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：我们提出了一种用于数据分析和可视化的新型意义建构过程模型。以往的意义建构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、人文主义的方式重新构建数据意义建构。我们的三阶段过程模型采用冰山类比，其中数据是底层图式的可见部分。在“添加”阶段，分析者获取数据，整合数据中的显性图式，并吸收数据与人员的隐性图式。在“检验”阶段，分析者依据现有图式解读数据，并评估图式与数据是否匹配。在“精炼”阶段，分析者考量权力因素，将隐性内容明确表述为图式，更新数据并形成研究结论。本模型具有四个重要特征：隐性/显性图式、图式优先且持续存在、数据作为图式产物、图式多重性。我们比较了图式在过往意义建构模型中的作用，并基于不同学术传统中图式的历史回顾提出概念区分。通过四个分析场景（关注未收集数据、学习数据整理、淡化不便数据、传感器测量），我们验证了模型的描述性与规范性能力。最后，我们讨论了解释主义的价值、认知谦逊的美德，以及该意义建构模型可能促进的多元主义。