High Tech — Data Normalization and Quality Assurance Pipeline

Overview

The primary purpose of this DAG is to ensure the normalization and quality of ingested data, which is critical for reliable operations in the high-tech industry. It begins by ingesting data from various sources, including ERP transaction logs, customer feedback datasets, and product specifications. The ingestion pipeline processes these data inputs through a series of defined steps to ensure they meet specific quality standards. The first step involves data validation, where the incoming data is

The primary purpose of this DAG is to ensure the normalization and quality of ingested data, which is critical for reliable operations in the high-tech industry. It begins by ingesting data from various sources, including ERP transaction logs, customer feedback datasets, and product specifications. The ingestion pipeline processes these data inputs through a series of defined steps to ensure they meet specific quality standards. The first step involves data validation, where the incoming data is checked against predefined criteria to identify any discrepancies. Next, data normalization is performed to standardize formats and values, ensuring consistency across the dataset. Following normalization, quality control measures are applied, including duplicate detection and anomaly identification. A lineage tracking mechanism is implemented to monitor data evolution throughout the process, which is essential for auditing and compliance purposes. The processed data is then stored in a compliance registry, which serves as the output of this DAG. Key performance indicators (KPIs) such as compliance rate and processing time are monitored to evaluate the effectiveness of the pipeline. The business value of this DAG lies in its ability to provide high-quality, reliable data that supports informed decision-making and enhances operational efficiency in high-tech environments.

Part of the Document Automation solution for the High Tech industry.

Use cases

• Enhances data reliability for critical business decisions
• Reduces operational risks associated with poor data quality
• Improves compliance with industry regulations
• Increases efficiency through automated quality checks
• Facilitates better customer insights and product development