Our latest EBook draws from Mining Your Own Business, A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics written by industry experts Jeff Deal and Gerhard Pilcher. The EBook includes Chapter 3 - Leading a Data Analytics Initiative which covers the key challenges and considerations for business leaders employing analytics to provide data-driven insight.
The forward for the book was written by Eric Siegel, founder of Predictive Analytics World and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. This blog includes excerpts from Eric's forward.
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. This book 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 Introduction). Elder Research, Inc., the consultancy that spawned this 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.
To capture this value, you must construct a durable bridge across the quant/business culture gap. The core technology—which learns from data to predict—is only half of the trick. Deploying it is more than just a technical process—it’s an organizational process. Existing business operations must change by way of implementing analytics. It’s no longer business as usual; science now drives the enterprise’s primary decisions and actions en masse. In this sense, data science is intrinsically revolutionary.
As a result, the greatest pitfall that hinders analytics is to not properly plan for its deployment. For each analytics initiative, it’s critical to build a pathway from the get-go that will lead to integration. This requires bridging the cultural gap. It takes the socialization of buy-in: Line of business staff must agree to make big changes. To that end, they must learn what a predictive model does for them and they must be willing to put their faith in it. That doesn’t always work out. The difference between success and failure is often whether the organization actually implements the fruits of analysis. No guts, no glory. It’s largely about educating the organization and opening discussions to understand the concerns of skeptics.
Predictive analytics’ value is simple and concrete: it helps run operations more effectively by way of predicting behavior, i.e., the outcome for each individual consumer, employee, healthcare patient, or suspect. These predictions are each just numbers, aka scores or probabilities. Since they directly drive decisions, by definition they are the most actionable deliverable you can get from analytics. One need only learn a limited bit about the (fascinating) “rocket science” that generates these predictive scores to integrate them and realize their value.
In a field propelled largely by data nerds, it may come as no surprise that most books serve the hands-on quant. Those books dive into the technical practice. After all, for a quant, the technology and software tools are much more tangible and easy to define than the more elusive, “human” arena of organizational processes and project management. As a natural-born geek, I know from personal experience.
This book is different. Jeff Deal and Gerhard Pilcher wrote it to serve the much neglected other side of the coin: you, the business leader. It delivers the two ingredients you need for success:
- an understanding of the technology so you can speak the quant’s language and
- a guide to analytics management best practices, including how to build your analytics team and avert the most costly pitfalls.
Download the EBook Mining Your Own Business (Chapter 3)
Learn about the Top 10 Data Mining Mistakes