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.
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.
Predictive Analytics Saves Lives – it really does!
Picture this: I read the overnight incident log and it contains a horrible murder of a vulnerable person in the community. My first thought is for his immediate family and friends. My second is to see if there was any chance of preventing it. If so, what can we learn? Most serious case reviews show that a lack of cross-agency information sharing and timely decision making directly contributes to these sad outcomes. Looking at this case as part of the serious crime review process revealed similar issues.
Fast Track Your Data Literacy Journey
As I meet with companies through my travels, there are recurring themes to the lack of data and analytical success: a lack of data and analytical strategy tied to organizational goals, skills gaps to making smart, data-informed decisions, and workplace culture. These roadblocks must be remedied for an organization to capitalize and truly realize success with their data and analytical objectives.
Do you have what it takes to make data-informed decisions?
In a previous post, we introduced a process and methodology for making data-informed decisions. The process defines the steps that should be systematically followed. The methodology outlines what needs to be done in each step of the process, including how and why to do it.In this post, we will add in the final piece of the puzzle: the skills that are needed to allow organizations to follow the process and apply the methodology accurately and successfully.