Date of Award

12-3-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Information Science

First Advisor

Elizabeth Pierce

Abstract

Experts agree that data governance is crucial for managing data as an asset, yet in practice, it remains inconsistently defined, unevenly implemented, and poorly understood. Earlier industry studies offered valuable descriptive benchmarks of governance adoption and professional roles, but provided limited theoretical insight and little empirical evidence explaining how governance functions within organizations. Building on this foundation, the current research offers a more substantial and analytically rigorous contribution by transforming descriptive observations into empirical explanations. Through a collaborative academic–industry partnership, the study utilizes a global survey of 565 respondents (348 valid cases) to analyze governance structures, motivations, challenges, and the adoption of emerging frameworks and technologies. Using inferential and predictive statistical modeling, formal multiplicity control, and explicit handling of missing data, the analysis delivers the most comprehensive empirical evaluation of data governance to date. The findings show that governance effectiveness relies less on formal frameworks and more on social architecture—such as communication, ownership, and organizational learning—reconceptualizing governance as an adaptive organizational capability rather than simply a compliance function. This research thus expands previous industry benchmarks into a theory-driven, statistically validated understanding of data governance as a sociotechnical system of coordination, accountability, and continuous learning.

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