Abstract
In the era of big data, automated visualization and advanced analytics, robust data visualization literacy frameworks are increasingly essential for supporting effective decision-making and the interpretation of complex information. Data visualization literacy refers to the ability to read, interpret, evaluate and communicate meaningful insights from visual representations of data. Despite its growing importance across domains such as healthcare, manufacturing, tourism, education and immersive analytics, research on structured frameworks remains fragmented and lacks standardization. This study presents a systematic literature review of data visualization literacy frameworks to synthesize current knowledge, examine evaluation approaches and identify research gaps. A total of 43 peer-reviewed studies published between 2013 and 2024 were analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review examines framework objectives, theoretical foundations, usability considerations, educational validation, empirical effectiveness and assessment methods. The findings indicate that many frameworks emphasize usability and cognitive support, but their empirical effectiveness is often weakly validated through limited samples, short-term testing, or prototype-based evaluation. The review also highlights emerging literacy concerns related to generative artificial intelligence and automated visualization systems, including transparency, interpretability and user trust. Overall, this study emphasizes the need for standardized, empirically validated and domain-adaptable frameworks that support cognitive effectiveness, educational application and responsible visualization practice.
Keywords: Data Visualization Frameworks, Data Visualization Literacy, Evaluation Methods, Systematic Literature Review, Visual Analytics.