ABSTRACT
This chapter explores the holistic concept of data science. Data science is concerned with the extraction of meaning from data. The chapter discusses why classical approaches alone are unlikely to keep pace with the needs of analysts in this domain, before moving on to the central issue of understanding and establishing cause-and-effect relationships. The reader may also have encountered the terms knowledge discovery and knowledge discovery in databases (KDD), which usually refer to drawing on big data and data visualisation challenges. The chapter further presents some time out of the pure complexity context to summarise some important principles regarding data integrity in the classical context. Such are the challenges that data science equips us better to explore, in particular, the trade-offs between KPIs and the interactions at lower levels with other performance indicators.
