At Qlik, we're witnessing a thrilling shift in the landscape of data analysis, customer engagement, and decision-making processes, all thanks to the advent of generative AI, especially Large Language Models (LLMs). The potential for transformation across all sectors is enormous, but the journey toward integration can be daunting for many businesses with many leaders wondering where to start in integrating the exciting capabilities of AI into their daily workflows.

In this blog series, I have been exploring the “sliding doors” – or divergent paths - organizations can take with data and analytics. Sometimes, grabbing the wrong door means missing out on creating the most value with your data. But in some instances, it can also lead you on a more serious path of breach of compliance with regulations.

Data engineers play a crucial role in leveraging the immense potential of SAP data to drive smarter decisions and foster innovation. However, integrating and analyzing this valuable information can be complex and time-consuming. But it doesn’t need to be. In this blog post, we will explore how Qlik Cloud Data Integration and Databricks together make it much easier to harness the power of SAP data and blend that data in Databricks, in a cost-effective manner.

Perhaps it's the awe-inspiring films about space exploration (my personal favorite – Apollo 13) that evoke the image of NASA as a place buzzing with activity, filled with screens displaying data, charts, and ALWAYS a big countdown clock. However, one of NASA's most recent challenges may surprise you - the task of cracking open a billion-dollar canister filled with ancient asteroid dust.

In an age of rapid technological transformation, governments are playing regulatory catch-up as they try to keep pace with technological developments and the increasing amount of personal identifiable information (“PII”) generated by our every-day lives. Privacy laws regulating the use of PII continue to strengthen (Gartner estimates that while 10% of the world’s population was covered by comprehensive privacy laws in 2020, by year-end 2024 it will be 75%).

In this blog series, I am exploring the “sliding doors”, or divergent paths, for creating value with data across different use cases, practices, and strategies. In this post, I want to discuss how to generate value with Data Products. As I reviewed in my last blog, grabbing the door to the better path for managing your data isn’t just about solving your particular use case: it’s ultimately about delivering value for your business.

Data engineers and architects are being asked to do more with their enterprise data than ever before. Yet, the knowledge gap between what businesses want to do with data and how they can accomplish it is growing daily—especially considering today's AI hype cycle. With all that noise in the market, it's easy to see how organizations struggle to keep pace with innovation.

It's summer of 1997, and I’m seven years old. I’ve just finished typing up my last homework assignment of the year, and a few loud keyboard taps later, it’s saved to a floppy disk, ready for printing at the school library. I grab my headphones and press play on my Walkman: Fleetwood Mac – Rumors.

In my first post of this new blog series, I introduced the concept of “sliding doors” for creating value with data: the divergent paths that organizations can take which can lead to great - or not so great - outcomes. In this post, I want to explore this for what has become a new data imperative for organizations today: effectively reporting their environmental, social and governance (ESG) data to meet new regulations.

In the popular 90’s film “Sliding Doors”, the life of the lead character, played by Gwyneth Paltrow, takes two divergent paths based on whether she catches or misses a train. Spoiler alert if you have not watched it: one path ultimately (and surprisingly) leads to a better outcome than the other.