Public Sector — Data Normalization and Quality Assurance Pipeline

Overview

The primary purpose of this DAG is to ensure that ingested data meets predefined quality and compliance standards, which is critical in the public sector for maintaining transparency and accountability. The data sources include ERP transaction logs, public service records, and compliance checklists, which are ingested into the system for processing. The pipeline begins with data normalization, where incoming data is standardized to a common format. This is followed by a validation step that empl

The primary purpose of this DAG is to ensure that ingested data meets predefined quality and compliance standards, which is critical in the public sector for maintaining transparency and accountability. The data sources include ERP transaction logs, public service records, and compliance checklists, which are ingested into the system for processing. The pipeline begins with data normalization, where incoming data is standardized to a common format. This is followed by a validation step that employs quality tests to identify anomalies and inconsistencies in the data. Governance rules are then applied to mask sensitive information, ensuring compliance with data protection regulations. The processed data is stored in a compliance register, which serves as an official record of data quality and conformity. Alerts are generated in real-time for any instances of non-compliance, allowing for immediate corrective actions. Key Performance Indicators (KPIs) related to data quality, such as accuracy, completeness, and timeliness, are continuously monitored to ensure the reliability of the data. The business value of this DAG lies in its ability to enhance decision-making processes by providing trustworthy data, thereby fostering public trust and improving service delivery.

Part of the Predictive Maintenance solution for the Public Sector industry.

Use cases

• Improves data reliability for informed decision-making
• Enhances compliance with regulatory standards
• Reduces risks associated with data anomalies
• Increases public trust through transparent data practices
• Optimizes resource allocation based on accurate data insights