4. Data Modeling & AI Readiness

Data modeling is a continuous program of turning raw, siloed data into a shared, AI-ready asset that humans, BI tools, and LLM agents can all reason over reliably.

Process Discovery & Conceptual Modeling

Start with the business, not the schema:

  • Stakeholder workshops — map existing company processes, vocabulary, and pain points across Sales, Operations, Finance, Product

  • 6 Core Questions — Who/Why/What/When/Where/How to scope the model and surface conflicting definitions before any table is built

  • Conceptual model — entities, relationships, and a business glossary agreed across silos, with AI agents treated as first-class data consumers

Logical & Physical Modeling

Pick the right pattern per workload — no single approach fits everything:

  • 3NF / Normalized — operational and transactional systems where integrity and update consistency matter most

  • Star & Snowflake Schemas — BI dashboards and analytics with predictable joins and fast aggregations

  • Data Vault 2.0 — auditable, history-preserving enterprise warehouse with parallel loads and graceful schema evolution

  • Wide tables / One Big Table — denormalized feature stores for ML training and low-latency serving

Physical implementation on your warehouse or lakehouse — Snowflake, Databricks, BigQuery, Iceberg — with naming conventions, surrogate keys, partitioning, and data quality tests built in.

Semantic Layer & Knowledge Graphs (AI Readiness)

Make the platform meaningful enough for agents and LLMs:

  • Semantic layer — single definitions for metrics and dimensions (dbt Semantic Layer, Cube) consumed by BI, APIs, and AI agents alike

  • Ontologies & controlled vocabularies — canonical entities and reason codes so the same concept means the same thing everywhere

  • Knowledge graphs — Neo4j, RDF, or property graphs that capture relationships, temporality, and lineage that flat tables lose

  • Entity resolution — deterministic, probabilistic, and ML-based matching with golden records and shared canonical IDs

Key Capabilities

Conceptual Discovery

Process mapping, glossaries, and definitions agreed across silos

Mixed Model Choice

Right pattern (3NF, star, Data Vault, graph) per workload, not dogma

AI Enablement

Semantic layers and knowledge graphs that LLMs and agents can trust

Approach (adjustable to your context)

  1. Discover business processes and stakeholders, document the 6 Core Questions

  2. Conceptual model — entities, relationships, glossary, AI use cases

  3. Logical model — choose 3NF, star schema, Data Vault, or a hybrid per domain

  4. Physical model — implement on your warehouse/lakehouse with tests and lineage

  5. Semantic & graph layer — metrics, ontologies, and knowledge graphs for AI