| > Sometimes it works. Sometimes it’s critical. But sometimes it fails, or results in unintended consequences that we may not notice for years. > Data-driven journalism gave us Buzzfeed > Data-driven music gave us X-Factor and Pop Idol > Data-driven movies gave us 25 Hollywood sequels planned for this year > Data-driven education gave us Key Performance Indicators and Teaching to the test These first three examples are awful examples. A ton of people love all of those things. The only failure of data-drivenness here is the failure to generate content that the author wants. Now, the author can attempt to make some argument about how websites, TV shows, and movies have a moral obligation to strive for whatever objectives the author prefers, but that's a separate issue to settle. The fourth example is a little different, because we're talking about mandatory education programs for children, rather than products that people choose to pay for or consume. Also, I don't think that data-drivenness itself is a significant contributor to those problems in education. |
Enough people might reliable go see 25 sequels, but none of those films will be memorable. None will advance the art of film-making. None will change anyone's life or mentality or affect the culture in any meaningful way.
Being data driven means chasing the biggest, loudest signal in your data set. It means pandering to that signal, because it swamps all others. A data-driven approach is not going to lead you anywhere new.
In machine learning / AI we refer to such algorithms as "greedy." A classic example would be simple hill climbing a.k.a. gradient descent. These algorithms are known to be very good at optimizing within the bounds of a simple well-behaved and regular fitness landscape, but they readily become stuck at local maxima when presented with any solution space with any complex structure.
We're living in what I'm tempted to call the dark age of the local maximum, the age of gradient descent.