Transport & Logistics — Logistics Data Normalization Pipeline

Overview

The primary purpose of this DAG is to normalize logistics data received from multiple sources, such as ERP and CRM systems, to ensure data quality and consistency. The workflow is initiated upon the arrival of new data, which is ingested into the system for processing. The first step involves validating the data formats to ensure compliance with predefined standards. Following validation, sensitive data is masked to protect privacy and adhere to data protection regulations. The next step involve

The primary purpose of this DAG is to normalize logistics data received from multiple sources, such as ERP and CRM systems, to ensure data quality and consistency. The workflow is initiated upon the arrival of new data, which is ingested into the system for processing. The first step involves validating the data formats to ensure compliance with predefined standards. Following validation, sensitive data is masked to protect privacy and adhere to data protection regulations. The next step involves maintaining a historical record of any modifications made to the data, ensuring traceability and accountability. Once the data has been normalized, it is stored in a data lake for further analysis and reporting. Throughout this process, key performance indicators (KPIs) related to data quality, such as accuracy and completeness, are monitored. In the event of any errors detected during processing, a recovery process is initiated to rectify issues and maintain data integrity. This comprehensive approach not only enhances the reliability of the data used for supply and demand forecasting but also provides significant business value by enabling more accurate decision-making and operational efficiency in the transport and logistics industry.

Part of the Supply/Demand Forecast solution for the Transport & Logistics industry.

Use cases

• Improves data accuracy for better forecasting
• Enhances operational efficiency through reliable data
• Reduces risks associated with data privacy violations
• Facilitates compliance with regulatory requirements
• Enables data-driven decision-making in logistics