In the late 1980s, Gene Lerner (with user input from Emanuel Schegloff) developed the first piece of data management and analysis software designed specifically for Conversation Analysis (CA) research. ‘The WorkBench’ (see Figure 9.1) enabled researchers to organize, index, search, code, and annotate audio and video clips, media-synced transcripts, and data collections. At the time, the few available software tools designed for qualitative data analysis provided only generic methods for coding and indexing textual data (see Tesch, 2013, pp. 150–166). The WorkBench, by contrast, provided a rich environment for hypertextual digital media composition and transcription that met CA’s methodological requirements for working flexibly with naturalistic observational data, addressing growing concerns that qualitative data analysis software tools could standardize and entrench reductive analytic practices (Coffey et al., 1996). Specialized use of new-media technologies has shaped the field of interaction analysis from its outset (Laurier, 2014), and new tools and empirical materials, from social media to 3D video, are continuously opening up new analytic affordances, research questions, and methodological challenges (McIlvenny, 2019; Meredith & Stokoe, 2014). While methodological discussions about novel research tools remain essential, technology-specific recommendations can quickly become outdated (Goodwin, 1993). In this chapter, therefore, we discuss the various practices, tools, and workflows interaction researchers use to analyze their data, without focusing specifically on digital tools. The choices we make among tools and techniques for data collection, comparison, and annotation are also analytic choices, so we focus on the affordances and constraints those choices entail. We begin at the point that the researcher has collected their primary data (see Hoey & Webb, Chapter 2, this volume), organized the field recordings (Albert & Hofstetter, Chapter 4, this volume), made some initial analytic observations (see Clift & Mandelbaum, Chapter 6, this volume) and are ready to build a collection (see Clayman, Chapter 8, this volume). We describe how conversation analysts review and segment recordings into clips, how they name and keep track of their files, and how data management processes inform and constitute analysis itself. We then focus on three practical methods researchers use to organize data for analysis using both analog and digital tools. Finally, we outline data management methods that analysts use to prepare clips for sharing in collaborative data sessions.