High Tech — Data Quality and Normalization Pipeline

Overview

The primary purpose of this DAG is to enhance the quality and standardization of data ingested from various sources within the high-tech industry. The data sources include ERP transaction logs, customer feedback databases, and product performance metrics. The ingestion pipeline begins by collecting these diverse datasets, followed by a series of processing steps designed to apply normalization rules and quality standards. During processing, the DAG performs quality checks to verify data conformi

The primary purpose of this DAG is to enhance the quality and standardization of data ingested from various sources within the high-tech industry. The data sources include ERP transaction logs, customer feedback databases, and product performance metrics. The ingestion pipeline begins by collecting these diverse datasets, followed by a series of processing steps designed to apply normalization rules and quality standards. During processing, the DAG performs quality checks to verify data conformity, ensuring that only accurate and reliable information is utilized. Additionally, a lineage tracking system is integrated to monitor and document any modifications made to the data throughout its lifecycle. The outputs of this DAG include a compliance register that records the validated data, along with alerts generated in the event of quality control failures. Key performance indicators (KPIs) related to data quality are monitored continuously to ensure data integrity and compliance with industry standards. The business value of this DAG lies in its ability to provide high-quality, standardized data that supports informed decision-making and enhances operational efficiency within high-tech organizations.

Part of the SOPs & Playbooks solution for the High Tech industry.

Use cases

• Improved decision-making with high-quality data insights
• Enhanced operational efficiency through data standardization
• Reduced risk of errors in data-driven processes
• Increased compliance with industry regulations
• Strengthened customer trust through data integrity