Transforming Sports Retail with AI-Driven Data Quality Management

The Challenge

• Increased Data Complexity: A large influx of data from expanded digital channels complicated data management.
• Ineffective Quality Checks: Traditional methods of data quality assurance were inadequate for addressing challenges such as data completeness, duplicate values, and file corruption.
• Manual Processes: The reliance on manual data ingestion and monitoring processes introduced delays and heightened the risk of errors.
• Impact on Decision-Making: Business-critical decisions were often made based on compromised data due to undetected issues, risking the company’s direction.

The Solution

We implemented an automated Data Quality Management (DQM) system using AI that ensured data integrity through advanced forecasting, measurement, and alerting. Prediction intervals for KPIs were established using best-fit models, and discrepancies were identified by comparing actual data with forecasted intervals. An exception system was built to notify stakeholders of anomalies in real time, enabling timely corrective actions. Exception reports were also visualized for easy understanding.