We
were supposed to be more connected, but we live in a world that is getting more
and more fragmented by the day. This is the case with data, but also with social
media, politics, regulations, education and business. Why is there so little
common ground?
Tag: Qlik Blog
With Data Literacy and Analytics More is More, Not Less
There is TED talk by Malcolm Gladwell during which Gladwell discusses the fact that we were actually better off when we only had two kinds of spaghetti sauce to choose from. Today, we stand in supermarket aisles in confusion amid so much more choice.
Don’t Take My Data Warehouse Advice, Take Snowflake’s
I’ve been involved with data warehousing in one way or another for over 15 years, and, in that time, earned a reputation for knowing a bit about the subject. I hesitate to call myself an “expert,” but folks often seek out my thoughts on data warehousing. I’m always happy to oblige. That being said, the most rewarding experience is when I read other expert advice from around the industry.
The Qlik Sense November 2019 Release
With last release of the year Qlik are happy to deliver several product enhancements that will set the stage for a successful new year ahead and close out on a host of exciting enhancements throughout 2019.
The Qlik Sense November 2019 release introduces expanded cloud connectivity, SOC 2 Type 2 certification, new visualization capabilities, mobile offline support for Android, reporting improvements and more.
The Future is Now – the 3rd Generation of BI and Analytics Is Right on Time
It was about this time last year that we started to define the vision for the 3rd generation of BI and analytics. Our idea was simple, that data-driven transformation required a new democratized approach to the way in which we drive value from our data. Subsequently in September we published our whitepaper which explained how the democratization of data, augmented intelligence and analytics embedded everywhere are the prerequisites for moving beyond the centralized and later decentralized approaches that went before.
The Hole Story and Bias in AI
AI and its enabled tools continue to enthrall business with its promise of efficiency and innovation. But, one of the things AI is also clearly enabling is the bias. We’ve all read the news and heard the scaremongering stories around potential flaws and biases in Artificial Intelligence systems. I believe for this technology to reach its full potential, addressing bias will need to be a top priority. In this blog post, I would like to talk about one of the major reasons for having bias in AI and share the “hole story” with a lesson learned from World War II.
Augmented Intelligence – So Much More Than Search
The hype around AI is deafening, creating a noisy and confused environment. Organizations are looking for clarity on the role and application of AI in bringing more value to data, and for help avoiding the pitfalls that surround so many of these projects. These pitfalls include error-prone decision making and unintended consequences based on flawed data or “black box” algorithms, and gimmicky approaches including simple search and bolt-on AI that overpromise and under deliver. We outline what you need to know as you think through these challenges in our new ebook, “Beyond the Hype: How to Get Real Value from AI in Analytics.”
Put me in coach!
Do you remember the time you wanted to learn to play an instrument or take on a new sport? The excitement and anticipation of acquiring the new skill that creates a buzz in your stomach. I remember that feeling when I decided to race a triathlon.
What You See Is Rarely All There Is
In last month’s blog, we introduced you to a 12-step systematic process to help guide you through making data-informed decisions. This process is unique because it is not just about understanding analytics. It is also about understanding psychology and business anthropology as well.
DataOps 101
There has been a lot of industry chatter lately on the notion of DataOps. In summary, this is a framework for moving the same kind of agility to the analytics space that many IT organizations have embraced with application development. The principles of iterative development, sprints, and failing fast can apply equally well here, but before we fall down the tech jargon rabbit hole, let’s get some perspective on what makes analytics slow in the first place.