Dave Rich is the president and chief executive officer of Revolution Analytics, a leading company in open source big data advanced analytics. A graduate of the U.S. Naval Academy, Dave has more than 28 years of analytics experience at companies such as as Accenture. In this interview Dave is joined by Michele Chambers, chief strategy officer of Revolution Analytics. They both talk about the role of Revolution Analytics in big data and about how the market has evolved and continues to evolve, giving examples of future trends and expectations.
Sramana Mitra: Dave, let’s start by setting the context for Revolution Analytics. Our audience does not know the company. Tell us about what you do, how big you are, and who you work with.
Dave Rich: Revolution Analytics was founded around 2007-2008. The initial effort at that point was backed by Intel Capital. Uncharacteristic for them, they actually granted a loan. The first chapter of the company was all about seizing the opportunity they saw in front of them relative to big data and big data analytics.
SM: Big data was not really a term people used in 2007-2008, right?
DR: That is correct. What they saw was the potential wave coming, the promise of looking at large volumes of data and doing more in the way of predictive analytics and predictive modeling in process. They saw the wave of next generation of analytics, staying more focused on predictive analytics at scale. The determination of this was to make it more effective than the historical offline process.
We had to bring the mass to the data. Those are my words for it. The algorithms, which are the backbone of most playbooks, had to be something that got closer to the data if big data analytics wanted to be something successful. The other challenge was making it cost effective from an infrastructure perspective. The goal was to try to run these events – analytics algorithms and applications – on commodity hardware. The first chapter of the company was all about the science behind doing so. It started with a group from Yale and later a combination of those people and a group that came over from TIBCO, which was the initial proprietary language based on an open source language called R, hence the term Revolution Analytics.
As the vast analytics programming language and tools became more probative with the open source language called R, there was a need for somebody to provide service and support, somebody to provide tools that would essentially make it more scalable to the enterprise. That morphed into much more of a rules, tools and schools company, as I like to say, that was built around the open source distribution of R, which is becoming the standard around the world for advanced analytics.
SM: How do you position yourself? Are you an analytics infrastructure layer company?
DR: We provide a platform. If we can provide a combination of CRAN R, which is the open source distribution – it was designed by academics for academics initially, by some of the best minds on the planet who try to apply the math and the modeling to the data. This is their language, and they build it for themselves. There are a lot of advantages to it, and in this regard it is not just the programming language. It is also tools that make it easier for people to build applications.
This is our challenge. Our opportunity is about how to make this available to a broader swathe of people, other than PhDs or people who are more comfortable with it. My words for it is that it has become more of a platform.