  We thought data to be simply given, but reality tells otherwise; it is
costly, situation-dependent, and muddled with dilemmas, constantly requiring
human intervention. The ML community's focus on quality data is increasing in
the same vein, as good data is vital for successful ML systems. Nonetheless,
few works have investigated the dataset builders and the specifics of what they
do and struggle to make good data. In this study, through semi-structured
interviews with 19 ML experts, we present what humans actually do and consider
in each step of the data construction pipeline. We further organize their
struggles under three themes: 1) trade-offs from real-world constraints; 2)
harmonizing assorted data workers for consistency; 3) the necessity of human
intuition and tacit knowledge for processing data. Finally, we discuss why such
struggles are inevitable for good data and what practitioners aspire, toward
providing systematic support for data works.
