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

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. 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