Business Analytics, Data Science, and AI: A Managerial Approach, 6th edition

Published by Pearson (March 31, 2026) © 2027
  • Ramesh Sharda
  • Dursun Delen
  • Efraim Turban

Title overview

For courses in Business Intelligence, Data Science, Business Analytics and MIS.

Help tomorrow’s managers master analytics and AI

Business Analytics, Data Science and AI: A Managerial Approach provides a complete guide to understanding and applying the technologies that drive business today. The text introduces the essentials of descriptive, predictive and prescriptive analytics as used in the business world. Vignettes and cases illustrate the real-world capabilities and costs of data science and analytics, as well as the latest advances in AI.

The 6th Edition offers coverage of emerging components of modern-day business decision systems such as deep learning, robotics, agentic AI and more.

Hallmark features of this title

  • Emphasizing methods, software and the role of artificial intelligence (AI), the text prepares tomorrow's managers for roles in decision support systems, and analytics.
  • Opening vignettes and application cases present analytics problems and solutions and illustrate how organizations use analytics and AI technology effectively.
  • Real-world cases and examples demonstrate capabilities, costs and justifications of business intelligence.
  • Boxed features call out key technology.
  • Color charts, graphs and figures illustrate data and processes.
  • Student-friendly features provide opportunities for practice and review. These include chapter highlights, key terms, section review questions, hands-on exercises, discussion questions and team projects.

New and updated features of this title

  • UPDATED: The title has been updated to Business Analytics, Data Science and AI: A Managerial Approach to better reflect the text's content and approach. New and updated topics in the 6th Edition include:
    • Analytics applications in international sports, agriculture and the gaming industry in Chapter 1
    • The latest AI tools and methods used in analytics, including discussion of the benefits to businesses, in Chapter 2
    • Emerging trends in socially responsible analytics, including a new section on using data for good, in Chapter 3
    • Geospatial and cloud-based analytics and conducting analytics explorations using AI tools in Chapter 10
    • Issues related to responsible AI and concerns about AI’s impact in Chapter 11

Key features

Features of MyLab MIS for the 6th Edition

NEW: Available for the first time with the 6th Edition, MyLab MIS lets instructors choose from a wide range of trusted, expert-created content that suits their teaching style.

  • AI Literacy Modules teach skills for applying AI responsibly and critiquing AI-generated content. Students receive a Credly® badge, recognized by colleges and employers, for each module they complete.
  • Using AI Projects provide ready-to-assign activities that teach students how to use generative AI effectively for school and at work.
  • Interactive Reading Assignments blend author content and media into a seamless learning path. These let you create assignments easily with a simple added step in your MyLab course setup.
  • Dynamic Study Modules pose a handful of questions and then respond to each student’s progress in real time. Learners deepen their grasp of concepts as they go.
  • Chapter Quizzes test student comprehension. MyLab lets instructors edit, customize and create their own quizzes as needed.
  • Excel Grader Projects, one per chapter, allow students to work live in Microsoft® Excel in real-world contexts. These projects help students learn to use the functionality of Excel for analytics tasks.

Features of Pearson+ eTextbook for the 6th Edition

  • Check Your Understanding questions at the end of each learning objective enable students to check their understanding of the material and provide feedback on answer choices. These questions help students make sure they comprehend the content.
  • NEW: AI-powered study tool gives students access to an expert chatbot for personalized support, simplified explanations and guided practice.

Table of contents

Part 1: Introduction

  1. An Overview of Business Analytics, Data Science and AI
  2. Artificial Intelligence Concepts, Drivers, Major Technologies and Business Applications

Part 2: Descriptive Analytics

  1. Descriptive Analytics 1: Nature of Data, Big Data and Statistical Modeling
  2. Descriptive Analytics 2: Business Intelligence Data Warehousing, and Visualization

Part 3: Predictive Analytics

  1. Predictive Analytics I: Data Mining Process, Methods and Algorithms
  2. Predictive Analytics II: Text, Web and Social Media Analytics
  3. Deep Learning and Cognitive Computing

Part 4: Prescriptive Analytics

  1. Prescriptive Analytics: Optimization and Simulation

Part 5: Software and Trends

  1. Landscape of Business Analytics Tools
  2. AI-Based Trends in Analytics and Data Science
  3. Ethical, Privacy and Managerial Considerations in Analytics

Author bios

About our authors

Ramesh Sharda (MBA, PhD, University of Wisconsin, Madison) is the ConocoPhillips Chair, and a Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). He served as the Vice Dean for Graduate Programs and Research from 2014 to 2025. He cofounded and directed OSU’s PhD in Business for the Executives Program. About 200 papers describing his research have been published in major journals, including Operations Research, Management Science, Information Systems Research, Decision Support Systems, and the Journal of MIS. He cofounded the AIS SIG on Decision Support Systems and Knowledge Management (SIGDSA). Dr. Sharda serves on several editorial boards, including those of Decision Sciences Journal, Decision Support Systems, and ACM Data Base. He has authored and edited several textbooks and research books and serves as the coeditor of several Springer book series (Integrated Series in Information Systems, Operations Research/Computer Science Interfaces, and Annals of Information Systems) with Springer. He served as the Executive Director of the Teradata University Network from 2013 to 2020. His current research interests are in decision support systems, business analytics, and technologies for managing information overload. Ramesh is a Fellow of INFORMS and AIS and was inducted into the Oklahoma Higher Education Hall of Fame in 2015. He was awarded a Fulbright Distinguished Chair at Aalto University of Finland for spring 2023.

Dursun Delen (PhD, Oklahoma State University) is the Spears Endowed Chair in Business Administration, Patterson Foundation Endowed Chair in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Regents 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 5 years, during which he led a number of decision support and other information systems-related research projects funded by several federal agencies including the Department of Defense (DoD), National Aeronautics and Space Administration (NASA), National Institute for Standards and Technology (NIST), Ballistic Missile Defense Organization (BMDO), and Department of Energy (DOE). Dr. Delen has published more than 200 peer-reviewed articles, some of which have appeared in major journals like Decision Sciences, Decision Support Systems, Communications of the ACM, Computers and Operations Research, Computers in Industry, Journal of Production Operations Management, Journal of the Association for Information Systems, Journal of the American Medical Informatics Association, Artificial Intelligence in Medicine, International Journal of Medical Informatics, Expert Systems with Applications, and IEEE Wireless Communications. He authored or coauthored 13 textbooks in the broad areas of business analytics, artificial intelligence, data mining, text mining, business intelligence, and decision support systems. He is often invited to national and international conferences for keynote addresses and to companies for technical training and consultancy engagements on topics related to AI, data science, business analytics, decision support systems, business intelligence, and knowledge management. He served as the General Cochair for the Fourth International Conference on Network Computing and Advanced Information Management (in Seoul, South Korea) and regularly chairs, tracks, and minitracks at various information systems and analytics conferences. He is currently serving as the Editor-in-Chief for the Journal of Business Analytics, Journal of AI in Business (a member of the Frontiers in AI family of journals), and as Senior Editor, Associate Editor, or Editorial Board Member for more than a dozen other academic journals. His research and teaching interests are in data and text mining, business analytics, decision support systems, data science, knowledge management, business intelligence, and enterprise modeling.

Efraim Turban (MBA, PhD, 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.

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