Hands-On Prescriptive Analytics

Preface

I have one overarching theme I painstakingly developed over the years for all my books: data consists of information obfuscated by noise. Data per se are not information; they are the repository for information. This is obvious, almost trivial, to data scientists, but it is profound to business decision-makers. They equate data with the information they need for their decisions.

Information, to be useful for decision making, must be extracted from data. My books, including this one, are concerned with extracting information from data. The topics I covered include business analytics, which incorporates Descriptive Analytics such as data visualization, preprocessing, and transformations; Predictive Analytics, which includes model building, mostly for forecasting; simulation analytics, especially for complex systems; and survey analytics.

Although my theme has been consistent, I felt something was missing. I kept looking back at my consulting, remembering I was always told by my clients that they appreciated the menu of choice options I gave them, but they did not have time to wade through them, even if it was just two or three options. They had many issues to handle; their time was precious. They simply wanted to be told what was the best option on the menu.

My inability to recommend reflected my training. As a classical economist, I was repeatedly told that economists must take a positive approach to analysis, not a normative one involving value judgments. We can only discuss ...