This is a short book, Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics” befitting its intended audience – managers and executives with responsibility for data science and analytics projects. It outlines the requirements for success – not technical model success, but rather successful implementation in a way that builds business value. It is based on the authors’ experience with Elder Research, Inc. (ERI), a data science and analytics consulting and training company – Gerhard Pilcher is the CEO and Jeff Deal the COO. Too often, they say, a project attains technical success but fails at the implementation stage for other reasons.
The book offers what Eric Siegel, founder of the Predictive Analytics World conference (now the Machine Learning Conference), terms a “treasure trove” of instructive anecdotes, such as the government fraud inspector who was stunned to see that ERI, from public data, had pegged as potential frauds several contractors who were then the subject of secret investigations. Each field, business, discipline, etc. has its own set of jargon and this book is a concise guide to the terminology of analytics. It also has lots of useful managerial advice, including some that runs counter to the Silicon Valley culture of “double down and fail fast.” This book stresses the value of starting small and proceeding incrementally, rather than going for a huge project out of the gate. There is also a walk-through of basic data-mining procedures, such as training and validation. While the book is pitched at managers, with little technical detail, I recommend it for technical people as well. If the latter are well briefed on the managerial pitfalls in the development and implementation of data science projects, collaboration is enhanced and all parties can help avoid the pitfalls.