  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.
