From Data To Profit

From Data To Profit

by Vin Vashishta
Released July 2023
Publisher(s): Wiley
ISBN: 9781394196210

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Book description

Transform your company’s AI and data frameworks to unlock the true power of disruptive new tech

In From Data to Profit: How Businesses Leverage Data to Grow Their Top and Bottom Lines, accomplished entrepreneur and AI strategist Vineet Vashishta delivers an engaging and insightful new take on making the most of data, artificial intelligence, and technology at your company. You’ll learn to change the culture, strategy, structure, and operational framework of your company to take full advantage of disruptive advances in tech.

The author explores fascinating work being undertaken by firms in the real world, as well as high-value use cases and innovative projects and products made possible by realigning organizational frameworks using the capabilities of new technologies. He explains how to get everyone in your company on the same page, following a single framework, in a way that ensures individual departments get what they want and need.

You’ll learn to outline a comprehensive technical vision and purpose that respects departmental autonomy over their core competencies while guaranteeing that they all get the tools they need to make technology their partner. You’ll also discover why firms that have adopted a holistic strategy toward AI and data have enjoyed results far beyond those experienced by those that have taken a piecemeal approach.

From Data to Profit demonstrates the proper role of the CEO during an intensive transformation: one of maintaining culture during the change. It offers advice for organizational change, including the 3-Phase Data Organizational Development Framework, the Core <-> Rim 3 Main People Groups Framework, and the way to implement new roles for a Chief Digital Officer and Technical Strategist.

Perfect for data professionals, data organizational leaders, and data product and process owners, From Data to Profit will also benefit executives, managers, and other business leaders seeking hands-on advice for digital transformation at their firms.

Table of contents

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

Product information

  • Title: From Data To Profit
  • Author(s): Vin Vashishta
  • Release date: July 2023
  • Publisher(s): Wiley
  • ISBN: 9781394196210