In the field of analytics and intelligent data analytics one of the open challenges regards to the metainformation model that allows an intelligent reporting system to preprocess and process correctly data coming from any kind of questionnaire, independently of number of variables, types of variables involved, including multiple response variables or other complex structures like compositional variables or temporal variables among others. INSESS is an intelligent system designed and used for the first time in INSESS-COVID19 project, specialized in automatic intelligent reporting for a parameterized questionnaire that includes different types of variables. From one INSESS-consultation to another, the questionnaire can change completely and the consultation may be oriented as well to different types of target populations going from citizens, to associates to an NGO or to special nonpublic populations and the pre and processing still works. The flexibility required to do this, relies on a well-founded and sufficiently general conceptual model for the metainformation associated to the questionnaire, so that the analytical module can properly reason on top of the metainformation and make intelligent decisions about the kind of preprocessing required or the kind of descriptive analysis to be applied. The metainformation model has been developed from a knowledge representation perspective.The paper presents the context of the research, the conceptual model defined and 4 real use cases where the INSESS methodology was used for different kinds of private/public consultations proving the flexibility of the tool.