On the marginal value gain by destroying your privacy. The whole thing is well worth a read.
I don’t mind letting your programs see my private data as long as I get something useful in exchange. But that’s not what happens.
A former co-worker told me once: “Everyone loves collecting data, but nobody loves analyzing it later.” This claim is almost shocking, but people who have been involved in data collection and analysis have all seen it. It starts with a brilliant idea: we’ll collect information about every click someone makes on every page in our app! And we’ll track how long they hesitate over a particular choice! And how often they use the back button! How many seconds they watch our intro video before they abort! How many times they reshare our social media post!
And then they do track all that. Tracking it all is easy. Add some log events, dump them into a database, off we go.
But then what? Well, after that, we have to analyze it. And as someone who has analyzed a lot of data about various things, let me tell you: being a data analyst is difficult and mostly unrewarding (except financially).
See, the problem is there’s almost no way to know if you’re right. (It’s also not clear what the definition of “right” is, which I’ll get to in a bit.) There are almost never any easy conclusions, just hard ones, and the hard ones are error prone. What analysts don’t talk about is how many incorrect charts (and therefore conclusions) get made on the way to making correct ones. Or ones we think are correct. A good chart is so incredibly persuasive that it almost doesn’t even matter if it’s right, as long as what you want is to persuade someone… which is probably why newpapers, magazines, and lobbyists publish so many misleading charts.
But let’s leave errors aside for the moment. Let’s assume, very unrealistically, that we as a profession are good at analyzing things. What then?
Well, then, let’s get rich on targeted ads and personalized recommendation algorithms. It’s what everyone else does!
Or do they?