Proactive Data Quality Engineering for Fabric Healthcare Data Pipelines
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#### Description
Modern healthcare organizations increasingly rely on Microsoft Fabric to unify data engineering, analytics, and reporting across cloud-native platforms. As Medicare datasets such as claims, eligibility, enrollment, and provider information traverse multiple ingestion, transformation, and analytics layers, maintaining data quality becomes essential for regulatory compliance, operational efficiency, and trusted decision-making. Traditional quality assurance approaches that validate data only after pipeline execution often identify defects too late, resulting in costly remediation and inconsistent analytical outcomes. This session presents a proactive data quality engineering framework designed for Microsoft Fabric environments, embedding validation throughout the end-to-end data lifecycle. The framework integrates source-level verification for schema conformity, completeness, and referential integrity; transformation-level validation for mapping accuracy, business rule enforcement, and aggregations within Data Factory and Synapse Data Engineering; and cross-layer reconciliation across OneLake and analytical datasets. By incorporating automated validation workflows into Fabric pipelines, organizations can detect anomalies earlier, improve traceability, and support continuous data quality monitoring at scale. The session also demonstrates how healthcare-specific validation rules can be aligned with Medicare business processes while leveraging Microsoft Fabric's unified architecture for orchestration and analytics. Attendees will gain practical guidance on implementing scalable validation strategies that enhance governance, strengthen compliance readiness, and establish reliable healthcare data pipelines capable of supporting high-quality analytics and business intelligence.