Putting predictive analytics to work

Predictive analytics involves both art and science, but getting started isn't for high rollers only. Here's how to ensure a successful outcome

When the Magic's Perez got started in 2010 he grossly miscalculated the time it would take to prepare the data. "We didn't set the right expectations. All of us were thinking that it would be easier than it was," he says. Pulling together data from Ticketmaster, concession vendors and other business partners into a data warehouse took much longer than anticipated. "We went almost the entire season without a fully functional data warehouse. The biggest thing we learned was that this really requires patience," he says.

"Everyone is embarrassed about the quality of their data," says Elder, but waiting until all of the data quality issues are cleaned up is also a mistake. Usually, he says, the data that really matters is in pretty good shape. "I urge people to go ahead and make a salad and see what you can get," he says.

Blue Health Intelligence (BHI) had no issues with the patient health care data coming from its 39 Blue Cross Blue Shield affiliates -- but with seven years of data about 110 million members, they had a lot of it. "Health care is way behind in analytics because of the complexity of our data," says Swati Abbot, CEO. "People tend to run after the data and not know why they need it." Clinical insights must come first, she says. "Then the math takes over."

BHI developed models to predict which of its highest risk members were most likely to be hospitalized, who had an avoidable risk, who was most likely to respond to intervention and which actions were most likely to work in each case.

Initially, BHI focused on diabetes patients at highest risk for hospitalization -- a patient group expected to cost the healthcare industry $500 billion by 2020. "The first round [of analytics projects] was not successful because the business stakeholders weren't involved. It became an IT exercise," she says, and because the reports didn't say why a given patient was at high risk, clinical professionals ignored them.

So Abbot made sure that they understood the underlying "risk drivers" in the model, explaining not just who was at high risk but why, and what interventions would be mostly likely to improve the outcome.

"Provide transparency," she advises, "and serve up the information in the right way, so it's not disruptive to workflows."

Iterate first, scale later

At Intuit every project starts small and goes through continuous cycles of improvement, says George Roumeliotis, data science team leader. "That's our process: Iterative and driven by small scale before going big." The financial services business started using predictive analytics to optimize its marketing and upsell efforts, and now focuses on optimizing the customer experience with its products.

Intuit developed predictive task algorithms to anticipate how customers will categorize financial transactions in products such as Mint and QuickBooks, and makes suggestions as they enter new transactions. It also proactively offers content and advice as customers use its products in an effort to anticipate questions before the user has to ask.

"Start with a clearly articulated business outcome, formulate a hypothesis about how the process will contribute to that outcome, and then create an experiment," he says. Through A/B testing, analysts can gain the confidence of business leaders by creating parallel business processes and demonstrating a measurable improvement in outcomes.

Just be sure to start by choosing an existing business process that can be optimized with minimal risk to the business, he advises. Customer support, retention and user experience are great places to get started.

While predictive analytics projects can require a substantial investment up front, return on investment studies show that it does make an impact, as Cisco's experience shows. Ultimately, even small-scale projects can have an enormous impact on the bottom line. "Predictive analytics is about projecting forward and transforming the company," says Peri.

The risks are high, but so are the rewards, says Informs' Robinson. "Take it to the end," she says. "Be successful. And act on what you learn."

 Organized analytics: Who runs the practice?

Where you locate an analytics practice may be as critical a success factor as the teams skills, experts say. Should you embed it within the IT organization, establish it as an independent group or embed the function within each business unit? Here are the tradeoffs.

Option 1: Run analytics as an IT operation. IT has the data expertise. Making the analytics team part of IT helps foster a common sense of purpose, and that collaborative relationship may foster faster integration of analytics with other enterprise applications. But without a close working relationship with stakeholders, such groups risk producing great models that no one uses. Analytics groups may also disappear into the IT fabric, says John Elder, principal at consultancy Elder Research. "I wouldn't embed them in IT because other priorities might take over."

Option 2: Let each business unit operate its own analytics group. Keeping data analysts embedded in the individual businesses ensures alignment with business needs and facilitates collaboration. However, the relationship with IT, which manages the data, may be more distant. While analysts want to innovate, IT may be more concerned about system availability and performance demands. Some members of the IT group may view analytics as an annoyance, says Dean Abbot, president of consultancy Abbott Analytics Inc., "because it means more hours in their day dedicated to things they don't care about."

Option 3: Create a shared services group. This approach allows for standardization of a common set of models and methods, eliminating redundancy and increasing productivity. But being outside of the business team can reduce buy-in by the business, as Bryan Jones, director at the USPS Office of Inspector Generals countermeasures and performance evaluation unit discovered. "We couldnt get much traction because we were an outside entity," he says, so he moved analysts into the business groups. Today, however, the organizations send auditors and investigators to his services group to help develop new models. "Were an organizational support group again," he says.

Procter & Gamble has a shared services group that reports to the CIO, but embeds some 300 analysts in the individual businesses to act as "trusted advisor" to the company's presidents and general managers, says director of business intelligence Guy Peri. Using an agile development model, the group can tweak an existing model within 24 hours, deploy new ones within 30 days and scale a good one out to other business units within 90 days.

Ultimately, the right decision depends on the existing structure of the organization, says Gartner Inc. analyst Gareth Herschel. "If youre IT-centric, put them in IT. If you have business units all doing different things, embed the groups in the business. If the businesses share customers and suppliers and have product overlap, use a shared services group."

Robert L. Mitchell is a national correspondent for Computerworld. Follow him on Twitter at twitter.com/rmitch, or email him at rmitchell@computerworld.com.

Read more about BI and analytics in Computerworld's BI and Analytics Topic Center.

This story, "Putting predictive analytics to work" was originally published by Computerworld.

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