  From dirty data to intentional deception, there are many threats to the
validity of data-driven decisions. Making use of data, especially new or
unfamiliar data, therefore requires a degree of trust or verification. How is
this trust established? In this paper, we present the results of a series of
interviews with both producers and consumers of data artifacts (outputs of data
ecosystems like spreadsheets, charts, and dashboards) aimed at understanding
strategies and obstacles to building trust in data. We find a recurring need,
but lack of existing standards, for data validation and verification,
especially among data consumers. We therefore propose a set of data guards:
methods and tools for fostering trust in data artifacts.
