One of the premises of system dynamics is that the modeler would make  assumptions about variable relationships with enough precision to make the model useful. A common validation method is to consult with field experts, but with the advent of the internet, and automated data collection methods, knowledge is diluted as companies store abundant information without time to process it. High turnover rates at companies paired with large amounts of data have reduced the number of "experts." Without experts, companies are data rich but not necessarily knowledge rich. Customers’ dislikes, perceptions, intentions, opinions, and service characteristics reside in data warehouses (e.g. survey data is stored as categorical, nominal, ordinal or qualitative without further analysis). We present an application of  Classification and Regression Trees (CART), Chi-Square Automatic Interaction Detection (CHAID) which are known nonparametric predictive methodologies to uncover/confirm significant variable relationships and build the equations to feed the model.  An illustrative example of CHAID/SEM to explore restructuring decisions in a large service organization will be briefly discussed.