  This paper introduces Data Stations, a new data architecture that we are
designing to tackle some of the most challenging data problems that we face
today: access to sensitive data; data discovery and integration; and governance
and compliance. Data Stations depart from modern data lakes in that both data
and derived data products, such as machine learning models, are sealed and
cannot be directly seen, accessed, or downloaded by anyone. Data Stations do
not deliver data to users; instead, users bring questions to data. This
inversion of the usual relationship between data and compute mitigates many of
the security risks that are otherwise associated with sharing and working with
sensitive data.
  Data Stations are designed following the principle that many data problems
require human involvement, and that incentives are the key to obtaining such
involvement. To that end, Data Stations implement market designs to create,
manage, and coordinate the use of incentives. We explain the motivation for
this new kind of platform and its design.
