30% Faster Data Processing with Databricks Lakehouse: How Quantzig Transformed a Global Electronics Retailer’s Analytics Ecosystem

Business Challenge

A leading global electronics retailer was struggling with data silos, slow processing speeds, and inefficient analytics workflows. With data coming from multiple sources—online sales, in-store transactions, supply chain systems, and customer interactions—the client faced challenges in consolidating, cleaning, and analyzing vast amounts of structured and unstructured data. Their existing data infrastructure was unable to support real-time analytics, leading to delays in decision-making and missed business opportunities.

Additionally, the client required advanced predictive analytics for personalized customer recommendations, demand forecasting, and inventory optimization. However, their legacy data platform lacked scalability and integration capabilities, preventing seamless data flow and AI-driven insights. The retailer sought a modernized data architecture that could improve operational efficiency and enable real-time decision-making across all business functions.

How Quantzig Helped

Quantzig implemented the Databricks Lakehouse platform to unify the client’s data infrastructure and enhance analytics performance. By leveraging cloud-based data processing, AI-driven analytics, and automated ETL pipelines, Quantzig enabled the retailer to gain real-time insights, optimize inventory management, and improve customer experience.

1. Data silos across multiple channels : Integrated all data sources into a centralized Databricks Lakehouse platform.
2. Slow data processing and reporting : Implemented automated ETL pipelines, reducing processing time by 30%.
3. Limited predictive analytics capabilities : Deployed AI-powered forecasting models for demand prediction and personalized recommendations.
4. Inefficient inventory management : Enhanced real-time inventory tracking and stock replenishment strategies.
5. Lack of real-time customer insights : Enabled real-time customer behavior analysis for targeted marketing.

By centralizing data with Databricks Lakehouse, Quantzig empowered the client to transition from batch processing to real-time analytics, significantly improving business agility. The retailer could now seamlessly manage high volumes of transactions, track customer sentiment, and optimize operations with AI-driven insights.

Results & Impact

With Databricks Lakehouse, the client achieved a 30% improvement in data processing speed, enabling real-time insights across multiple business functions. Faster data access allowed for dynamic pricing adjustments, personalized marketing campaigns, and optimized stock replenishment, resulting in a 15% increase in operational efficiency.

Additionally, predictive analytics enhanced customer engagement, leading to a 20% increase in conversion rates for personalized product recommendations. The improved data infrastructure also reduced IT costs by 25%, as the retailer moved from an expensive on-premises setup to a scalable cloud-based architecture.