Business Intelligence, 2nd Edition
It has been nearly ten years since I began the process of writing the first edition of this book; in some ways that span of years seems like an eternity, while in others, a blink of the eye. At that time, business intelligence (BI), business analytics, OLAP, and data mining were still maturing techniques. But one interesting observation, looking back at the text of the first edition: our objective in developing the book (titled Business Intelligence – The Savvy Manager’s Guide) was to provide an overview of a collection of techniques that were gradually being adopted to help business understand ways to work better, and this objective anticipated the eventual democratization of the capabilities for repurposing information from many sources in ways that could lead to business value. At that time, I suggested that:
The boundary that divides business and technology is a fuzzy one, and this border erodes more and more as organizational managers recognize how integral knowledge and information management are to the bottom line. A natural development of this is the concept of business intelligence (BI), which (loosely defined) incorporates the tools, methods, and processes needed to transform data into actionable knowledge.
What I find curious about BI is that it is not just technology, nor is it just practices and methods. It is more a combination of the best of both the business world and the technical world—using advanced algorithms and data management techniques to better implement the way a business works. But what prevents BI programs from being successful is precisely what forms the dividing line between business and technology. If we are moving toward a business environment where profits are driven by the exploitation of information, then it is critical for those who run, or, more properly, improve, the business to understand what kinds of value lie within a company’s information and how to unlock that value and transform it into profits.
As I was completing my previous book, The Practitioner’s Guide to Data Quality Improvement, I was approached by Rick Adams at Morgan Kaufmann (MK) to revise the material in the first edition into a second edition, which I agreed to do because I thought that many things had changed in the ways that we guide our customers in implementing a BI program. I embarked on the set of tasks of updating the material in the book as well as adding material about what has changed in the industry and expanding sections and chapters about things and ideas that have changed over time. But what I found interesting is that as I reviewed the material, I was struck by how much of the first edition has remained relevant, which I believe has contributed to the long shelf-life of the original version.
And in turn, it seems that Morgan Kaufmann’s interest in business intelligence has matured as well; I was invited to be the series editor for the business intelligence series, and (after some fits and starts) found a strong MK editor partner, Andrea Dierna. At the same time that I have been revising this book, Andrea and I have been diligently working at laying the foundation for expanding the catalog of books to guide data management and business intelligence professionals. A lot of the material that is introduced in this book is, or will be treated in much finer detail in the upcoming books of the series. This book is intended to be a cornerstone piece of that strategy. My overall goal is to paint a broad-brush overview of the objectives and practices for designing and deploying a BI program, and other books in the series will provide greater depth about many of the topics we discuss here.
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