Iceberg Sensemaking: A Process Model for Critical Data Analysis and Visualization
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We offer a new model of the sensemaking process for data science and visual analytics. Whereas past sensemaking models have been built on theoretical foundations in cognitivism and positivism, this model adopts interpretivist foundations in order to reframe data sensemaking in humanistic terms. We identify five key principles centered on the concept of schemas: Tacit and Explicit Schemas, Schemas First and Always, Data as a Schematic Artifact, Schematic Multiplicity, and Sensemaking Over Time. Our model uses the analogy of an iceberg, where data is the visible tip of the schema underneath it. The analysis process iteratively refines both the data and its schema in tandem. 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, predictive, and explanatory power of our model through four analysis scenarios: uncovering data injustice, investigating official data, teaching data wrangling, and producing data mashups.
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