To accomplish a given computational task, a number of algorithmic and heuristic approaches can be employed to act upon the ever-varying input data. Depending upon the assumptions made regarding the data, the algorithm and the task, the end result from each of these approaches could be different. Currently, there does not exist an automatic, robust, precise, simple, and algorithm-independent measure to rate the accuracy of a multiplicity of algorithms to accomplish a given task on the given data. Lack of such a measure severely restricts the integration of "datacentric" computational tools. This paper proposes a Fourier-domain based method to robustly assess and rank the accuracy of a multiplicity of abstractions vis-a-vis the original data. The method is scalable across dimensions and data types and is blind to the task associated with the generation of the competing to-be-rated abstractions.