Predictive Analytics in 2015: What Are the Experts Saying?
The ability to predict the future has always fascinated the human imagination. Being able to foresee risks would allow us to avoid dangerous situations. Being able to foretell the outcomes of different options would allow us to make better decisions. While predicting the future has long been a staple of science fiction, over the past few years, data science has become a fact of everyday life, making predictive analytics an increasingly realistic and relevant topic. Below we’ll discuss what experts in the field are saying about predictive analytics and what the future of the field may look like.
In his 2013 book Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie, or Die, Eric Siegel defined predictive analytics as “technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.” Although experience has always been a great teacher, most of our experience never became data (in the past). This left it inaccessible to technology that could discover patterns and relationships in our experience that could help predict our future behavior and guide our future decisions.
But in the 21st century, the digital explosion known as big data is directly capturing our experience as data. What we say is captured in our e-mails, voicemails, text messages, and social networking status updates. What we see is captured in the photos and videos we share. Where we go is tracked by GPS and location-based apps on our mobile devices. Other data captures how we work, shop, vote, and date.
“Everything is connected to everything else—if only indirectly—and this is reflected in data,” Siegel explained. “Data embodies a priceless collection of experience from which to learn. Data always speaks. It always has a story to tell, and there’s always something to learn from it. Data scientists see this over and over again across predictive analytics projects. Data is always predictive.”
The Value of Data
Before we start believing it has clairvoyant properties, it’s important to understand that “big data, by itself, is essentially useless,” digital futurist Chris Riddell explained. “It’s just numbers, words, and pictures—mountains and mountains of information being constantly gathered and stored. The real value of big data comes when you ask the right questions and big data becomes smart data.”
The essence of predictive analytics, Riddell said, is “asking the right questions” so that predictive analytics can tell a story and “influence what is going to happen in the future. They can give you a say in how the story ends.” In addition, predictive analytics can explain what is occurring within and outside businesses.
The Data Behind Moneyball
One predictive analytics story that’s endlessly repeated is the bestselling book Moneyball by Michael Lewis. It’s the story of how general manger Billy Beane used predictive analytics to transform the low-budget Oakland Athletics into one of the highest-winning teams in Major League Baseball in the early 21st century. This oft-cited success story from the world of sports is used as a clarion call to all fields to adopt predictive analytics.
However, as Nate Silver explained in his blog post, “Rich Data, Poor Data,” one of the reasons sports have been able to make rapid analytical progress decades before other fields is sports data. Sports don’t just have big data, Silver explained. Sports have “something much better: rich data. By rich data, I mean data that’s accurate, precise, and subjected to rigorous quality control.”
This is simply not the case for the data available to most other fields. Additionally, Silver explained that the real lesson of Moneyball was not whether data should be used to make better decisions, but what data should be taken into account. In baseball, for example, predictive analytics revealed that on-base percentage is more highly correlated with scoring runs and winning games than batting average is. This finding, however, long went under-appreciated by traditionalists within the industry. This resistance of the status quo to accept the discoveries of predictive analytics has slowed, and will continue to slow, its implementation in other fields.
Silver explained that the essence of Beane’s philosophy was to “collect as much information as possible, but then be as rigorous and disciplined as possible when analyzing it. Rigor and discipline [are] applied in the way the organization processes the information it collects, and not in declaring certain types of information off limits. One hallmark of analytically progressive fields is the daily collection of new data that allows researchers to rapidly test ideas and chuck the silly ones.”
In his research report “Making Predictive Analytics Pervasive,” business intelligence and analytics thought leader Wayne Eckerson forecasted the 2015 market for predictive analytics based on a 2014 survey of business and information technology professionals, compared with similar research he conducted in 2006. He discovered there’s more widespread awareness of predictive analytics now than there was in 2006. This has been helped, in part, by predictive analytics making strides in a variety of fields, including health care (e.g., hospitals and surgical and imaging centers), electric utilities (e.g., state power utilities such as the New York Power Authority), political campaigns (e.g., presidential campaigns), finance, marketing, and accounting.
Eckerson sees business intelligence professionals leading the charge for predictive analytics in 2015, viewing it as the next wave of technology that can help their organizations leverage their investments in data to better serve customers and to implement more competitive ways of doing business. Almost three quarters of the companies he surveyed planned to increase their investments in predictive analytics in 2015.
According to Riddell, predictive analytics in 2015 will “help business leaders to better understand how things happening at the macro level—world financial shifts, geopolitics, climate changes—will affect their business on a day-to-day, moment-by-moment basis.”
In addition, predictive analytics is a tool that can help predict situations in the future. Riddell noted that those predictions would not be completely accurate, but “as long as the predictions are more accurate than simply guessing, businesses will be winning. While predictive analytics is not a crystal ball, it will provide increasingly accurate insights into what is happening inside and outside businesses.”