The design and maintenance of an aero-engine generates a signiﬁcant amount of documentation. When designing new engines, engi- neers must obtain knowledge gained from maintenance of existing engines to identify possible areas of concern. Firstly, this paper investigate the use of advanced business intelligence tenchniques to solve the problem of knowledge transfer from maintenance to design of aeroengines. Based on data availability and quality, various models were deployed. An asso- ciation model was used to uncover hidden trends among parts involved in maintenance events. Classiﬁcation techniques comprising of various algorithms was employed to determine severity of events. Causes of high severity events that lead to ma jor ﬁnancial loss was traced with the help of summarization techniques. Secondly this paper compares and evalu- ates the business intelligence approach to solve the problem of knowl- edge transfer with solutions available from the Semantic Web. The re- sults obtained provide a compelling need to have data mining support on RDF/OWL-based warehoused data.