I've always taken part of my job as a data scientist is to articulate when data is not sufficient to meet a particular a goal (and to outline what data would be necessary). For some goals doing special data sampling and building a model on small (complete) data is better than using the fragmented overall database.

I work mostly on claims data now, and building models to identify problematic claims I assure folks is feasible.