AbstractThis paper could have been subtitled “Was Johannes Kepler a precursor of analytics?” Models constitute a key tool for achieving a deep understanding of how complex systems behave, and they have been central to building the body of knowledge that we understand as “science.” Since its origins, operations research has claimed to be a scientific discipline and, as such, it is rooted in the model‐building process. Epistemologically, it is a process that is schematically represented by the methodological chain: facts→laws→theories. One well‐known example of the paradigm of modern science is Kepler's quest to interpret Brahe's astronomical observations in terms of a model, which resulted in his laws of planetary motion. Since then, we have learned that reality is more complex than we thought, and that it is uncertain and dynamic in both probabilistic and chaotic terms. Technological evolution currently provides us with quality data in unprecedented amounts, and this has forced us to develop appropriate methods for dealing with them. However, this only means that the methodological chain for discovering knowledge is now richer with respect to our capabilities of analyzing the facts, and therefore the door is now open to greater possibilities of finding laws and formulating explanatory theories. The search for Higgs boson, frequently presented as an archetype of analytics process, is essentially supported by the same epistemological principles as Kepler's work. This paper is devoted to making some remarks on these similarities and, in doing so, will highlight the relationships between the model‐building processes and the set of techniques under the umbrella of analytics. The above‐mentioned comments took place within the framework of the theme of the IFORS 2014 conference: The Art of Modeling. Relationships are illustrated in a typical, contemporary case, the time‐dependent analysis of mobility patterns in urban areas exploiting information and communications technologies, in which pervasive sensoring makes massive amounts of data available.