CRISP-DM as a framework for discovering knowledge in small and medium sized enterprises´ data

Discovering knowledge from a waste amount of data has become a promising area nowadays, but at the same time it is a very intricate, uncertain and time consuming process. The complexity of a data collection, the oscillations in data quality and their impact on the discovery process, as well as the applicability of results, urge for an extensive research and gain of experience to overcome the difficulties that can jeopardize the knowledge in data discovery (KDD) process as a whole. In this article we described the limitations and challenges of discovering knowledge, that we have experienced analyzing small and medium sized enterprises´ (SMEs) data.