I think this is the wrong takeaway.
Everything has become organized around measurable things and short-term optimization. "Disparate impact" is just one example of this principle. It's easy to measure demographic representation, and it's easy to tear down the apparent barriers standing in the way of proportionality in one narrow area. Whereas, it's very hard to address every systemic and localized cause leading up to a number of different disparities.
Environmentalism played out a similar way. It's easy to measure a factory's direct pollution. It's easy to require the factory to install scrubbers, or drive it out of business by forcing it to account for externalities. It's hard to address all of the economic, social, and other factors that led to polluting factories in the first place, and that will keep its former employees jobless afterward. Moreover, it's hard to ensure that the restrictions apply globally instead of just within one or some countries' borders, which can undermine the entire purpose of the measures, even though the zoomed-in metrics still look good.
So too do we see with publically traded corporations and other investment-heavy enterprises: everything is about the stock price or other simple valuation, because that makes the investors happy. Running once venerable companies into the ground, turning merges and acquisitions into the core business, spreading systemic risk at alarming levels, and even collapsing the entire economy don't show up on balance sheets or stock reports as such and can't easily get addressed by shareholders.
And yet now and again "data-driven" becomes the organizing principle of yet another sector of society. It's very difficult to attack the idea directly, because it seems to be very "scientific" and "empirical". But anecdote and observation are still empirically useful, and they often tell us early on that optimizing for certain metrics isn't the right thing to do. But once the incentives are aligned that way, even competent people give up and join the bandwagon.
This may sound like I'm against data or even against empiricism, but that's not what I'm trying to say. A lot of high-level decisions are made by cargo-culting empiricism. If I need to choose a material that's corrosion resistant, obviously having a measure of corrosion resistance and finding the material that minimizes it makes sense. But if the part made out of that material undergoes significant shear stress, then I need to consider that as well, which probably won't be optimized by the same material. When you zoom out to the finished product, the intersection of all the concerns involved may even arrive at a point where making the part
easily replaceable is more practical than making it as corrosion-resistant as possible. No piece of data by itself can make that judgment call.