Business intelligence (BI) applications within an enterprise range over enterprise reporting, cube and ad
hoc query analysis, statistical analysis, data mining, and proactive report delivery and alerting. The most
sophisticated applications of BI are statistical analysis and data mining, which involve mathematical and
statistical treatment of data for correlation analysis, trend analysis, hypothesis testing, and predictive
analysis. They are used by relatively small groups of users consisting of information analysts and power
users, for whom data and analysis are their primary jobs. We present an ontology-based approach for BI
applications, specifically in statistical analysis and data mining. We implemented our approach in financial knowledge management system (FKMS), which is able to do: (i) data extraction, transformation and
loading, (ii) data cubes creation and retrieval, (iii) statistical analysis and data mining, (iv) experiment
metadata management, (v) experiment retrieval for new problem solving. The resulting knowledge from
each experiment defined as a knowledge set consisting of strings of data, model, parameters, and reports
are stored, shared, disseminated, and thus helpful to support decision making. We finally illustrate the
above claims with a process of applying data mining techniques to support corporate bonds classification.