Information Governance – EduDataSci – Educating the world about data and leadership

When Facts Don’t Live in a Domain: Why Data Products Beat Pure Domain-Driven Data Engineering

There’s a pattern I see in mature analytics organizations: as soon as the data platform gets big enough to feel “enterprise,” someone reaches for domain-driven design (DDD) as the organizing principle for data engineering and governance.

It’s an understandable move. DDD gives us language for ownership, boundaries, and “this team is responsible for that thing.” And when you’re trying to untangle a spaghetti warehouse, that sounds like oxygen.

But here’s the catch: the most valuable data in a warehouse—the fact tables in traditional star schemas—often doesn’t live neatly inside a single domain. It lives at the intersections.

In this post, I’m going to make three points:

why domain boundaries tend to break down precisely where the business value shows up (facts)
how a data product model handles “intersection data” without inventing reference domains and mega-joins
why Microsoft Fabric’s workspace + lakehouse + OneLake tooling makes the product approach easier to execute in practice (especially with shortcuts, security, and materialized lake views)

And yes—domains still matter. Just not as the unit of delivery.

Analytics Governance: the Missing Middle of the Information Governance Stack

Most organizations have matured data governance (quality, ownership, catalogs) and are racing to formalize AI governance (risk, bias, safety, model monitoring). Application governance (SDLC, access, change control) keeps production systems stable.

But the layer where business decisions actually touch numbers—analytics—often sits in a gray zone. KPI definitions live in wikis, dashboards implement subtle variations of the “same” metric, and spreadsheets quietly fork the math. Analytics governance fills that gap: it is the set of controls, roles, artifacts, and workflows that make calculations consistent, auditable, and reusable across the enterprise.

Information Governance: The Backbone That Unifies Data, AI, Applications, and Analytics

Information governance (IG) is the strategy, accountability, and control system for how an organization collects, classifies, uses, protects, shares, retains, and disposes of information across its entire lifecycle. It is:

Scope‑wide: Covers structured data, unstructured content, model artifacts, code, dashboards, and records (including legal/records management and privacy).
Lifecycle‑aware: From intake and creation → active use → archival → retention/disposition and legal holds.
Outcome‑driven: Balances value (insights, automation, personalization) with risk (security, privacy, ethics, legal/regulatory).

Where data governance focuses on data as an asset, information governance focuses on information as a liability and an asset—linking value creation with lawful, ethical, and secure handling.