Business Intelligence and Analytics: Systems for Decision Support, Global Edition

Series
Pearson
Author
Efraim Turban / Ramesh Sharda / Dursun Delen  
Publisher
Pearson
Cover
Softcover
Edition
10
Language
English
Total pages
688
Pub.-date
March 2014
ISBN13
9781292009209
ISBN
1292009209
Related Titles


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Business Intelligence and Analytics: Systems for Decision Support, Global Edition
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Description

Appropriate for all courses in Decision Support Systems (DSS), computerized decision making tools, and management support systems.

Decision Support and Business Intelligence Systems 10e provides the only comprehensive, up-to-date guide to today's revolutionary management support system technologies, and showcases how they can be used for better decision-making.

The 10th edition focuses on Business Intelligence (BI) and analytics for enterprise decision support in a more streamlined book. In addition to traditional decision support applications, this edition expands the reader’s understanding of the various types of analytics by providing examples, products, services, and exercises by discussing Web-related issues throughout the text.

Features

Appropriate for all courses in Decision Support Systems (DSS), computerized decision making tools, and management support systems.

Decision Support and Business Intelligence Systems 10e provides the only comprehensive, up-to-date guide to today's revolutionary management support system technologies, and showcases how they can be used for better decision-making.

The 10th edition focuses on Business Intelligence (BI) and analytics for enterprise decision support in a more streamlined book.

Introduction of management support systems (MSS) technologies.
The tenth edition concentrate on three main areas: BI, data mining, and automated decision support (ADS).

BI and analytics for enterprise decision support.
In addition to traditional decision support applications, this edition promotes students’ understanding of the Web by providing examples, products, services, and exercises and by discussing Web-related issues throughout the text. Web intelligence/Web analytics are highlighted, which parallel BI/business analytics (BA) for e-commerce and other Web applications.

Extensive supply chain and ERP coverage.
Detailed coverage of decision support in supply chain and ERP applications helps students address the crucial supply chain and logistics questions that enterprises focus on.

Comprehensive coverage of data warehousing.
Coverage of topics such as warehouse access, analysis, mining, visualization, and modeling introduces students to state-of-the-art solutions for the entire data warehouse lifecycle.

Comprehensive coverage of knowledge-based decision support.
All the techniques students will need to build advanced knowledge-based decision support systems are covered—knowledge acquisition and representation, inference techniques, and intelligent systems development.

Organizational and societal impacts.
By learning about the organizational and societal implications of advanced decision support technology, students gain a grasp on the ethics, politics, and other non-technical issues associated with electronic decision support.

Detailed coverage of implementation and integration.

The coverage of the real-world challenges of integrating new decision support tools into existing technical and business infrastructures gives students insight into the issues that will make or break their decision support projects.

Links to Teradata University Network (TUN).
Most chapters include links to TUN (www.teradatauniversitynetwork.com). The student side of the Teradata site (Teradata Student Network [TSN]; www.teradatastudentnetwork.com) mainly includes assignments for students. A visit to TSN allows students to read cases, view Web seminars, answer questions, search material, and more.

Software Support.
The TUN website provides software support at no charge. It also provides links to free data mining and other software. In addition, the site provides exercises in the use of such software.

New to this Edition

  • New Organization. The book is now organized around three types of analytics: descriptive, predictive, and prescriptive, a classification promoted by INFORMS. After introducing the topics of DSS/BI and analytics in Chapter 1 and covering the foundations of decision-making and decision support in Chapter 2, the book begins with an overview of Data Warehousing and data foundations in Chapter 3. This part then covers descriptive or reporting analytics, specifically, visualization and business performance measurement. Chapters 5-8 cover predictive analytics. Chapters 9-12 cover prescriptive and decision analytics as well as other decision support systems. Some of the coverage from Chapter 3-4 in previous editions will now be found in the new Chapters 9 and 10. Chapter 13 introduces big data and analytics. The book concludes with emerging trends and topics in business analytics including location intelligence, mobile computing, cloud-based analytics, and privacy/ethical considerations in analytics. This chapter also includes an overview of the analytics ecosystem to help the user explore all of the different ways one can participate and grow in the analytics environment. Thus the book marks a significant departure from the earlier editions in the organization. Of course, it is still possible to teach a course with a traditional DSS focus with this book by covering Chapter 1-4 and chapter 9-12 and possibly 14.
  • New chapters. The following chapters have been added:
    • Chapter 8, “Web Analytics, Web Mining & Social Analyticscovers the popular topics of web analytics and social media analytics. It is an almost entirely new chapter. [95% new material]
    • Chapter 13, “Big Data & Analytics” introduces the hot topics of big data and analytics. It covers the basics of major components of big data techniques and characteristics. It is also a new chapter. [99% new material]
    • Chapter 14, “Business Analytics: Emerging Trends and Future Direction” examines several new phenomena that are already changing or are likely to change analytics. It includes coverage of geospatial in analytics, location-based analytics applications, consumer oriented analytical applications, mobile platforms, and cloud-based analytics. It also updates some coverage from the last edition on ethical and privacy considerations. It concludes with a major discussion of the analytics ecosystem. [90% new material]
  • Streamlined coverage. We have made the book shorter by keeping the most commonly used content. We also mostly eliminated the preformatted online content. Instead, we will use a Web site to provide updated content and links on a regular basis. We also reduced the number of references in each chapter.
  • Revamped author team. ;Building upon the excellent content that has been prepared by the authors of the previous editions (Turban, Aronson, Liang, King, Sharda and Delen), this edition was revised primarily by Ramesh Sharda and Dursun Delen. Both Ramesh and Dursun have worked extensively in DSS and analytics and have industry as well as research experience.
  • All new figures for PowerPoint. Although the figures in the print edition have been retained from previous editions and new figures added for the new content, all the figures have been redrawn in color and are available through the image library for use in PowerPoint presentations.
  • A live update Web site. Adopters of the textbook will have access to a Web site that will include links to news stories, software, tutorials, and even YouTube videos related to topics covered in the book. This site is accessible at http://dssbibook.com
  • Revised and updated content. Almost all of the chapters have new opening vignettes and closing cases that are based on recent stories and events. In addition, Application Cases throughout the book have been updated to include recent examples of applications of a specific technique/model. New Web site links have been added throughout the book. We also deleted many older product links and references. Finally, most chapters have new exercises, Internet assignments, and discussion questions throughout.
  • Table of Contents

    PART I: DECISION MAKING AND ANALYTICS: AN OVERVIEW

    1. An Overview of Business Intelligence, Analytics, and Decision Support

    2. Foundations and Technologies for Decision Making

     

    PART II: DESCRIPTIVE ANALYTICS

    3. Data Warehousing

    4. Business Reporting, Visual Analytics, and Business Performance Management

     

    PART III: PREDICTIVE ANALYTICS

    5. Data Mining

    6. Techniques for Predictive Modeling

    7. Text Analytics, Text Mining, and Sentiment Analysis

    8. Web Analytics, Web Mining, and Social Analytics

     

    PART IV: PRESCRIPTIVE ANALYTICS

    9. Model-Based Decision Making: Optimization and Multi-Criteria Systems

    10. Modeling and Analysis: Heuristic Search Methods and Simulation

    11. Automated Decision Systems and Expert Systems

    12. Knowledge Management and Collaborative Systems

     

    PART V: BIG DATA AND FUTURE DIRECTIONS FOR BUSINESS ANALYTICS

    13. Big Data and Analytics

    14. Business Analytics: Emerging Trends and Future

    Author

    Ramesh Sharda (M.B.A., Ph.D., University of Wisconsin—Madison) is Director of the PhD in Business for Executives Program and Institute for Research in Information Systems (IRIS), ConocoPhillips Chair of Management of Technology, and a Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). About 200 papers describing his research have been published in major journals, including Operations Research, Management Science, Information Systems Research, Decision Support Systems, and Journal of MIS. He cofounded the AIS SIG on Decision Support Systems and Knowledge Management (SIGDSS). Dr. Sharda serves on several editorial boards, including those of INFORMS Journal on Computing, Decision Support Systems, and ACM Transactions on Management Information Systems. He has authored and edited several text and research books and serves as the co-editor of several book series (Integrated Series in Information Systems, Operations Research/Computer Science Interfaces, and Annals of Information Systems) with Springer. He is also currently serving as the Executive Director of the Teradata University Network. His current research interests are in decision support systems, business analytics, and technologies for managing information overload.

    Dursun Delen (Ph.D, Oklahoma State University) is the Spears and Patterson Chairs in Business Analytics, Director of Research for the Center for Health Systems Innovation and Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). Prior to his academic career, he worked for a privately-owned research and consultancy company, Knowledge Based Systems Inc., in College Station, Texas, as a research scientist for five years, during which he led a number of decision support and other information systems related research projects funded by federal agencies, such as DoD, NASA, NIST and DOE. Dr. Delen’s research has appeared in major journals including Decision Support Systems, Communications of the ACM, Computers and Operations Research, Computers in Industry, Journal of Production Operations Management, Artificial Intelligence in Medicine, Expert Systems with Applications, among others. He recently published four textbooks: Advanced Data Mining Techniques with Springer, 2008; Decision Support and Business Intelligence Systems with Prentice Hall, 2010; Business Intelligence: A Managerial Approach, with Prentice Hall, 2010; and Practical Text Mining, with Elsevier, 2012. He is often invited to national and international conferences for keynote addresses on topics related to Data/Text Mining, Business Intelligence, Decision Support Systems, and Knowledge Management. He served as the general co-chair for the 4th International Conference on Network Computing and Advanced Information Management (September 2-4, 2008 in Soul, South Korea), and regularly chairs tracks and mini-tracks at various information systems conferences. He is the associate editor-in-chief for International Journal of Experimental Algorithms, associate editor for International Journal of RF Technologies and Journal of Decision Analytics, and is on the editorial boards of five other technical journals. His research and teaching interests are in data and text mining, decision support systems, knowledge management, business intelligence and enterprise modeling.

    Efraim Turban (M.B.A., Ph.D., University of California, Berkeley) is a visiting scholar at the Pacific Institute for Information System Management, University of Hawaii. Prior to this, he was on the staff of several universities, including City University of Hong Kong; Lehigh University; Florida International University; California State University, Long Beach; Eastern Illinois University; and the University of Southern California. Dr. Turban is the author of more than 100 refereed papers published in leading journals, such as Management Science, MIS Quarterly, and Decision Support Systems. He is also the author of 20 books, including Electronic Commerce: A Managerial Perspective and Information Technology for Management. He is also a consultant to major corporations worldwide. Dr. Turban’s current areas of interest are Web-based decision support systems, social commerce and collaborative decision making.