This chapter explores three dangers a researcher commonly encounters in the digital humanities, each posed by imperfect and incomplete data. First, the data of the past may be occluded – historians are trained to look for silences and gaps in historical accounts, and a digital scholar must develop literacy in these matters lest their analysis be riddled with inaccuracies and distortions. A second danger is dirty data, not in the sense of transmission errors, but rather, cultural biases and conceptual distortions in the source material itself that, when left unrecognized, can result in distorted narratives. A third danger in text mining is fantasy – the gross misapprehension that models of the past can function as a prediction machine. Critically reviewing projects framed by misunderstandings of history that resulted from data-driven inquiries, this chapter explores a crucial aspect of research in the humanities: the imperfection of archives, the skew of libraries, and the inherent bias of language, and how literacy in these matters means approaching even the best archives with caution.