ABSTRACT

The data revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the practice of data analysis is design thinking—the problem-solving process to understand the people for whom a solution is being designed. For a given problem, there can be significant or subtle differences in how a data analyst (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow. These choices can affect the data analysis products themselves and the experience of the consumer of the data analysis. Therefore, the role of a producer can be thought of as designing the data analysis with a set of design principles. Here, we introduce design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide data showing variation of principles within and between producers of data analyses. Our work suggests a formal mechanism to describe data analyses based on design principles. These results provide guidance for future work in characterizing the data analytic process. Supplementary materials for this article are available online.

摘要

数据革命导致人们对数据分析实践的兴趣日益浓厚。虽然关于统计思维的文章已经很多，但数据分析实践中出现的一种补充思维形式是设计思维，即理解解决方案所针对的人群的解决问题的过程。对于给定的问题，数据分析师（或数据分析的生产者）构建、创建或设计数据分析的方式可能存在显着或细微的差异，包括方法、工具和工作流程选择的差异。这些选择会影响数据分析产品本身以及消费者的体验的数据分析。因此，生产者的角色可以被认为是用一套设计原则来设计数据分析。在这里，我们介绍数据分析的设计原则，并描述如何以定量和信息丰富的方式将它们映射到数据分析。我们还提供显示数据分析生产者内部和之间原则差异的数据。我们的工作提出了一种基于设计原则描述数据分析的正式机制。这些结果为未来表征数据分析过程的工作提供了指导。本文的补充材料可在线获取。