The industrial Internet of Things (IIoT) provides increasing amounts of real-time data. These data bring potential, as the information can be used to identify faults in machines or optimize processes to remain competitive. Human decision-makers are involved in many of these decisions as a supervising and intervening human-in-the-loop. They use this information to make decisions such as emergency shutdowns. While there are significant advances in efficiently analyzing and processing large amounts of data to draw conclusions for business processes, research on the adequacy of supporting decision-makers is relatively scarce. Yet, it is crucial that decision-makers are supported at work. Due to the amount of sensor data, decision-makers can be consistently engaged in the process with repetitive decisions. It is comprehensible that continuous and repetitive work results in increased cognitive load, leading to errors and oversights. As the data come from sensitive production systems, errors can have consequences on these IIoT systems. The failure to detect errors leads to the shutdown of the entire production process due to the failure of essential machines. We aim to develop design principles and an architecture that is aware of the cognitive load of decision-makers, with the goal of reducing the cognitive load of decision-makers.