An active human role is essential in big data-led decisions and data-intensive science

Area of Science:

• Data Science
• Knowledge Discovery
• Decision Making

Background:

• Big data is transforming decision-making and knowledge production across sectors.
• The increasing reliance on data-intensive science necessitates examining evolving data interaction paradigms.
• Current trends raise concerns about human involvement and the interpretation of big data.

Purpose of the Study:

• To analyze three critical issues impacting how big data is read and utilized.
• To explore the shift towards data-led models and their implications for knowledge discovery.
• To address the diminishing human role and the persuasive allure of big data.

Main Methods:

• Conceptual analysis of big data trends and their impact on scientific practices.
• Examination of the changing paradigms in data utilization and knowledge production.
• Discussion on the necessity of human expertise in data interpretation and decision-making.

Main Results:

• A shift towards complex, data-led models challenges traditional principles like Occam's razor.
• Decreasing human involvement in data processing raises concerns about accountability.
• The 'seductive allure' of big data can overstate the significance of its findings.

Conclusions:

• An active human role is crucial for unbiased incorporation of expertise into data-driven processes.
• Robust monitoring and appraisal mechanisms are needed to ensure data addresses relevant questions.
• Rethinking human-data interaction is essential to align big data practices with human needs.