"Consider three cases. First, suppose a clever chart-reader thinks he has spotted a pattern in the old price records—say, every January, stock prices tend to rise. Can he get rich on that information, by buying in December and selling in January? Answer: No. If the market is big and efficient then others will spot the trend, too, or at least spot his trading on it. Soon, as more traders anticipate the January rally, more people are buying in December—and then, to beat the trend for a December rally, in November. Eventually, the whole phenomenon is spread out over so many months that it ceases to be noticeable. The trend has vanished, killed by its very discovery. In fact, in 1976 some economists spotted just such a pattern of regular January rallies in the stocks of small companies. Many investors close their losing positions towards the end of the year so they can book the loss as a tax deduction—and the market rebounds when they reinvest early in the new tax year. The effect is most pronounced on small stocks, which are more sensitive to small money movements. Alas, before you rush out to trade on this trend, you should know that its discovery seems to have killed it. After all the academic hoopla over it, it no longer shows up as clearly in price charts."
-Benoit Mandelbrot, The Misbehavior of Markets
Then we begged "Can't we open a couple of presents on Christmas Eve?" So we got to open a few that night.
Next year was "Well, how about Christmas Eve Morning? Maybe just one or two?"
And the next year was "The 23rd is practically Christmas Eve, isn't it? It's just a few hours apart. Can't we open all our presents on the evening of the 23rd?" And we did!
We didn't push it past that: we were already so happy that we got our presents a day and a half before all our friends!
If that's not a law, it should at least be a rule-of-thumb.
One issue is that some things, like vulnerability to supply chain disruptions, are intrinsically harder to track because they are based on rare occurrences. Thus, they will tend to get sacrificed in favor of measures which are more frequent, leading to an emphasis on short-term strategies.
It is wrong to suppose that if you can’t measure it, you can’t manage it – a costly myth.
It wasn't Drucker either.
https://medium.com/centre-for-public-impact/what-gets-measur...
This whole mindless must measure, must measure mentality has been criticized since the 50s. Measurement is a tool. There are many tools.
The problem is that people tend to think that all measurement is necessarily quantitative. I think that this might be a version of the streetlight effect? Quantitative measurements tend to be much easier to collect and analyze than qualitative measurements. Oftentimes you can let it all run on autopilot, whereas doing good qualitative work always requires concentration, effort, and expertise.
That would be true, if measurement was free. It never is, and it often is quite costly.
I suppose this is part of why "chaos engineering" has gained popularity- introducing artificial disruptions at a known rate makes it easier to quantify the impact of otherwise-unusual events.
That wasn't Deming’s position, Deming was pretty strong on the idea that there are measurable things that it is not cost effective to measure.
A big contribution of Deming’s here is the importance of understanding uncertainty in measurements relations between them to understand the degree of control.
And we're often not entirely wrong if we do pick reasonable proxies and have reasonable control systems in place. Because throwing up our hands and saying metrics are useless is usually not the answer either.
A particular instance for this has been (to keep it simple) quantity (speed) and quality in production environments (factories and the like). Daily throughput measures paired with less frequent quality measures. The desire is to keep throughput high, and quality ends up suffering as a result. By integrating quality measures into the process you make the two measures compete on more equal footing, forcing a balance. At least one factory I worked in (well, adjacent to, I was in the software portion not the assembly line) massively reduced their quality problems by integrating quality checks between each station. This contrasted with the prior years where throughput, being measured and reacted to daily, drove them to make things so fast that they had piles of rework at the end. Integrating the quality measures between stations slowed them down, but their rework numbers turned into a rounding error (over a decade ago so I've forgotten the exact numbers, but they went from having items needing rework nearly every day to maybe one or two a month). As a result their real (deliverable to customers) production increased and their cost per unit dropped.
The tiers (kwh rates) are in hunks of the a day measured in hours.
But the reporting is only available to the consumer in the form of a monthly bill, so by the time you discover you were eating pixies in the Peak Cost hours the heat wave is over and your bill is already through the roof.
(Any local SoCal residents please feel free to pick my analysis apart, but that was my first take when I heard about the legal action.)
To understand what the big problems are today, you just have to think about the kinds of data which people in government (and the public) haven't been thinking about or aiming for. For example: Happiness, honesty, altruism, sanity... These are not measured and not targeted so they got completely crushed.
In the past, large, powerful religious groups would target these characteristics but nowadays, society is more secular so these aspects of our lives have suffered significantly.
Okay, yeah I'm sure it does. Because it looks terrible and clashes with literally everything on the screen.
As long as metrics are increasing someone can write shit code and design a bloated product.
When you're writing a procurement contract, it's relatively easy to describe what the requested system must do, but almost impossible to enforce a great UI design, as great UI design isn't objectively measurable.
As a contractor, you're optimizing for minimum money spent, so if good design is not required, good design gets sacrificed first.
One solution to this specific problem would be to conduct user surveys on how pleasant the system is to use, requiring a specific score before the contract is deemed completed.
This trend manifests more generally in bigger organizations. Smaller orgs let people judge things subjectively, so all possible aspects are taken into account, making those things relatively good; this is why startups succeed. In a bigger org, there are often objective judgement measures to prevent the influence of personal biases, politics or even bribes. However, those measures poorly reflect how good the thing in question actually is. This is why a big corp might produce worse software, even when competing against a small and underfunded startup.
As an example, Apple exempted the first iPhone crew from most internal company procedures, creating a quasi-startup inside Apple. Steve Jobs always had the final say, and his opinions were based on what he thought personally, not on how many points in a requirements specification were satisfied. I believe this was one of the reasons for the iPhone's success.
This is not limited to governments. Although it's a common naive bias to assert that governments are worse and less efficient than private industry, what is really happening is that government budgets and projects are open to the public, done in the open. For every failed Healthcare.gov, there are dozens of private industry failures that don't make the news because the operations are not subject to the public disclosure rules.
[0]: https://en.wikipedia.org/wiki/McNamara_fallacy