  Data analysis impacts virtually every aspect of our society today. Often,
this analysis is performed on an existing dataset, possibly collected through a
process that the data scientists had limited control over. The existing data
analyzed may not include the complete universe, but it is expected to cover the
diversity of items in the universe. Lack of adequate coverage in the dataset
can result in undesirable outcomes such as biased decisions and algorithmic
racism, as well as creating vulnerabilities such as opening up room for
adversarial attacks.
  In this paper, we assess the coverage of a given dataset over multiple
categorical attributes. We first provide efficient techniques for traversing
the combinatorial explosion of value combinations to identify any regions of
attribute space not adequately covered by the data. Then, we determine the
least amount of additional data that must be obtained to resolve this lack of
adequate coverage. We confirm the value of our proposal through both
theoretical analyses and comprehensive experiments on real data.
