Human-in-the-Loop Processes as Enabler for Data Analytics in Digitalized Organizations

Abstract

As a key driver for innovation in science, economy and society, digitalization affects almost every aspect of our daily working and living environments. The opportunity to track data about processes, persons, and other entities in organizations allows new opportunities for digitalized working scenarios and the creation of new perspectives on matters such as inter- and intra-organizational relationships. The aim of this paper is to elaborate on these perspectives on the basis of studies that are currently a part of our research activities. Firstly, a framework is outlined that combines topic modeling of textual data and machine learning to derive thematic synergies in the data, for example, between organizations or research projects. Secondly, classical benchmarking approaches are extended by developing a suitable text-mining process for interdisciplinary research. Lastly, a brief concept about evolution as a method for further optimizations and its implications for the human-in-the-loop process is outlined. Altogether, the approaches contribute to a comprehensive human-in-the-loop model – defined as a model that combines intelligent data technologies with human interaction – in the culture of innovation amongst modern, highly digitalized organizations.