storytelling with data podcast: #94 the uncharted territory of data and music with Chris Dalla Riva
storytelling with data podcastShow Notes
Chris Dalla Riva is a musician, data analyst, and the author of Uncharted Territory: What Numbers Tell Us about the Biggest Hit Songs and Ourselves. In this episode, he joins data storyteller Mike Cisneros to talk about musical myths, hidden industry incentives, chart data, and underappreciated hits, as well as the thorny questions about authenticity, categorization, and what it really means to tell an honest story with numbers. RELATED LINKS Uncharted Territory: What Numbers Tell Us about the Biggest Hit Songs and Ourselves | Chris Dalla Riva’s book
Can’t Get Much Higher | Chris’s newsletter
Audiomack | Music streaming service where Chris works on analytics and personalization
Max Martin Knows How to Create a #1 Hit | Chris’s The Economist article on song intros
Suno AI | Generative AI tool for music creation discussed in the authenticity segment
Follow Chris at @cdallarivam
SONGS AND ARTISTS MENTIONED IN THIS EPISODE “Want Ads” by Honey Cone
“House of the Rising Sun” by The Animals
“Hey Jude” by The Beatles
“Blinded by the Light” (written by Bruce Springsteen, recorded by Manfred Mann’s Earth Band)
“Dancing in the Dark” by Bruce Springsteen
“When Doves Cry” by Prince “Believe” by Cher
“Golden” by HUNTR/X
More from storytelling with data podcast
“We’ve already tried that.” “Those numbers don’t look right.” “I’m not convinced.” What do you do when your audience pushes back on your data or recommendations? In this episode, Alli and Simon tackle eight common questions from storytelling with data workshops about winning over a tough audience. From handling disagreement and challenging questions to presenting unpopular findings, they share practical strategies to build trust, navigate resistance, and turn tense presentations into productive conversations.
Bump charts are great at showing who's rising, who's falling, and who's staying on top—but they don't tell you the whole story. In this episode, Alli explains what a bump chart is, when to use one, and what to watch out for when comparing rankings instead of actual values. Plus, she highlights three creative examples from the storytelling with data community that use color, added context, and an unexpected layout to make changes in rankings easier to see and understand.
Does telling a story with data introduce bias? In this episode, Cole explores the tension between objectivity and perspective—and why simply “showing the data” isn’t as neutral as it may seem. If you’ve ever worried that storytelling could compromise rigor, learn how to bring your expertise and point of view to the data while still communicating uncertainty, limitations, and alternative interpretations responsibly.
Cole recently appeared on the Analytics Power Hour, and this was such a great conversation, we're rebroadcasting it here! ———— There’s a version of this episode where AI replaces the data visualization expert entirely, and there’s a version where it’s useless without one. Neither is quite right, according to Cole Nussbaumer Knaflic of Storytelling with Data, who joined Tim, Moe, and Julie to sort out where AI genuinely earns its keep in data storytelling and where it’s just producing a shinier first draft of the same shitty slide. Cole—admittedly a skeptic-turned-convert who once joked she’d retire before having to deal with any of this—walks through why the humans who benefit most from AI are the ones who already have the foundation to know when to ignore it, why “let me look at some options” beats “give me the final chart” as a prompt, and why the analog, pencil-and-paper parts of the process might be the most valuable friction in the whole workflow. Bring your own opinions on whether Claude should ever be handed “the rules” and told to just go.
What can AI help us see, try, or explore that we couldn’t easily do before? Alli shares three standout talks from the 2026 Outlier conference—and three lessons for using AI in data visualization: - Use AI to expand your options without outsourcing your judgment. - Give AI the information it needs to critique its work. - Use AI to find what’s worth visualizing in the first place.