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<i>Background:</i> Using personal data as Open Data is a pervasive topic globally, spanning various sectors and disciplines. Recent technological advancements, particularly in artificial intelligence and algorithm-driven analysis, have significantly expanded the capacity for the automated analysis of vast datasets. There’s an expectation that Open Data analysis can drive innovation, enhance services, and streamline administrative processes. However, this necessitates a legally and ethically sound framework alongside intelligent technical tools to comprehensively analyze data for societal benefit. <i>Methodology:</i> A systematic review across seven databases (MEDLINE, CINAHL, BASE, LIVIVO, Web of Science, IEEExplore, and ACM) was conducted to assess the current research on barriers, support factors, and options for the anonymized processing of personal data as Open Data. Additionally, a supplementary search was performed in Google Scholar. A total of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mspace width="0.166667em"></mspace><mo>=</mo><mspace width="0.166667em"></mspace><mn>1192</mn></mrow></semantics></math></inline-formula> studies were identified, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mspace width="0.166667em"></mspace><mo>=</mo><mspace width="0.166667em"></mspace><mn>55</mn></mrow></semantics></math></inline-formula> met the inclusion criteria through a multi-stage selection process for further analysis. <i>Results:</i> Fourteen potential supporting factors (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mspace width="0.166667em"></mspace><mo>=</mo><mspace width="0.166667em"></mspace><mn>14</mn></mrow></semantics></math></inline-formula>) and thirteen barriers (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mspace width="0.166667em"></mspace><mo>=</mo><mspace width="0.166667em"></mspace><mn>13</mn></mrow></semantics></math></inline-formula>) to the provision and anonymization of personal data were identified. These encompassed technical prerequisites as well as institutional, personnel, ethical, and legal considerations. These findings offer insights into existing obstacles and supportive structures within Open Data processes for effective implementation.