Cognitive complexity in data modeling: causes and recommendations

Data modeling is a complex task for novice designers. This paper conducts a systematic study of cognitive complexity to reveal important factors pertaining to data modeling. Four major sources of complexity principles are identified: problem solving principles, design principles, information overload, and systems theory. The factors that lead to complexity are listed in each category. Each factor is then applied to the context of data modeling to evaluate if it affects data modeling complexity. Redundant factors from different sources are ignored, and closely linked factors are merged. The factors are then integrated to come up with a comprehensive list of factors. The factors that cannot largely be controlled are dropped from further analysis. The remaining factors are employed to develop a semantic differential scale for assessing cognitive complexity. The paper concludes with implications and recommendations on how to address cognitive complexity caused by data modeling.