How the list is made
Methodology
Every leader receives a score from 50 to 99, a weighted composite of four criteria assessed by the editors. Rank is determined by score; ties are broken alphabetically by name.
Scoring rubric
- Scale of influence30%
How many people, organisations and datasets their decisions reach: users, customers, capital, compute and data under their control.
- Innovation25%
Original contributions to how data is stored, processed, modelled or governed: papers, products, open-source projects and new categories.
- Industry impact25%
Measurable effect on the data and AI industry: companies built, markets moved, standards set, talent trained.
- Public voice20%
Their role in shaping public understanding and policy: writing, teaching, testimony and advocacy.
How the longlist was built
We started from ten categories of data power, the distinct places where control over data is concentrated, and built a longlist for each from public reporting, company filings, academic citations, open-source activity and policy records. Each candidate was then scored against the rubric and the highest scorers across all categories make up the list. Categories represented in this edition:
- Cloud & Compute
- AI Labs
- Research Pioneers
- Data Platforms
- Global Power
- Policy & Ethics
- Data Founders
- Open Source
- Infrastructure & Analytics
- Data Science Voices
Editorial independence
The Datagarchy 100 is compiled independently. No one pays to be included, no one can pay to be removed, and people on the list are not consulted on their ranking. We do not accept sponsorship tied to any individual or company featured.
Sources
Profiles are compiled from public sources, and those sources are cited, numbered and linked at the foot of every profile. Where sources disagree, we prefer primary documents and reputable reporting.
Image policy
We use only freely licensed images, such as those on Wikimedia Commons, and credit the photographer and licence on each profile. Where no suitable free image exists, we show a monogram portrait instead.
Corrections & removal requests
If you spot an error, or you are featured and wish to request a correction or removal, email corrections@datagarchy.com with the profile URL and details, or see our contact & corrections page. We review every request and correct factual errors promptly.