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AI-Ready Data vs. Analytics-Ready Data
Identifiers of "ready," data consumption patterns, maturity paths, and possibilities of "upgrading" from Analytics-ready states to AI-ready states.
 • Animesh Kumar
Where Exactly Data Becomes Product: Illustrated Guide to Data Products in Action
Concrete Step-by-Step Journey, Debunking Confusion and Dilution, and Importance of Social / Cultural Structures
 • Animesh Kumar and Travis Thompson
How the Ontology Pipeline Powers Semantic Knowledge Systems
The Need for a Structured Approach, Elements of the Ontology Pipeline, the Pipeline as a Framework for Developing Knowledge Management Systems, and…
 • Jessica Talisman
Data Products: A Case Against Medallion Architecture
The Significance of Medallion, Crux of the Differences between the two 3-Tiered DataFlow Models, and a Colourful Visual Journey!
 • Animesh Kumar, Shubhanshu Jain, and Samadrita Ghosh
Introducing Lakehouse 2.0: What Changes?
Transitioning to the 2nd Generation, an Architectural Overview, Pivots for Decentralisation, Design that brings freedom to both Business & the Stack
 • Animesh Kumar and Travis Thompson
The Semantic Infrastructure Opportunity: Building Meaningful Operational Frameworks
Ontology Pipeline as a strategic framework for semantic engineers to prove their professional value by linking abstract models to functional entity…
 • Jessica Talisman
AI-Ready Data: A Technical Assessment. The Fuel and the Friction.
Why Data Infrastructure Must Evolve for Practical AI, How Data Pipelines are Passively Killing the AI ROI, and the Impact of Distributed Semantic…
 • Sagar Paul
So I Have A Data Product... Now What?
Contents of a Data Product Portfolio, Portfolio Management, Implementation Guidance & Other Considerations After Data Product Adoption
 • Ryan Duffy
🎯 End-to-End Guide to Building Data Products and Modular AI-Systems
Collection of our Top Resources, from Strategy to Data Product Evolution, and more!
 • Modern Data 101
Data Lineage is Strategy: Beyond Observability and Debugging
Gaps in passive lineage, how and why Data Products change and uplift lineage, and notes on stepping up to the AI-native era.
 • Travis Thompson
Modeling Semantics: How Data Models and Ontologies Connect to Build Your Semantic Foundations
Ontology vs. Conceptual Data Modeling, Implementation of Semantic Foundations, and Using Agents in the Right Places for Enrichment
 • Juha Korpela
Evolution of the Data Stack: The story of how we interpret ever-growing data | Issue #6
Traditional Data Stack → Modern Data Stack → Data-First Stack
 • Animesh Kumar
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