Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics

In the last decade, advanced approaches to the analysis and exploitation of large amounts of heterogeneous data ("big data") have gained tremendous attention, particularly on the part of corporate decision-makers but also from academic researchers [1, 2, 3]. The term "advanced analytics" generally refers to various methods beyond traditional multivariate statistics, mainly from the field of machine learning (ML), that leverage big data to drive decisions and actions (e.g., in organizations) [4, 5, 1, 6]. While researchers started to emphasize the suitability of these approaches mostly for (i) the design of innovative artifacts (e.g., decision support or process automation systems) and (ii) the induction of knowledge from quantitative studies [7, 1, 8, 9], companies increasingly deploy analytics applications in order to exploit their promising business potential [4, 6]. Several research articles show that such applications--especially those driven by modern ML algorithms--may considerably improve efficiency and/or effectiveness in important business areas, such as predictive maintenance, financial fraud detection, capacity planning, and product recommendation [10, 11, 12, 13, 14, 15, 16, 17]. The average return on investment of modern data analytics applications in a business context is estimated at an almost inconceivable rate of 1,301% [18].