Building a Context Layer: Where Data Engineering, AI, and Governance Meet

In a recent webinar, Lorenz Kindling from Scalefree and Samuel from Dawiso made a compelling case for something most modern data platforms are still missing: a dedicated context layer. Their argument is simple but consequential. Data without context isn’t just incomplete, it’s risky.

The Problem With Context-Free Data

When data lacks clear business meaning, everything downstream suffers. Meanings become ambiguous, trust erodes, ownership goes unclaimed, transparency drops, and compliance exposure grows as metadata fragments across systems. Teams end up guessing what a column really represents or which report depends on it, which is a poor foundation for both analytics and AI.

A context layer solves this by acting as an advanced data catalog, one that layers business meanings, glossary definitions, visibility, and accountability onto data products across the entire pipeline.

From Warehouses to Full Data Platforms

The webinar framed this need within the industry’s broader evolution. We’ve moved from isolated data warehouses and data lakes toward integrated hybrid architectures and, now, full data platforms. That shift raises the stakes. Modern platforms need a strict core model, such as Data Vault, paired with a comprehensive context layer to safely fuel AI applications, meet regulatory requirements, and stay genuinely usable for business users.

The Dawiso Demo: Four Pillars

This is exactly where Data Vault comes back into the picture.

The framing of “Lakehouse vs. Data Vault” is, in my view, the wrong comparison. A Lakehouse is a platform and architecture pattern. Data Vault is a modeling and integration methodology. They operate on different layers. The Lakehouse describes where and how data can live. Data Vault describes how to model, integrate, historize, and audit data across multiple sources.

Historization: Time Travel vs. Data Vault

Samuel walked through how Dawiso tackles the four core pillars of a context layer:

1. Adding meaning. AI agents automatically populate asset descriptions and link technical metadata directly to business glossaries and KPIs.
2. Ownership. An intuitive UI lets business users claim ownership and manage the status of data objects without friction.
3. Lineage. An interactive, end-to-end map of the data flow enables fast impact analysis, such as seeing exactly which reports break if a column is renamed.
4. Governance. AI automatically assigns classifications (like GDPR status), while approval workflows keep glossary definitions current.

Feeding Context Into AI via MCP Servers

The most forward-looking part of the session covered how Dawiso pushes its collected context into AI agents using Model Context Protocol (MCP) servers. Two use cases stood out.

The first is talk-to-my-data chatbots. By referencing Dawiso’s explicit definitions, an AI agent working against physical data in Databricks can explain analytics using precise company logic rather than guessing from general internet knowledge.

The second is an impact analysis agent. Before a developer agent rewrites SQL or renames a table, it can check Dawiso first to evaluate downstream consequences, avoiding accidental breakage of the platform.

Getting Started

Attendees were invited to connect with Scalefree or Dawiso on LinkedIn to explore proof-of-concept engagements, since standard developer trial licenses aren’t publicly hosted. Scalefree also noted that its Data Vault 2.0 training and certifications remain available online.

The takeaway: as data platforms mature and AI moves closer to production, context is no longer a nice-to-have. It’s the layer that makes everything else trustworthy.