2014: A Year In New Music

Reflections from the annual summary of my year in music discovery

This year marks the 5th time, in as many years, that I have documented and visualized some aspect of my minor obsession with music**. This year is also the most complete and ambitious effort to date. I had two main goals. One, I sought to put context to listening and discovery of music. And two, I tallied the conversations I had about new releases. The former required the broad collection of large quantities of my activity, on a daily basis. The latter hinged on spontaneous and detailed documentation of transitory moments.

**For those who haven’t been following along, the rules for my year in new music are as follows. The focus is on new music, meaning music that was released during that calendar year. As the year goes, I collect the data, and then before digging into the data, I make a top ten list of my favorite albums of the year. This acts as a set of qualitative data to overlay on the larger set of qualitative data. I then prepare a report, visualizing the stories I find in my data from the year.

Over the course of the year, I relied on 4 methods to cobble together the right data to answer those questions. I primarily used Reporter to randomly sample my day and capture the context. I relied on Twitter as a type of electronic diary to document the conversations I had. I also used Spotify playlists and good old fashioned, hand written notes to fill in the gaps. By year’s end, I had accumulated over 2300 reports, 350 tweets, and 285 songs. This added up to just under 35,000 data points. Needless to say, there were many more stories I uncovered as I sorted, filters, queried, and visualized the data. Here are a few of the stories that stood out to me. The entire report can be found on my website and limited amounts of the printed report can be ordered here.


There are some pretty clear patterns when it comes to music related activity, especially while I am at work. Just before lunch and just after lunch are the hot spots of the day. The part of this chart that I identified with the most, as a reflection of myself, is the swath between 5:30 PM and around 9:00 PM. There is a spike in music activity up until about 6 o’clock, followed by a few hours where it really dies down before picking up a little bit around 9 o’clock. There is a shift in my priorities during this time, a push to end the work day followed by arriving at home and focusing on my family to get through the nighttime routines with my kids.

Here, every report logged is charted by the minute of the day that it was captured. Music related activity is black, top ten music activity is magenta, all other reports are gray.


A little more than half of my conversations about music happened over instant messenger. A quarter of them happened in person. But the big discovery for me was that I talk about my favorite music with those I talk to the most. In the passing conversations I had, where I talked just once with someone, I never mentioned an album that became a top ten for the year.

This chart represents the different people I talked about new music with, proportionally weighted. Sections are shaded by percentage of Top Ten Albums discussed with that person.

Songs of 2014

Using Spotify playlists, I was able to catalogue all the songs I liked during the year. What I found by visualizing this data was a shift that takes place as the year goes along. The first quarter of the year is weighted towards older music, but as new records are released, new music, and especially top ten music, starts to tilt in favor of new music.

The thin black line represents the total number of songs logged each month, the interior circles represent the number of songs from top ten albums or prior years

Volume of Discovery

Maybe it’s better record keeping, maybe it’s more focus on this on-going project, but one thing is clear, I’m casting my net much wider now when it comes to discovering new music.

I’ve more than doubled the amount of new albums I’ve listened to each year


There are myriad ways to slice the data I collected in 2014. Surely there are more stories to tell than I’ve included in my summary. Each one of these pie charts represents a different dimension of the data. What I found most interesting, after all that has been described and visualized, can be found on the left-most chart. That is that a very tiny percentage of my music listening happens without something else taking place at the same time. Be it working, commuting, or exercising, it seems that concerts are the only significant portion of time where I’m solely focused on listening to music.

Categorical breakdowns of different dimensions of the cumulative data set

Maybe it’s time to correct that. After all, what good is this exercise in extreme self-reflection if it doesn’t catalyze even a small amount of change?