Consumer Products — Data Normalization and Quality Assurance Pipeline

Overview

The purpose of this DAG is to enhance the quality and normalization of data ingested from various sources within the Consumer Products industry, specifically targeting fraud and anomaly analytics. The data sources include ERP transaction logs, customer feedback databases, and sales records. The ingestion pipeline begins with collecting data from these sources, followed by a series of processing steps designed to enforce normalization rules and quality checks. During processing, the data undergoe

The purpose of this DAG is to enhance the quality and normalization of data ingested from various sources within the Consumer Products industry, specifically targeting fraud and anomaly analytics. The data sources include ERP transaction logs, customer feedback databases, and sales records. The ingestion pipeline begins with collecting data from these sources, followed by a series of processing steps designed to enforce normalization rules and quality checks. During processing, the data undergoes validation tests to identify any anomalies, ensuring that only high-quality data is retained for further analysis. Additionally, the DAG implements historical logging of the data to maintain traceability, which is crucial for compliance and auditing purposes. Outputs from this workflow include a compliance register that documents the quality assessment results, alerts for any detected non-conformities, and a secure data repository for sensitive information. Monitoring key performance indicators (KPIs) such as the rate of anomalies detected and the percentage of data passing quality checks is essential to measure the effectiveness of the DAG. The business value derived from this pipeline includes improved data integrity, enhanced fraud detection capabilities, and increased trust in data-driven decisions within the Consumer Products sector.

Part of the Fraud & Anomaly Analytics solution for the Consumer Products industry.

Use cases

• Enhances accuracy of fraud detection algorithms
• Improves compliance with industry regulations
• Reduces operational risks associated with data quality
• Increases efficiency in data processing workflows
• Builds consumer trust through reliable data practices