Predictive analytics lets organizations look ahead in an effort to optimize business strategies. But there has to be a purpose to the analytics efforts, and a solid plan behind them.
From recommending additional purchases based on the items that customers place in online shopping carts to pinpointing hospital patients who have a greater risk of readmission, the use of predictive analytics tools and techniques is enabling organizations to tap their collections of data to predict future business outcomes — if the process is managed properly.
Predictive analytics has become an increasingly hot topic in analytics circles as more people realize that predictive modeling of customer behavior and business scenarios is “the big way to get big value out of data,” said Mike Gualtieri, an analyst at Forrester Research Inc. As a result, predictive analytics deployments are gaining momentum, according to Gualtieri, who said that he has seen an increase in adoption levels from about 20% in 2012 to “the mid- to high-30% range” now.
That’s still relatively low — which creates even bigger potential business benefits for organizations that have invested in predictive analytics software. If a company’s competitors aren’t doing predictive analytics, it has “a great opportunity to get ahead,” Gualtieri said.
Predictive analytics projects can also provide those benefits across various industries, said Eric King, president and founder of The Modeling Agency LLC, an analytics consulting and training services firm based in Pittsburgh. “Everyone is overwhelmed with data and starving for information,” King noted.
But that doesn’t mean it’s just a matter of rolling out the technology and letting analytics teams play around with data. When predictive analytics is done well, the business benefits can be substantial — but there are “some mainly strategic pitfalls” to watch out for, King said. “Many companies are doing analytics to do analytics, and they aren’t pursuing analytics that are measurable, purposeful, accountable and understandable by leadership.”
One common mistake is putting too much emphasis on the role of data scientists. “Businesses think the data scientists have to understand the business,” Gualtieri said. With that in mind, they end up looking for experienced data analysts who have all the required technical skills and also understand their business practices, a combination that he warned can be nearly impossible to find. “That’s why they say, ‘A data scientist is a unicorn.’ But it doesn’t have to work that way.”
Instead, he recommended, business managers should be the ones who walk through customer experience management operations or other business processes and identify the kinds of behaviors and trends they’d like to predict, “then go to the data scientists and ask if they can predict them.”
King agreed that organizations often give data scientists too much responsibility and leeway in analytics applications.
“They’re really not analytics leaders in a lot of cases,” he said, adding that data scientists often aren’t very effective at interviewing people from the business side about their needs or defining analytics project plans. Echoing Gualtieri, King said a variety of other people, from the business and IT, should also play roles in predictive analytics initiatives. “When you have the right balance with your team, you’ll end up with a purposeful and thriving analytics process that will produce results.”
Companies looking to take advantage of predictive analytics tools also shouldn’t just jump into projects without a plan.
“You can’t approach predictive analytics like you do a lot of other IT projects,” King said. It’s important, he advised, to think strategically about an implementation upfront, plotting out a formal process that starts with a comprehensive assessment of analytics needs and internal resources and skills. “That’s where we’re seeing not only a greater adoption of predictive analytics, but far greater results,” he said.