Business Intelligence and Data Mining

180 Pages·2014·5.8 MB·English

Big Data and Business Analytics
Mark Ferguson, Editor
Business
Intelligence and
Data Mining
Anil K. Maheshwari, Ph.D.
Business Intelligence
and Data Mining

Business Intelligence
and Data Mining
Anil K. Maheshwari, PhD
Business Intelligence and Data Mining
Copyright © Anil K. Maheshwari, PhD, 2015.
All rights reserved. No part of this publication may be reproduced,
stored in a retrieval system, or transmitted in any form or by any
means—electronic, mechanical, photocopy, recording, or any other
except for brief quotations, not to exceed 400 words, without the prior
permission of the publisher.
First published by
Business Expert Press, LLC
222 East 46th Street, New York, NY 10017
www.businessexpertpress.com
ISBN-13: 978-1-63157-120-6 (print)
ISBN-13: 978-1-63157-121-3 (e-book)
eISSN: 2333-6757
ISSN: 2333-6749
Business Expert Press Big Data and Business Analytics Collection.
Cover and interior design by S4Carlisle Publishing Services Private Ltd.,
Chennai, India
Dedicated to my parents,
Mr. Ratan Lal and Mrs. Meena Maheshwari.

Abstract
Business is the act of doing something productive to serve someone’s
needs, and thus earn a living, and make the world a better place. Business
activities are recorded on paper or using electronic media, and then these
records become data. There is more data from customers’ responses and
on the industry as a whole. All this data can be analyzed and mined using
special tools and techniques to generate patterns and intelligence, which
reflect how the business is functioning. These ideas can then be fed back
into the business so that it can evolve to become more effective and ef-
ficient in serving customer needs. And the cycle continues on.
Business intelligence includes tools and techniques for data gather-
ing, analysis, and visualization for helping with executive decision making
in any industry. Data mining includes statistical and machine-learning
techniques to build decision-making models from raw data. Data mining
techniques covered in this book include decision trees, regression, artifi-
cial neural networks, cluster analysis, and many more. Text mining, web
mining, and big data are also covered in an easy way. A primer on data
modeling is included for those uninitiated in this topic.
Keywords
Data Analytics, Data Mining, Business Intelligence, Decision Trees,
Regression, Neural Networks, Cluster analysis, Association rules.

Contents
Abstract ..................................................................................................v
Preface ................................................................................................xiii
Chapter 1 Wholeness of Business Intelligence and Data Mining ........1
Business Intelligence .........................................................2
Pattern Recognition ..........................................................3
Data Processing Chain ......................................................6
Organization of the Book ................................................16
Review Questions ............................................................17
Section 1 .....................................................................................19
Chapter 2 Business Intelligence Concepts and Applications .............21
BI for Better Decisions ....................................................23
Decision Types ................................................................23
BI Tools ..........................................................................24
BI Skills ..........................................................................26
BI Applications ..............................................................26
Conclusion......................................................................34
Review Questions ............................................................35
Liberty Stores Case Exercise: Step 1 .................................35
Chapter 3 Data Warehousing ...........................................................37
Design Considerations for DW .......................................38
DW Development Approaches ........................................39
DW Architecture ............................................................40
Data Sources ...................................................................40
Data Loading Processes ...................................................41
DW Design .....................................................................41
DW Access ......................................................................42
DW Best Practices ...........................................................43
Conclusion......................................................................43