Business Intelligence Strategy and Big Data Analytics

A General Management Perspective

Nonfiction, Computers, Advanced Computing, Management Information Systems, Database Management, General Computing
Cover of the book Business Intelligence Strategy and Big Data Analytics by Steve Williams, Elsevier Science
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Author: Steve Williams ISBN: 9780128094891
Publisher: Elsevier Science Publication: April 8, 2016
Imprint: Morgan Kaufmann Language: English
Author: Steve Williams
ISBN: 9780128094891
Publisher: Elsevier Science
Publication: April 8, 2016
Imprint: Morgan Kaufmann
Language: English

Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability.  It is written from a general management perspective, and it draws on observations at 12 companies whose annual revenues range between $500 million and $20 billion.  Over the past 15 years, my company has formulated vendor-neutral business-focused BI strategies and program execution plans in collaboration with manufacturers, distributors, retailers, logistics companies, insurers, investment companies, credit unions, and utilities, among others.  It is through these experiences that we have validated business-driven BI strategy formulation methods and identified common enterprise BI program execution challenges.

In recent years, terms like “big data” and “big data analytics” have been introduced into the business and technical lexicon.  Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness.  Accordingly, we will use the terms BI and business intelligence throughout the book, and we will discuss the newer concepts like big data as appropriate.  More broadly, the goal of this book is to share methods and observations that will help companies achieve BI success and thereby increase revenues, reduce costs, or both.

  • Provides ideas for improving the business performance of one’s company or business functions
  • Emphasizes proven, practical, step-by-step methods that readers can readily apply in their companies
  • Includes exercises and case studies with road-tested advice about formulating BI strategies and program plans
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Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability.  It is written from a general management perspective, and it draws on observations at 12 companies whose annual revenues range between $500 million and $20 billion.  Over the past 15 years, my company has formulated vendor-neutral business-focused BI strategies and program execution plans in collaboration with manufacturers, distributors, retailers, logistics companies, insurers, investment companies, credit unions, and utilities, among others.  It is through these experiences that we have validated business-driven BI strategy formulation methods and identified common enterprise BI program execution challenges.

In recent years, terms like “big data” and “big data analytics” have been introduced into the business and technical lexicon.  Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness.  Accordingly, we will use the terms BI and business intelligence throughout the book, and we will discuss the newer concepts like big data as appropriate.  More broadly, the goal of this book is to share methods and observations that will help companies achieve BI success and thereby increase revenues, reduce costs, or both.

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