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    Data alone is insufficient for effective decision-making. Contextual knowledge, including data limitations and subject matter expertise, is crucial for robust analysis and improved outcomes in science and industry.

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    Area of Science:

    • Data Science
    • Information Science
    • Decision Analysis

    Background:

    • Data-driven decision-making is prevalent across science, industry, and policy.
    • Data is an incomplete representation of reality, often insufficient for sound analysis.
    • Contextual knowledge about datasets is vital for effective data interpretation.

    Purpose of the Study:

    • To investigate the role of contextual knowledge in data analysis workflows.
    • To understand how analysts incorporate diverse knowledge sources beyond raw data.
    • To explore how data knowledge influences analysis outcomes.

    Main Methods:

    • Conducted an interview study with analysts from various domains and experience levels.
    • Inquired about the application and impact of contextual knowledge in their work.
    • Analyzed qualitative data to identify patterns in knowledge integration.

    Main Results:

    • Data alone is insufficient for analysts; contextual knowledge is essential.
    • Analysts integrate domain expertise and data hunches into their workflows.
    • Understanding data's context significantly shapes analysis outcomes.

    Conclusions:

    • Effective data analysis requires a blend of data and contextual knowledge.
    • Design opportunities exist to better support the integration of knowledge and data.
    • Future systems should robustly incorporate both data and analyst knowledge.