  We present a case that the newly emerging field of synthetic data in the area
of recommender systems should prioritize `doing data right'. We consider this
catchphrase to have two aspects: First, we should not repeat the mistakes of
the past, and, second, we should explore the full scope of opportunities
presented by synthetic data as we move into the future. We argue that explicit
attention to dataset design and description will help to avoid past mistakes
with dataset bias and evaluation. In order to fully exploit the opportunities
of synthetic data, we point out that researchers can investigate new areas such
as using data synthesize to support reproducibility by making data open, as
well as FAIR, and to push forward our understanding of data minimization.
