<p>Data Governance and Data Privacy are critical aspects of organizational management that are widely utilized across all organizational scales. However, this research focused specifically on the significance of Data Governance and Data Privacy in Small and Medium Enterprises (SMEs). While the importance of maintaining these systems is paramount across all organizations, the challenges faced by SMEs in maintaining these systems are greater due to their limited resources. These challenges include potential errors such as data leaks, use of corrupted data, or insufficient data, as well as the difficulty in identifying clear roles and responsibilities regarding data handling. To address these challenges, this research investigated the impact of utilizing Lean Six Sigma (LSS) tools and practices to overcome the anticipated gaps and challenges in SMEs. The qualitative methodology utilized is a grounded theory design, chosen due to the limited understanding of the best LSS practices for achieving data governance and data privacy in SMEs and how LSS can improve the adoption of data governance concerning privacy in SMEs. Data were collected using semi-structured interview questions that were reviewed by an expert panel and pilot tested. The sampling method included purposive, snowballing, and theoretical sampling, resulting in 20 participants being selected for interviews. Open, axial, and selective coding were performed, resulting in the development of a grounded theory. The obtained data were imported into NVivo, a qualitative analysis software program, to compare responses, categorize them into themes and groups, and develop a conceptual framework for Data Governance and Data Privacy. An iterative data collection and analysis approach was conducted to ensure that all aspects were considered. The applied grounded theory resulted in retrieving the themes used to generate a theory from the participants’ descriptions of LSS, SMEs, data governance, and data privacy. Finally, ISM technique has been applied to identify the relationships between the concepts and factors resulted from the grounded theory. It helps arranging the levels the criteria, drawing the relationships in a flowchart, and providing valuable insights to the researcher. </p>