Blog
Field notes on enterprise data
Short essays from our team on governance, integration, and delivery—what we see in real programs and how we think about fixing it. For readers who want clarity on the problem before they choose a path or a partner.
Recent writing
Demand forecasting for brands: why integrated data has to come first
Why a demand forecast is only as good as the history behind it, what integrated data means for brand-led companies (beyond one big server), and what to fix before you trust a model.
The semantic layer: what it is and how to build it
What a semantic layer is in practice, how Iceberg and a catalog fit together, how DataHub and OpenLineage unify lineage, and a simple phased rollout for platform teams.
AI agents, memory, and databases: the shared-knowledge gap and why the semantic layer matters
Why chat memory isn’t organizational memory, how fragmented databases break agent answers, why a semantic layer matters, why it’s hard to build, and how teams fix it in practice.
A document-centric knowledge base: authority, metadata, and when to skip RAG
What a knowledge base is for, when organizations need one, and how versioned docs with structured metadata can ground programs—and agents—without vector retrieval as the core.
The enterprise AI agent problem is data, not the model
The limiting factor is usually data, not the model: why integration and clear definitions matter more than a bigger model.
Analytics agents in the enterprise: what actually works
Why chat-with-data isn’t enough, how to ground agents in trusted metrics, and the guardrails real programs use.
Why we're building this—and what breaks in enterprise data
The problem isn't missing tools; it's fragmented definitions, integrations, and trust. Here's why we're focused on coherence.