Ramesh Sharda

co-author and editor of "Business Intelligence, Analytics, and Data Science: A Managerial Perspective," Fourth Edition

January 27, 2017

Ramesh Sharda is the Vice Dean for Research and Graduate Programs, Watson/ConocoPhillips Chair and a Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. He has coauthored two textbooks ("Business Intelligence and Analytics: Systems for Decision Support," Tenth Edition, Prentice Hall and "Business Intelligence, Analytics, and Data Science: A Managerial Perspective," Fourth Edition, Pearson). His research has been published in major journals in management science and information systems including Management Science, Operations Research, Information Systems Research, Decision Support Systems, Decision Science Journal, EJIS, JMIS, Interfaces, INFORMS Journal on Computing, ACM Data Base and many others. He is a member of the editorial boards of journals such as the Decision Support Systems, Decision Sciences, and Information Systems Frontiers. He is currently serving as the Executive Director of Teradata University Network and received the 2013 INFORMS HG Computing Society Lifetime Service Award. He was inducted into the Oklahoma Higher Education Hall of Fame in 2016.

The field of business intelligence and business analytics (BI & BA) has evolved rapidly to become more focused on innovative applications for extracting knowledge and insight from data streams that were not even captured some time back, much less analyzed in any significant way. New applications turn up daily in healthcare, sports, travel, entertainment, supply chain management, utilities, and virtually every industry imaginable. The term analytics has become mainstream. Indeed, it has already evolved into other terms such as data science, and the latest incarnation is deep learning and Internet of Things.

This edition of "Business Intelligence, Analytics, and Data Science: A Managerial Perspective" provides a managerial perspective to business analytics continuum beginning with descriptive analytics (e.g., the nature of data, statistical modeling, data visualization, and business intelligence), moving on to predictive analytics (e.g., data mining, text/web mining, social media mining), and then to prescriptive analytics (e.g., optimization and simulation), and finally finishing with Big Data, and future trends, privacy, and managerial considerations.

URL: https://library.okstate.edu/news/celebratingbooks/2017-honorees-b/ramesh-sharda

Last Updated: 12 January 2022