The treatment of many-valued data with FCA has been achieved by means of scaling. This method has some drawbacks, since the size of the resulting formal contexts depends usually on the number of di erent values that are present in a table, which can be very large. Pattern structures have been proved to deal with many-valued data, offering a viable and sound alternative to scaling in order to represent and analyze sets of many-valued data with FCA. Functional dependencies have already been dealt with FCA using the binarization of a table, that is, creating a formal context out of a set of data. Unfortunately, although this method is standard and simple, it has an important drawback, which is the fact that the resulting context is quadratic in number of objects w.r.t. the original set of data. In this paper, we examine how we can extract the functional dependencies that hold in a set of data using pattern structures. This allows to build an equivalent concept lattice avoiding the step of binarization, and thus comes with better concept representation and computation.