Unifying Business, Data, and Code

Table of contents

1. 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. 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
2. 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
         1. Minification
         2. Alternative Representations
   3. Creating a JSON Document
      1. A Product Entry
      2. A Store Order
   4. Summary
3. 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 Teams Up for Success
   4. Incorporating a Product Management Mindset
      1. Defining Data Users’ Needs
      2. Defining Product Features
      3. Defining and Measuring Success
   5. Unifying vs. Aligning
   6. Summary
4. 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 Unify
   4. Mapping the Conceptual Terrain: Assessing Concepts
   5. Facilitating Assessments of Conceptual Alignment across Technical and Non-Technical Teams
   6. Smooth Is Slow, Slow Is Fast
   7. Summary
5. 5. A Universal Language for Data
   1. What Is JSON Schema?
      1. What Is a Schema?
   2. The Building Blocks of JSON Schema
      1. Dialects and Vocabularies
      2. Metachemas: Schemas that Describe Other Schemas
   3. Understanding a JSON Schema Definition
      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 Circular Data Structure
      1. Referencing Schemas
   5. Your First JSON Schema Project
      1. Writing a Schema: Step by Step
      2. Generating a Web Form
   6. Summary
6. 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
7. 7. The Science of Synchronization
   1. An Introduction to Thinking in Networks
      1. Example of Thinking in Networks: Artists vs. Athletes
      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. Short-Sightedness
      4. Indecisiveness
   7. The Power of Design in Collaborative Networks
   8. Summary
8. 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
9. 9. Illuminating Pathways of Acceleration
   1. How Ambiguity and Knowledge Gaps Influence Decisions and Progress Toward Goals
   2. Which Is Bigger: Greenland or the U.S.?
   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