How Unibail-Rodamco-Westfield Democratizes Access to Its Data with Generative AI

CHALLENGE

All of URW's data is hosted in Snowflake. It is available but practically inaccessible to most employees. Getting a simple answer ("Which are our busiest centers this quarter?") requires either mastering SQL or requesting assistance from the Data teams, creating a bottleneck that slows down decision-making at all levels.

Prior to the project, several key challenges were identified:

• Enable business teams to query data without technical skills
• Reduce reliance on Data teams for routine queries
• Accelerate decision cycles by eliminating intermediary delays
• Ensure Snowflake is adopted and usable by non-technical users

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SOLUTION

Qolaig designed and deployed a Natural Language to SQL POC: a conversational interface that transforms a question asked in natural language into an SQL query executed directly on Snowflake, then returns the answer in a clear and understandable way without the user needing to know the database structure.

01. From Question to Data, Without SQL

The user asks their question as they would phrase it to a colleague:

• "How many stores do we have in the United States?"
• "What is the average occupancy rate per shopping center?"
• "Which are the busiest centers this quarter?"

The system understands the intent, identifies relevant tables, generates the appropriate SQL query, executes it on Snowflake, then interprets the results via a second AI layer before returning them in natural language.

02. A Business Glossary at the Core of Reliability

The project's main challenge wasn't technical; it was semantic. Every company has its own vocabulary: in-house indicators, internal synonyms, specific calculation rules, and undocumented table relationships.

Qolaig built a custom business glossary for URW, enabling the AI to understand the exact definitions of indicators, the synonyms used by field teams, and the group's specific calculation rules. This layer is what ensures the relevance and reliability of the generated queries.

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RESULTS

• Natural Language Data Access for All Business Teams
• Reduced Workload for Data Teams on Routine Queries
• Easier Adoption of Snowflake by Non-Technical Users
• Accelerated Decision-Making by Eliminating Intermediation Delays

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RETURN ON INVESTMENT

Every question that reached the Data teams consumed skilled time on low-value-added tasks. By delegating these routine queries to an AI agent, URW frees up its data engineers and analysts for higher-impact work: modeling, strategic projects, governance, all while accelerating the decision cycles of business teams.