Define what enterprise data means.
Build the Snowflake foundation around how it will be used.
Business documents, metric definitions, reports, SQL, and representative user questions inform the design. AI supports analysis and drafting. Business and data experts approve what is implemented in the Enterprise Data Model and Semantic Views, and the results are tested against known outputs.
Data in Snowflake still needs shared definitions, controls, and evidence
When systems use different structures and codes, or when calculation logic is spread across reports, SQL, and documents, the same question can produce different results. AI inherits the same ambiguity. The foundation must organize both the data and the business criteria used to interpret it.
Duplicated data structures
The same customer, product, or transaction may be represented differently across source systems and data marts.
Scattered metric logic
Revenue, cost, inventory, and other metrics may rely on formulas distributed across reports, SQL, programs, and operating documents.
Unclear lineage and ownership
Users may not be able to identify a value’s source, transformation history, quality owner, or the impact of a change.
Inconsistent access criteria
Data exposure and sensitive-data handling may not be defined consistently for each role and business purpose.
Move from existing business evidence to reviewed models and tested results
Examine how the data is used
Review documents, metric definitions, reports, SQL, and user questions to identify the terms and calculation rules already used by the business.
Prepare an AI-assisted draft
Use AI to prepare an initial draft of business entities, relationships, metrics, formulas, synonyms, and items that still require clarification.
Confirm definitions with experts
Business and data experts review the draft and approve the definitions, exceptions, and criteria that will be adopted.
Implement and validate the model
Incorporate approved criteria into the Enterprise Data Model and Semantic Views, then test the results against representative questions and known outputs.
AI supports analysis and drafting. Designated business and data experts decide what is approved, implemented, and accepted.
Define the model, business meaning, and controls as one foundation
Data Rationalization
Assess duplicated data marts, tables, codes, and formulas. Define the data and history required for the intended use and identify complexity that can be removed.
Enterprise Data Model & Standards
Separate domains, master data, transactions, and history, then define grain, keys, codes, naming, and common dimensions.
Semantic Layer & Semantic Views
Define business entities, relationships, dimensions, facts, metrics, calculation rules, descriptions, and synonyms. Semantic Views add business meaning to a validated physical and logical model; they do not replace it.
Data Quality & Lineage
Establish quality rules, ownership, source and transformation lineage, and change-impact criteria, then apply them in the Snowflake environment.
Security & Access Control
Define access by role and business purpose, together with criteria for sensitive-data protection and permission changes.
Source-data preparation → Enterprise Data Model → Semantic Layer & Semantic Views → quality, lineage, and access controls → analytics and AI use
Use access patterns suited to the type of data and question
Structured data analysis
Semantic Views and Cortex Analyst can support natural-language questions translated into SQL aligned with reviewed business definitions. Representative questions and verified SQL provide the basis for evaluation and regression checks after changes.
Document and unstructured-data retrieval
Prepare document metadata, access rules, and retrieval units, then apply Cortex Search as a retrieval layer for Enterprise Search and RAG use cases.
Validate reconciled data and analytical results—not only the load
Source reconciliation
Compare counts, amounts, key fields, and historical data with the source systems.
Metric and calculation review
Check metric definitions and aggregation logic against the reports and calculation results used by the business.
Evaluation with representative questions
Use frequently asked business questions and verified SQL as criteria for evaluating analytical results.
Access and change-impact checks
Verify role-based data exposure and the effect of model or metric changes on existing analysis.
Set the foundation around the work it must support
Our experience in BI and data warehousing, Data Governance, SAP ERP, and enterprise-system implementation helps us examine the data, reports, and business criteria already in use.
The design is guided by the required data scope, history, calculation rules, and operating constraints—not by the number of tables or platform features.
Start with the systems, definitions, and questions already in use
Tell us about your source systems, existing data warehouses or data marts, priority analytical questions, known metric definitions, and access constraints. We will help identify the first data scope and delivery approach to examine.
Discuss an AI-Ready Data Foundation →