How to Do Test Data Management in Agile: 8 Best Practices

how to do test data management in agile

Once upon a time, application testing was really easy. All you had was a single mainframe and limited data sets. Users were satisfied with a simple application that provided the basic features. But nowadays the competition to develop the best app is intense. Companies are focusing on developing applications that provide the best user experience and features. This means companies are also focusing on application testing to ensure the features they implement work. And with testing, there comes a need to understand how to do test data management in agile. Why?

When it comes to testing, there are many factors that require consideration. Compliance standards like the European Union’s General Data Protection Regulation (GDPR) have added new challenges to testing, and overlooking even one area can expose an organization to massive risks, not the least of which are lawsuits and compliance fines. So how do you avoid these pitfalls? What do you think should be the number one task for testing teams?

With an increase in companies adopting agile, the need to accurately and efficiently manage test data has also increased. Proper test data is a must when shifting to a more flexible development process. After all, it’s the best way to improve the quality of tests. Therefore, it’s important to understand how to do test data management (TDM) in agile. Because there are various boxes to check before you can mark a test complete, TDM should be a high priority when it comes to streamlining testing.

Because agile is relatively new, handling TDM can be a challenge. So, in this post, we’ll take a detailed look at how to handle TDM in agile. Before we get started, though, it’s important to understand exactly what TDM means.

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How Can Data Analytics Improve the Measurement of Employees’ Performance?

how can data analytics improve the measurement of employees' performance

A report from Deloitte shows that in recent years, the need for people analytics has reached high points of over 70 percent. More companies rank people analytics as a “high priority” in the organization. However, the same report lays down a frightening truth: Less than 10 percent of those organizations actually have usable data. So, are we not asking the right questions? Do we even know what to look for? This isn’t yet another post on complicated algorithms; after all, you’re not here to learn data science.

This post is about answering one question: how can data analytics improve the measurement of employees’ performance? It’s not going to be easy— assessing your human capital is always easier said than done—but I’ve got your back.

But before we dig any further, it’s important to make sure that we’re on the right page. There are a lot of buzz words out there, but data-driven management is by no means a new concept. Since the concept of “the cloud” came up, companies started moving their workforce to it. Big item metrics like acquisition (e.g. cost per filling, training ROI) led companies to also ask questions about revenue, retention, and, more importantly, the question we’re here to talk about: how to track performance.

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