In 1985–1986, Doug Maynard and Courtney Marlaire recorded videos of thirteen children going through developmental diagnostic processes. After duplicating the soon-to-be obsolete Betamax tapes onto VHS tapes (now also obsolete), they each stored a separate working copy of the data. Incredibly, they later discovered that they had each, independently, lost the recording of one particular interview that became central to their foundational work on interaction in autism diagnosis (Maynard, 2005). Thankfully, the clips and audiotape copies made while doing transcription had preserved enough of this recording for analytic purposes, but the anecdote illustrates that careful data management is necessary for any Conversation Analysis (CA) project. Data collected by the first generation of conversation analysts were recorded onto tape or film, duplicated, transcribed on a typewriter, and mimeographed before being shared by post or passed from hand to hand. Some corpora that were central to early teaching materials and core publications became “classic data,” often returned to as familiar examples of a phenomenon, reanalyzed, and reused in analytic collections (Bolden, 2015). Even today, half-centuryold data with titles like “Chicken Dinner,” “Two Girls,” and “Auto-Discussion” are frequently used and cited in CA research (e.g., Bolden, 2018; Clift, 2020; Lerner, 1993; Schegloff, 1988, 2007; Sidnell, 2006). Some classic data such as Jefferson’s (2007) Newport Beach transcripts have been digitized and published online, but most only circulate as excerpts in publications, at workshops, between mentors and students or – as among the first generation of conversation analysts – informally between colleagues (Drew et al., 2015). While the reuse and reexamination of classic data has contributed to the robustness and longevity of CA findings (De Ruiter & Albert, 2017), informal methods of data management have restricted access for many (Hoey & Raymond, 2022). For example, key data extracts that are simply published as transcripts are only available for further analysis by researchers who can gain access to original clips and recordings. T his chapter outlines the first steps to mitigate these risks and ensure the security, reusability, and accessibility of data as soon as field recordings are complete (see Hoey & Webb, Chapter 2, this volume), from initial data capture and backup, to cataloguing and organizing multiple data sources, to anonymizing and otherwise preparing data for sharing. We reserve discussion of working with clips and collections for a separate chapter (Hofstetter & Albert, Chapter 9, this volume).