Observing Team Dynamics and Communication using Sensor-Based Social Analytics (ODYN)

Digitalization spans the whole field of business. In this digital transition, the focus is mainly on what they do and how to do this in a better and smarter way. However, there is still limited data available on who is doing it: the people. Observing and capturing that data and information leads to Big Data, which requires intelligent ICT solutions for sensor-based social analytics. In this project, we will measure team collaboration, structure and performance, towards making sense out of this data and visualize the outcomes, enabling human teams to communicate and act in a better and smarter way. We will develop approaches using wearable sensors to observe data relating to, e.g., vocal non-verbal features, proximity and potentially accelerometer data, including the Rhythm badges and the Openbeacon sensors for estimating social signals in human interactions, which have shown to be effective for capturing social interactions. For that, we will also investigate and adapt proven questionnaires and assessments, where we will collect data on team structures and individual skills/personalities of every member. This results in Big Data on the individual and team level. By linking this Big Data to the business performance we can correlate human behaviors and team structure to the business outcome. One important part is computational sensemaking, in order to make the data interpretable and explainable. Here, we aim to specifically study subgroup discovery, pattern mining, complex network analysis, specifically connected to explicative data analytics methods. We can build on existing methods and implementations, e.g., the VIKAMINE and Ubicon platforms, for enabling interpretable analysis and visualization.