High Tech — Data Lineage Tracking for High-Tech Governance

Overview

The purpose of this DAG is to manage data lineage tracking across various processing stages, ensuring compliance and governance within the high-tech industry. It ingests data from multiple sources, including ERP transaction logs, CRM data exports, and sensor data feeds. The ingestion pipeline captures raw data and prepares it for processing by validating input formats and ensuring data integrity. Processing steps include transformation logging, where each modification is documented, and lineage

The purpose of this DAG is to manage data lineage tracking across various processing stages, ensuring compliance and governance within the high-tech industry. It ingests data from multiple sources, including ERP transaction logs, CRM data exports, and sensor data feeds. The ingestion pipeline captures raw data and prepares it for processing by validating input formats and ensuring data integrity. Processing steps include transformation logging, where each modification is documented, and lineage tracking, which records the origin of data elements. Quality controls are implemented at each stage to verify data accuracy and consistency, ensuring that any anomalies are flagged for review. The final outputs are stored in a centralized data catalog, which includes comprehensive lineage reports and key performance indicators (KPIs) related to data traceability. Monitoring KPIs such as data transformation counts, source reliability scores, and processing times provide insights into the efficiency of the data handling process. The business value of this DAG lies in its ability to enhance transparency and accountability in data management, facilitating compliance with industry regulations and improving decision-making processes based on reliable data.

Part of the Governance & Compliance solution for the High Tech industry.

Use cases

• Improves regulatory compliance and reduces audit risks.
• Enhances data transparency for better decision-making.
• Facilitates faster identification of data quality issues.
• Supports data governance initiatives across departments.
• Increases trust in data-driven insights and analytics.