Embedded DMAIC Methodology into Financial Knowledge Management System

We present a “define, measure, analyze, improve, and control” (DMAIC) methodology to model the knowledge discovery process for BI applications, specifically in statistical analysis and data mining. We implemented the DMAIC methodology embedded into the existed Financial Knowledge Management System (FKMS), which can be used as an effective means to dramatically improve knowledge product and process quality. It is meant for the achievement of total financial industry power users’ satisfaction by producing near defect-free financial knowledge. We have emphasized the knowledge engineering perspective for developing knowledge sets into historic reference decision with regard to formed knowledge management and sharing architecture in the knowledge discovery process in addition to the importance of leveraging structures and DMAIC methodology in design as well as implementation. 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 made helpful to support decision making. We finally illustrate the above claims with a process of applying data mining techniques to support corporate bonds classification.