F
Furō
Data Platform Transformation

Leading Retail Chain

How we built a real-time analytics platform using Databricks that improved inventory efficiency by 45%, accelerated customer insights by 300%, and drove 28% revenue growth across 200+ stores.

8-month implementation
200+ stores connected
50TB+ data processed daily

Business Impact

45%
Inventory Efficiency
Reduced stockouts by 60%
300%
Faster Customer Insights
Real-time personalization
28%
Revenue Growth
$45M additional revenue
85%
Forecast Accuracy
ML-powered predictions

The Challenge

Siloed Data Systems

Data scattered across 15+ legacy systems with no unified view

Delayed Insights

Weekly reports taking 3-5 days to generate, missing market opportunities

Inventory Inefficiencies

$12M in excess inventory and frequent stockouts affecting customer satisfaction

Limited Personalization

Generic customer experiences due to lack of real-time behavioral data

Our Solution

Unified Data Platform

Databricks lakehouse architecture consolidating all data sources

Real-time Analytics

Streaming data pipelines with sub-second latency for instant insights

ML-Powered Forecasting

Advanced machine learning models for demand prediction and optimization

Customer 360 Platform

Unified customer profiles enabling real-time personalization

Data Platform Architecture

Lakehouse Architecture

Data Ingestion

Real-time streaming from POS, inventory, and customer systems

Data Lake

Databricks Delta Lake for unified batch and streaming processing

ML Platform

MLflow for model lifecycle management and automated retraining

Analytics

Power BI dashboards with real-time data visualization

Technology Stack

Data Platform

Databricks
Delta Lake
Apache Spark
MLflow

Cloud Infrastructure

Azure Data Factory
Azure Event Hubs
Azure Storage
Azure Key Vault

Analytics & Visualization

Power BI
Databricks SQL
REST APIs
Implementation Timeline
1

Data Discovery & Architecture

Data mapping and platform design (6 weeks)

2

Core Platform Build

Databricks setup and initial data pipelines (10 weeks)

3

ML Model Development

Forecasting and recommendation models (8 weeks)

4

Analytics & Rollout

Dashboard creation and phased store rollout (8 weeks)

Key Use Cases

Demand Forecasting

ML models analyzing historical sales, weather, events, and trends to predict demand with 85% accuracy.

  • Seasonal trend analysis
  • External factor integration
  • Store-level optimization
Customer Personalization

Real-time customer profiles enabling personalized recommendations and targeted marketing campaigns.

  • Behavioral segmentation
  • Product recommendations
  • Dynamic pricing
Inventory Optimization

Automated inventory management with real-time stock level optimization across all store locations.

  • Automated reordering
  • Cross-store transfers
  • Waste reduction
"The data platform Furō built has transformed our business. We now make decisions based on real-time insights rather than gut feeling. The 28% revenue growth speaks for itself – this investment paid for itself in the first quarter."
Michael Rodriguez
Chief Data Officer
Leading Retail Chain

Ready to Unlock Your Data's Potential?

Let's discuss how we can build a data platform that drives similar results for your business.

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