Advanced Analytics, Big Data and the Power of R: A Q&A Spotlight with David Rich, CEO of Revolution Analytics

Originally published May 15, 2012

BeyeNETWORK Spotlights focus on news, events and products in the business intelligence ecosystem that are poised to have a significant impact on the industry as a whole; on the enterprises that rely on business intelligence, analytics, performance management, data warehousing and/or data governance products to understand and act on the vital information that can be gleaned from their data; or on the providers of these mission-critical products.

Presented as Q&A-style articles, these interviews conducted by the BeyeNETWORK present the behind-the-scene view that you won’t read in press releases.

This BeyeNETWORK spotlight features Ron Powell's interview with David Rich, CEO of Revolution Analytics. David and Ron talk about predictive analytics and how Revolution Analytics provides advanced analytic solutions built on open source R.

David, let’s talk about “big data.” Relational databases have been the norm for decades, but now there are new technologies like Hadoop and dedicated high-performance appliances such as IBM Netezza. When it comes to predictive analytics on big data, where do you see the industry going?

David Rich: Well, most of the CIOs that I engage with have been doing all they can just to store the massive amounts of data that are coming their way. Many organizations are struggling with the volume, velocity, and variety of data.  I think that’s why you’re hearing as much as you are about “big data.” The real question is this: What is the right data? We haven’t seen it yet, but I think the focus will be on providing more and more intelligence at the point of collection by doing some filtering or sifting. Unfortunately, that requires a greater synthesis and a collaboration between IT and the business that might not exist today, or between the data scientists, perhaps, and IT that doesn’t exist today. Where that synthesis and collaboration does exist, it only does so in pockets.

I think the tools to do predictive analytics on big data is exactly why organizations are turning to companies like Revolution Analytics to help address this because it’s not just the simple counting, averaging and tables and things that people have been doing for generations and decades for management reporting. I call that the “what” – getting to the “so what” and the “now what” is what people can get now – the promise of having all this data and the insights you can gather from it.

You bring up statistical insights. People have been doing statistical modeling and predictive analytics for 50 years now, SAS and SPSS have been around since the early ‘70s. What’s different now -- what’s making this move toward other statistical and “big data” areas?

David Rich: Well, I think obviously SAS and SPSS have been around, as you pointed out, for decades. We call that sort of the first generation of analytics and insight-driven solutions. In my perspective, having been in the business for more than three decades, it reminds me a bit of what COBOL was back in the day relative to business software. I see R as the more modern language. In this analogy, R would represent Java or C++. What happened in the middle of the nineties when the shift occurred is very similar to where we are now with R. Open source is a worldwide collaboration innovation. It’s a way to tap into that channel for research, and I think the role that Revolution Analytics can play – very similar to what Red Hat did back in the Linux days – is to be the conduit between the community and enterprise deployment.

You make a good analogy with COBOL to Java and C++. They really changed the dynamics just like we’re seeing open source and R changing the dynamics of the predictive analytics space. Looking forward, what changes do you see coming related to the impact of advanced analytics on business?

David Rich: New technologies like Hadoop and NoSQL are going to drive some of the innovation as well as the need to have in-database analytics. We have to bring the analytics to the data and, as I mentioned earlier, I think even providing some intelligence into what is the right data at the point of collection even, and having that be a very tight, closed-loop integration. I think that’s going to be the next wave of focus.

Clearly, organizations need the ability to integrate predictive analytics  into their ongoing processes. The way I look at this is that this is being driven as more of a management change. This is a management discipline of being much more analytical in your key decision-making processes and bringing insights to key decision makers in order for them to make more insightful decisions. That’s what this is all about. And, there have been previous generations of this – even back in the day with executive information systems and other types of decision support tools.  I think the key to those – which is also the key to this – is how to best create the APIs that integrate this with the tools that many people have on their desktops today.

What do you see as some of the benefits of an analytics-driven enterprise, and how can Revolution Analytics help organizations achieve that goal of bringing the analytics to the right data?

David Rich: One of the benefits is the ability to ask questions that you haven’t been able to even think about yet. As I said, I call it the “so what” or the “now what” and being much more forward-looking and predictive as opposed to looking more at historical data. You have to look backward to look forward some would say, but I would say that the real promise here is to bring real-time decisioning to the fore with critical decision-making processes and decision makers, whether it’s someone on a trading floor or somebody who’s making a big bet relative to markdown pricing in a retail setting. People are turning to Revolution Analytics for these key decision makers that are placing big bets every day for the company. When they create these role-based workbenches and need some help with the advanced thinking about how to look at a business problem, how to bring math to the problem-solving and make it easily understandable for them. We call it the “rules, tools and schools,” and that’s why companies are turning to us.

Traditionally, analytics has been costly to deploy. How does R help them from both a cost and an implementation perspective?

David Rich: Well, R is open  source, and I think that alone speaks to some orders of magnitude with regard to cost. I also think that just the technology platforms that are more conducive to R lend themselves to being less costly. Again, if you can bring the analytics to the data as opposed to having to bring the data out to a stand-alone analytics engine of some sort, the total cost of ownership is a lot cheaper just in terms of all the ancillary hardware and software you bring to that type of decision-support system.

That’s a good point. How about ease of use? How easy is it for a casual user or business analyst to get started with R. Is it pretty easy for them to wrap their arms around R?

David Rich: Well, candidly, R by itself can be challenging, but people are learning it for the first time every day – some who are former SAS or SPSS users and even Excel users. And, we’re making it easy through our web services and MS Office integration to extend the work of the R statisticians throughout the enterprise. . But again I think companies are turning to Revolution Analytics because of our approach to making “R for dummies.” This includes software as well as training and services. Some might wince at that, but my view is that with the right tools and templates, we can shield casual R users or the consumers of R – the actual business analysts or the decision makers that have to use these models as part of their decision-making process – from the complexity. I think the ability to push the work of the R programmers out to the masses and shield them from the heavy lifting is an important part of what we bring to the table.

You make a good point. You’re moving it closer to the business and by shielding the technical side of the statistics from the business that really allows for greater adoption and allows the business to do more. I think that’s really good.

David Rich: One additional comment is that there just aren’t enough data scientists in the world right now. I think that’s the hot new role. I’m sure you’ve studied and read about it, and I think that’s an opportunity. Where Revolution Analytics comes in is that with the right rules, tools and schools, we can be the right conduit to the community of power users and even to the point of crowd sourcing for critical decisions or critical business problems and make this more available. We have the right framework and the tool so that you don’t have to be a power user to get access to it. That’s the role that we play and again why our clients are turning to us. They see it clearly that way, and they’re struggling with how to build that kind of critical mass of talent and so they have no choice but to leverage that talent.

You’re right – talent is always an issue. David, I want to thank you for taking the time to talk with me about Revolution Analytics and R.

  • Ron PowellRon Powell
    Ron, an independent analyst and consultant, has an extensive technology background in business intelligence, analytics and data warehousing. In 2005, Ron founded the BeyeNETWORK, which was acquired by Tech Target in 2010.  Prior to the founding of the BeyeNETWORK, Ron was cofounder, publisher and editorial director of DM Review (now Information Management). Ron also has a wealth of consulting expertise in business intelligence, business management and marketing. He may be contacted by email at rpowell@wi.rr.com.

    More articles and Ron's blog can be found in his BeyeNETWORK expert channel. Be sure to visit today!

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