We offer a new model of the sensemaking process for data analysis and visualization. Whereas past sensemaking models have been grounded in positivist assumptions about the nature of knowledge, we reframe data sensemaking in critical, humanistic terms by approaching it through an interpretivist lens. Our three-phase process model uses the analogy of an iceberg, where data is the visible tip of underlying schemas. In the Add phase, the analyst acquires data, incorporates explicit schemas from the data, and absorbs the tacit schemas of both data and people. In the Check phase, the analyst interprets the data with respect to the current schemas and evaluates whether the schemas match the data. In the Refine phase, the analyst considers the role of power, articulates what was tacit into explicitly stated schemas, updates data, and formulates findings. Our model has four important distinguishing features: Tacit and Explicit Schemas, Schemas First and Always, Data as a Schematic Artifact, and Schematic Multiplicity. We compare the roles of schemas in past sensemaking models and draw conceptual distinctions based on a historical review of schemas in different academic traditions. We validate the descriptive and prescriptive power of our model through four analysis scenarios: noticing uncollected data, learning to wrangle data, downplaying inconvenient data, and measuring with sensors. We conclude by discussing the value of interpretivism, the virtue of epistemic humility, and the pluralism this sensemaking model can foster.

翻译：我们提出了一个针对数据分析和可视化的意义构建过程新模型。过去的意义构建模型基于关于知识本质的实证主义假设，而我们通过解释主义视角，以批判性、人本主义的方式重新构建数据意义构建。我们的三阶段过程模型使用冰山类比，其中数据是潜在模式可见的尖端。在添加阶段，分析师获取数据，融入来自数据的显性模式，并吸收数据和人员双方的隐性模式。在检验阶段，分析师根据当前模式解释数据，并评估模式是否与数据匹配。在精炼阶段，分析师考虑权力的作用，将隐性内容转化为明确表述的模式，更新数据，并形成结论。我们的模型具有四个重要区别性特征：隐性与显性模式、模式优先且贯穿始终、数据作为模式化产物、以及模式多元性。我们比较了过去意义构建模型中模式的作用，并基于不同学术传统中对模式的历史回顾进行了概念区分。我们通过四个分析场景验证模型的描述性和规定性能力：注意未收集的数据、学习处理数据、淡化不便的数据、以及使用传感器测量。最后，我们讨论了解释主义的价值、认知谦逊的美德，以及这一意义构建模型能够促进的多元主义。