Making good data easier to find and use with Google's Prem Ramaswami
About this episode
The episode discusses the challenges of making official statistics accessible and usable for both humans and AI systems.
As AI assistants, search engines and chatbots become intermediaries between users and information, official statistics face a new usability test: can authoritative public data be found, understood, sourced and used correctly by both humans and machines? In the second episode of Statistics in the AI Era, OECD Chief Statistician Steve MacFeely speaks with Prem Ramaswami, Head of Google Data Commons, about the practical work needed to make trusted statistics understandable to people and legible to AI systems. Prem argues that the challenge is not only to publish reliable data, but to make it easier to discover, connect and use. He explains why fragmented datasets impose a hidden “data tax” on analysts, why AI systems need data that is well sourced, auditable and reproducible, and why statistical organisations should start with machine-readable metadata. The conversation also explores practical AI use cases across the statistical pipeline - including data collection, data cleaning, sharing and tailored policy outputs - as well as the need for public-private partnerships where technology companies help, and statistical institutions hold them to account. Host: Steve MacFeely | Chief…
People in this episode
Host: Steve MacFeely
Guest: Prem Ramaswami
Topics covered
- data usability
- AI and statistics
- public data accessibility
- data management
- public-private partnerships
Keywords
- data discovery
- AI systems
- metadata
- data cleaning
- statistical pipeline
Mentioned in this episode
Organizations: Google, OECD, Google Data Commons
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