Solving messy information problems. By a combination of big data analyics and system Dynamics

Abstract

The present study gives an answer on the question: Which possibilities exist for solving messy information problems when using a combination of system dynamics and big data analytics? Messy information problems exists of five main characteristics: ambiguity, incompleteness, biases and faults, building the dynamic complexity structure and understand the dynamic complexity structure. Based on a literature review a model was build , validated with multiple sources and two possibilities were found. The first archetype is fixes that fails and consists of two balancing loops and two reinforcing loops. The two balancing loops in this structure showed the processes of data mining and deep learning. The two reinforcing loops represent the application of expert and theoretical knowledge. The second archetype is success to the successful and consists of two reinforcing loops. One loop about parameter specification using model structure characteristics and one loop about parameter specification using. However, these loops are limited by the effects of the consequences the project have on participants who work with expert and theoretical knowledge. Another important limitation is not all data will be suitable for the combinations. Furthermore, a good examination of the big data analytics results, can prevent rejecting them to early.