Q&A with David Gutelius, Founder, Data Guild and former Chief Social Scientist, Jive Software
When the concept of Big Data first began to gain traction in the mid-2000s, it was declared a boon for B2B marketers and sales organizations — promising access to a seemingly limitless vault of real-time customer intelligence, buyer behavior insight, and critical operations analyses.
To some degree, Big Data has delivered on some of those promises. But it’s also created a new problem. Namely, marketers are now inundated with endless streams of information and, as a result, are struggling to make sense of (and act on) the incredible volume and velocity of data at their fingertips.
Making matters worse, existing marketing tools are designed to produce more data, rather than to help make smarter, faster, and more confident decisions from existing data sources. In fact, most legacy business intelligence and analytics tools are simply overwhelmed by the sheer volume of marketing data being pumped into them.
So, where does that leave us?
According to David Gutelius, principal and co-founder of The Data Guild, marketers must adopt new processes and a new way of thinking if they’re ever going to fully leverage the potential benefits of the Big Data revolution.
To build on that point, I recently asked David (disclosure: a BrightFunnel advisor) to join me for a brief conversation about that issue and several others:
Nadim: Can you start by talking a little bit about the role of data in decision-making, and how the existing suite of marketing technology is or isn’t addressing it?
David: Absolutely. Being someone who works with data on a regular basis, I’m of course interested in this sudden tidal wave of interest in Big Data. Frankly, it’s difficult for most humans to make sense of the sheer volume of data that’s being created and analyzed today. While the purpose of that data is to provide new insight and allow you to make more confident decisions, it’s not really accomplishing that.
In fact, in many instances, Big Data is actually creating more problems than it’s solving. The key challenge for everyone — but marketers in particular — is not just to make sense of what’s out there in real-time, but also to act on that information in some kind of meaningful way. Unfortunately, most legacy marketing platforms are burdened by the dynamic nature of Big Data or limited by data silos. And that’s keeping marketers from generating truly meaningful, predictive, and actionable insight.
The good news is that new platforms are helping marketers experiment in a very targeted way to see what does and doesn’t work, and then act more quickly on that information. Ultimately, that facilitates a decision-making process that’s much more efficient and effective.
David: Incredibly important. Some legacy platforms have attempted to address each of those things, but there’s a lot of room for improvement. The problem is that those technologies often create a sense of overconfidence when the data insight isn’t really there. And because people generally assume that machines and algorithms have done the work for them, they blindly trust the outputs.
The problem is that machines and algorithms sometimes lack proper context, or they pull from incomplete or improperly targeted data sets. The purpose of these tools isn’t to predict the future with 100 percent certainty. It’s to help marketers make higher quality decisions at a more rapid rate, and then react when things change or something new comes up in the market.
Nadim: How valuable are recommendations and alerts as decision tools?
David: Again, it depends on how they’re interpreted and used. Those tools aren’t valuable on their own. They need to be smart enough to learn from you and optimize their recommendations and alerts based on what they know about you, your market, your customers, and your strategy. When alerts and recommendations are customized for specific actions or goals, they can be really powerful.
Nadim: I know you’re a big fan of applying OODA (observe-orient-decide-act) Loops to business decision-making. Can you briefly explain how that decision cycle applies to B2B marketing?
David: It’s a term that was developed by Air Force Colonel John Boyd to help intelligence professionals and fighter pilots make better decisions in a much more rapid, strategic way. By operating at a faster tempo, the theory was that the military could stay one step ahead of its competition and in a key position of strategic advantage — and it worked.
For marketers, the application is simple: If you can make smarter, more insightful decisions at a much faster rate than your competition, then you’ve got a built-in advantage, as well. You’re a step ahead, which allows you to respond to new intelligence, changing market conditions, or big opportunities much faster than businesses without that same capability.
Nadim: How is data-driven decision-making different in marketing different than in other domains?
David: I think the biggest difference is that, for years, marketing wasn’t a data-driven discipline. As a result, many marketing pros are still adjusting to their new way of life. The good news is that the majority of good B2B marketers — even if they’re old-school creative types — are interested in using Big Data more effectively. They just need access to tools and technology that help them bridge the gap between interest and execution.
Nadim: That leads to my last question — what impact is data visualization having with marketers who have access to rich data, but maybe struggle to tell a compelling story with that data?
David: That’s the beauty of data visualization. With Excel, you can create these robust charts and graphs that attempt to bring data to life, but data visualization really takes it to the next level. It takes interesting data stories and quickly converts them into predictive insight and compelling marketing narratives. Ultimately, those visualizations can make it easier for an entire marketing organization to digest Big Data, get through the OODA Loop or decision-making cycle more efficiently, and arrive at meaningful action much faster. At the end of the day, that’s really the ultimate purpose of Big Data.
About David: David previously drove data science and technology innovation at Jive Software as Chief Social Scientist, which he joined in 2011 after Jive acquired the company he founded, Proximal Labs (large-scale machine learning to personalize enterprise social networks). Previously, David was co-founder and CTO at Social Kinetics, as well as co-founder of the Social Computing Group at SRI’s Artificial Intelligence Center, serving as Product Manager for the largest machine learning project in the U.S. Government’s history, DARPA’s CALO, and working extensively with national security customers. He holds a B.A. in History from Principia College and M.A and Ph.D. degrees in Economic History from Johns Hopkins University.
[Original Image Source: GigaOm]