Watson: Business Analytics

Business Analytics

Managing, Building, and Using Analytical Systems

Hugh J. Watson, University of Georgia

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.

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.

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 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 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 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 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

Hugh Watson is a Professor of MIS in the Terry College of Business at the University of Georgia. He is a leading scholar and authority on analytics, having authored 24 books and over 200 scholarly journal articles. Hugh helped develop the conceptual foundation for decision support systems in the 1970’s, researched the development and implementation of executive information systems in the 1980’s, and for the past twenty years has specialized in data warehousing, BI, and analytics. Hugh has consulted with numerous global organizations, including the World Bank, Intel, IBM, Conoco, and Glaxo, and conducted hundreds of executive development programs. Hugh is a Fellow of the Association for Information Systems and The Data Warehousing Institute and was the Senior Editor of the Business Intelligence Journal for 25 years. ScholarGPS ranks Hugh as the #1 worldwide scholar for lifetime research in Business Intelligence.

Instructor Resources

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Business Analytics

Managing, Building, and Using Analytical Systems

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Overview

Features

Table of Contents

About the Author

Instructor Resources

Ordering Information

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