Date of Award

4-15-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Information Science

First Advisor

John Talburt

Abstract

Enterprise Architecture (EA) is a discipline, approach, and practice that aims to align the enterprise’s technical infrastructure, strategy, processes, and technology investments with the organization’s strategy to fulfill the mission and vision. Data quality is conformance to data specifications (Talburt, 2015, p. 192), and low data quality has significant implications for decisions and outcomes across the enterprise. Scant literature and research exist surrounding the intersection, integration, and impact of data quality within EA practices. This research examines the awareness and understanding of Enterprise Architect and Technology Architect populations regarding data quality concepts, artifacts, and tools. The research aims to answer two primary research questions: 1) What is the level of understanding of data quality among the Enterprise Architect or Technology Architect population? 2) Do Data Quality concepts, theories, and tools require explication among the Enterprise Architect or Technology Architect population (e.g., Applications, Business, Cloud, Data, Security, Solutions)? The research indicates that Enterprise and Technology Architects and Information Technology Leaders need to be made aware of whether their organizations are measuring data quality. It also reveals that further training and education regarding data quality concepts, theories, and tools are warranted. The findings suggest gaps exist in incorporating data quality frameworks, specifications, references, and requirements within EA practices. The research findings also suggest that referral to existing educational resources or programs focused on Enterprise and Technology Architects, surrounding Data Quality principles, and their application within an organization's practice, may be beneficial. The findings may lead to further investigation surrounding the incorporation of explicit Data Quality artifacts, components, or tools within an organization's existing EA framework, to help the EA practices address enterprise data challenges and build the foundation for a data-centric enterprise. Focusing on data quality within an organization's EA program may ensure that organizations produce high-quality data, leading to better enterprise decision-making and ensuring that prevailing technologies utilized by an organization, such as artificial intelligence, leverage high-quality data.

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