Abstract: Techniques are disclosed for statistically and ontologically correlated business intelligence (BI) analytics. An example method includes performing an ontological analysis on relevant data defined for a BI analytics query to determine correlations with ontological concepts. The method includes performing a statistical analysis on direct analytics output data to rank the direct analytics output data in order of influence on the direct analytics output. The method includes performing a statistical analysis on the relevant data set relative to the direct analytics output data to determine data in the relevant data set that influence the direct analytics output data, thereby generating a list of key drivers ranked in order of influence. The method includes revising the ranking of the key drivers based on correlations of the key drivers with the ontological concepts. The method includes generating a correlated analytics output comprising information on the key drivers based on the ranking.

Abstract: Techniques are disclosed for statistically and ontologically correlated business intelligence (BI) analytics. An example method includes performing an ontological analysis on relevant data defined for a BI analytics query to determine correlations with ontological concepts. The method includes performing a statistical analysis on direct analytics output data to rank the direct analytics output data in order of influence on the direct analytics output. The method includes performing a statistical analysis on the relevant data set relative to the direct analytics output data to determine data in the relevant data set that influence the direct analytics output data, thereby generating a list of key drivers ranked in order of influence. The method includes revising the ranking of the key drivers based on correlations of the key drivers with the ontological concepts. The method includes generating a correlated analytics output comprising information on the key drivers based on the ranking.