This paper examines data governance frameworks from both academic research and industry practices, focusing on their suitability for small and medium-sized enterprises (SMEs) with limited resources. Given the crucial role SMEs play in the economy, effective data governance is essential. However, our review found a gap in research specifically addressing SME needs. Existing frameworks, originally designed for larger organizations, often involve complex processes and high resource demands, lacking in evidence of successful SME adaptation, and underscoring the need for frameworks tailored to SMEs. Using a structured approach based on “People, Process, and Technology” (PPT), this study analyzes two widely used industry data governance frameworks, DAMA and DGI, to assess their practicality for SMEs. Our analysis reveals that DAMA offers a comprehensive approach centered on data management, whereas DGI provides a more structured, phased implementation of data governance policies. Although DGI may be a better starting point for SMEs, both frameworks still require significant resources, which can be a challenge for SMEs. The paper also explores how emerging technologies, such as AI, knowledge graphs, and machine learning, could help SMEs implement data governance with less manual effort by streamlining processes and reducing the burden on limited personnel. Accordingly, this study calls for the development of simplified, scalable data governance frameworks that leverage these technologies to enhance efficiency. Additionally, it highlights the importance of collaboration between academia and industry to create practical, cost-effective solutions that support SMEs in managing their data effectively.