Leveraging Data Warehouse Custom Functions in Sigma

Making Advanced Business Logic Directly Usable in Reporting

THE BUSINESS PROBLEM

The Disconnect Between Warehouse based AI Functions and Insights

Organizations today rely heavily on modern cloud data warehouses such as Snowflake, BigQuery and Databricks. These platforms are not just storage engines—they are computational platforms that support sophisticated custom functions built by data engineering teams. These functions often encode mission-critical business logic, including classification rules, scoring models, forecasting formulas, and machine-learning inference.

At the same time, business users rely on Sigma as their primary reporting and analytics tool. Sigma enables fast, spreadsheet-like analysis directly on warehouse data and is widely adopted by analysts, finance teams, operations managers and decision-makers.

Despite this, a critical gap exists.

Business logic lives in the data warehouse, but business decisions are made in Sigma.

What Goes Wrong Today

Even when a Data warehouse analytics fully supports custom functions, Sigma users cannot easily consume those functions in a simple, reusable way. As a result:

1. Business users attempt to recreate logic manually in Sigma formulas
2. Different teams implement the same logic differently
3. Engineering teams are pulled into routine reporting requests
4. Advanced analytics remain underutilized
5. Governance and consistency suffer

Real-World Example: Sentiment Classification

Consider a common scenario illustrated by the Sigma table shown in the screenshot.

The dataset contains a column named Feedback, populated with free-text customer comments such as:

1. “I'm captivated by this band's unique sound.”
2. “The customer service at this hotel was terrible.”
3. “The food at this restaurant was mediocre.”

The data warehouse already contains a custom sentiment classification function that accepts text and returns one of three values:

1. GOOD
2. BAD
3. AVERAGE

The function works correctly and consistently in the warehouse.
The challenge is making this logic usable inside Sigma—directly on the pasted feedback column—without forcing users to write SQL or understand warehouse internals.

THE SOLUTION — THE WRAPPER APPROACH

Introducing the Wrapper Function Pattern

The solution is a Sigma Custom Function Wrapper.

A wrapper function is a Sigma-level custom function that:

1. Calls an existing warehouse custom function
2. Hides all technical details (database, schema, syntax)
3. Exposes a simple, business-friendly interface

Think of it as a translator:

1. The warehouse function does the heavy computation
2. The Sigma wrapper makes it easy to use

How the Wrapper Works Conceptually

With a wrapper, users simply write:

ClassifyFeedback(Feedback)

That single line invokes the warehouse function behind the scenes and returns GOOD, BAD, or AVERAGE directly in Sigma data analytics.

Applying the Wrapper to the Feedback Table

Once the wrapper is in place, the Sigma table behaves intuitively:

Importantly:

1. Sigma is not performing sentiment analysis
2. The warehouse function remains the source of truth
3. Sigma simply displays the result

Why This Approach Works

1. Performance: Computation stays in the warehouse
2. Consistency: Everyone uses the same logic
3. Simplicity: Business users see a familiar function
4. Abstraction: Warehouse complexity is hidden
5. Scalability: The same pattern applies to any custom function

GOVERNANCE, AND BUSINESS VALUE

User Experience After Implementation

Before wrappers:

1. Users submit requests to engineering
2. Sentiment tagging is manual or inconsistent
3. Advanced logic is avoided

After wrappers:

1. Users apply “ClassifyFeedback(Feedback)” 
2. Works in tables, filters, and dashboards
3. No SQL knowledge required
4. Results are immediate and trusted

This dramatically improves adoption of advanced analytics.

Governance and Maintainability Benefits

The wrapper pattern also strengthens governance:

1. Business logic remains centralized in the warehouse
2. Updates are made once and reflected everywhere
3. Sigma admins control visibility and naming
4. No duplicated logic across dashboards
5. Clear separation of responsibilities:

• Engineers build logic
• Sigma exposes logic
• Business consumes logic

Beyond Sentiment Analysis

While sentiment classification is a simple example, the same pattern applies to:

1. Risk scoring models
2. Fraud detection rules
3. Customer lifetime value calculations
4. Forecasting and prediction functions
5. Compliance and validation checks
6. Machine-learning inference functions

If the warehouse accepts a custom function, Sigma can expose it using a wrapper.

Business Impact

By bridging the gap between warehouse logic and reporting tools, organizations achieve:

1. Faster decision-making
2. Reduced engineering dependency
3. Higher trust in metrics
4. Better use of advanced analytics
5. Scalable, governed reporting architecture

CONCLUSION

The real challenge was never whether the data warehouse could support custom functions—it already does.

The challenge was making those functions usable inside Sigma, where business decisions are made.

The Sigma wrapper pattern solves this by exposing warehouse-based custom functions through simple, business-friendly interfaces. In the sentiment classification example, users apply a single function to a feedback column and instantly receive GOOD, BAD, or AVERAGE—powered entirely by warehouse logic.

By keeping computation in the warehouse and accessibility in Sigma, this approach delivers the best of both worlds: powerful analytics without complexity.

Contact us at: [email protected]   
For consultations or custom inquiries: https://dataplatr.com/contact-us   
Follow us in Linkedin: https://www.linkedin.com/company/dataplatrinc