AI-Enabled Analytics for Business

AI-Enabled Analytics for Business
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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.

Оглавление

Lawrence S. Maisel. AI-Enabled Analytics for Business

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

AI-Enabled Analytics for Business. A Roadmap for Becoming an Analytics Powerhouse

Acknowledgments

Introduction

CHAPTER 1 A Primer on AI-Enabled Analytics for Business

AI AND ML—SIMILAR BUT DIFFERENT

MACHINE LEARNING PRIMER

ANALYTICS VS. ANALYSIS

BI AND DATA VISUALIZATION VS. ANALYTICS

BIASED VS. UNBIASED

AI AND ROI

CONCLUSION

NOTES

CHAPTER 2 Why AI-Enabled Analytics Is Essential for Business

COMPETITIVENESS

HUMAN JUDGMENT AND DECISION-MAKING

Group Decision-Making

Individual Bias in Decision-Making

CONCLUSION

NOTES

CHAPTER 3 Myths and Misconceptions About Analytics

DATA SCIENTIST MISCONCEPTION AND MYTH

SHOT IN THE DARK

BASS-ACKWARD

AI IS NOT IT

BIG IS NOT BETTER

NOT NOW

NOTE TO EXECUTIVES

CONCLUSION

NOTES

CHAPTER 4 Applications of AI-Enabled Analytics

FINANCE

SALES

MANUFACTURING AND SUPPLY CHAIN

DEMAND PLANNING AND INVENTORY

CONCLUSION

NOTES

CHAPTER 5 Roadmap for How to Implement AI-Enabled Analytics in Business

CULTURE

MINDSET

PEOPLE

PROCESS

Data Governance

Decision Governance

SYSTEMS

Spreadsheets

Data Visualization

AI-Enabled Analytics

Toolbox and Persona

THE ROADMAP FOR IMPLEMENTING AI-ENABLED ANALYTICS

LAUNCHING THE CULTURE OF ANALYTICS

CONCLUSION

NOTES

CHAPTER 6 Executive Responsibilities to Implement Analytics

EXECUTIVE COMMITMENT

Budget

Bandwidth

Focus

ANALYTICS CHAMPION

CHANGE MANAGEMENT

Communications

Collaboration

Cultural Influences

Behaviors and Recognition

CONCLUSION

NOTES

CHAPTER 7 Implementing Analytics

DEFINE THE PROBLEM

SELECT AN ANALYTICS SOFTWARE POC VENDOR

PERFORM THE ANALYTICS POC

BENCHMARK PEOPLE SKILLSET

SCALE ANALYTICS

ILLUSTRATIVE EXAMPLE OF THE ANALYTICS POC

ANALYTICS POWERHOUSE

CONCLUSION

NOTE

CHAPTER 8 The Role of Analytics in Strategic Decisions

HOW WE TRICK OURSELVES

TACTICS THAT AFFECT STRATEGY

Sandbagging

The Big Ego

KEY PERFORMANCE INDICATORS (KPIs) AND STRATEGIC OBJECTIVES

THE ANALYTICS SCORECARD™

CONCLUSION

NOTES

CHAPTER 9 Cases of Analytics Failures from Deviation to the Roadmap

MINDSET COMMITMENT

INSUFFICIENT PEOPLE AND PROCESSES

TOOLBOX CONFUSION

CONCLUSION

NOTE

CHAPTER 10 Use Case: Grabbing Defeat from the Jaws of Victory

POC RESULTS—REALIZING THE THREE GOALS

Goal 1—Optimize Store Staffing—Balance Customer Service vs. Labor Cost

Goal 2—Increase Forecast Accuracy of Liquor Demand

Goal 3—Finding Insights Not Being Looked For

THE ROI OF AI

FAILURE IS A CHOICE

NOTE

CHAPTER 11 Use Case: Incremental Improvements to Gain Insights

STARTING ANALYTICS

TEST AND LEARN

ASSESSING ANALYTICS PERSONAS

MOVING FORWARD

NOTE

CHAPTER 12 Use Case: Analytics Are for Everyone

THE ROAD TO ANALYTICS

STEPPING INTO ANALYTICS

ANALYTICS IS FOR ALL

Epilogue

NOTES

APPENDIX: Analytics Champion Framework: The Fundamental Qualifications, Skills, and Project Steps for the Analytics Champion. INTRODUCTION

ANALYTICS CHAMPION QUALIFICATIONS

Experience and Education

Project Management Primer

Project Manager Key Characteristics

Project Management Key Principles

Project Manager Key Responsibilities

Project Manager Status Reporting

Project Manager Wrap-up

Analytics Champion Position

Soft Skills

Communication

Collaboration

Business Acumen

Strategic Leadership

Character

Know the Ground Rules

ANALYTICS CHAMPION SKILLSETS

Systematic Thinking™

Data Definition

Skills Supporting Analytics

Storytelling

STARTING AN ANALYTICS PROJECT

Eliminate and Automate

Eliminate

Automate

Analytics Project Framework

Step 1: Problem Definition

Step 2: Data

Step 3: Analytics Insights

Step 4: Insight to Action

Step 5: Solution Adoption

Loop Back and Measure Success

EPILOGUE

NOTES

About the Authors. LAWRENCE S. MAISEL

ROBERT J. ZWERLING

JESPER H. SORENSEN

About the Website

Index

WILEY END USER LICENSE AGREEMENT

Отрывок из книги

Lawrence S. MaiselRobert J. ZwerlingJesper H. Sorensen

To Dana, forever in my heart.

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Today, legacy BI tools have essentially become data-marts for data extraction into spreadsheets for reporting. BI tools are largely maintained by IT and require programming to build cubes (specialized BI databases) to respond to predefined questions. However, legacy BI is too rigid and complex for most users, so IT departments often program user-requested reports and data extractions (for download to other applications).

The complexity of BI gave birth to data visualization tools that were introduced in the 2000s and offered graphic representations of data in many forms, often combined into dashboards to render a story about key aspects of the business. Dashboards can be informative but typically not analytical.

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