Organizations can build an Enterprise Context Layer on Snowflake, Databricks, or Microsoft Fabric. Its value depends less on individual platform features than on how well the organization defines Meaning, connects Evidence, represents Relationships, and enforces Controls. The platform enables the capability. The organization owns it.
In the first article in this series, we argued that most organizations don’t have an AI model problem. They have a business context problem. We showed why even sophisticated AI struggles with a seemingly simple question:
Why did customer retention decline among premium customers in the Northeast last quarter, and what should we do about it?
The obstacle isn’t a lack of data. Most organizations already hold transactions, product telemetry, support interactions, survey feedback, renewal records, and account information. The obstacle is turning that fragmented information into context AI can understand, reason over, and use responsibly.
We described that context through four dimensions:
- Meaning: shared business definitions, such as what retention and premium customer actually mean
- Evidence: authoritative information that grounds conclusions in fact
- Relationships: the connections between customers, products, incidents, service experiences, and outcomes
- Controls: ownership, security, lineage, and accountability that make the result trustworthy
Meaning, Evidence, Relationships, and Controls combine into an Enterprise Context Layer that supports trusted decision intelligence.
This article moves from strategy to execution. The question is no longer why AI can’t explain churn. It’s how organizations put business context into practice on the platforms they use, so AI can help explain churn, support a defensible conclusion, and inform better decisions.
Context is a capability, not a feature
One of the most common misconceptions in enterprise AI is that context can be purchased. Organizations often start by asking which platform has the most advanced AI, the strongest agent framework, the most integrated experience, or the fastest path to generative AI.
Those are valid technology questions. They aren’t where successful context initiatives begin.
Context is an enterprise capability created by combining shared business definitions, trusted evidence, connected business relationships, metadata and lineage, governance and accountability, AI-accessible knowledge, and an operating model that spans business and technology. Platforms can help organizations govern, retrieve, organize, and activate context. They can’t decide what customer retention means, which source is authoritative, or who validates business-critical conclusions.
You don’t buy an Enterprise Context Layer. You build one.
In the first article, we traced how transactions, usage data, support records, telemetry, surveys, and account notes each explain part of a retention decline, and how the full explanation emerges only when those signals are connected through shared definitions and weighed against authoritative evidence. The sections below look at how each platform supports that work, one dimension at a time.
Operationalizing Meaning
The first challenge is a shared understanding of business concepts. For customer retention, success means consistent definitions, governed customer classifications, agreed calculation logic, a common business vocabulary, and accountable ownership.
| Snowflake | Databricks | Microsoft Fabric | |
|---|---|---|---|
| Key capabilities | Semantic Views, Cortex Analyst, and Horizon Context (announced 2026; several components in preview) | Unity Catalog metric views, governed metadata, and lineage | Power BI semantic models, the OneLake catalog, and the Fabric IQ ontology (preview) |
| Approach | Semantics built into the governed data platform, with natural-language analysis over structured data | Semantics treated as part of lakehouse governance, with flexibility in how definitions and measures are modeled | Semantics anchored in the BI models business users already work in, extended by an ontology for AI agents |
| Watch for | Horizon Context capabilities are still maturing; confirm what is generally available | Flexibility can produce competing definitions without strong ownership | Definitions spread across many Power BI models need consolidating before AI can rely on them |
What success looks like
- Definitions are governed and ownership is clear.
- AI works from approved business meaning.
- Competing definitions are explicit rather than hidden.
Operationalizing Evidence
Once meaning is established, AI needs reliable evidence: analytical data, customer feedback, product documentation, support interactions, operational events, and institutional knowledge.
| Snowflake | Databricks | Microsoft Fabric | |
|---|---|---|---|
| Key capabilities | Cortex Search for hybrid retrieval | AI Search (formerly Vector Search) for vector, hybrid, and full-text retrieval | OneLake and the OneLake catalog, plus broader Microsoft search and AI services |
| Approach | Retrieval runs alongside structured Snowflake data in the same platform | Search indexes are registered and governed in Unity Catalog with the source data | Governed analytical data lives in OneLake; knowledge outside Fabric may need other services |
| Watch for | Deciding which content is authoritative, how it is indexed, and who can access it | Row- and column-level permissions on source tables don’t carry through to search indexes | OneLake is part of the evidence architecture, not the complete retrieval solution |
What success looks like
- Supporting information is authoritative and current.
- Every result can be traced to its source.
- Access to evidence is permitted and enforced.
- Contradictory evidence is surfaced and evaluated.
Without evidence, AI produces assertions. With governed evidence, AI produces defensible insights.
Operationalizing Relationships
Evidence alone rarely explains business outcomes. Value emerges when information is connected across domains: customers, products, subscriptions, incidents, support interactions, sentiment, and renewals. Those connections don’t appear on their own. They’re created through shared business entities, semantic models, metadata, identity resolution, domain modeling, and lineage.
| Snowflake | Databricks | Microsoft Fabric | |
|---|---|---|---|
| Key capabilities | Relational models, governed data products, Semantic Views, metadata, and lineage | Governed lakehouse models, shared entities, Unity Catalog lineage, and AI frameworks | Power BI semantic models, OneLake, OneLake catalog lineage, and the Fabric IQ ontology |
| Approach | Relationships defined in Semantic Views and governed data products | Operational, analytical, and unstructured data connected across the lakehouse | Entity relationships modeled across semantic models, lakehouses, and eventhouses |
| Watch for | Shared keys, customer hierarchies, and relationship logic still have to be established | Flexibility calls for discipline in identity resolution, data products, and relationship ownership | The Fabric IQ ontology is in preview; validate it before relying on it in production |
What success looks like
The organization can explain how operational events, customer experiences, and commercial activity connect to business outcomes. Relationships turn disconnected evidence into a coherent business narrative.
Operationalizing Controls
Even a sophisticated AI system fails if its outputs can’t be trusted. Every executive eventually asks: How do we know? Controls answer that question through ownership, security, lineage, governance, explainability, and accountability.
| Snowflake | Databricks | Microsoft Fabric | |
|---|---|---|---|
| Key capabilities | Horizon Catalog: role-based access, masking and row access policies, metadata, and lineage | Unity Catalog: governance, access controls, lineage, and auditing across data and AI assets | OneLake catalog: governance insights, domains, endorsement, lineage, and permissions |
| Extending to AI | Horizon Context builds on Horizon Catalog governance | AI Search indexes are governed as Unity Catalog objects | Microsoft Purview adds sensitivity labels, data loss prevention, and compliance controls |
| Watch for | Governance for newer context capabilities depends on preview features | Index permissions don’t include row- and column-level controls from source tables | Controls that extend beyond Fabric depend on how Purview is configured |
What success looks like
- The definitions and sources behind a conclusion are visible.
- Authorization to access evidence is enforced.
- Observed facts are distinguished from inferred conclusions.
- Accountability for validation and approval is clear.
Controls turn AI-generated insight into an accountable business process.
Context is also an operating model
Putting context into practice takes coordinated ownership across business, governance, data, and AI teams:
- Business and domain owners own definitions, measures, policies, exceptions, and how they’re interpreted in practice.
- Data and platform teams own architecture, pipelines, data products, metadata, identity resolution, and platform services.
- Governance teams establish ownership, lineage, quality expectations, access policies, and accountability.
- AI and analytics teams design AI experiences, retrieval workflows, analytical models, evaluations, and human review.
- Executive sponsors tie the capability to measurable business outcomes and resolve cross-functional accountability.
Organizations that treat the Enterprise Context Layer purely as a technical build may end up with better metadata or more sophisticated retrieval without better decisions. Organizations that pair architecture with an accountable operating model are better positioned to create lasting value.
Comparing Enterprise Context Layer approaches
An Enterprise Context Layer isn’t delivered by a single feature or product. It emerges from the combination of semantics, evidence, relationships, governance, and AI experiences. Each platform offers different mechanisms for putting those pieces together.
| Context need | Snowflake | Databricks | Microsoft Fabric |
|---|---|---|---|
| Meaning | Semantic Views, Cortex Analyst, Horizon Context (announced 2026) | Unity Catalog, metric views, governed metadata | Power BI semantic models, Fabric IQ ontology (preview), OneLake catalog |
| Evidence | Cortex Search for hybrid retrieval alongside structured data | AI Search for vector, hybrid, and full-text retrieval | OneLake, Fabric data services, broader Microsoft search and AI services |
| Relationships | Semantic View relationships, governed data products, metadata, lineage | Governed lakehouse models, shared entities, Unity Catalog lineage | Semantic model relationships, Fabric IQ ontology, OneLake catalog lineage |
| Controls | Horizon Catalog governance, role-based access, masking and row access policies, lineage | Unity Catalog governance, access controls, lineage, auditing | OneLake catalog governance and security, Purview sensitivity labels and DLP, lineage |
| General orientation | Integrated platform | Composable, engineering-led | Analytics- and business-user-centric |
The comparison is directional, not absolute. Product names and availability reflect vendor documentation as of October 2026; several capabilities are in preview.
Enterprise architectures may also combine native services with third-party catalogs, semantic layers, identity services, knowledge graphs, search technologies, and AI frameworks.
What the comparison tells us
- Snowflake generally favors an integrated approach that brings governed data, semantics, retrieval, and AI services together within the platform.
- Databricks generally favors a composable, engineering-led approach built around the lakehouse, Unity Catalog, retrieval, and AI frameworks.
- Microsoft Fabric generally favors an analytics- and business-user-oriented approach built around semantic models, OneLake, integrated workloads, and the broader Microsoft ecosystem.
Every implementation still has to answer the same organizational questions. Who owns critical business definitions? Which evidence is authoritative? How are relationships across domains represented? Which controls govern access, lineage, and AI usage? How will uncertainty and human validation be built into decisions?
The platform can accelerate an Enterprise Context Layer, but it doesn’t create one. Advantage comes from the organization’s ability to operationalize Meaning, Evidence, Relationships, and Controls, and make them available to both people and AI.
Why platform selection isn’t the first decision
Many organizations start in the wrong order: choose a platform, identify use cases, then try to build context. A more effective sequence starts with the business problem.
Starting with business outcomes, context requirements, and the operating model leads to a better platform decision.
For customer retention, that means defining the decisions the organization wants to improve before selecting a technology pattern:
- Identify customers at risk of leaving
- Explain the likely contributors to churn
- Prioritize retention interventions
- Support renewal conversations
- Evaluate whether an intervention improved outcomes
The organization can then determine the Meaning, Evidence, Relationships, and Controls those decisions require. Only then should it evaluate which platform and architecture fit best. A weak context strategy will struggle on Snowflake, Databricks, or Microsoft Fabric. A strong one can succeed on any of them.
Technology enables. Context differentiates.
The bottom line
The ability to explain customer churn isn’t a Snowflake problem, a Databricks problem, or a Microsoft Fabric problem. It’s a context problem. Each platform offers a viable path to an Enterprise Context Layer, and none creates business context automatically.
The critical question isn’t which platform has the best AI. It’s how business meaning will be defined, which evidence will be trusted, how relationships will be represented, and who owns governance and accountability. Enterprise AI succeeds when technology is grounded in a shared understanding of how the business operates, and that understanding has to be deliberately built, governed, and sustained.
Next in the series
In the final article, Choosing the Right Enterprise Context Layer Strategy for AI, we move beyond platform capabilities to a practical decision framework for CIOs, CDOs, enterprise architects, and AI leaders. It covers business objectives, governance maturity, operating model readiness, skills, ecosystem fit, and long-term AI strategy. The goal isn’t to name a universal winner. It’s to find the strategy that fits your organization.
Insights By
Building on Snowflake, Databricks, or Microsoft Fabric?
AIM helps organizations design and modernize data platforms that give AI trusted business context.
Frequently asked questions
None is universally best. Snowflake generally favors an integrated platform approach, Databricks a composable, engineering-led approach, and Microsoft Fabric an analytics- and business-user-centric approach within the Microsoft ecosystem. The right fit depends on existing investments, governance maturity, operating model, and AI ambitions. A strong context strategy can succeed on any of them.
No. Platforms provide semantic models, catalogs, retrieval, and governance tools that accelerate the work, but they cannot decide what a metric means, which source is authoritative, or who validates conclusions. The organization has to define and own that context.
Snowflake offers Semantic Views, Cortex Analyst, and Horizon Context within Horizon Catalog. Databricks offers Unity Catalog with metric views and governed metadata. Microsoft Fabric offers Power BI semantic models, the OneLake catalog, and the Fabric IQ ontology, which is in preview.
Ownership is shared. Business and domain owners own definitions and policies; data and platform teams own architecture and data products; governance teams own lineage, access, and accountability mechanisms; AI and analytics teams own AI experiences and evaluation; and executive sponsors tie the capability to business outcomes.
