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Automated spectrum analytics inform critical decisions in dynamic spectrum access networks such as (i) how to allocate network resources to clients, (ii) when to enforce penalties due to malicious or disruptive activity, and (iii) how to chart policies for future regulations. The insights gleaned from a spectrum trace, however, are as objective as the trace itself, and artifacts introduced by sensor imperfections or improper configuration will inevitably affect analysis outcomes. Yet, spectrum analytics have been largely developed in isolation from the underlying data collection and are oblivious to sensor-induced artifacts. To address this challenge, we develop VIA, a framework that attributes sensor properties and configuration to spectrum data fidelity, and models the relationship between spectrum analytics performance and data quality. VIA does not require expert input or intervention and can be used to profile the fidelity of unknown sensors. VIA takes as an input a spectrum trace and the sensor configuration, and benchmarks data quality along three dimensions: (i) Veracity, or how truthfully a scan captures spectrum activity, (ii) Intermittency, characterizing the temporal persistence of spectrum scans and (iii) Ambiguity quantifying the likelihood of false detection. We employ VIA to measure the data fidelity of five common sensor platforms. We then predict the outcome of several spectrum analysis tasks including occupancy and transmitter detection, and modulation recognition using both controlled and real-world measurements. We demonstrate high prediction performance with an average mean squared error of 0.0013 across all tasks using both regression and neural network models.