The human insights missing from Big Data

To seek medical treatment

To get advice from the gods about the future

To learn new scientific discoveries

To train as philosophers

Supercomputers

Artificial intelligence and big data analytics

Social media influencers

Political leaders

knowledge

technology

big data

artificial intelligence

A software engineer who builds AI models

A data scientist who only works with big data

An ethnographer who studies human behavior and collects thick data

A market analyst who predicts financial trends

They believed their current data was sufficient

They thought her research methods were outdated

They were already investing in new smartphone technology

They wanted to focus on feature phones instead of smartphones

Big data is qualitative, while thick data is quantitative

Big data provides numerical insights, while thick data provides deep human understanding

Big data is small-scale, while thick data is large-scale

There is no real difference; both are used in data analysis

The tendency to trust qualitative data over quantitative data; it can be avoided by focusing only on numbers

The belief that only numbers provide reliable insights; it can be avoided by combining big data with human-centered thick data

The practice of underestimating big data results; it can be avoided by investing in more AI tools

The assumption that all data is equal; it can be avoided by ignoring qualitative insights