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Data Governance, Accuracy, And Validation Frameworks for Google Analytics and Looker Studio Reporting Systems

Uchechukwu Nkechinyere Anene, Asmita Basnet, Elebe Oghenemaiga

Abstract

Data governance, accuracy, and validation frameworks are critical to ensuring trust, consistency, and decision reliability in analytics environments powered by Google Analytics and Looker Studio. This abstract examines structured approaches for governing data flows, assuring metric accuracy, and validating reports across web, application, and business intelligence use cases. The study focuses on governance principles including data ownership, stewardship, access control, versioning, and documentation, emphasizing their role in reducing ambiguity and misinterpretation in self-service analytics. Accuracy challenges such as event misconfiguration, sampling bias, attribution discrepancies, schema drift, and transformation errors are analyzed in the context of Google Analytics event-based models and downstream visualization layers. The abstract proposes a validation framework that combines automated rule-based checks, reconciliation against source systems, statistical anomaly detection, and dashboard-level verification to ensure metric integrity. Particular attention is given to aligning definitions between Google Analytics properties and Looker Studio data sources to prevent metric inflation, duplication, or loss during aggregation. The framework highlights the use of centralized data models, controlled calculated fields, and audit trails to support reproducibility and comparability across reports. In addition, the study discusses governance workflows for change management, including pre-deployment testing, approval processes, and rollback mechanisms for reporting assets. Privacy, consent management, and regulatory compliance considerations are integrated to ensure governance frameworks align with data protection requirements while preserving analytical utility. Through a conceptual synthesis and applied reporting scenarios, the abstract demonstrates how robust governance and validation practices improve reporting confidence, stakeholder trust, and organizational analyt

Keywords

Data governance; Data accuracy; Validation frameworks; Google Analytics; Looker Studio; Reporting integrity; Business intelligence

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