Getting inconsistent or inaccurate results from Snowflake Cortex Analyst? You’re not alone. While Cortex Analyst’s text-to-SQL engine often boasts over 90% accuracy, real-world use cases frequently expose gaps in precision, context, and consistency. The culprit — a missing semantic layer.
This guide breaks down the core challenges and shows how the AtScale semantic layer platform bridges the gap to deliver accurate, governed insights from natural language queries.
Why Cortex Analyst Struggles with Accuracy
1. The Semantic Gap
Cortex Analyst interprets natural language, but without business context, it can misread key terms. For example, a user might ask, “What’s our forecast accuracy?” Cortex might calculate the answer using percentage differences, while your team defines it using absolute variance. The SQL it generates may be technically correct, but it is wrong business-wise.
2. Inconsistent Business Definitions
When each department has different definitions for metrics like “customer churn” or “gross margin,” you inevitably end up with conflicting answers depending on who’s asking and how. Without a centralized semantic model, AI-driven queries risk reinforcing inconsistencies.
3. Weak Understanding of Complex Data Relationships
Cortex Analyst often stumbles when:
- Handling low-cardinality categorical fields
- Dealing with multi-table joins and complex data models
- Querying semi-structured fields like ARRAY, VARIANT, or OBJECT
These limitations can result in slow performance, invalid joins, or incorrect answers.
AtScale + Cortex Analyst: Accurate Answers in Seconds
AtScale integrates directly with Snowflake Cortex Analyst, enabling AI-driven insights that are accurate, consistent, and aligned with business logic.
Enhanced Query Accuracy
AtScale provides a business-oriented semantic layer that maps natural language terms to precise metric definitions and calculations. Cortex Analyst uses this metadata to:
- Generate accurate SQL
- Reflect how your organization defines each KPI
Cortex and AtScale achieved 100% accuracy in benchmark testing using AtScale’s NLQ Benchmark. Consistency Across Tools
With AtScale as your single source of truth:
- The same metric queried via Cortex will match what’s reported in Tableau, Power BI, or Excel
- No more reconciling conflicting numbers
- Trust in AI-generated insights increases
Faster, Smarter Queries
Cortex Analyst sends simple queries to AtScale’s logical tables. AtScale then:
- Translates them into optimized multi-table SQL
- Handles joins, aggregations, and filters under the hood
- Delivers sub-second performance even on large, complex datasets
Stronger Governance and Guardrails
AI hallucination is a real risk. AtScale mitigates it by:
- Only allowing queries within the defined semantic model
- Returning error prompts when users ask out-of-scope questions
- Ensuring security and access controls carry through to every query
Real-World Impact
Organizations using AtScale with Cortex Analyst report:
- Drastic improvements in accuracy and query reliability
- Reduced time spent verifying AI-generated SQL
- Better trust and adoption of natural language analytics
- Improved query speed and reduced Snowflake compute usage
“Our integration with AtScale improves the quality of the semantic layer… By combining Cortex Analyst with AtScale’s semantic layer, users can get correct answers in seconds.”
— Abhinav Vadrevu, Senior Product Manager, Snowflake
Optimize the Power of Cortex Analyst
Cortex Analyst is a breakthrough in natural language querying — but without proper context and governance, even the best AI gets things wrong. That’s where AtScale comes in.
With AtScale’s semantic layer:
- Business logic is embedded into every query
- Users get consistent, accurate results across every tool
- Governance and performance are built-in
Ready to bring accuracy and trust to your Cortex Analyst implementation? Watch AtScale and Cortex Analyst in action or reach out for more information.
SHARE
Case Study: Vodafone Portugal Modernizes Data Analytics