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.

翻译：数据分析师面临的一个挑战是构建对特定受众有用的数据分析。此前，我们定义了一套描述数据分析的原则，这些原则可用于创建数据分析并刻画不同分析之间的差异。本文中，我们引入了一个概念，称之为数据分析师与受众之间的数据分析"契合度"。我们将成功对齐的数据分析定义为：分析师与为其开发分析的受众之间在原则上的匹配。本文提出了一种评估数据分析契合度的统计模型，并描述了其若干性质。我们认为，该框架为描述契合度提供了一种语言，并可作为实践数据科学家和数据科学课程学生构建更优数据分析的指导工具。