One promising application of diagnostic classification models (DCM) is in the area of cognitive diagnostic assessment in education. However, the successful application of DCM in educational testing will likely come with a price--and this price may be in the form of new test development procedures and practices required to yield data that satisfy the defining characteristics of these models. By implication, this means that "retrofitting" DCM to much of the achievement testing data that is currently available in education is likely to yield unsatisfactory diagnostic classification results. Retrofitting can be described as the addition of a new technology or feature to an older system. Similarly, the authors might consider cognitive diagnostic retrofitting as the application of a new statistical or psychometric model, such as a DCM, to student response data from an existing testing system that uses traditional test development procedures and practices. In this paper, the authors contend that conducting cognitive diagnostic assessment through retrofitting will yield few successful applications, precisely because of the DCM's unique requirements. The authors work with three of the defining characteristics to describe and illustrate why retrofitting DCM to existing educational data will often prove unsuccessful. They also propose one additional defining characteristic for these psychometric and statistical models.