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An essential goal of all qualitative researchers is getting close to data. Recently, that goal has gained prominence without critical examination. Pursuit of closeness poses risks and requires careful specification. This article identifies four very different meanings of "closeness to data." Although being close to data in each of these senses is essential to researchers, the challenge of getting distance is critical to good analysis and far more difficult. Recent changes in the literature, especially on qualitative computing, have not addressed the balance of closeness and distance. Tools for either purpose too often require that data be kept in ways that are flat and static. The author concludes that the challenge defining the next generation of method writing and software development is the ability to support qualitative researchers in achieving distance and maintaining the vitality of data.