I think metrics can be very useful but as with many things, you need to know how to interpret the data.
I'm in the restaurant industry, so my #1 metric is profit (over the long run anyhow). Now, the next metric is sales, since profit is a % of sales. So to get those sales, we're looking at how many new guests are coming through the door, repeat guests, average guest check, etc... After that, it's margins. Sales mixes play into that (relating to COGS), and labour. Now, to determine what we're getting out of our labour, more metrics. How much each server is selling, what products they're selling, how many guests they can serve in a night, etc... For cooks, it's about productivity and consistency.
Anyhow, there's a lot of shit restaurants and workers out there (due to low barrier of entry education-wise), lots of people misinterpret the data, leaving to a feedback loop of shit managers and employees. Often restaurants will focus too much on one of those metrics, leading to places that just gauge guests, or others that entertain guests who are fishing for free shit.
Now, as for metrics in the programming world which probably don't matter, I always hear about lines of code written. Obviously it's easy to game and hard to discern, as code can be too terse or too verbose. Programmers are also usually so far removed from the sales part of the business that there's no objective sales metric to use either. And that's where you need good managers. I'm sure there's a good set of metrics that give some idea of performance, but they need to be interpreted by someone.
And all of this reminds me of stats/economics (what I did in university), where you're bombarded with data and need to interpret it. Like GDP per capita can indicate general well being, but then you adjust it to PPP to get a better metric of quality of life, and add inequality calculations to understand how it affects different segments of the population, and you can go even deeper with specific stats underlining quality of life (amount of disposable income, amount spent on housing, amount spent on entertainment, etc...).
Anyhow, tldr here is that metrics matter, but interpreting them is a skill and makes all the difference.
I'm in the restaurant industry, so my #1 metric is profit (over the long run anyhow). Now, the next metric is sales, since profit is a % of sales. So to get those sales, we're looking at how many new guests are coming through the door, repeat guests, average guest check, etc... After that, it's margins. Sales mixes play into that (relating to COGS), and labour. Now, to determine what we're getting out of our labour, more metrics. How much each server is selling, what products they're selling, how many guests they can serve in a night, etc... For cooks, it's about productivity and consistency.
Anyhow, there's a lot of shit restaurants and workers out there (due to low barrier of entry education-wise), lots of people misinterpret the data, leaving to a feedback loop of shit managers and employees. Often restaurants will focus too much on one of those metrics, leading to places that just gauge guests, or others that entertain guests who are fishing for free shit.
Now, as for metrics in the programming world which probably don't matter, I always hear about lines of code written. Obviously it's easy to game and hard to discern, as code can be too terse or too verbose. Programmers are also usually so far removed from the sales part of the business that there's no objective sales metric to use either. And that's where you need good managers. I'm sure there's a good set of metrics that give some idea of performance, but they need to be interpreted by someone.
And all of this reminds me of stats/economics (what I did in university), where you're bombarded with data and need to interpret it. Like GDP per capita can indicate general well being, but then you adjust it to PPP to get a better metric of quality of life, and add inequality calculations to understand how it affects different segments of the population, and you can go even deeper with specific stats underlining quality of life (amount of disposable income, amount spent on housing, amount spent on entertainment, etc...).
Anyhow, tldr here is that metrics matter, but interpreting them is a skill and makes all the difference.