Unifying Business, Data, and Code

Unifying Business, Data, and Code

by Ron Itelman, Juan Cruz Viotti
Released January 2024
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781098145002

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Book description

In the modern symphony of business, each section-from the technical to the managerial-must play in harmony. Authors Ron Itelman and Juan Cruz Viotti introduce a bold methodology to synchronize your business and technical teams, transforming them into a single, high-performing unit.

Misalignment between business and technical teams halts innovation. You'll learn how to transcend the root causes of project failure-the ambiguity, knowledge gaps, and blind spots that lead to wasted efforts.

The unifying methodology in this book will teach you these alignment tools and more:

  • The four facets of data products: A simple blueprint that encapsulates data and business logic helps eliminate the most common causes of wasted time and misunderstanding
  • The concept compass: An easy way to identify the biggest sources of misalignment
  • Success spectrums: Define the required knowledge and road map your team needs to achieve success
  • JSON Schema: Leverage JSON and JSON Schema to technically implement the strategy at scale, including extending JSON Schema with custom keywords, understanding JSON Schema annotations, and hosting your own schema registry
  • Data hygiene: Learn how to design high-quality datasets aligned with creating real business value, and protect your organization from the most common sources of pain

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Table of contents

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

Product information

  • Title: Unifying Business, Data, and Code
  • Author(s): Ron Itelman, Juan Cruz Viotti
  • Release date: January 2024
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098145002