Lights-off Data Factory: A Governance-First Architecture for Level-5 Autonomous Data Systems

Abstract

Enterprise data governance systems increasingly fail to scale under the demands of autonomous analytics and AI-driven data consumption. Existing data management practices rely heavily on human-in-the-loop decision authority, introducing latency, inconsistency, and systemic risk. This paper proposes a governance-first framework for autonomous data systems composed of three orthogonal components: the Autonomous Data Governance Maturity Model (ADGMM), the Autonomous Epistemic Governance & Integrity System (AEGIS), and Canonical Entity Reasoning & Epistemic Stewardship (CERES). The framework repositions operational domains such as Master Data Management, Data Quality, and Data Integration as governed execution layers rather than sources of authority. Autonomy is defined as a system’s ability to estimate uncertainty, reason semantically, and self-correct without continuous human arbitration.