Unifying Business, Data, and Code Ron Itelman, Juan Cruz Viotti

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• Preface
  • What You Cant See Can Kill You, and the Same Is True for Data
    • Hidden Threats to Organizations: A Modern Parallel
    • Your AI Is Only as Good as Your Data
    • Aligning Problem-Solving Strategies, Data, and AI
  • A New Paradigm to Optimize Data Management and Business Strategy for the Age of AI
  • The Origin Story of Unifying
  • Orchestrating Alignment at Organizational Scale
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. The Need for a Unifying Data Strategy
  • Your Quest for Data-Driven Breakthroughs Begins
    • There Are Usually Multiple, Conflicting North Stars
    • The Good, the Bad, and the Ugly of Data Problems
    • The Problem with Problems
    • Unifying Concepts: The Key to Innovation
  • What a Unifying Data Strategy Will Do for Agile
    • Defining Being Agile
    • Agile Theater
    • Agile, Waterfall, and Unifying
    • Defining a Unifying Data Strategy Approach
  • Understanding the Phrase Being Data Driven
    • To Be Data Driven, Be Data Centric
    • Bottlenecks Preventing Teams from Being Data Driven
  • This Books Project: Intelligence.AI Coffee Beans
  • Summary
• 2. The Lingua Franca of Data: JSON
  • Introducing JSON
    • A Simple JSON Example
    • JSON Viewing and Authoring Tools
      • JSON Hero
      • OK JSON
      • Visual Studio Code
  • Overview of JSON Grammar
    • Booleans
    • Numbers
    • Strings
    • Arrays
    • Objects
    • Null
    • Learning More
    • Minification
    • Alternative Representations
      • Textual alternatives
      • Binary alternatives
      • JSON versus the JSON data model
  • Creating a JSON Document
    • A Product Entry
    • A Store Order
  • Summary
• 3. Data-Centric Innovation: A Guide for Data Champions
  • Data Transformations Require Data Champions
  • The Rise of the Data Product Manager
  • Alignment Is a Journey, Not a Destination
    • Evaluating Alignment from a Holistic Perspective
    • The Goal Isnt Alignment, Its Effective Alignment
    • Strategies for Setting Up Teams for Success
  • Incorporating a Product Management Mindset
    • Defining Data Users Needs
    • Defining Product Features
    • Defining and Measuring Success
  • Unifying Versus Aligning
  • Summary
• 4. Concept-First Design for Data Products
  • Packaging and Products: An Example Using Coffee
  • The Four Facets of a Data Product
  • Getting Started with Concept-First Design
  • A Blueprint for Unifying
  • Mapping the Conceptual Terrain: Assessing Concepts
  • Facilitating Assessments of Conceptual Alignment Across Technical and Nontechnical Teams
  • Smooth Is Slow, Slow Is Fast
  • Summary
• 5. A Universal Language for Data
  • What Is JSON Schema?
    • What Is a Schema?
  • The Building Blocks of JSON Schema
    • Vocabularies and Dialects
    • Meta-Schemas: Schemas That Describe Other Schemas
  • Understanding JSON Schemas
    • Step 1: Determining the Schema Dialect: The $schema Keyword
    • Step 2: Determining the Schema Vocabularies
      • Inspecting meta-schemas
      • The $vocabulary keyword
    • Step 3: Understanding Schema Vocabularies
      • Using the reference documentation
      • Keyword namespacing (or lack thereof)
    • Step 4: Understanding Schema Keywords
      • A first pass on top-level keywords
      • The validation vocabulary
      • The applicator vocabulary
  • JSON Schema as a Recursive Data Structure
  • Referencing Schemas
    • What does duplication look like?
    • Local referencing
    • Remote referencing
  • Your First JSON Schema Project
    • Writing a Schema: Step by Step
    • Generating a Web Form
  • Summary
• 6. The Art of Alignment
  • Enemies of Alignment: Ambiguity and Assumptions
    • Ambiguity: The Culprit in the Illusion of Communication
    • Assumptions: Ambiguitys Best Friend
  • Defining Success: Symmetry Between Concepts and JSON Schema Equals Minimal Ambiguity
  • Illuminating Misalignment with a Concept Compass
    • Step 1: Harmonizing the What
    • Step 2: Harmonizing the Way
    • Step 3: Harmonizing the How
    • Harmonized Concepts
  • Validating Concepts: Belief Scoring and Hypotheticals
    • Counterfactuals
    • Belief Scoring
  • Summary
• 7. The Science of Synchronization
  • An Introduction to Thinking in Networks
    • Example of Thinking in Networks: Athletes Versus Artists
    • Graphs: The Visual Language of Networks
  • Networks of Entities: Knowledge Graphs
    • A Simple Knowledge Graph
    • Challenges with Knowledge Graphs
    • Aligning Knowledge for the 99%
  • Fundamentals of CLEAN Data Governance
    • Collaboration
    • Knowledge
    • Business Logic
    • Activity
  • CLEAN Data Governance in Practice
  • The Four Facets of Data Products and CLEAN
  • The Four Horsemen of Data Death
    • Ignorance
    • Siloed Incentives
    • Shortsightedness
    • Indecisiveness
  • The Power of Design in Collaborative Networks
  • Summary
• 8. The Two Fundamental Operations of Schemas
  • Validating the Structure of Data
    • Using an Online Validator
    • Validation Example
    • JSON Schema as a Constraints Language
    • Boolean Schemas
    • Heterogeneous Data Structures
    • The format Keyword
  • Using Annotations to Define Meaning
    • Annotation Extraction Example
    • A Simple Use Case: Deprecations
    • Runtime Extraction
    • Standard Output Formats
    • Revisiting the format Keyword
    • Using an Online Validator
  • Thinking in Schemas
  • Summary
• 9. Illuminating Pathways of Acceleration
  • How Ambiguity, Knowledge Gaps, and Blind Spots Influence Decisions and Progress Toward Goals
  • Which Is Bigger: Greenland or the US?
  • Mapping Pathways of Processes and Progress
    • Measuring Progress Toward Goals
    • Defining Decisions and Steps with Process Maps
    • How Process Maps Reveal Ambiguity
  • Visualizing and Removing Ambiguity in Processes
    • Enriching Process Maps with Annotations
    • Process Maps Reveal Innovation Opportunities
  • Summary
• 10. Spectrums of Success
  • An Introduction to Knowledge Frameworks
    • Knowledge Experiences and Pathways
    • A Tool for Designing Knowledge Experiences
    • From Structured Knowledge to Computational Knowledge
  • Success Spectrums
    • Mapping Progress and Value
    • Visualizing and Adding Next Best States
    • Removing Blind Spots
    • Embracing Multiperspective Design and Road Maps
    • Defining KPIs for Success Measures and Metrics (Assessments)
    • Using Demons and Magical Thinking for Innovation
    • Faster Horses
    • Imagining Magical Possibilities
    • Problem Landscapes: Quantifying Pain Points Threatening Value
  • Nudges: The Right Information at the Right Time
  • A Real-Life Problem Landscape and Demon Example That Led to a Unified Data Product Model
    • Understanding the Problem Landscape
    • The Staggering Impact
    • A Meeting of Minds and the Birth of a Solution
  • Beyond Data Products: Data Product Management
  • The Circular Nature of Unifying
  • Summary
• 11. Deploying a JSON Schema Registry
  • Schemas Over HTTP
  • Step 1: Setting Up a GitHub Repository
    • Creating a GitHub Repository
    • Uploading Your First Schema
  • Step 2: Deploying to Cloudflare Pages
    • Creating a New Cloudflare Pages Website Project
  • Step 3: Configuring HTTP Headers
    • Inspecting the Current HTTP Headers
    • Declaring Custom HTTP Headers on Cloudflare Pages
    • Checking the Results
  • Step 4: Creating a Landing Page
    • Adding an HTML Entry Point
  • Step 5: Adding a Custom Domain
    • Configuring a Custom Domain in Cloudflare Pages
    • Setting Up a CNAME DNS Record
    • Checking the Results
  • Best Practices
    • Schemas Are Immutable
    • Adopt a Versioning Strategy
  • Summary
• 12. Designing Data Products Using JSON Schema
  • First Facet: Data
    • An Example CSV Dataset
    • A JSON Row Representation
  • Second Facet: Structure
    • General-Purpose Concepts
      • Timestamp
      • IP address
      • Email
      • US state
      • Cost and currency
    • Application-Specific Concepts
    • Dataset Entries
    • The Dataset Schema
  • Third Facet: Meaning
    • Timestamp
    • IP Address
    • Email
    • US State
    • Currency
    • Price
    • Milestone
    • Analytics Entry
  • Fourth Facet: Context
    • The Signup Analytics Schema
  • Summary
    • Automated Schema Extraction
    • Next Steps
• 13. Extending JSON Schema
  • Simple Case: Unknown Keywords
    • Extracting Unknown Keywords as Annotations
    • Pros and Cons of This Approach
  • Complex Case: Authoring Vocabularies
    • The JSON Schema Vocabulary System
    • Step 1: Writing a Specification
      • Vocabulary identifiers
      • The context vocabulary specification
    • Step 2: Writing a Vocabulary Meta-Schema
      • Official vocabularies meta-schemas
      • SPDX licenses
      • The context vocabulary meta-schema
        • Setting schema identifiers
        • Configuring schema extension
    • Step 3: Extending an Implementation
      • Diversity of JSON Schema implementations
      • Extending Hyperjump
  • Consuming Vocabularies
    • Defining a Dialect
    • Making Use of the Dialect
    • Example: Extracting Annotations with Hyperjump
      • Adding the dialect
      • Getting annotations
  • Summary
• 14. Introducing JSON Unify
  • Introducing the Dataset Vocabulary
    • Revisiting the Signup Analytics Example
  • JSON Schema Bundling
    • The Bundling Process
    • Bundling Our Example Data Product
  • Referencing Remote Data
    • The Problem of Streaming JSON
    • Introducing JSON Lines
  • Extracting Meaning
    • A Simple Example
    • Using Logic Operators
    • The Signup Analytics Example
  • Dataset Lineage
    • Filtering
    • Transforming
    • Aggregation
  • Summary
• 15. Principles of Designing Intelligence
  • Your Unifying Journey So Far
  • A Constellation of Deeper Principles Guides Unifying
  • 1. The Principle of Alignment
    • Transforming the Abstract to Concrete
    • What You See Can Kill You, and the Same Is True in Data
  • 2. The Principle of Information
    • Understanding Uncertainty
  • 3. The Principle of Learning
    • Defining Learning
    • Defining Errors
  • 4. The Principle of Integrated Simplicity
    • Complexity Reduction
    • Decomposition
    • Compression
    • Memoization
    • Integrating in Communication Networks
  • 5. The Principle of Continuums
    • Making Things Measurable
    • The Dangers of Misusing Measurements
    • A Continuum Example for a Control Strategy Problem
  • 6. The Principle of State Transitions
    • A Simple State Machine
    • Simplifying State Transitions
  • 7. The Principle of Decidability
    • What Is Decidability?
    • Two Key Approaches to Problem Solving
    • Making Informed Decisions
    • Real-World Decidability to Reduce Misalignment in Teams
  • 8. The Principle of Heuristics
    • Awareness and Ethical Considerations
    • Connection to Decision Making in Business
  • 9. The Principle of Mastery
    • Levels of Mastery in Knowledge
    • Strategies for Mastery
  • 10. The Principle of Wisdom
  • Summary
• 16. Toward Unified Intelligence
  • Functional Artificial Intelligence
    • Your AI Is Only as Good as Your Data
    • Beware Illusions Within Vetting Processes
    • Question Assumptions
  • Collective Intelligence
  • Collaborative Intelligence
  • Unified Intelligence
    • Collaborative Learning Networks
    • Personalized Knowledge
    • Anticipatory Design: Personalization and Digital Twins
  • Codifying Principles of Intelligence
    • Continuous HumanMachine Learning Loops
    • Applying Wisdom in Practice
    • Conceptual Zoomability
  • Wisdom Graphs: Connecting Concepts, Actions, and Outcomes
    • Cognitive Primitives: Standardizing Cognitive Experience Design
  • The Value of Unifying
    • Prioritize Knowledge Before AI
    • A Tale of Simple Knowledge Versus Complex Intelligence
    • Follow the Principle of Integrated Simplicity
  • Summary
  • Going Beyond This Book
• Index

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