This paper presents a proposed taxonomy of data modeling techniques. Each technique was classified with regards to its primary source of domain knowledge, recommended/intended use with regards to system size, and whether the technique was analytical or more synthesis oriented. We predict that our taxonomy will prove valuable to both academics and practitioners, and form the basis of future endeavors aimed at developing more robust methodologies for developing standardized data models (see Appendix B), large data models, and applications of ontologies to real-world database design.