Transport & Logistics — Logistics Data Quality Normalization and Validation Pipeline

Overview

The Logistics Data Quality Normalization and Validation Pipeline is designed to enhance the integrity of transport and logistics data by normalizing and validating extracted datasets. The primary purpose of this DAG is to ensure that data meets predefined quality standards and compliance requirements essential for operational efficiency. The data sources include ERP transaction logs, shipment records, and customer feedback forms, which are ingested into the pipeline for processing. The archite

The Logistics Data Quality Normalization and Validation Pipeline is designed to enhance the integrity of transport and logistics data by normalizing and validating extracted datasets. The primary purpose of this DAG is to ensure that data meets predefined quality standards and compliance requirements essential for operational efficiency. The data sources include ERP transaction logs, shipment records, and customer feedback forms, which are ingested into the pipeline for processing. The architecture consists of several processing steps that include data extraction, normalization, validation against quality criteria, and lineage tracking. Initially, the data is extracted from various sources, followed by normalization to standardize formats and structures. Next, the validation step checks the data against specific quality expectations, such as completeness, accuracy, and consistency. Quality control tests are conducted to identify any discrepancies or anomalies. In case of validation failures, a robust retry mechanism is triggered to reprocess the problematic data, ensuring that quality issues are addressed promptly. The results of these processes are cataloged, allowing for comprehensive lineage tracking, which is crucial for compliance audits and operational transparency. The outputs of this DAG include validated datasets ready for reporting, quality assurance logs, and lineage documentation. Monitoring key performance indicators (KPIs) such as data accuracy rates, validation failure rates, and processing times provides insights into the effectiveness of the pipeline. The business value lies in improved data quality, enhanced compliance with regulatory standards, and increased operational efficiency in the transport and logistics sector.

Part of the Governance & Compliance solution for the Transport & Logistics industry.

Use cases

• Ensures compliance with industry regulations and standards
• Reduces operational risks associated with poor data quality
• Improves decision-making with accurate and reliable data
• Enhances customer satisfaction through better service delivery
• Increases efficiency in logistics operations and reporting