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Extend your Simon Data deployment with enhanced metadata, semantics, and contextual intelligence to make your customer data more powerful, discoverable, and actionable.

What is Data Model Enrichment?

Data model enrichment is the process of layering meaning and context onto your raw data. In Simon, this means transforming datasets into business-aware, AI-ready assets that make it easier for both people and systems to understand your customer data.

By enriching your model, you enable:

• Human understanding: Business users can explore datasets with clear context and definitions.
• Machine intelligence: AI Agents and automations leverage structured metadata to deliver smarter insights.
• Governed access: Teams can discover and use data safely without losing visibility or consistency.

Why It Matters

Your Snowflake data warehouse already contains the facts—your customers, purchases, and events. Enrichment makes that data usable by assigning meaning, relationships, and relevance.

With Simon’s enrichment features, you can:

• Bridge the gap between data engineering and marketing operations
• Automate data discovery and reduce manual mapping
• Enable self-service analytics and segmentation
• Feed AI Agents with business context to generate accurate, explainable results

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Result: faster time to insight, consistent metrics, and smarter personalization across every touchpoint.

Key Components

Semantic Layer

The foundation of Simon’s enrichment framework. The Semantic Layer integrates with your Snowflake environment to add metadata, relationships, and marketing-specific context to your datasets. It powers both Simon’s Data Hub and Composable AI Agents with structured, explainable intelligence.

Schema Builder

Visualize and manage your Snowflake tables within Simon. Configure identity, property, and event relationships to make data available for segmentation, flows, and AI use cases.

Datasets

Use Simon’s self-service SQL editor to build, transform, and publish business-ready views of your data directly from Snowflake.

Identity Model

Define and maintain a single source of truth for your customer profiles. Combine multiple identifiers (email, customer ID, phone) into one unified identity graph.

Benefits

• Unified Data Understanding: Consistent definitions across teams and tools.
• Faster Time to Activation: Build segments and campaigns on enriched, ready-to-use data.
• AI Empowerment: Provide context for predictive models and AI Agents.
• Cross-Team Collaboration: Share semantic views directly through Snowflake.