The practice of business is changing. More and more companies are amassing larger and larger amounts of data, and storing them in bigger and bigger data bases. Consequently, successful applications of data-driven decision making are plentiful and increasing on a daily basis. This book will motivate the need for data and data-driven solutions, using real data from real business scenarios. It will allow managers to better interact with personnel specializing in analytics by exposing managers and decision makers to the key ideas and concepts of data-driven decision making.

Business Analytics for Managers conveys ideas and concepts from both statistics and data mining with the goal of extracting knowledge from real business data and actionable insight for managers. Throughout, emphasis placed on conveying data-driven thinking. While the ideas discussed in this book can be implemented using many different software solutions from many different vendors, it also provides a quick-start to one of the most powerful software solutions available.

The main goals of this book are as follows:

· To excite managers and decision makers about the potential that resides in data and the value that data analytics can add to business processes.

· To provide managers with a basic understanding of the main concepts of data analytics and a common language to convey data-driven decision problems so they can better communicate with personnel specializing in data mining or statistics.

Autorentext

Wolfgang Jank is Associate Professor in the Department of Decisions, Operations & Information Technologies and Director, Center for Complexity in Business, at the Smith School of Business, University of Maryland. He is interested in using ideas from statistics and data mining to solve problems in marketing, information systems, and operations management. Dr. Jank has authored over seventy refereed articles, book chapters and conference papers. He has co-authored two books, Statistical Methods in e-Commerce Research and Modeling Online Auctions.

Inhalt

Introduction.- Exploring & Discovering Data.- Data Modeling I - Basics.- Data Modeling II - Making Models More Flexible.- Data Modeling III - Making Models More Selective.- Data Modeling IV - Fine Tuning Your Model.- Introduction to the Statistical Software R.