  A challenge that data analysts face is building a data analysis that is
useful for a given consumer. Previously, we defined a set of principles for
describing data analyses that can be used to create a data analysis and to
characterize the variation between analyses. Here, we introduce a concept that
we call the alignment of a data analysis between the data analyst and a
consumer. We define a successfully aligned data analysis as the matching of
principles between the analyst and the consumer for whom the analysis is
developed. In this paper, we propose a statistical model for evaluating the
alignment of a data analysis and describe some of its properties. We argue that
this framework provides a language for characterizing alignment and can be used
as a guide for practicing data scientists and students in data science courses
for how to build better data analyses.
