The popular dynamic Markov compression algorithm (DMC) offers state-of-the-art compression performance and matchless conceptual simplicity. In practice, however, the cost of DMC's simplicity and performance is often outrageous memory consumption. Several known attempts at reducing DMC's unwieldy model growth have rendered DMC's compression performance uncompetitive. One reason why DMC's model growth problem has resisted solution is that the algorithm is poorly understood. DMC is the only published stochastic data model for which a characterization of its states, in terms of conditioning contexts, is unknown. Up until now, all that was certain about DMC was that a finite-context characterization exists, which was proved in using a finiteness argument. This paper presents and proves the first finite-context characterization of the states of DMC's data model Our analysis reveals that the DMC model, with or without its counterproductive portions, offers abstract structural features not found in other models. Ironically, the space-hungry DMC algorithm actually has a greater capacity for economical model representation than its counterparts have. Once identified, DMC's distinguishing features combine easily with the best features from other techniques. These combinations have the potential for achieving very competitive compression/memory tradeoffs.