Behind every gripping data-driven headline is a journalist elbow-deep in messy, inconsistent, sometimes maddening data. In this episode, we explore the unsung labor of data preparation in journalism — and how it compares to the workflows of data scientists.
Based on a recent study, we uncover how journalists tackle dirty data from fragmented sources, PDFs from FOIA requests, and tables that change over time like shapeshifting monsters. From regionally inconsistent COVID stats to detective-level entity matching, this is the side of data journalism the public rarely sees — but desperately needs to understand.
🎙️ Topics include:
What “dirty data” really means in journalism
The 4 integration nightmare archetypes
Why journalists can’t just impute missing values
A fresh way to categorize data quality issues
The tension between storytelling and spreadsheet chaos
If you’ve ever cursed a CSV or tried to make sense of government data, this one’s for you.
🎧 Hosted by Sébastien Deschamps
