Energy — Data Governance Compliance Pipeline

Overview

The Data Governance Compliance Pipeline is designed to ensure that data management within the energy sector adheres to regulatory requirements. The primary purpose of this DAG is to collect metadata from diverse data sources, including operational databases, market data feeds, and compliance logs. The ingestion pipeline begins with the extraction of metadata, which is then processed through a series of governance rules to verify compliance with existing regulations. Each step in the processing l

The Data Governance Compliance Pipeline is designed to ensure that data management within the energy sector adheres to regulatory requirements. The primary purpose of this DAG is to collect metadata from diverse data sources, including operational databases, market data feeds, and compliance logs. The ingestion pipeline begins with the extraction of metadata, which is then processed through a series of governance rules to verify compliance with existing regulations. Each step in the processing logic includes checks for data integrity and quality, ensuring that the metadata remains accurate and reliable. Quality control measures are implemented to validate the integrity of the metadata throughout the process. Upon completion, the results are stored in a centralized data warehouse, where compliance reports are generated. Additionally, alerts are triggered in cases of non-compliance, allowing for immediate corrective actions. Monitoring key performance indicators (KPIs) such as compliance rates and data quality scores provides insights into the effectiveness of the governance processes. The business value of this DAG lies in its ability to mitigate regulatory risks, enhance data reliability, and support informed decision-making in trading and market intelligence.

Part of the Scientific ML & Discovery solution for the Energy industry.

Use cases

• Reduces risk of regulatory penalties and fines
• Enhances data reliability for trading decisions
• Improves operational efficiency through automation
• Supports compliance with evolving regulations
• Facilitates better data-driven decision-making