1. A data classification system, comprising: an input interface configured to receive documents comprising line-item data entries, at least some of the line-item data entries having associated attributes represented directly in the documents; a data warehouse backed by a non-transitory computer readable storage medium and configured to store curated and classified data elements; a classification model stack including (a) a plurality of classification models, (b) a plurality of confidence models, and (c) a related multi-level taxonomy of classifications applicable to line-item data entries included in documents received via the input interface; and processing resources including at least one processor and a memory, the processing resources being configured to at least: execute classification models from the classification model stack to associate the line-item data entries included in the documents received via the input interface with potential classifications at each level in the related taxonomy; execute confidence models from the classification model stack to assign probabilities of correctness for each potential classification generated by execution of the classification models; determine, for each of the line-item data entries included in the documents received via the input interface, a most granular level of potential classification that meets or exceeds a threshold value; designate a classification result corresponding to the determined most granular level of potential classification for each of the line-item data entries included in the documents received via the input interface; store each line-item data entry, with or without additional processing, to the data warehouse, along with an indication of its associated classification result; and enable records in the data warehouse to be queried from a computer terminal.