Watson, Hugh: Business Analytics

Business Analytics

Business Analytics

Managing, Building, and Using Analytical Systems

Edition 1.1 | Copyright 2027 | Publication Date: October 2024

Richard T. Watson, University of Georgia

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Overview

Business Analytics: Managing, Building and Using Analytical Systems provides an extended foray into analytic techniques and their application in business including decision support systems, data warehousing, analytical portals, business intelligence, big data analytics, artificial intelligence, and more. These applications and related technologies are designed to support decision making, enhance organizational performance, and create business value. Coverage includes how these analytical applications are managed, built, and used. Insights, examples, and stories draw heavily on Dr. Hugh Watson's extensive experience teaching, researching and especially consulting in this area.

For students aiming for a career in analytics or expecting to encounter it in their professional career, this book is designed to equip them with the necessary concepts, understandings, and skills. For those pursuing a specialized path in analytics, it provides a comprehensive foundation. For others, it offers the insights needed to participate confidently in discussions about the essential elements of business analytics. By the end of this book, students will be able to engage thoughtfully with analytics professionals, managers, and other stakeholders on a broad range of analytics topics.

Edition 1.1 includes updated material on Generative AI.

Features

Emphasize a Systems Perspective.
Businesses use analytics for much more than just finding answers to one-time problems. Analytics techniques and models are integral to systems that provide ongoing insight that promote business value. This text examines the process of developing, managing, and maintaining such analytical systems.

Animate Learning with Stories.
Most concepts are illustrated with real-world accounts that engage students and provide them with practical insights.

Highlight Alignment of Business and Analytics Strategies.
The value of analytics and broader analytics strategies are presented with regard to their larger connection to business strategy.

Prepare Students for AI.
A dedicated section covers generative AI which is transforming business in numerous ways.

Advance Students' Career Potential.
Understanding the business use of analytics prepares students for a productive career whether they build, design, or use analytical systems or for those times when their responsibilities involve engaging those who do.

Table of Contents

Detailed Table of Contents PDF

Part I: Introduction to Understanding Business Analytics
Chapter 1: What is Business Intelligence, Analytics, and Artificial Intelligence?
Chapter 2: The Skills Needed for Analytics
Chapter 3: Supporting Business Strategies with Analytics
Chapter 4: Enduring Keys to Analytics Success
Part I Cases

Part II: Descriptive Analytics
Chapter 5: Decision Support Systems
Chapter 6: “Slicing and Dicing” Data with OLAP
Chapter 7: Meeting the Information Needs of Executives
Chapter 8: Analytics Portal Provides a Window for Accessing Information
Chapter 9: Selecting the Best Visualizations
Chapter 10: Data and Information Visualization, Data Interpreters, and Storytelling
Chapter 11: Dashboard and Scorecards to Monitor, Evaluate, and Motivate Performance
Chapter 12: Data Literacy Is Self-Service Analytics’ Missing Link
Part II Cases

Part III: Creating the Analytics Technology Infrastructure
Chapter 13: Selecting Analytics Software
Chapter 14: Data Marts and Warehouses Provide a “Single Version of the Truth”
Chapter 15: Creating the Big Data Infrastructure
Part III Cases

Part IV: Analytics Project Life Cycle
Chapter 16: ROI and Getting Analytics Projects Approved
Chapter 17: Analytics Project Success is in the Eye of the Beholder
Chapter 18: A Deep Dive into the Analytics Life Cycle

Part V: Predictive Analytics
Chapter 19: Predictive Modeling for Supporting Decision-Making and Improving Organization Performance
Chapter 20: The CRISP-DM Methodology for Building Predictive Models
Chapter 21: Finding Patterns and Relationships with Machine Learning
Part V Software Project

Part VI: Artificial Intelligence
Chapter 22: Artificial Intelligence and Its Applications
Chapter 23: Using and Building Generative AI Applications
Chapter 24: Managing Artificial Intelligence

Part VII: Prescriptive Analytics
Chapter 25: Monte Carlo Simulation
Chapter 26: Optimization Using Linear Programming

Part VIII: Business Intelligence, Analytics, and AI Governance
Chapter 27: Getting Started with Business-Driven Data Governance
Chapter 28: Avoid Being Creepy in the Use of Personal Data and Algorithms

Part IX: Careers in Analytics
Chapter 29: Career Opportunities in Analytics

About the Author

Richard T. Watson is the J. Rex Fuqua Distinguished Chair for Internet Strategy in the Department of MIS at the University of Georgia's Terry College of Business. In 2006, Dr. Watson was appointed a Fellow of the Association for Information Systems (AIS) and in 2007 a Fellow of the Royal Society of Arts. In 2011, he received the AIS LEO Award, which is given for exceptional lifetime achievement in Information Systems.

He has published over 150 articles in leading academic and practitioner journals; written or edited more than 10 books (including a data Management textbook and the first book on Energy Informatics). His most recent research focuses on Information Systems (IS) leadership, the business of open source, net-based customer service systems, and the role of IS in creating sustainable business practices as well as ecological sustainability.

Richard T. Watson

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