At the AIR Forum in Orlando, I led a facilitated discussion on the meaning of the term “democratized data”. Here are some of the insights I gained and also a chance for you to weigh in on the discussion. My goal is to get more people thinking about this, since it’s really the most important aspect of data-work. Watched pots never boil, and uncommunicated data never leads to change! Let us know what you envision when you hear “democratized data”, regardless of your background and experience working with data.

The consequences of black swan events are troublesome for analysts because the events themselves are extremely difficult to predict, they cause ripple effects elsewhere in a system, and they leave a legacy that must be accounted for when future observations are made of those systems. Since these events, by their definition, lie outside the realm of regular expectations, it’s not only difficult to gauge their likelihood of occurrence, but it’s also difficult to quantify the size and scope of the impact across the system.

If you follow us on Twitter you know that we at Rapid Insight love two things; Data and Dogs. I adopted my lab shepherd mix, Rogue, back in 2008 and we have been together since. It was love at first sight and we bonded maybe a little more than I had hoped for. Her separation anxiety was a problem from the get go, to the point where I once received a phone call from a roommate telling me

May is a busy season on any college campus. Last week, I experienced this firsthand as I helped my son move back home after he finished his freshman year. Cars were scattered around the campus as two semesters of belongings were loaded in, students were saying their goodbyes and commencement-related activities had begun. The first year was a really positive experience for my son and he’s already looking forward to sophomore year.

Understanding the relationships between your business outcomes and the data points that result in those outcomes can be the difference between those who are succeeding in their marketplace and those who are struggling. As it turns out, small and medium-sized enterprises (SMEs) are the fastest adopters of business intelligence (BI) solutions – three times more than large businesses

Last week, Rapid Insight team members attended #ELIVE18 in sunny San Diego.  We were greeted with a keynote address from Dr. Jill Biden and Malala Yousafzi, enlightened by collaborative sessions, visited by attendees from all walks of the higher education industry and kissed by the sun, a pleasant change from snowy New England in our opinion!

A diversity of backgrounds, perspectives, and skillsets are attributes that help any organization to succeed, regardless of industry. The same is true of data. One conflict between those two truths, is that, not everyone in an organization feels equally comfortable working with data nor do they have the background required to do so. That is exactly why we’re all so excited to introduce Veera Bridge

Rapid Insight’s powerful and easy to use analytics products are now aligned under one simple, clean, modern brand and platform called Veera. Veera enables any organization to build a truly data-driven culture. Affordable and easy to use, Veera enables exploration, drives curiosity, and makes critical business insights accessible to more people, and more quickly, than ever before.

Being a data analyst, I am always interested in the data behind any sport, so I decided to take a look at the Biathlon World Cup data to see what things could be discovered. The Biathlon World Cup started in November of this year and will continue till March so I have 7 events worth of information that I can analyze (including the Olympics). Using Rapid Insight Veera I was able to pull data directly from the International Biathlon Union website

Did you know that American Pharaoh, the first horse to win the grand slam of thoroughbred racing, was chosen early on as a once in a lifetime horse simply because of how large his left ventricle was?  Or that Walmart stocks up on Strawberry Frosted Pop Tarts before big storms, since they sell seven times faster during those times?  These are just two of the facts that I learned after reading Seth Stephens-Davidowitz’s book ‘Everybody Lies’.

This past October, I attended TC17 where I had the chance to learn about Hyper, Tableau’s new in-memory data engine technology. Hyper enables a completely new level of analytical query processing for large and complex data sets. There has been a lot of excitement in the Tableau user community and so it was important for Veera to support Hyper right out of the gate.