Energy — Data Governance Framework for Energy Sector

Overview

The primary purpose of this DAG is to implement a robust data governance framework tailored for the energy industry. It focuses on ensuring data traceability and security while integrating essential policies for access management and protection of sensitive information. The architecture begins with data ingestion from various sources, including ERP transaction logs, operational data records, and compliance documentation. The ingestion pipeline processes these inputs through a series of well-defi

The primary purpose of this DAG is to implement a robust data governance framework tailored for the energy industry. It focuses on ensuring data traceability and security while integrating essential policies for access management and protection of sensitive information. The architecture begins with data ingestion from various sources, including ERP transaction logs, operational data records, and compliance documentation. The ingestion pipeline processes these inputs through a series of well-defined steps. The first step involves data normalization, where disparate data formats are standardized to ensure consistency. Following normalization, data cataloging is performed, which allows for efficient data discovery and management. Quality control checks are then applied to verify the integrity and accuracy of the data, ensuring that it meets established standards. The results of these processes are compiled into compliance reports, which provide transparency and accountability in data management practices. Monitoring key performance indicators (KPIs) such as data accuracy rates, compliance adherence, and access control effectiveness is crucial for ongoing governance. The business value of this DAG lies in its ability to enhance data security, improve regulatory compliance, and facilitate informed decision-making, ultimately leading to operational efficiencies and risk mitigation in the energy sector.

Part of the Enterprise Search solution for the Energy industry.

Use cases

• Enhanced data security reduces risk of breaches
• Improved regulatory compliance minimizes legal liabilities
• Informed decision-making drives operational efficiencies
• Increased transparency fosters stakeholder trust
• Streamlined data management reduces operational costs