Our client, a subscription-based meat delivery services company, was seeking to optimize their data processes that integrated multiple data sources and power analytics and AI modeling to extract customer behavioral patterns and help reduce customer churn.
Managing data from 26 diverse sources, including e-commerce and ERP data
Needed to provide personalized customer experiences to stay competitive
Data quality and reporting issues
Lack of visibility into data pipelines
Data used by dependent processes before completion, leading to errors
Monitoring and troubleshooting difficulties
Utilized Databricks platform for data integration, warehousing, and analytics
Integrated Azure data lake with Databricks
Connected data from various sources, including Shopify, Infor M3, and sensor data
Employed FiveTran to get data from multiple sources to create machine learning models
Implemented Unity Catalog for data governance
Automated data lineage tracking for data quality
Introduced an alerting system using Azure logic app
Streamlined data flow through bronze, silver, and gold layers
Developed multiple ML models for customer insights and churn prediction
Weekly monitoring and maintenance of infrastructure and ML models
Achieved 7x better performance with Databricks
Ensured data quality for various business users
Reduced manual efforts for data error fixing by 31%
Lowered overall cloud computing costs by 20%
Enabled faster data-driven decision-making
Improved employee productivity and operational efficiency
