies, investments in data infrastructure, and building AI teams to monitor and fine-tune AI models.
See also: QA Increasingly Benefits from AI and Machine Learning
Overlooking Data-Driven Testing Leads to High Stakes Consequences
Failure to prioritize data in the app testing phase can lead to significant consequences for organizations across the board. One of the biggest risks is the introduction of bugs and errors into the production environment, which can result in both sizable financial losses and damage to your organization’s reputation. Poorly tested applications can lead to customer dissatisfaction, reduced user adoption, and potential legal liabilities, especially if sensitive data is compromised.
Overlooking data in the testing process also undermines the effectiveness of the entire DevOps approach. Without accurate and up-to-date data, the results of testing become unreliable, rendering the entire CI/CD pipeline less effective. This, in turn, impacts the organization’s ability to innovate and compete in the market and may lead to delays in releasing new features or updates.
Additionally, organizations may find it challenging to keep up with industry trends and demands if they do not utilize data-driven testing practices. As AI and process discovery become more prevalent in the testing landscape, organizations that overlook data in their testing efforts risk falling behind their competitors who have embraced these transformative technologies.
No matter the context, organizations must recognize that quality and quantity of data in the testing process is the answer to streamlined operations, and staying up to date on the supporting technology is not just a “leg up” against competitors but rather critical to losing out on time, energy, money, and resources that could put your organization out of business.
