AUDIT DATA ANALYTICS GOVERNANCE: A FRAMEWORK FOR EVIDENCE QUALITY, MODEL CONTROL, AND PROFESSIONAL SKEPTICISM

Abstract

Background: Organizations increasingly depend on audit data analytics governance, yet visible activity does not by itself demonstrate reliable capability or sustainable value. Fragmented responsibilities, weak evidence, and locally optimized metrics can cause decisions about model and technology controls to create unmanaged exposure elsewhere. Aim: This article develops a governance framework that connects audit data analytics governance, model and technology controls, and human judgment through five mutually reinforcing capabilities: data integrity, model and technology controls, human judgment, accountability, and continuous monitoring. Method: The paper uses an integrative conceptual review. Established management research, professional standards, and institutional guidance are synthesized through construct clarification, mechanism mapping, risk-control analysis, and proposition development. It does not report respondents, sample statistics, or causal estimates. Results: The synthesis indicates that performance becomes more resilient when decision rights, data definitions, controls, escalation paths, and learning routines are designed as one management system. The proposed model links each capability to observable evidence and balanced indicators. Contribution: The article offers an auditable implementation sequence and propositions that can be tested in later empirical research.