The 2021 holiday season is over, and, with a bump we’ve landed in 2022, with something of a repetitive tune playing. The COVID pandemic continues; we are a full two years post the first cases being reported; and the statistics are no less shocking today than they were at the outset.
I have written many a post about the importance of keeping your data moving. As Mathew Wilder sang in “Break My Stride,” it’s “got to keep on moving” to enable you to act within the business moment. Real-time data analytic pipelines are the solid foundation to achieving the state of Active Intelligence, and you can only start that when you work on the freshest data available to you.
When AI came on the scene in analytics, Qlik took a very different approach than ‘black box’ tools. We felt strongly that AI should be utilized to enhance human intuition, instead of replacing it. And of course, the term augmented analytics is now well known. That’s not a coincidence.
The start of a new year is a great time for reflection, and, when I look at the progress organizations are making on their data journey, I’m feeling quite positive as we welcome 2022.
While COVID-19 continues to cause devastating disruption to the global economy more than two years into the pandemic, it is also continuing to force remarkable innovation across different industries. Companies have found new ways to sell, service and operate during the crisis. For me, there is one common theme for these innovative companies, including Qlik, and it is “Jobs to be Done.”
Since I’m now migrating NodeGraph’s processes to Qlik, I thought it may be a good time to talk about migrating data during a merger or acquisition. There are many aspects to consider. Here are some of my thoughts on why companies merge or migrate data landscapes, common M&A migration pitfalls and how to avoid them, the time and cost involved migrating data during a merger or acquisition, and other topics.
Over the course of my next two blog posts, I would like to share my thoughts around a debate raging in data architecture circles. The bone of contention? That the 21st century needs a new data management paradigm for modern analytics. First up, I’ll frame the argument and explain the two prominent approaches of data hub and data fabric. Then, I’ll cover data mesh and compare all three architectures. As always, I’d love to get your input, feedback, queries and comments!
