Business Intelligence: Making Decisions Through Data Analytics

Front Cover
Business Expert Press, Mar 6, 2011 - Business & Economics - 150 pages
0 Reviews
This book is about using business intelligence as a management information system for supporting managerial decision making. It concentrates primarily on practical business issues and demonstrates how to apply data warehousing and data analytics to support business decision making. This book progresses through a logical sequence, starting with data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery, and finally the actual use of discovered knowledge. All examples are based on the most recent achievements in business intelligence. Finally this book outlines an overview of a methodology that takes into account the complexity of developing applications in an integrated business intelligence environment. This book is written for managers, business consultants, and undergraduate and postgraduates students in business administration.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Introduction
1
An Introduction to Business Intelligence
5
The Data Warehouse
17
The Basics of Business Analysis
35
Advanced Business Analysis
67
Customer Intelligence
91
Business Intelligence and ValueBased Management
103
Conclusion
127
Notes
131
References
139
Index
145
Copyright

Other editions - View all

Common terms and phrases

About the author (2011)

Dr. Jerzy Surma is an assistant professor at Warsaw School of Economics and a director of postgraduate studies on business intelligence. Prior to his teaching positions, he worked as a business consultant in an information management group and was responsible for the design and implementation of business intelligence solutions for international companies. He is a member of the Strategic Management Society and IESE Business School Alumni. Currently, Dr. Surma has been working on applying advanced data mining techniques in reality mining and analytical customer relationship management.

Bibliographic information