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    This study introduces a new interpretivist model for data sensemaking, viewing data analysis through schemas. It offers a three-phase process to better understand and refine data insights.

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    Area of Science:

    • Data Science
    • Information Science
    • Human-Computer Interaction

    Background:

    • Existing data sensemaking models often rely on positivist assumptions.
    • A critical, humanistic perspective is needed for a more nuanced understanding of data analysis.
    • Interpretivism offers a valuable lens for reframing data sensemaking.

    Purpose of the Study:

    • To propose a novel, interpretivist model for the data sensemaking process.
    • To reframe data analysis and visualization through a critical, humanistic lens.
    • To provide a structured, three-phase model for understanding how analysts make sense of data.

    Main Methods:

    • Developed a three-phase process model (Add, Check, Refine) using an iceberg analogy.
    • Incorporated the concepts of tacit and explicit schemas throughout the sensemaking process.
    • Validated the model through four distinct data analysis scenarios.

    Main Results:

    • The proposed model emphasizes schemas as fundamental to data interpretation, treating data as a schematic artifact.
    • It highlights the importance of tacit knowledge, power dynamics, and schematic multiplicity in sensemaking.
    • The model effectively addresses scenarios like identifying missing data, data wrangling, and handling inconvenient data.

    Conclusions:

    • The interpretivist approach fosters epistemic humility and promotes pluralism in data sensemaking.
    • The new model offers a more comprehensive framework for understanding the complexities of data analysis.
    • This approach enhances the descriptive and prescriptive power of sensemaking models.