I’ve known Matt Sheret for a few years now: we met at a hauntology do in London. Our first conversation must’ve gone on for about an hour, sitting on the south bank of the Thames and watching the world go by. He’s been a writer, a publisher and a public speaker, and for the last year or two has described himself as a Data Griot for the music service last.fm. So I asked him what that meant:
Griots were the bard/spin-doctors of the African Continent, taking traditional histories and reworking them to satisfy the needs of the audiences they found.
The ‘facts’ of their legends and their histories remained a constant, the raw tendons and sinew of their stories a carefully preserved structure, passed from griot to griot. But the flesh and bearing of every tale was twisted to suit the audience; they used context to weaponise content, to fill every telling with a meaning pertinent to the people listening at only that moment.
Hundreds of years ago we started to fix those stories, to lock them in place. We started to lose our bards and our minstrels and our griots, instead allowing ourselves to “recollect by the external aid of foreign symbols”, in Plato’s words.
But then those symbols started to lose their meaning, numbers in particular. Big data got bigger and bigger, and all the digits started to lose context. They became separated from the stories that got them there in the first place.
Companies started to look for ways of filling that gap (Chris Heathcote wrote really nicely about that last year), and the likes of The Guardian and OK Cupid started to spin the numbers into pictures and the pictures into paragraphs, connecting the numbers – at last – back to their sources.
Last.fm started looking for someone of their own to do that, and named the role, this new/old caste, in the process. The Data Griot. Me.
Last.fm users send tiny bits of information about the music they’re listening to to their Last.fm profile, and we call each piece of data a scrobble. What users get out of scrobbling is music and live event recommendations. What we get out of scrobbling is enough data to drown in.
Our Music Information Retrieval (MIR) team turn all that data into meaningful connections for the people who use Last.fm; chart data, radio algorithms, data-visualisation tools. The guys in MIR are respected scientists; they are much cleverer than you, and have a tremendous capacity for booze.
But sometimes people need paragraphs. My job is to humanise the numbers, to turn that huge quantity of data into stories, the kind of mini-narratives that could surface anywhere. I turn the facts and sinew into simple blog posts, into ticker-tape copy running beneath celebrity gossip shows, and into audio scripts broadcast to 9 million listeners every single day.
I can’t see in scrobbles alone. If I’m reading the numbers and not fleshing out the context I am not doing my job. I have to see through the 98,000 people screaming “Baby I was born this way” last week and look at how badly they want to feel liberated. I have to find out why witch house has given way to yacht bounce has given way to cloud rap will give way to hazy beach. I have to instinctively know how differently an Xbox listener behaves to an iTunes die-hard.
On the good days, with the charts pointing in the right direction, I see the grins of listeners spinning Kendrick Lamar’s “Ronald Reagan Era” on repeat. And on the bad days I see the booted foot of Sir Paul McCartney stamping on humanity’s headphones, forever.
And then, one day, the griot after me will need to see through those same numbers and tell a whole new story.
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