Data does not just magically spring into existence. Someone, somewhere, has to decide what data gets created and the rules for its creation. We would claim that this often starts as a pretty simple exercise, and then, over time, that simplicity balloons to be pretty complex! What if, for instance, you decided to listen to every #1 song on the Billboard Hot 100 going back to its inception in 1958? You may start by just capturing the song name, the artist, and the week(s) it was the #1 song. But, before you know it, you may find that you’re adding in artist details…and songwriter details…and producer details…and genre details…and instrumentation details, and your dataset has 105 columns! But, oh, the questions that dataset could answer! And that’s exactly the dataset that our guest for this episode, Chris Dalla Riva, created. He uses it (with a range of supplemental datasets) for his pieces in his Substack, Can’t Get Much Higher, as well as the underlying raw material for his upcoming book, Uncharted Territory: What Numbers Tell Us about the Biggest Hit Songs and Ourselves. While the underlying material was music, the parallels to more staid business data were many when it comes to the underlying processes and challenges for doing that work!

This episode’s Measurement Bite from show sponsor Recast is an explanation of the miracle of randomization when it comes to addressing unobserved confounders from Michael Kaminsky!

Links to Resources Mentioned in the Show

• (Chris’s book) Uncharted Territory: What Numbers Tell Us about the Biggest Hit Songs and Ourselves


• (Chris’s newsletter) Can’t Get Much Higher


• Billboard Hot 100 Number Ones Database—the dataset Chris created (Google Sheets)


• Want Ads by Honey Cone


• Disco Duck by Rick Dees


• Macarena by Los Del Rio


• Bennie and the Jets by Elton John


• All About that Bass—99% Invisible podcast episode


• Santa Tell Me by Ariana Grande


• Underneath the Christmas Tree by Kelly Clarkson


• Rick Beato and the Miracle of YouTube


• Every Noise at Once


• Same Data, Different Questions: Transforming Time Series Data for Better Insights


• A Dispassionate Examination of the Empirical Evidence Regarding Positional Punctuation in SQL


• Analytics the Right Way: A Business Leader’s Guide to Putting Data to Productive Use

Photo by Long Truong on Unsplash

Episode Transcript

00:00:05 .73 [Announcer]: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

00:00:14 .44 [Tim Wilson]: Hi, everyone. Welcome to the Analytics Power Hour. This is episode number 282. And technically what we’re going to talk about on the show is pop culture, which can feel like a pretty kind of vibey and nebulous and subjective topic. And I’m Tim Wilson, and as Michael Helbling inadvertently reminds me on a regular basis, pop culture is something where I seem to be pretty locked into like a pretty narrow window of the 1980s. I did, after all, weave in the mid-80s sitcom 227 into the introduction I did for episode 227 of this show. In my defense, that was one of Regina King’s earliest appearances, and she is still going strong. But I digress. I’m going to give a shot at facilitating a discussion centered around pop culture ago, even though maybe not my forte, because I think this show is going to uncover some useful perspectives about a range of kind of things that we want to measure and analyze that are inherently kind of vibey and nebulous. consumer sentiment, employee engagement, social influence. Anyone? Anyone? Luckily, I’m joined by a couple of much more hip cats, as we said back in the day, than I am for this discussion. Julie Hoyer, would you agree that the data shows that I am the least likely co-host to get a Chapel Roan reference?

00:01:38 .31 [Julie Hoyer]: Yeah, that’s probably a good conclusion, honestly.

00:01:42 .08 [Val Kroll]: At least he said her name right.

00:01:45 .88 [Tim Wilson]: I do have a 20-year-old daughter.

00:01:47 .27 [Julie Hoyer]: See, that helps.

00:01:49 .23 [Tim Wilson]: Yeah. I get a lot of dads on those sorts of things. And Val Crowell, we heard you. What about you? Have you ever considered checking in with me to learn about what’s trending in the zeitgeist?

00:02:04 .21 [Val Kroll]: You are quite plugged in, Tim. Moere than you think.

00:02:09 .22 [Tim Wilson]: Perfect. Not. The funny thing is that I’ve been a regular reader of our guest’s sub-stack, which is called Can’t Get Much Higher. I’ve read that for several years now. That newsletter is a weekly data-driven analysis about the musical trends of yesterday and today. So I like to think it provides me with some analyses that help me kind of fake my pop culture knowledge. The author, Chris Dalareva, has a day job as a senior product manager of data and personalization at Audio Mac, which is a creator-friendly music streaming service. Chris is also a musician and the author of an upcoming book called Uncharted Territory, what numbers tell us about the biggest hit songs and ourselves. For the book, Chris went back and created a pretty fascinating and extensive data set by listening to every Billboard number one song from 1958 until earlier this year, which wound up being just over 1100 songs. He then conducted a bunch of analyses with that data and the results are what kind of fed into the book. And today he is our guest. Welcome to the show, Chris.

00:03:15 .36 [Chris Dalla Riva]: Thank you for having me. I’m looking forward to chatting with the other hip cats here.

00:03:20 .13 [Tim Wilson]: The fact that I am the oldest one on this mic by a lot. So I think hip cats might even be a little dated for me. So maybe a good place to start would be just by getting a little bit deeper on kind of the what, the why, the like, what were you thinking kind of in the how of the book. Like what prompted you to even tackle that project in the first place? And then how did you kind of land on Billboard number one songs as a place to do that?

00:03:56 .41 [Chris Dalla Riva]: Yeah, good question. I don’t think if I had some grand scheme, this listening journey would have ever started or a book would have ever been written. In fact, I think to write a book, you have to be a little bit insane because it starts to feel like a fool’s errand at multiple pa