Systems | Development | Analytics | API | Testing

Blog Contents hide How many ads will users tolerate before you risk them deleting your app? Your retention strategy shouldn’t be one-size-fits-all. This data story will help for those times when you’ve struggled with churn and trying to grow your product’s usage We are using a music streaming app as an example, but this strategy can apply to any business. The goal of this story is to help you think critically with a data-driven perspective to inform your decision-making process.

The Australian Prudential Regulation Authority (APRA) introduced the CPS 234 prudential standard to set a clear benchmark for cybersecurity resilience. Complying with CPS 234 is a key step for organisations to protect sensitive information and build trust. As businesses rely more on data-driven operations, protecting customers’ information — especially in non-production environments that are often overlooked — is more important than ever.

The world of software testing isn’t slowing down anytime soon. Teams are releasing updates faster, systems are getting more complex, and users expect everything to “just work.” It’s a lot to juggle. The good news is that testing itself is evolving to meet those challenges. As we move into 2026, a few clear trends are starting to shape how QA teams think and operate. Here’s what’s on the horizon, and why it matters.

CWE and CWE Top 25 secure coding practices help you safeguard your code against rising software security risks. Here we explain what is CWE and the CWE Top 25. Read along or jump ahead to the section that interests you the most.

When you embed traditional BI tools, you work with platforms originally designed for internal analysts who expect to explore data directly. Embedded capabilities came later, and while these tools expose APIs, every variation in experience requires development work. The challenge is that traditional BI tools aren’t built for the full spectrum of embedded use cases. Most embedded analytics implementations must serve several distinct user types inside your customers’ organizations.

Most AI analytics projects are failing to deliver on their promises, and the cause isn’t what you might expect. This creates widespread project failures and undermines confidence in AI-driven analytics. What are the problems with AI analytics and how can organizations address them?

Gartner has once again named Qlik a Leader in the Magic Quadrant for Data Integration Tools, a position we have held for a decade. In that time, the landscape around data integration has shifted. Hyperscalers are moving up, large vendors are tightening their stacks, and acquisitions are reshaping customer choice. For CIOs and CDOs, that consolidation changes the question. It is less about who sits where in the quadrant, and more about how much control you still have over your own data and AI strategy.

A comprehensive guide to DreamFactory's evolution from a monolithic platform to a modular, container-ready architecture—and how administrators should approach version upgrades.

In today’s product landscape, analytics isn’t just about showing insights. It’s about moving data into the tools where work actually happens, so that users can make informed decisions in context. Users expect analytics to integrate with their systems, whether that means sending a lead to a CRM, creating a Jira ticket, or triggering a workflow downstream. As product owners, that means one thing: your analytics experience should enable seamless data flows across systems.

In today’s fast-paced software environment, getting product features out the door quickly is the minimum. Getting features out the door quickly + with reliability is what separates high performing teams opening up larger opportunities. This is where DevOps testing comes into play, testing not just at the endpoint of the development + operations lifecycle but as an integrated process throughout every step of the life of the product.