Iceberg Sensemaking: A Process Model for Critical Data Analysis and Visualization

TL;DR

The paper critiques the dominance of positivist assumptions in data sensemaking and proposes Iceberg Sensemaking, an interpretivist three-phase Add-Check-Refine process in which data grow from Tacit and Explicit Schemas. It reframes data as a Schematic Artifact, emphasizes Schemas First and Always, and advocates Schematic Multiplicity to avoid single-perspective bias, validated through four diverse scenarios. The approach foregrounds power, context, and ethical considerations, promoting epistemic humility and pluralism as core methodological virtues for critical data visualization. This framework aims to improve transparency, accountability, and justice in data-driven reasoning by making the interpretive acts behind data explicit and contestable.

Abstract

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.

Figures (4)

• Figure 1: Pirolli and Card (2005) model of sensemaking. The figure has been redesigned to highlight the placement of schemas and schematization.
• Figure 2: Grolemund and Wickham (2014) model of sensemaking. The figure has been redesigned to highlight the placement of schemas and process of searching for a relevant schema.
• Figure 3: Sacha et al. (2014) model of sensemaking. The figure has been redesigned to highlight the placement of knowledge, the closest approximation of a schema in this model.
• Figure 4: The Iceberg Sensemaking Model depicts how a data analyst may use Datasets, Explicit Schemas, and Tacit Schemas to develop Findings through a critical sensemaking process. The wavy waterline separates the Tacit Schemas below from the Explicit Schemas, Datasets, and Findings above. Data Sources and People are external sources of the schemas that eventually become Datasets. The analyst is directed to consider the role of power, in the broad sense of social influence or ideology, to inform their articulation of Tacit Schemas. Actions are classified into three phases (Add, Check, and Refine). The sensemaking process can loop back to any previous phase before reaching a conclusion when Findings are finalized.