In this blog post we are going to look at using JMeter to support business testing in production. This is a slightly different topic to the one discussed in this post on testing in production. The one in the link above is around running your performance testing in production for reasons that are discussed in that post. This post is going to focus on how you can leverage your performance testing tools to support this activity, as discussed above we will focus on JMeter in some of the examples.

When you look at stories of great success like these, emotionally it’s tempting to feel like these companies just got the “unfair” advantage – that lucky, elusive edge that set them apart and put them ahead of their competition and into the hearts of their customers. But often, it is something else… In my role as Chief Strategy Officer at Qlik, I have been looking at this more formally with my team.

Delivering quality software is as much about the people behind the code as it is about building the software itself.

An operational data fabric focuses explicitly on integrating and managing disparate data in the context of operational systems and processes. It’s tailored to meet the needs of operational data management, often in real time or near real time, to support business operations. Let’s break down some key features of operational data fabric: To fully appreciate the transformative power of operational data fabric, it’s important to understand how it compares to data fabric.

Fan expectations pose an existential threat to fan-centric businesses. And that's because what it means to be a fan has changed. Fandom used to be watching the Monday night game on TV or getting tickets for a concert when your favorite artist rolled through town.

Gartner predicts that 70% of engineering teams will shift to "continuous quality" by 2026. But what is continuous quality, anyway?

With an ever-growing user base of approximately 6.7 billion active smartphone users globally, mobile apps have become an integral part of our modern existence and digital transformation. Recognizing their omnipresence and revenue-generating potential, it becomes imperative not to overlook their significance. Nevertheless, one of the initial queries posed by clients while presenting their app concepts to us revolves around understanding the app development cost.

Shaping the future of AI goes hand in hand with discussions of diversity, equity, inclusion (DE&I), and women’s leadership. As AI increasingly pervades, revolutionizes industries, and reshapes the daily lives of people, the question of addressing intrinsic biases and ensuring inclusivity in the development of AI becomes critical.

There is a growing recognition that insurers can introduce data, analytics and AI into virtually all of the important insurance functions and workflows, including product development, pricing and risk selection, underwriting, claims management, contact center optimization, distribution management, reinsurance, and understanding and shaping customer journeys. Here are some of the exciting ways insurance companies can put data to work.