  Big Data involves both a large number of events but also many variables. This
paper will concentrate on the challenge presented by the large number of
variables in a Big Dataset. It will start with a brief review of exploratory
data visualisation for large dimensional datasets and the use of parallel
coordinates. This motivates the use of information theoretic ideas to
understand multivariate data. Two key information-theoretic statistics
(Similarity Index and Class Distance Indicator) will be described which are
used to identify the key variables and then guide the user in a subsequent
machine learning analysis. Key to the approach is a novel algorithm to
histogram data which quantifies the information content of the data. The Class
Distance Indicator also sets a limit on the classification performance of
machine learning algorithms for the specific dataset.
