Relationship between the data, such as direct observations of nature and recorded measurements, and the models is very complicated in the ‘water domain’. It is not at all as clear and explicit as it is often presented by teachers to students, by consultants to clients, or by authors to readers of publications. A number of aspects of this relationship are discussed using examples to illustrate the author's views. Limitations of data-driven tools (correlations, Artificial Neuronal Networks, Genetic Algorithms, etc.) and data-mining, when applied without physical knowledge of the relevant phenomena, are discussed, as are those of deterministic models. The currently used ‘good practice’ paradigm in modelling (the model is to be set up, calibrated, validated and run) is rejected when deterministic models are concerned. They should not be calibrated. A new paradigm, a new ‘code of good practice’, is proposed instead. Strategic and tactical aspects of various available approaches to modelling of physical phenomena and data exploitation have practical engineering and financial consequences, most often immediate and sometimes very important: hence the significance of the subject that concerns the everyday occupations of modellers, their clients and end-users.