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.
Many websites and even applications online are built on top of a CMS. According to recent survey data, WordPress has a 60% market share, making it the most popular CMS by far. The next closest competitor, Joomla, has only 5.2%! But unlike bespoke software, many people don’t test their WordPress website. While the core of WordPress is fairly well tested by it’s creators, users, and the open source community, the same cannot be said for every plugin and theme.
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.
In October 2018, we launched two new products into the Yellowfin Suite: Signals, an automated discovery product that discovers critical changes in your data as they happen, and Stories, a data storytelling product which enables users to provide better context to the numbers and create a common, consistent understanding across the organization. What did we do next? Drink our own champagne, of course.
In my last post I laid out our reasoning for moving from Heroku to Google Kubernetes Engine (GKE) and other GCP services. Now I'll describe the actual migration process in detail. This isn't designed as a how-to guide for migrating from Heroku to GKE—Google has their own excellent tutorial for that—but rather a description of some of the challenges of migrating real-world production applications and how we overcame them.
