This week, SiliconANGLE’s theCUBE broadcast from both the ServiceNow Knowledge 2014 event at the Moscone Center in San Francisco and the IBM Impact Conference held at Las Vegas’ Venetian Resort and Casino. Helming theCUBE desk for IBM Impact were John Furrier and Paul Gillin. On Day 1, they welcomed the CEO and Principal Consultant for Decision Management Solutions, James Taylor.

Taylor’s biography explains that he is a 20-plus year veteran in the field of Decision Management and is regarded as a leading expert in decisioning technologies. He is also the author of Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.

At the start of the conversation, Furrier noted how we are in an age that really should be considered one of the most dynamic times for IT. He cites a recognition by the top line of business focusing on utilizing IT for revenue and business growth and no longer employing their IT simply as a means of cost reduction. He attributes this to significant converging trends that are changing the role of IT, like increased speed and agility, unlimited compute in the Cloud and the fact everything is now instrumentable.

Interjecting on this thought, Taylor stated, “I think what has really changed is the acceptance of analytics. When I wrote the book, people were uncertain about it.” He continued, “There were ways to use small data that weren’t predictive analytics. If you don’t process it and turn it into a usable prediction, it’s hard to consume it.” As the landscape has changed, he believes predictive analytics is stepping to the front and center in the business world.

Watch the interview in its entirety here:

Much of that change is being driven by the habits and expectations of the emergence of a more technologically savvy consumer base. Talking about his own 25-year-old son having to purchase auto insurance, Taylor said, “When he gives you his data, he expects a quote. If you say you’ll let him know what the quote is, he’ll just go somewhere else. He wants the answer now.” This expectation requires real time responses even when the proposition is relatively complex and involves assessments of risk. “You have to use analytics to determine how risky he is and you have to answer now.” Taylor claims that capability has to be embedded not only into call center scripts but also into a company’s mobile applications and website where no human-to-human interaction ever occurs. “Because if you don’t, you’re missing the point,” he stated.

Understanding the Principles of Decision Management

Much of the rest of the conversation centered on Decision Management principles Taylor outlined in his book. The first such principle, ‘Begin With The Decision in Mind’ was recounted by Gillin. “That struck me as kind of obvious,” he said. “Isn’t that how you would go about this? Obviously there is a reason why you said that. Do you find that people typically don’t,” he asked.

“The reason for misquoting the late Stephen Covey there is really two fold,” Taylor began. “The first is that when you look at Decision Support Systems, and there’s obviously a long history there, people are often very unclear what decision it is, in fact, somebody’s going to make with the data.” He goes on to state that all sorts of data is put in without any thought to the eventual decision that will need to be made. Companies, he claims, expect the employee, whom they regard as smart and experienced will be able to use the data to arrive at a decision. “What happens when I try to make a decision about what offer to make to a customer who’s on the phone to the call center right now,” he posited. “[The employee] has seven seconds. And they were hired yesterday. And they got three hours of training. They’re not in a position to know what decision they should be making.” He continued, “So, if you don’t know what decision you’re trying to embed the analytics into, you can’t do a good job with the analytics. I put [that principle] in because there was this sense that people were very (sic) lacksadaisical about what the decisions were they were supporting with their Decision Support Systems.”

Another of Taylor’s principles outlined in his book is to ‘Be Predictive and Not Reactive.’ Gillin stated, “I think you’re right. Using data to support decisions reactively is more intuitive.” He then asked, “What is the mind shift that is involved in moving toward predictive analytics?”

“It turns out to be one of those things where it’s very easy to build things that are predictive,” Taylor stated. “But if they don’t change people’s decision making behavior, they don’t help. Talk to anyone who does predictive analytics and they’ll tell you stories of building highly predictive models that didn’t change the business,” he explained. “You have to be clear how it’s going to affect the decision you’re going to make before you can build the predictive.”

One reason Taylor believes there has been resistance thus far is because the whole notion of predictive analytics shifts the operation of business from the realm of absolutes to the realm of probabilities. “If I’m measuring last month’s results,” he said, “I can give you an absolute number. I can tell you exactly what you sold last month.” Projecting demand for the future doesn’t give you that same certainty. “I can give you a probability or a range,” he stated. “You have to start dealing with a little bit of uncertainty. That’s why it’s important to wrap some rules around these predictions.” However, Taylor says we, as humans, operate in probabilities and chance subconsciously every day. Once that is understood, the mind shift to predictive analytics becomes easier to adopt.

Embracing the Opportunities of Unstructured Data

One key to building a robust predictive analytic structure is incorporating multiple data streams, including unstructured data. “There are a slew of startups getting funding around Business Intelligence and data warehousing,” Furrier pointed out. “That market is shifting. But how does loose data affect some of the opportunities,” he asked.

Taylor pointed out that it is a strength to be able to store data before you’ve figured out how you might use it. “That’s a key advantage,” he said. “I did some surveys recently on predictive analytics in the Cloud,” he explained. “We asked about some of these Big Data sources and what we found was that people who are getting value from Big Data and these unusual sources were people who had some experience with more advanced kinds of analytics.” He further explained that the combination of traditional data with data from e-mails, texts and other unstructured data could only serve to produce a more fine-grained model, improving accuracy. “Its ability to refine existing predictions is really strong. Right now, that’s the biggest use case I see in customers,” he stated.

For more on this interview and others broadcast by SiliconANGLE’s theCUBE from both of this week’s events, be certain to visit SiliconANGLE’s YouTube Channel.

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