An AWS-Powered Retail DQM Transformation

Situation

The global operations team of a leading retailer relied on large volumes of data from stores and digital platforms to drive decisions in merchandising, marketing, inventory, and finance. As their ecosystem scaled, maintaining data quality became increasingly complex and critical.

Problem

Manual data validation was unsustainable. Data from systems like Snowflake and CCM Merkle was often incomplete, inconsistent, or inaccurate – causing delays in reporting, flawed insights, and operational inefficiencies across forecasting, promo planning, and executive dashboards. A scalable, automated quality assurance mechanism was missing.

Solution

Mu Sigma designed an automated Data Quality Management (DQM) framework with AWS SageMaker as the core engine. It validated high-priority datasets (sales, transactions, inventory etc.) – across digital and physical channels in multiple markets.

Key Components:

• Data Integration: Scheduled Python jobs pulled data from Snowflake and CCM Merkle, orchestrated via SageMaker for low-latency, batch validation across regions.
• Validation Logic: Checking for completeness by flagging missing records, accuracy through outlier detection using statistical thresholds, and consistency by cross-validating sources (actuals vs. forecasts and traffic vs. transactions.)
• Modular Architecture:
  • Reusable object-oriented Python modules in SageMaker
  • Metadata-driven engine rule updates (no code changes)
  • Adaptive thresholds based on historical data
  • Multi-threaded, parallel processing scripts optimized for SageMaker
  • Full audit logs in S3 with full traceability
• Automation & Monitoring: Alerts and Live dashboard to flag real time trends and anomalies.

AWS SageMaker enabled intelligent, scalable data validation by combining historical baselining with adaptive thresholds and high-performance parallel processing, ensuring accurate anomaly detection and reliable SLA adherence across global datasets.

Impact

• 85% reduction in manual effort for data validation by eliminating redundant checks across 20+ datasets and markets.
• 3x faster detection and resolution of data issues, scaling from 2–3 hours of manual review per dataset to ~30-minute automated turnaround across key global markets (NA, Western Europe, Greater China etc.)
• Real-time visibility into data quality trend, leading to:
  • 60% fewer reporting delays, especially during business-critical periods like Cyber Week and Month-End Closures
  • 40% faster upstream corrections, minimizing operational disruptions

By leveraging SageMaker, we enabled reliable, scalable orchestration—powering faster campaign launches, accurate financial closures, and better promo tracking.