From Data to Profit: How Businesses Leverage Data to Grow Their Top and Bottom Lines

Cover
Title Page
Copyright Page
Contents
Introduction
A Novel Asset Class with a Greenfield of Opportunities
The Road from Laggard to Industry Leadership
Technical Strategy as a New Top-Level Construct
Playbook for the Enterprise
Systems, Models, and Frameworks
Introducing Data to the Enterprise
Chapter 1 Overview of the Frameworks
Continuous Transformation
Three Sources of Business Debt
Evolutionary Decision Culture
The Disruptor’s Mindset
The Innovation Mix
Meet the Business Where It Is
The Technology Model
The Core-Rim Model
Transparency and Opacity
The Maturity Models
The Four Platforms
Top-Down and Bottom-Up Opportunity Discovery
Large Model Monetization
The Business Assessment Framework
The Data and AI Strategy Document
Data Organizational Development Framework
More to Come
Chapter 2 There Is No Finish Line
Where Do We Begin? With Reality
Defining a Transformation Vision and Strategy
Paying Off the Business’s Digital Debt
Managing the Value Creation vs. the Technology
A Master Class in Continuous Transformation Strategy
Evaluating Trade-Offs
What Happens When the Business Loses Faith in Data and AI?
What’s Next?
Chapter 3 Why Is Transformation So Hard?
Cautionary Tales
Data-Driven Transparency
The Nature of Technology and FUD
The Business Has Been Lied to Before
Is It Sci-Fi or Reality?
The Coming Storms
Time Travel
Time Travel in the Real World
Data-Driven, Adaptive Strategy
What’s Next?
Chapter 4 Final vs. Evolutionary Decision Culture
Implementing Change and Taking Back Control
Paying Off Cultural and Strategic Debt
Playing Better Poker Means Folding Bad Hands
Fixing the Culture to Reward Data-Driven Decision-Making Behaviors
A Changing Incentivization Structure
What’s Next?
Chapter 5 The Disruptor’s Mindset
The Innovation Mix
Exploration vs. Exploitation
What Happens with Too Much or Too Little Innovation?
Innovate Before It’s Too Late
EVs and Innovation Cycles
Putting the Structure in Place for Innovation
Building the Culture for Innovation
An Innovator’s Way of Thinking
Managing Constant Change and Disruption
Preventing Data-Driven and Innovation from Spiraling Out of Control
What’s Next?
Chapter 6 A Data-Driven Definition of Strategy
How Quickly the Innovators Became Laggards
Using Strategy to Balance the Scales
Redefining Strategy
Resistance and Autonomy
The Cost of Resisting Change
What’s Next?
Chapter 7 The Monolith—Technical Strategy
The Business Model
A Few Examples of Business Models
The Need for Technical Strategists
The Operating Model
Scale to Infinity and Super Platforms
The Implications of an Automated Operating Model
The Technology Model
The Best Tool for the Job
Making the Connection to Value from the Start
What’s Next?
Chapter 8 Who Survives Disruption?
Using Frameworks to Maintain Autonomy
Reducing Complexity While Maintaining Autonomy
Technology Cannot Solve All Our Problems
Making Decisions with Core-Rim and the Technology Model
Defining the Value Proposition
How Technology First-Businesses Scale
Can We Be Confident That Business Units Won’t Be Completely Erased?
What’s Next?
Chapter 9 Data—The Business’s Hidden Giant
Does the Business Really Understand Itself?
Moving from Opaque to Transparent
Getting Deeper into Workflows and Experiments
Data Gathering and Business Transparency
Understanding the Workflow
Improving Workflows with Data
Designing a Better Framework
What’s Next?
Chapter 10 The AI Maturity Model
Capabilities Maturity Model
Data Gathering, Serving, and Experimentation
Starting with Experts
A Race Against Complexity and Rising Costs
The Product Maturity Model
The Data Generation Maturity Model
What’s Next?
Chapter 11 The Human-Machine Maturity Model
What Happens When Technology Adapts to Us?
The Human Machine Maturity Model
Hidden Changes as Models Take Over
Human-Machine Collaboration Is a New Paradigm
Holding Machines and Models to a Higher Standard
Understanding Reliability Requirements
What’s Next?
Chapter 12 A Vision for AI Opportunities
The Zero-Sum Game: Winners and Losers
Near- and Mid-Term Opportunities
Best-in-Breed Solutions
Preparing Products for Transformation
Opportunity Discovery Gets the Business Off the Sidelines
Top-Down Opportunity Discovery
Monetization Assessment
Just Because It Can Be Built. . .
What’s Next?
Chapter 13 Discovering AI Treasure
Bottom-Up Opportunity Discovery
Giving Frontline Teams a Framework to Leverage Data and AI
The AI Product Governance Framework
What Happens if No One Brings Opportunities Forward?
It May Be Bottom-Up, But It Still Starts at the Top
What’s Next?
Chapter 14 Large Model Monetization Strategies—Quick Wins
AI Operating System Models
AI App Store
Quick-Win Opportunities
The Digital Monetization Paradigm
Understanding the Risks
What’s Next?
Chapter 15 Large Model Monetization Strategies—The Bigger Picture
What Are the Costs?
How the Models Work
Flaws Are Opportunities
Disrupting College
Advanced Content Curation
How Microsoft Successfully Monetized Their $10 Billion Investment
Large Models Enabling Leapfrogging
Workflow Mapping Becomes Even More Critical
What’s Next?
Chapter 16 Assessing the Business’s AI Maturity
Starting the Assessment
Culture
Leadership Commitment
Operations and Structure
Skills and Competencies
Analytics-Strategy Alignment
Proactive Market Orientation
Employee Empowerment
The Data Monetization Catalog
What’s Next?
Chapter 17 Building the Data and AI Strategy
Defining the Data and AI Strategy
The Executive Summary
The Introduction
Strategy Implementation
Introducing the Data Organization
Next Steps
Needs, Budget, and Risks
What’s Next?
Chapter 18 Building the Center of Excellence
The Need for an Executive or C-level Data Leader
Navigating Early Maturity Phases
The Data Organizational Arc
Benefits of the Center of Excellence Model
Connecting Hiring to the Infrastructure and Product Roadmaps
Getting Access to Talent
Common Roles for Each Maturity Phase
What’s Next?
Chapter 19 Data and AI Product Strategy
The Need for a Single Vision
Defining Data and AI Products
The Business’s Four Main Platforms
Leveraging Data and AI Strategy Frameworks
Workflow Mapping and Tracking
Assessing Product and Initiative Feasibility
Pricing Strategies for Data and AI Products
Problem, Data, and Solution Space Mapping
Managing the Research Process
The AI Evangelist: Community Building for Platform Success
What’s Next?
Index
EULA