For over a decade now, I’ve had the pleasure – and the frustration – of being a data scientist in the video games industry. I’ve always loved problem-solving. I’ve always loved video games, computers, and digital products of all kinds (okay, most kinds). They entertained me, fascinated me, and gave me worlds to explore in ways that the people around me never really seemed to care about when I was growing up.
I’ve also always loved science – all science, though none more than astronomy. Getting to use the scientific method, my problem-solving instincts, my personal understanding of digital products, and my technical skills to help create better video games has been a huge privilege. One I never could have imagined when growing up in rural Atlantic Canada.
I’ve had the opportunity to work with some truly amazing people – including many I never quite managed to form meaningful personal connections with. If any of my former colleagues at Ubisoft or Prodigy are reading this, please know that you had a profound impact on me. I’m deeply grateful to have crossed paths with you, even if my quiet, awkward self never quite figured out how to fully engage. Working alongside artists, designers, engineers, producers, and monetization teams gave me years of chances to reflect on how my own profession can – and should – intersect with theirs. In many cases, perhaps too late, I learned how data could genuinely support their work and, I hope, make their jobs a little easier.
Along the way, though, I’ve also encountered some… uncomfortable ideas about what data science and analytics are supposed to be. And more often than not, those ideas came from within my own field. I’ve come to realize that my goals as a data scientist often diverge sharply from the goals of many others who share my title. That the “science” in “data science” is, for some, little more than a word of distinction, rather than a signal of methodology, ethos, or intent. I’ll admit: when that finally sank in, it was a pretty profound shock.
There are plenty of people who see data science as little more than the automation wing of software engineering – marching steadily toward a future where decision-making itself is automated away, responsibility is handed off to an inscrutable black box, and human judgment quietly exits the room.
Man, oh man, is that terrifying. And the thing is, it’s the natural endpoint of organizations that proudly embrace the phrase “data-driven decision making.”
After all, if the data is the one doing the driving, then you’re just a passenger.
I find that vision bleak, and now that I’m looking for my next role, it’s one I know I need to be careful not to stumble into.
See, I’ve always thought of my job as being that of a knowledge facilitator, not a process automator. As someone who works to understand what my colleagues are trying to accomplish, what problems they’re actually trying to solve, and then uses that context (along with my own experience) to understand how real people engage with our products. To try to understand what those users’ motivations and goals are, what the designer’s intentions are, and then to understand where real behaviour diverges from those, and why.
I want to understand everything, always. That includes why one part of an application is heavily used while another is ignored. Why one user makes a purchase and another walks away. What isn’t working, why it isn’t working, and what – if anything – we can do about it.
I desperately want to understand it all. Everything else follows from that.
And I want to bring that understanding to others. Not to replace their judgment, but to inform it. To create knowledge that makes people’s work a little easier, their decisions a little clearer, and – if we’re lucky – the products we build a little better for the people who use them.
I worry, though, that this isn’t where my field is headed. That too many businesses aren’t run by people who value understanding, but by people eager to offload responsibility – while clinging to the illusion of control.
More and more, I worry that we’re becoming comfortable being passengers in our own vehicles.