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Here's what you need to know about my data: Exploring Expert Knowledge's Role in Data Analysis
IEEE Transactions on Visualization and Computer Graphics
|December 10, 2025
Summary
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.
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.
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