"Predictive business analytics refers to the skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions. It focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions--or it may drive fully automated decisions. We are entering the era of digital analytics where human and artificial intelligence (AI) work hand in hand to achieve better analytical results. Today, more than ever, businesses are expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions. More and more organizations are seeking better processes and tools to ensure thatthe right people have the right information at the right time, to make smarter decisions. This process, in essence, reflects an organizational capability to improve managerial decision making across many core performance and financial areas. For years, organizations have sought to develop and deploy an effective process to capture and filter forward-looking measures that enable it to understand significant patterns, relationships, and trends in order to facilitate better and more insightful decisions about the future. This book is intended to promote clarity and ensure that the application of AI-Enabled Predictive Business Analytics is relevant to all business functions"--
We are entering the era of digital transformation where human and artificial intelligence (AI) work hand in hand to achieve data driven performance.
Today, more than ever, businesses are expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions. AI-Enabled Analytics in Business is your Roadmap to meet this essential business capability. To ensure we can plan for the future vs react to the future when it arrives, we need to develop and deploy a toolbox of tools, techniques, and effective processes to reveal forward-looking unbiased insights that help us understand significant patterns, relationships, and trends. This book promotes clarity to enable you to make better decisions from insights about the future.
- Learn how advanced analytics ensures that your people have the right information at the right time to gain critical insights and performance opportunities
- Empower better, smarter decision making by implementing AI-enabled analytics decision support tools
- Uncover patterns and insights in data, and discover facts about your business that will unlock greater performance
- Gain inspiration from practical examples and use cases showing how to move your business toward AI-Enabled decision making
AI-Enabled Analytics in Business is a must-have practical resource for directors, officers, and executives across various functional disciplines who seek increased business performance and valuation.
| Acknowledgments |
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ix | |
| Introduction |
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Chapter 1 A Primer on Al-Enabled Analytics for Business |
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Chapter 2 Why Al-Enabled Analytics Is Essential for Business |
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Chapter 3 Myths and Misconceptions About Analytics |
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Chapter 4 Applications of Al-Enabled Analytics |
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Chapter 5 Roadmap for How to implement Al-Enabled Analytics in Business |
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Chapter 6 Executive Responsibilities to implement Analytics |
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87 | (10) |
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Chapter 7 Implementing Analytics |
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97 | (12) |
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Chapter 8 The Role of Analytics in Strategic Decisions |
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Chapter 9 Cases of Analytics Failures from Deviation to the Roadmap |
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Chapter 10 Use Case: Grabbing Defeat from the Jaws of Victory |
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133 | (10) |
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Chapter 11 Use Case: Incremental improvements to Gain insights |
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Chapter 12 Use Case: Analytics Are for Everyone |
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151 | (6) |
| Epilogue |
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157 | (2) |
| Appendix - Analytics Champion Framework |
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159 | (50) |
| About the Authors |
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209 | (6) |
| About the website |
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215 | (2) |
| Index |
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217 | |
LAWRENCE S. MAISEL is President of DecisionVu, a management consultancy specializing in Performance Management, Data Analytics, and Operations Improvements. He is an experienced executive with proven leadership to drive efficiency and control through planning and analysis, AI-enabled analytics, and operating process redesign. Maisel is a CPA, MBA, and CGMA and received AICPAs Thought Leader award for creating its Center for Excellence in Financial Management. He authored Predictive Business Analytics, co-created with Drs. Kaplan and Norton, the Balanced Scorecard Approach, and co-authored with Drs. Kaplan and Cooper Implementing Activity-Based Cost Management. He is a former Senior KPMG Partner and an Adjunct Professor, Columbia Universitys Graduate School of Business.
ROBERT J. ZWERLING is a high-tech entrepreneur founding and growing software companies across telecom, manufacturing, distribution, high data availability, analytics, and AI. He is a noted speaker, thought leader, and author on AI and analytics, and has co-authored with Jesper H. Sorensen dozens of papers and the groundbreaking book, Implementing an Analytics Culture for Data Driven Decisions. Zwerling holds two degrees in engineering and is a registered Professional Engineer. He is Managing Director at Aurora Predictions, providing AI-enabled analytics with an intuitive/no-code interface to automatically reveal insights that moves the businesss needle. He is co-founder of the Finance Analytics Institute, which teaches how to implement analytics through papers, surveys, benchmarks, and the Analytics Academy.
JESPER H. SORENSEN is a Finance Executive with a proven track record of advancing the analytics agenda. He is currently a Vice President of Finance at Oracle, leading a large global finance team for a multi-billion-dollar business. Prior to Oracle he held leading positions with DuPont and IBM. He holds several advisory positions including advisory board member for Aurora Predictions. He co-authored with Robert J. Zwerling many articles and papers on analytics and the book Implementing an Analytics Culture for Data Driven Decisions. Sorensen is also the co-founder of the Finance Analytics Institute. He holds a Master in Economics and Management from the University of Aarhus, Denmark, and is certified in Risk Management and Strategic Decision Making from Stanford University
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