Enterprise Data Management: Design of a Conceptual Model for an Effective Data Governance Framework in Large Organizations

Abstract

Large organizations generate and process vast amounts of data, making effective data governance frameworks essential for maintaining data quality, security, and compliance. However, implementing governance structures presents challenges, including organizational resistance, lack of expertise, and integration complexities. This dissertation investigates the measurable impact of data governance frameworks on data quality and business outcomes, the key success factors for effective implementation, and the influence of stakeholder perceptions on adoption and success.
The study employs a quantitative research approach, utilizing survey data from 255 professionals across industries such as finance, healthcare, and technology. The analysis applies regression models, correlation analysis, and factor analysis to examine governance effectiveness. Findings indicate a strong positive correlation between governance implementation and data accuracy, consistency, and regulatory compliance
Organizations with well-structured governance models experience enhanced business efficiency, strategic alignment, and improved decision-making capabilities.
Key success factors identified for governance effectiveness include senior management support, role clarity, interdepartmental collaboration, continuous training, and resource availability. Despite these benefits, resistance to change and governance complexity remain significant barriers. Stakeholder perceptions strongly influence adoption, with most professionals recognizing the advantages of governance frameworks but citing complexity and resource constraints as limiting factors.
The research underscores the need for business-aligned, adaptable governance strategies that integrate emerging technologies like AI and automation to streamline processes and enhance compliance. Organizations should focus on fostering a data-driven culture and implementing structured change management initiatives to overcome resistance and optimize governance adoption.
This study contributes empirical insights into enterprise data governance, offering recommendations for improving governance models, refining regulatory compliance strategies, and leveraging technological advancements. Future research should explore longitudinal studies on governance impact, industry-specific governance applications, and AI-driven automation in governance practices.
Data governance frameworks play a critical role in ensuring data integrity, optimizing decision-making, and driving business performance. By addressing implementation challenges and aligning governance with organizational objectives, businesses can enhance operational efficiency, regulatory compliance, and long-term competitiveness in the evolving digital landscape.