Big data technologies are fundamental in managing large volumes of data and transforming data into information. However, the mere availability of technological resources is not sufficient to extract meaningful insights. The maturity of these technologies and users’ perceptions of their use play a crucial role in determining their potential for value creation. This study proposes a maturity model that integrates both the technological maturity perceived by users and users’ own maturity in utilizing these tools. Focusing on three major cloud-based services for big data, namely Google Cloud Platform, Amazon Web Services, and Microsoft Azure, the research explores user perceptions regarding the capabilities and usability of these platforms in supporting big data initiatives. The research methodology involved the design of a structured questionnaire aimed at investigating two main constructs: “Technological Maturity”, referring to the extent to which the technology meets user needs; “User Maturity”, concerning users’ competence, awareness, and confidence in utilizing the investigated platforms. The questionnaire was administered to big data professionals, who were asked to evaluate one or more technologies based on their own experience. The analysis revealed significant trends, including a general increase in perceived technological maturity with growing user experience, and a non-linear relationship between experience and user maturity. The high number of “Don’t know” responses among less experienced participants, and their decrease among more experienced users, highlights a progressive increase in self-awareness and knowledge acquisition over time. Notably, evidence of the “Dunning-Kruger” effect emerged, indicating that less experienced users tend to overestimate their abilities. This research proposes an innovative framework for assessing technological maturity in the big data context. The model not only enhances academic understanding but also offers practical guidance for organizations in the selection, adoption, and integration of cloud technologies, while supporting effective strategies for internal skills development. The findings underscore the importance of considering, alongside technological characteristics, human factors related to perception and usability of the adopted technologies.