The biggest takeaway from Gartner’s Magic Quadrant (the MQ) this year for me is that organizations, analysts, and vendors now realize that analytics is not linear. While many businesses are looking to artificial intelligence and augmented analytics, these don’t replace other types of analytics. There’s very little point in delivering sophisticated advanced automated analytics if you haven’t got your ‘bread and butter’ reporting and governance working.

In the previous blog, we initially discussed how Yellowfin Signals discovered a surprising website traffic spike hidden in our Google Analytics data. So how did we set up Signals? And did we learn anything along the way? Read on below for our learnings and suggested best practice (this is going to be a deep dive, so grab a coffee and enjoy!)

Lots of organizations use Google Analytics and Google Insights to monitor the effectiveness of their digital marketing. While it looks appealing, some of the information it delivers is almost meaningless. It’s so complicated that it doesn’t help you understand what’s happening in your business.

The introduction of AI, automation and data storytelling to the world of analytics has not only had an immediate impact on the end users of analytics but also the people that work in the field. While many analysts may fear they will be replaced by automation and AI, CEO of Yellowfin, Glen Rabie, believes that the role of the data analyst will increase in significance to the business and breadth of skills required.

So much of our decision making is made based on firmly held beliefs and stories we have absorbed in our lifetimes. Generally referred to as type one thinking – this is fast, emotional and generally unconscious. It is a type of thinking that is very useful for making day to day decisions like what to wear, what to have for lunch or how to get to work. However, this type of thinking is inherently flawed and full of bias.

Today at Qonnections we outlined new innovations in multi-cloud and augmented intelligence that are setting the stage for Qlik as the leading platform for SaaS-based analytics. Leveraging our unique Associative Difference and Kubernetes-based architecture, Qlik is now able to deliver the full Qlik Sense Enterprise product on Qlik Cloud Services.

Qlik has been hard at work the past year transforming its customer success and support organization to truly align with customer needs in a SaaS first world. Today at Qonnections we announced a series of enhancements that put the customer at the center of everything we do at Qlik.

In part 1 of my post on data governance fundamentals, I introduced the "5 Ws and 1 H" of problem-solving—"What”, “Why”, “Who”, “When”, “Where”, and “How”— and applied the first three to data governance. This part covers how you can apply the last three pieces and suggest some next steps. Let's get started!

Jumping to conclusions has always been considered a bad way to do business (and most things in life). It implies making a rash, poorly considered decision without the facts and without an understanding of the wider implications associated with that decision. But there’s a new wave of technology that is changing everything.

Recently, I read an article by Jill Dyché which was a wrap-up of TDWI Las Vegas. She talked about speaking to an analytics professional who works for a bank and was building analytics on top of their applications. This comment really struck me because it means the bank’s software vendor is missing out on a great opportunity to create an enormous amount of value for their customer and their own business.