Interview with CBC's Christopher Berry

on YouTube ↗05m

" summary

CBC extracts podcast server logs to reconstruct download patterns and infer reach and engagement (no third‑party demographics, but popular shows and topic-level depth emerge), overcomes organizational fear about unpopular results and questions of source quality, reuses existing log tailings rather than buying new streams, and prototypes audience‑to‑content linkage using neo4j graph matching to map listening pathways and identify surprisingly large niche audiences from underused data.

AI-written summary · about

" chapters (4)

1. 0:00Introduction
2. 0:32How is data exhaust applied
3. 1:29Biggest roadblocks
4. 2:47Tips for organizations

" related talks

• 02h 53mMining digital exhaustS. Anand · The Fifth Elephant 2025 Winter Edition
• 03mCombining creativity and analyticsDavid Boyle · Strata Data Conference 2019 - London, United Kingdom
• 01h 26mSaturday KeynotesKarthik Ramasamy, Jeanne Holm, Karen Borchgrevink, Todd Terrazas · Data Con LA 2020
• 40mData Doesn't Lie: How to Influence Open Source Programming Through..Hilary Carter, Steve Hendrick · OSPOCon @ Open Source Summit North America 2023
• 11mData science: Next-gen performance analyticsKen Gardner · Velocity 2016 New York
• 33mCOVIDcast: An ecosystem for COVID-19 tracking and forecastingRyan Tibshirani · AWS Machine Learning Summit 2021
• 47mData for good: Lessons from the frontlineEmma Deraze, Charlie Harrison · PyData London 2017
• 16mThe Benefit of Talking to the "Non-Datas"Caro Buck · rstudio::conf(2022)