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Business Intelligence: A Managerial Approach, Global Edition

Paperback Engels 2017 9781292220543
Verkooppositie 2464Hoogste positie: 2464
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For courses on Business Intelligence or Decision Support Systems.
A managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.

New to this Edition
With the goal of improving the text, this edition marks a major reorganization of the text to reflect the focus on business analytics. This edition is now organized around three major types of business analytics (i.e., descriptive, predictive, and prescriptive). The new edition has many timely additions, and the dated content has been deleted. The following major specific changes have been made:

New Organization- The book recognizes three types of analytics: descriptive, predictive, and prescriptive, a classification promoted by INFORMS. Chapter 1 introduces BI and analytics with an application focus in many industries. This Chapter also includes an overview of the analytics ecosystem to help the user explore all the different ways one can participate and grow in the analytics environment. It is followed by an overview of statistics, importance of data, and descriptive analytics/visualization in Chapter 2. Chapter 3 covers data warehousing and data foundations including updated content, specifically data lakes. Chapter 4 covers predictive analytics. Chapter 5 extends the application of analytics to text, Web, and social media. Chapter 6 covers Prescriptive Analytics, specifically linear programming and simulation. It is totally new content for this book. Chapter 7 introduces Big Data tools and platforms. The book concludes with Chapter 8, emerging trends and topics in business analytics including location analytics, Internet of Things, cloud-based analytics, and privacy/ethical considerations in analytics. The discussion of analytics ecosystem recognizes prescriptive analytics as well.

New Chapters- The following chapters have been added:
- Chapter 2: 'Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization'
This chapter aims to set the stage with a thorough understanding of the nature of data, which is the main ingredient for any analytics study. Next, statistical modeling is introduced as part of the descriptive analytics. Data visualization has become a popular part of any business reporting and/or descriptive analytics project; therefore, it is explained in detail in this chapter. The chapter is enhanced with several real-world cases and examples (75% new material).

- Chapter 6: 'Prescriptive Analytics: Optimization and Simulation'
This chapter introduces prescriptive analytics material to this book. The chapter focuses on optimization modeling in Excel using the linear programming technique. It also introduces the concept of simulation. The chapter is an updated version of material from two chapters in our DSS book, 10th edition. For this book it is an entirely new chapter (99% new material).

- Chapter 8: 'Future Trends, Privacy and Managerial Considerations in Analytics'
This chapter examines several new phenomena that are already changing or are likely to change analytics. It includes coverage of geospatial in analytics, Internet of Things, and a significant update of the material on cloud-based analytics. It is also updates some coverage from the last edition on ethical and privacy considerations (70% new material).

Revised Chapters- The remaining chapters have been revised and updated:
- Chapter 1: 'An Overview of Business Intelligence, Analytics, and Data Science'
This chapter has been rewritten and significantly expanded. It opens with a new vignette covering multiple applications of analytics in sports. It introduces the three types of analytics as proposed by INFORMS: descriptive, predictive, and prescriptive analytics. As noted earlier, this classification is used in guiding the complete reorganization of the book itself (earlier content but with a new figure).


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Over Ramesh Sharda

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.

Andere boeken door Ramesh Sharda

Over Dursun Delen

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.

Andere boeken door Dursun Delen

Over David King

David King (1970) studeerde in Cambridge en doceerde Europese geschiedenis aan de University of Kentucky. Hij heeft al verschillende New York Times-bestsellers op zijn naam staan.

Andere boeken door David King


Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science
Chapter 2 Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization
Chapter 3 Descriptive Analytics II: Business Intelligence and Data Warehousing
Chapter 4 Predictive Analytics I: Data Mining Process, Methods, and Algorithms
Chapter 5 Predictive Analytics II: Text, Web, and Social Media
Chapter 6 Prescriptive Analytics: Optimization and Simulation
Chapter 7 Big Data Concepts and Tools
Chapter 8 Future Trends, Privacy and Managerial Considerations in Analytics

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