Transport & Logistics — Data Quality Normalization and Validation Pipeline

Overview

The primary purpose of this DAG is to ensure that data ingested from various sources within the Transport & Logistics industry adheres to established quality standards. The pipeline begins with the ingestion of data from sources such as ERP transaction logs, shipment tracking data, and customer feedback records. Once the data is ingested, a series of processing steps are executed to validate and normalize the data. This includes applying predefined validation rules to check for completeness, acc

The primary purpose of this DAG is to ensure that data ingested from various sources within the Transport & Logistics industry adheres to established quality standards. The pipeline begins with the ingestion of data from sources such as ERP transaction logs, shipment tracking data, and customer feedback records. Once the data is ingested, a series of processing steps are executed to validate and normalize the data. This includes applying predefined validation rules to check for completeness, accuracy, and consistency. Following validation, normalization rules are applied to standardize data formats and values, ensuring uniformity across datasets. Quality reports are generated to provide insights into the data quality status, which are crucial for compliance and operational efficiency. Validated data is then updated in the data warehouse, making it readily available for analysis and reporting. In cases where data does not meet quality standards, alerts are triggered to notify relevant teams, enabling prompt corrective actions. Monitoring key performance indicators (KPIs) such as validation success rates and normalization efficiency helps to continuously improve the data quality processes. The business value of this DAG lies in its ability to enhance data reliability, support compliance with industry regulations, and ultimately improve decision-making processes within the Transport & Logistics sector.

Part of the Data & Model Catalog solution for the Transport & Logistics industry.

Use cases

• Improved decision-making through reliable data insights
• Enhanced compliance with industry regulations and standards
• Increased operational efficiency by reducing data errors
• Faster response times to data quality issues
• Greater trust in data-driven strategies and initiatives