Why Predictive Analytics is Within Everyone’s Grasp
Predictive analytics is not hard to understand or implement. It just takes a fundamental understanding of the effects you ultimately want to achieve.
That’s the word from Eric Siegel, PhD, in the latest update to his book, Predictive Analytics: The Power to Predict Who Will Click, Lie, Buy or Die. Siegel, who is founder of the Predictive Analytics World conference series, provided the refresh to reflect the upward growth and embrace of predictive analytics in industries of all sizes.
“The heady, sophisticated notion of learning from data to predict may sound beyond reach,” Siegel writes, “but the basic idea is clear, accessible, and undeniably far-reaching.”
And it’s worth the effort. “Predictive analytics is intuitive, powerful, and awe-inspiring,” he adds. Here are the five key effects that should be expected as predictive analytics efforts unfold:
The Prediction Effect: “A little prediction goes a long way.” Siegel observes that “each application of predictive analytics is defined by what’s predicted – the kind of behavior to predict for each individual, stock or other kind of element”; and “what’s done about it – the decisions driven by prediction; the action taken by the organization in response to or informed by each prediction.”
The Data Effect: “Data is always predictive,” Siegel observes, noting that “the number of predictors at our disposal grows along with an unbridled trend: Exploding quantities of increasingly diverse data are springing forth, and organizations are innovating to turn all this unprocessed sap into maple syrup. To fully leverage predictor variables, we must deftly and intricately combine them with a predictive model. To this end, you can’t just stir the bowl with a big spoon. You need an apparatus that learns from the data itself how best to mix and combine it.”
The Induction Effect: “Art drives machine learning; when followed by computer programs, strategies designed in part by informal human creativity succeed in developing predictive models that perform well on new cases.” Siegel states that trough machine learning algorithms, predictive analytics “transforms risk to opportunity.” And to reach this point, it’s a matter of generating a predictive model from data, “to learn from examples and form an electronic Sherlock Holmes that sizes up an individual and predicts. You’re inches away from the key to one of the coolest things in science, the most audacious of human ambitions: the automation of learning.”
The Ensemble Effect: “When joined in an ensemble, predictive models compensate for one another’s limitations so the ensemble as a whole is more likely to predict correctly than its component models.” Siegel observes that “whether assembled by the thousands or pasted together manually, ensemble models triumph time after time…. By simply joining models together, we enjoy the benefit of cranking up our model’s structural complexity while retaining a crucial ingredient: robustness against overlearning.” Example of ensemble models cited by Siegel is IBM’s Watson, the IRS for tax fraud, the Nature Conservancy for donations and Netflix for movie recommendations.
The Persuasion Effect: “Although imperceptible, the persuasion of an individual can be predicted by uplift modeling, predictively modeling across two distinct training data sets that record, respectively, the outcomes of two competing treatments.” Only by combining these two practices – uplift modeling and predictive modeling – “does the newfound ability to predict persuasion for each individual become possible,” Siegel observes.