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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010327975 | HD38.7 B54 2013 | Open Access Book | Book | Searching... |
Searching... | 33000000010186 | HD38.7 B54 2013 | Open Access Book | Book | Searching... |
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Summary
Summary
"The chapters in this volume offer useful case studies, technical roadmaps, lessons learned, and a few prescriptions to 'do this, avoid that.'"
--From the Foreword by Joe LaCugna, Ph.D., Enterprise Analytics and Business Intelligence, Starbucks Coffee Company
With the growing barrage of "big data," it becomes vitally important for organizations to make sense of this data and information in a timely and effective way. That's where analytics come into play. Research shows that organizations that use business analytics to guide their decision making are more productive and experience higher returns on equity. Big Data and Business Analytics helps you quickly grasp the trends and techniques of big data and business analytics to make your organization more competitive.
Packed with case studies, this book assembles insights from some of the leading experts and organizations worldwide. Spanning industry, government, not-for-profit organizations, and academia, they share valuable perspectives on big data domains such as cybersecurity, marketing, emergency management, healthcare, finance, and transportation.
Understand the trends, potential, and challenges associated with big data and business analytics Get an overview of machine learning, advanced statistical techniques, and other predictive analytics that can help you solve big data issues Learn from VPs of Big Data/Insights & Analytics via case studies of Fortune 100 companies, government agencies, universities, and not-for-profitsBig data problems are complex. This book shows you how to go from being data-rich to insight-rich, improving your decision making and creating competitive advantage.
Author Jay Liebowitz recently had an article published in The World Financial Review.
www.worldfinancialreview.com/?p=1904Author Notes
Dr. Jay Liebowitz is the Orkand Endowed Chair of Management and Technology, the only endowed chair at the University of Maryland University College. He previously served as a full professor in the Carey Business School at Johns Hopkins University. He was ranked one of the top 10 knowledge management (KM) researchers/practitioners out of 11,000 worldwide and was ranked number two worldwide in KM strategy according to the January 2010 Journal of Knowledge Management. He is a prolific author, Fulbright Scholar, Computer Educator of the Year (IACIS), IEEE Executive Fellow, and the founder and editor-in-chief of Expert Systems with Applications: An International Journal.
His most recent books are:
Knowledge Management Handbook: Collaboration and Social Networking, Second Edition Beyond Knowledge Management: What Every Leader Should Know Knowledge Management in Public Health Knowledge Management and E-Learning Knowledge Retention: Strategies and SolutionsTable of Contents
| Foreword | p. vii |
| Preface | p. xv |
| About the Editor | p. xvii |
| Contributors | p. xix |
| Chapter 1 Architecting the Enterprise via Big Data Analytics | p. 1 |
| Chapter 2 Jack and the Big Data Beanstalk: Capitalizing on a Growing Marketing Opportunity | p. 21 |
| Chapter 3 Frontiers of Big Data Business Analytics: Patterns and Cases in Online Marketing | p. 43 |
| Chapter 4 The Intrinsic Value of Data | p. 69 |
| Chapter 5 Finding Big Value in Big Data: Unlocking the Power of High-Performance Analytics | p. 87 |
| Chapter 6 Competitors, Intelligence, and Big Data | p. 103 |
| Chapter 7 Saving Lives with Big Data: Unlocking the Hidden Potential in Electronic Health Records | p. 117 |
| Chapter 8 Innovation Patterns and Big Data | p. 131 |
| Chapter 9 Big Data at the U.S. Department of Transportation | p. 147 |
| Chapter 10 Putting Big Data at the Heart of the Decision-Making Process | p. 153 |
| Chapter 11 Extracting Useful Information from Multivariate Temporal Data | p. 171 |
| Chapter 12 Large-Scale Time-Series Forecasting | p. 191 |
| Chapter 13 Using Big Data and Analytics to Unlock Generosity | p. 211 |
| Chapter 14 The Use of Big Data in Healthcare | p. 229 |
| Chapter 15 Big Data: Structured and Unstructured | p. 249 |
| Index | p. 269 |
