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Qualitative data can be gathered from an array of rich sources of research information. One of the popular ways to collect this data is by interviewing a range of experts on the topic, followed by transcription, resulting in a database of written documents, often supplemented by other documented data that informs the topic. Thematic or Content Analysis can then be used to explore the data and identify themes of meaning that enlighten the research topic, with the themes being gathered into nodes. The researcher now has an array of nodes, which needs to be organised into a coherent model, and more importantly, one that represents the views of the research informants. To do this with some degree of rigour, the researcher needs some way of ranking the nodes in terms of their relative importance. The node ranking can be based on experience, or on the literature, but neither of these approaches looks to the data itself. If the database contains new or unexpected knowledge, neither experience nor the literature will guide us to it, and vital new insights may easily be missed. The framework outlined in this paper aims to provide a sound first‑cut analysis of the data, based on the evidence in the research interviews themselves. Clearly the literature and research experience have an important role to play in shaping the results of any research. However this paper argues that one should proceed only after the data itself has been offered "the first chance to speak".The node classification matrix detailed here, identifies distinct node categories, each ranging in significance and with particular characteristics that reveal key aspects of the informants' views. In this way the researcher can use the nodes to reveal the voice of the experts, and build a scientifically rigorous set of results from a qualitative database.