Executive Summary
A leading premium apparel company partnered with Altimetrik to modernize its enterprise master data ecosystem and improve operational efficiency across merchandising and planning operations. The organization needed a scalable, reliable framework to manage product, vendor, and location data while reducing delays in downstream data availability.
Altimetrik implemented a modernized data engineering architecture with advanced validation, parallel processing, near real-time data streaming, and enhanced telemetry. The transformation significantly accelerated data delivery, improved data quality, optimized infrastructure efficiency, and enhanced the overall retail customer experience.
Key Outcomes
- 76% improvement in overall data processing time
- Near real-time master data availability for downstream consumers
- 80% reduction in CPU utilization through query optimization
- 90% reduction in outbound processing time through parallelization
- Enhanced data quality and reliability across enterprise systems
Powering Retail Operations with Enterprise Master Data (Client)
A leading premium apparel retailer managing enterprise-wide merchandising and planning operations across product, vendor, and location master data systems.
When Data Bottlenecks Began Slowing the Business (Challenge)
The client’s existing master data architecture struggled with frequent business rule changes, increasing data complexity, and monolithic processing pipelines. Data ingestion and validation were slow and inefficient, with new data processing taking nearly 25 hours and updates requiring up to 3 hours.
Large file-based processing created infrastructure bottlenecks, high server utilization, and delayed downstream data availability. Limited visibility into data movement and error tracking further impacted operational efficiency and customer experience.
Transforming Batch Processing into Real-Time Data Flow (Approach)
Altimetrik implemented an additional validation layer to proactively detect flawed data and notify support teams before downstream distribution.
The existing monolithic architecture was redesigned using parallel processing, breaking large files into smaller streams to dramatically reduce processing times. The solution also introduced Kafka-based real-time data streaming, eliminating delays caused by physical file generation and transfer.
To improve observability and operational efficiency, Altimetrik deployed end-to-end telemetry dashboards and a correlation ID framework for complete data tracking across systems. Query optimization further reduced infrastructure strain and improved overall platform performance.
Unlocking Faster Data, Better Visibility, and Greater Scale
- Overall data processing time reduced from 25 hours to approximately 6 hours
- Near real-time master data availability achieved for downstream consumers
- CPU utilization reduced from 90% to approximately 20%
- Data quality and reliability significantly improved across enterprise systems
- Enhanced visibility into data movement, debugging, and operational bottlenecks
- Improved scalability and readiness for enterprise-wide data-as-a-service initiatives
Altimetrik’s Data Engineering practice. Technology stack included Kafka, enterprise MDM systems, telemetry dashboards, parallel processing architecture, and real-time data streaming solutions.
