Data Management Platforms: An Empirical Taxonomy

Abstract

Data management platforms (DMPs) are a widely used means of placing targeted advertising, for example, commercial or political advertisements. However, only a few academic papers shed light on the platforms’ mechanisms. These mechanisms’ opacity makes it hard for consumers to understand what happens with their data, and regulators struggle to implement effective regulations. Hence, we develop a taxonomy to understand and compare different characteristics of DMPs. Following Nickerson’s (2013) method and combining an inductive and deductive approach, eight dimensions emerge that differentiate DMPs. We evaluate the taxonomy’s applicability and test it with a set of nine DMPs, which we select by feasibility, relevance, and popularity. The application shows that the eight dimensions cover the significant features that explain most of the variance in characteristics between DMPs. The evaluation revealed opportunities for further development of the taxonomy