Information Quality (IQ) frameworks, such as Wang’s and Strong’s (1996), treat IQ dimensions as discrete categories as seen above the sea surface. Yet scant research has examined the actual causal structure among these dimensions beneath the water, a phenomenon we termed the “iceberg effect.” This study aims to examine the iceberg effect in Building Information Modeling (BIM) by using Industry Foundation Classes (IFC)-based data exchange as an example. It does so by conducting a systematic review of 25 studies, a secondary analysis of 18 practitioner interviews, and a Decision-Making Trial and Evaluation Laboratory (DEMATEL)-Interpretive Structural Modelling (ISM) causal modelling with 10 domain experts. The results reveal that practitioners systematically over-report visible downstream symptoms (data loss 72%) while under-recognizing upstream root causes (classification errors 17%), leading to a perception-causality gap, i.e., the “iceberg effect.” The classification of dimension (D3) is the principal CAUSAL driver (D−R = +1.37), followed by data loss as a downstream effect (D−R = −0.57), forming a causal model chain as follows: Classification → Data Relations/Semantics → Data Loss/Geometry. This model indicates a causal asymmetry between IQ dimensions. It extends Wang’s and Strong’s framework by demonstrating that IQ improvement in standardized data exchange should follow the causal hierarchy rather than practitioner-reported frequency.