Kyvos: Global Retail Chain Analyzes 3 Years of Data on Azure

About the Customer

A leading global retail chain

A global retail leader, operating over 40,000 stores across 80+ countries and serving millions of customers daily. With revenues surpassing $36 billion, the organization continues to redefine the retail experience worldwide.

Challenges

Performance bottlenecks demanded more manual work and restricted visibility

The analytics environment on Azure and Databricks had structural and operational constraints that limited scalability and prevented granular insights.

Scalability issues: The system struggled to scale effectively for extended time-period analyses as data volumes grew. OBIEE users also faced scalability challenges as they had to repeatedly build aggregates.

Coding overhead: There was heavy reliance on engineers who had to write complex Spark SQL to prepare aggregate tables. The process took weeks and was further complicated by performance issues on massive datasets.

BI query slowdowns: Tableau slowed significantly as data volumes increased, reducing interactivity for business users. Query runtimes also stretched from hours to even days.

Limited data exploration: Dashboards were largely static and disallowed business users to drill down, roll up, slice and dice, or fully explore the data.

Short analytical window: The team could only process data for only one or two quarters which restricted trend and YoY analysis.

Reporting bottlenecks: Spark users on Databricks faced challenges generating on-demand reports due to processing limitations and performance lags.

Business Goals

Move from slow, limited reporting to fast, scalable analytics

The organization wanted to analyze its point-of-sale data across all stores to get a holistic view of their business and enable quick reporting. Strategic goals included:

Consolidate POS data to help business teams answer questions across customers, products and stores.

Conduct granular analytics on transaction-level data without waiting on engineering.

Enable year-over-year analysis across multiple fiscal periods.

Remove performance constraints in Tableau while maintaining existing BI workflows.

How Kyvos Helped

Enabled instant ad hoc analysis on billions of transactions

Kyvos semantic layer allowed their business teams to query all their data directly and drill-down to the most granular details in seconds.

Unified business view: Provided in-depth insights into all aspects of their business, including products, stores and customers – all within a single, consolidated platform.

Simplified, consistent responses: Created a universal semantic model using AI-powered smart aggregation to support all user groups and eliminate the need for use case-specific reporting.

Extended historical analysis: Scaled analysis from a few months to three years of data, enabling advanced year-over-year analysis and accurate distinct count across 6 billion transactions.

Granular and hierarchical analysis: Supported complex 5-level hierarchy with drill down to the day/time-of-day level detail on product and store data.

Seamless ecosystem integration: Enabled native connectivity with Azure, Databricks and Tableau—the existing data stack.

Automated data refreshes: Eliminated expensive manual SQL rewriting and freed data engineers for higher-value work like predictive analysis and model development.

Self-service analytics: Delivered interactive analytics with self-service access for business users.