Core: Data-as-a-Product Evolution
AI Data Products
From raw data to intelligent data products (quality + context + AI readiness).
- Raw Data → Cleaned Data → Curated Datasets → Data Products → Intelligent Data Products
Data Interfaces
How consumers access data: BI to APIs to AI-ready semantic interfaces.
- Tables → Reports → Dashboards → APIs → AI-ready Data Interfaces
Data Utility
Why data exists: from storage/analytics to decisions and automation.
- Data for Storage → Data for Analytics → Data for Decisions → Data for Automation
Data Ownership
Org model shift: centralized IT to domain/data product ownership.
- Data Ownership by IT → Domain-owned Data → Product Teams Own Data
Architecture Evolution (Very Important)
These are the foundation layers that make “data-as-a-product” real at scale (platform, contracts, pipelines, and mesh).
Data Architecture
Storage/compute architecture evolution culminating in semantic + feature layers.
- Monolith DB → Data Warehouse → Data Lake → Lakehouse → Semantic + Feature Layer
Data Pipelines
Movement & processing: batch to streaming and event-driven.
- Batch ETL → Near-real-time → Streaming → Event-driven Pipelines
Schema Strategies
How schemas are governed: write/read to contracts.
- Schema-on-write → Schema-on-read → Contract-driven Schemas
Pipelines & Data Mesh
Delivery model: centralized to federated and mesh.
- Centralized Pipelines → Federated Pipelines → Data Mesh
Consumption Evolution
Data Consumption Patterns
How usage evolves: ad-hoc to embedded to agents.
- Ad-hoc Queries → BI Reports → Self-serve Analytics → Embedded Analytics → Agent Consumption
Humans → Apps → Agents
Interaction shift: humans interpret, apps integrate, agents reason.
- Humans Read Data → Apps Consume Data → Agents Reason Over Data