  Choosing the right Visualization techniques is critical in Big Data
Analytics. However, decision makers are not experts on visualization and they
face up with enormous difficulties in doing so. There are currently many
different (i) Big Data sources and also (ii) many different visual analytics to
be chosen. Every visualization technique is not valid for every Big Data source
and is not adequate for every context. In order to tackle this problem, we
propose an approach, based on the Model Driven Architecture (MDA) to facilitate
the selection of the right visual analytics to non-expert users. The approach
is based on three different models: (i) a requirements model based on
goal-oriented modeling for representing information requirements, (ii) a data
representation model for representing data which will be connected to
visualizations and, (iii) a visualization model for representing visualization
details regardless of their implementation technology. Together with these
models, a set of transformations allow us to semi-automatically obtain the
corresponding implementation avoiding the intervention of the non-expert users.
In this way, the great advantage of our proposal is that users no longer need
to focus on the characteristics of the visualization, but rather, they focus on
their information requirements and obtain the visualization that is better
suited for their needs. We show the applicability of our proposal through a
case study focused on a tax collection organization from a real project
developed by the Spin-off company Lucentia Lab.
