"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."