Anonymous Crowd vs. Managed Team

A Study on Quality Data Processing at Scale

Webinar + Discussion

Structuring Quality Data at Scale

Data-science tech developer Hivemind designed a quantitative experiment to determine which type of workforce completed a series of increasingly complex tasks to deliver the highest-quality structured datasets. Which workforce performed better?

Was it CloudFactory’s managed workforce or a top crowdsourced workforce?

This webinar reveals the study’s results and dives into the:

  • Strategic implications of using different types of workforces for structuring data
  • Positive impact of paying workers by the hour as opposed to the task
  • Pros and cons of an anonymous crowdsourced team versus a managed team of data workers

WATCH THE WEBINAR Tell us about yourself

Philip Tester Moderator

Philip is Director of Business Development at CloudFactory, where he creates partnerships to help solve data-production problems for AI innovators.

Daniel MitchellPresenter

Daniel is CEO of Hivemind, a software company that helps its clients build, clean, and enrich datasets from messy or unstructured information.

Mark Roulston Presenter

Mark is Co-founder and Senior Data Scientist for Hivemind.


For over a decade, CloudFactory has powered quality data at scale. Its managed workforce processes pipelines of big data with high accuracy on virtually any platform, with the expertise and communication of a trained internal team. As a global leader in impact sourcing, CloudFactory creates economic and leadership opportunities for talented people in developing nations.


Hivemind is a data science and technology company that specialises in creating structured data from an unstructured world. Hivemind combines computational methods with distributed human intelligence to help companies distil messy or unstructured raw material into pertinent, valuable, and accurate data assets.