Decision mining in information technology processes - a case study of the new idea discovery process

Effective knowledge-based processes are essential for companies operating in the information technology industry. These processes rely on the expertise of skilled workers and play a crucial role in the value chain of such organizations. Decision-making is a critical element of knowledge-based processes, highlighting the need to identify decision rules and models accurately. In this paper, we examine the process of identifying and deciding on proposed ideas in the software industry, analyzing decision logs from a leading software company. The Rough sets theory and fast Reduction algorithm are employed to provide a step-by-step approach to data analysis and decision mining. The algorithm identifies vital features used in decision-making and presents the decision model as if-then rules, utilizing existing equivalence rules between data. The results demonstrate that this model can significantly reduce the direct involvement of decision-makers and the duration of the decision-making process.