Machine Learning Times
Machine Learning Times
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8 years ago
The Trick to Predictive Analytics: How to Bridge the Quant/Business Culture Gap


This article is excerpted from Eric Siegel’s foreword to the recently released book, Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics, by Jeff Deal and Gerhard Pilcher. Those two authors head up the programming of two of the event series Siegel founded, Predictive Analytics World: PAW Healthcare and PAW Government, respectively.

For predictive analytics to work, two different species must cooperate in harmony: the business leader and the quant. In order to function together, they each have to adapt. On the one hand, the quant needs to attain a business-oriented vantage. And on the other, the business leader must navigate a very alien world indeed. Deal and Pilcher’s new book, Mining Your Own Business, helps with that second bit.

Bridging this gargantuan divide is worth the effort. Take for example a tax fraud detection story worth ten digits (covered in the book’s Introduction). Elder Research, Inc. (ERI), the consultancy that spawned the book, delivered predictive models to the IRS that increased the agency’s identification of a certain type of tax fraud by a factor of twenty-five. This saved the feds billions (with a b).

This success exemplifies a widely applicable paradigm. Across commercial and government sectors, predictive targeting achieves a multiplicative improvement to broad scale operations (albeit often a single-digit multiplier rather than that whopping twenty-five-fold improvement). In addition to deciding which tax returns to audit, predictive models determine which customers to contact for marketing, which debtors to approve for increased credit limits, which patients to clinically screen, which employees to woo away from quitting, which persons of interest to investigate, and which equipment to inspect for impending failure.

Thus data science earns its status as hot, lucrative, and sexy. This is the Information Age’s latest evolutionary step, technology that taps data to drive decisions more effectively. It’s the very act of scientifically optimizing resource allocation for… just about all processes. Various outlets have dubbed data scientist as the best, most in-demand, and even “sexiest” job. And if you haven’t heard, data is the new oil. Industry research forecasts that demand will continue to grow and estimates the global predictive analytics market could reach as high as $9 billion by 2020.

The above is an excerpt—read the full article as originally published in Analytics Magazine

Eric Siegel, Ph.D., is the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Revised and Updated Edition (Wiley, January 2016—this and the book discussed in the article nicely complement one another), the founder of the Predictive Analytics World conference series which includes events for business, government, healthcare, workforce, manufacturing, and financial services —executive editor of The Predictive Analytics Times, and a former computer science professor at Columbia University.


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