🤖 AI Summary
Existing data-analytic models, grounded in positivism, neglect critical dimensions of power, tacit knowledge, and cognitive schemata. Method: This paper proposes an interpretivist “iceberg model of meaning construction” (Add-Check-Refine), treating data as schematized artifacts and distinguishing explicit from implicit cognitive schemata; it emphasizes schema primacy, multiplicity, and epistemic humility. Validation employs historical conceptual analysis and four empirically grounded scenarios—e.g., sensor measurement bias and data neglect—to demonstrate interpretivist coherence and explanatory power. Contribution/Results: The model precisely identifies canonical analytical dilemmas while offering actionable remediation pathways. Crucially, it constitutes the first systematic integration of humanistic critique with data practice, thereby establishing both theoretical foundations and methodological scaffolding for institutionalizing interpretivism within data science.
📝 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.