Information visualization limits itself, per definition, to the domain of symbolic information. This paper discusses arguments why the field should also consider forms of data that are not symbolically encoded, including physical traces and material indicators. Continuing a provocation presented by Pat Hanrahan in his 2004 IEEE Vis capstone address, this paper compares physical traces to visualizations and describes the techniques and visual practices for producing, revealing, and interpreting them. By contrasting information visualization with a speculative counter model of autographic visualization, this paper examines the design principles for material data. Autographic visualization addresses limitations of information visualization, such as the inability to directly reflect the material circumstances of data generation. The comparison between the two models allows probing the epistemic assumptions behind information visualization and uncovers linkages with the rich history of scientific visualization and trace reading. The paper begins by discussing the gap between data visualizations and their corresponding phenomena and proceeds by investigating how material visualizations can bridge this gap. It contextualizes autographic visualization with paradigms such as data physicalization and indexical visualization and grounds it in the broader theoretical literature of semiotics, science and technology studies (STS), and the history of scientific representation. The main section of the paper proposes a foundational design vocabulary for autographic visualization and offers examples of how citizen scientists already use autographic principles in their displays, which seem to violate the canonical principles of information visualization but succeed at fulfilling other rhetorical purposes in evidence construction. The paper concludes with a discussion of the limitations of autographic visualization, a roadmap for the empirical investigation of trace perception, and thoughts about how information visualization and autographic visualization techniques can contribute to each other.