In this paper, we design a new model for Big data analytics – data-driven axes creation model. In Big data environment, the one of the important technologies is a correlation measurement. We cannot define a protocol of measurement on Big data era, because there are many varieties of data. However, almost current data analytics and data mining method cannot apply to Big data environment, because the big data environment is opened assumption and we have to consider new methods for opened assumption. That is, we have to design a new data-driven axes creation model for correlation measurement method. Our proposed model creates axes for correlation measurement on Big data analytics. Specifically, this model infers in the Bayesian network and measures correlation in the coordinate axes. Therefore, this model maps the Bayesian network into measure correlation mutually. This model contributes to a paradigm shift of Big data analytics.