Your data has a story.

On the surface data is just a collection of bits and bytes separated by columns, rows, and data types. Do these snippets of information have a bigger story to tell? Do they just need the right microphone?

The end of the year is filled with best lists, worst lists, and year in review countdowns. That was before the Internet stormed into our lives and started regurgitating back the stuff we vomited into it.

Now you can’t scroll past the fold on Facebook without seeing a friend’s Facebook Year in Review. It’s kind of fun, but en masse the hipster in all of us says, “ugh — I don’t want to relive your ice bucket challenge, bucko.”

How did we get here and where are you going with this? Fair question.

I received an email from Spotify with the subject like: Your Year in Music. Intrigued, I opened the email. My top song of 2014 was Lose Yourself to Dance by Daft Punk. They was enough of a nudge for me to want to learn more, and I did.

I went to Spotify’s Year in Music landing page and checked out what they had to offer. First off, it’s beautiful. But the thing that really struck me was their ability to spot trends and to tell stories based on their usage data.

Like it or not the stuff you put on the Internet, your online shopping habit, and the apps that you use will generate a lot of information about you.

Music is one of those magical mediums that we all participate in as makers or listeners. Spotify has the software and the business contracts to deliver it to us at scale. The data that they and their users generate is a veritable gold mine. Thei Year In Music landing page gives us a sneak peak at it.

Globally, they’ve learned a lot about how people listen to music.

Take New Years Day, for instance, and the playlist data that they’ve gleamed.

Or how about January 7th? Who knew that was such a big day for working out?

By the way, if Taylor Swift (like her or hate her) didn’t pull her music off of Spotify — I bet she would have unseated Katy Perry as the Top Female Artist.

Interestingly enough, Spotify has data on when we workout because everybody listens to music when they’re working out. Spotify was just smart enough to make their playlists purpose driven and name them in a way (Workout, Chill Out, Data Night, Valentine’s Day Love Vibes, etc) that they could draw correlations.

If they could tell stories like this from global usage information, what can they do on the individual level? A lot. The know that I listen to a ton of indie rock, hip-hop, and hippie jam band music. In fact, they know that the Grateful Dead are my most played artist (no judging, music is personal after all).

That’s a fair amount of listening time. 435.966667 hours of listening to be exact. There’s 8,760 hours in a year. I spent 20% of my time listening to music. But was I making dinner, carpooling, or working to it?

I’m willing to bet I spent the vast amount of listening time I accrued was spent while working. In fact, this next screenshot kind of lends to that story.

It makes sense that those would be my busiest days — those are the same days that I’ve setup my calendar to focus on work. Spotify doesn’t necessarily know that because I’m generally not listening to a “Focus on Work” or “Get Shit Done” playlist and I don’t think they’re pulling in calendaring information.

It’s not an exact science, but Spotify has some amazing data and it’s starting to lend itself to telling better stories and creating engagement between jamokes like me and their brand. Shit, I’m writing a piece on it.

What’s the teaching moment though? Well, we all have data. We have data about our clients that we lock into our brains/task management/CRM systems. We have data about our users locked into relational databases, silos, and other weird places.

The point is that all of that data is locked away. It doesn’t come out to see the light of day and when it does, it’s not being related to real world stuff.

I had a friend tell me once that you can sit behind your computer and tweet at people all you want, but real relationships are created by getting out from behind your computer and meeting people.

You can draw a parallel between that idea and most organization’s data. Are you taking usage patterns and trying to derive some meaning from them? Are you figuring out what your user’s story is?

You should. It just may lead to some freaking whimsy, user delight, a higher valuation, or more importantly — a deeper and more engaged relationship with your audience.

Or, it could lead to utter disaster as the world finds out that you’re no-longer a secret One Direction fan and ridicules you for choices in music.

With great power comes great responsibility (and opportunities to tell a good story).