Systems | Development | Analytics | API | Testing

As a design leader at Qlik, I have the privilege of working with an incredibly talented team of designers dedicated to creating thoughtful, intuitive, and impactful user experiences. We see design as more than aesthetics—it’s about problem-solving, innovation, and ensuring that every interaction adds value. Our mission is to craft experiences that are not just functional but also delightful, trustworthy, and consistent across our products.

One of the fastest-growing trends in the datasphere today is the development and use of data products. As we move deeper into 2025 and beyond, data product usage is going to explode. In fact, according to the Gartner 2024 Hype Cycle for Data Management, Data Products are currently at the Peak of Inflated Expectations, signaling strong interest and rapid evolution in this space. You can download the entire report here. The advantages and impact of this new paradigm extend beyond data engineers.

We know the role that AI can play in modern business, and the benefits it brings to employees and customers. But launching and sustaining a successful AI project remains a critical challenge for many organizations. Technology leaders across the globe are being tasked with using AI to drive business success, and it is becoming a vital pillar in reaching strategic goals.

Adopting AI? Risky. Not adopting AI? Riskier… It’s an interesting conundrum illustrating a broader truth: playing it safe is sometimes risky. To succeed, companies need to take smart risks—and those who do often achieve the greatest rewards. But a risk that is smart for one organization may not be as smart for another, so what risk is right for you?

A data lakehouse is an innovative data architecture that blends the strengths of data lakes and data warehouses into a single, cohesive system. It retains the cost-effectiveness and flexibility of data lakes while incorporating the structured data management and performance optimization capabilities of data warehouses.

When Elizabeth Barrett Browning wrote her love sonnets, she may not have been thinking about the largest ERP system in the world. Here at Qlik, SAP is just one of the many data sources we love, and we think about them every day. This blog is our sonnet to all the SAP endpoints we have made available for you when it comes to getting data out of SAP. Many SAP customers right now are full of fear, uncertainty and doubt with SAP moving the goalpost for accessing their data inside SAP systems.

AI and analytics are only as powerful as the data they rely on. Without accurate, consistent, and governed data, even the most advanced AI models can produce misleading results. That’s why data quality isn’t just nice to have — it’s essential. At Qlik, our customers across the globe rely on our solutions to drive AI-powered insights, optimize decision-making, and mitigate risk. And it all starts with trusted data.

The typical data engineer is balancing many priorities. In addition to the day-to-day tasks like pipeline construction, connecting sources, and establishing data trust and governance, pressure is increasing to contribute to the organization’s bottom line. This may happen by unlocking the full value of the organization’s data or devising ways to accelerate business outcomes. One way to accomplish both is through data products.

Some companies have created that unfair advantage by unlocking data’s hidden treasures, or by leveraging a powerful ecosystem, or by overcoming adversity, or through other great means like a unique company culture. Another great way is driving excellence in partner engagements to unlock value quicker than your competitors. So, what are the key ingredients for that particular recipe?

I recently saw a post on LinkedIn that said, “Qlik isn’t the same company it was in 2016.” I’m pretty sure that it wasn’t meant as a compliment. But here’s the thing: they’re right. And that’s a good thing. Because if we were the same company we were in 2016, we wouldn’t be prepared for the challenges businesses are facing today. The world of data and AI has changed. Businesses have changed. So, of course, Qlik has changed too.