We offer a new model of the sensemaking process for data science 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 the schema underneath it. 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 philosophical 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 and the virtue of epistemic humility.

翻译：我们提出了一种面向数据科学和可视化的新型认知建构过程模型。不同于以往基于实证主义知识假设的认知建构模型，我们通过阐释学视角，以批判性、人本主义的术语重新审视数据认知建构过程。该三阶段过程模型采用冰山类比，将数据视为其底层认知图式的可见尖顶。在"添加"阶段，分析者获取数据、融入数据的显性图式，并吸收数据与人员双方的隐性图式；在"校验"阶段，分析者基于当前图式解读数据，评估图式与数据的匹配程度；在"精炼"阶段，分析者审视权力作用、将隐性要素转化为显性图式、更新数据并形成研究发现。本模型具有四个关键特征：隐性图式与显性图式、图式优先与持续存在、数据作为图式化产物、以及图式多样性。我们对比了过往认知建构模型中图式的作用，基于不同哲学传统对图式概念的历史梳理进行了概念区分。通过四个分析场景验证了模型的描述性与规范性效力：识别未采集数据、学习数据清洗、弱化干扰性数据、以及传感器测量。最后，我们探讨了阐释学的价值与认知谦逊的美德。