Data Quality Standards - IBR-015

Program Architecture Layer	Business Logic Layer
Module	Data Management
Component	Data Matching Engine
Level of Importance	Optional
Priority	Low
Social Protection Delivery Chain Stage	Manage

Requirement Description	IBR ideally should implement a data quality validation service that can be invoked by external systems for ensuring data standards, updated quarterly.
Justification	Ensures data integrity and reliability, maintaining ecosystem quality.
Use Case	Provide a data quality validation service for external systems to ensure data standards.
Data Elements Required	Beneficiary ID, Data Quality Metrics, Validation Reports
Minimum Technical Specifications	Validation Module: Python-based script for running quarterly data validation checks. Integration: REST API for invoking validation services from external systems. Reporting: CSV reports summarizing data quality issues.
Standard Technical Specifications	Validation Module: Automated data quality checks with Apache Nifi. Integration: Microservices architecture for data validation. Reporting: Interactive data quality dashboards with Power BI.
Advanced Technical Specifications	Validation Module: AI-driven data quality assessment with anomaly detection. Integration: Event-driven microservices with Apache Kafka. Reporting: Real-time data quality monitoring with AI-driven insights.
Security & Privacy Requirements	Quarterly automated validation checks, secure integration.
Scalability Considerations	Event-driven microservices for real-time data validation scaling.
Interoperability Requirements	Integration with external systems for data quality validation.
Compliance with International Standards	GDPR compliance for data quality and integrity.
User Interface Requirements	N/A

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Data Quality Standards - IBR-015

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