Background: In an era defined by the proliferation of Artificial Intelligence (AI) and big data, the 
governance of information assets has transitioned from a back-office compliance function to a critical 
strategic imperative. As organizations seek to leverage AI for competitive advantage, the quality, 
security, and ethical management of underlying data have become paramount.  
Methods: This study employs an integrative review of contemporary literature, industry frameworks 
(such as DAMA-DMBOK), and empirical studies to analyze the evolving landscape of data governance. 
The research specifically examines the distinct challenges faced by Small and Medium-sized Enterprises 
(SMEs) compared to large corporations, alongside the critical role of governance in enabling secure AI 
adoption.  
Results: The analysis reveals that while foundational principles of governance remain consistent, 
implementation strategies must be highly adaptive. Large enterprises often struggle with siloed data 
and bureaucratic inertia, whereas SMEs face significant resource constraints that make comprehensive 
frameworks difficult to operationalize. Furthermore, the integration of AI introduces complex ethical 
variables, particularly regarding data privacy in sectors like healthcare and wearables. 
Conclusion: Effective data governance requires a hybrid approach that balances rigid compliance with 
agile decision-making. For AI to be secure and reliable, organizations must adopt metric-driven 
governance models that prioritize data quality and ethical stewardship. The study suggests that future 
frameworks must incorporate decentralized technologies, such as blockchain, to enhance trust and 
transparency in multi-stakeholder environments. 

Data Governance, Artificial Intelligence, SMEs, Data Ethics

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