Entertainment Data Science: Streaming vs. Theatrical
In my Next Frontiers in Entertainment Data Science article on Towards Data Science, I refer to how data science can be applied at various phases of the content lifecycle, from greenlight to production to release. Though it's easy to conceptualize how applications of data science might differ between, say, deciding what scripts should be greenlit and determining how production costs can be optimized, there can be stark differences even across contexts that, at first sight, might seem relatively similar.
About a year and a half ago, started a new job at a major movie studio. Coming from the streaming tech side of the business, I expected things to be more or less similar, except that this time I'd be working with movie data exclusively rather than both TV and movie data. Predicting how popular things are gonna be using data, how crazy different could things be?
Boy, did I have no clue.
The business is totally different. The questions are different, the stakeholders are different, the data is different, etc. So I wanted to write this piece with two goals in mind. The first more obvious goal is to show aspiring and junior Entertainment data professionals how data science work can differ between theatrical and streaming contexts. But I imagine this kind of dynamic can manifest in a lot of different industries – where you think you'll be doing largely the same predict Y using X thing you always did only to find out it's entirely different takes on X and Y – so the second broader goal is to give data professionals in all fields an idea of how even though two jobs may seem functionally similar to a remarkable degree on the surface, they can be totally different in various ways once you really start digging in to the data and the business questions at hand.
With that, below are some of my key observations after making the leap from streaming entertainment data science to theatrical entertainment data science. I skip over some of the more blatantly "no duh" points (oh, there's no theatrical TV show releases, what a surprise), but I touch on some of the major trends. And of course, none of this is some biblical statement of truth; YMMV based on company, team leadership, and the like. Furthermore, although data science can play a role in earlier phases of the entertainment content lifecycle as I allude to above, this piece derives from my experience with more downstream processes nearer to release. If I ever seem a bit ambiguous, that's deliberately because I don't wanna spill any of the secret sauce
