A common feature of organizations in the software technology industry (but certainly not only in that industry) is their fixation on metrics, measurements, and quantifiers. I understand that this is frequently done and advocated for in the spirit of making management more objective, less arbitrary, more scientific, and perhaps fairer. But since they say that the road to hell is often paved with good intentions, here’s a quick summary of what we know about about the undesirable side effects of such an approach.
British economist Charles Goodhart wrote in 1975, in an article about British monetary policy:
Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.
— Charles Goodhart, “Problems of Monetary Management: the U.K. Experience” (1975)
That’s a mouthful of somewhat niche technical jargon, but let me try to paraphrase it like this:
- You collect some data.
- You crunch the numbers using statistics.
- You observe a pattern.
- You distill a value (a “statistical regularity”) from it.
- Someone decides that that value should change: it is too high or too low.
- Someone — an individual or a group — is tasked with bringing that value up or down, and then keeping it high or low, or rising or falling, or above or below a particular threshold.
- That value now is no longer a useful statistical indicator.
When a measure becomes a target, it ceases to be a good measure.
— Marilyn Strathern, “‘Improving ratings’: audit in the British University system” (1997)
Why is that so? It’s because once you make the measure a target that has an influence on people (for example, meeting it gets them a bonus, failing at it gets them a demotion), you have wired them to improve the measure, and not necessarily to improve the underlying conditions that the measure originally arose from. Therefore, they might opt for gaming the measure, because that gets them to their goal (a promotion, for example) more quickly and at less effort to them.
Furthermore, even keeping the option of fudging the numbers aside: when faced with a choice between doing something that might have a negative effect on the measure and something else that might have a negative effect on something other than the measure, people will tend to choose the latter. This may lead to situations where people avoid an activity with significant inherent value, just to avoid depressing a measurement — a concept known as creaming.2
For example, a hospital may be interested in measuring individual surgeons’ intraoperative death rates: the percentage of a surgeon’s patients that die in the middle of surgery. On its face, this metric could help weed out bad surgeons. If a particular surgeon is an outlier and has way more patients dying on their operating table than their peers, it’s possible that that surgeon might be doing something wrong: they could be incompetent, or frequently intoxicated, or even be a Dr. Death type serial killer.
It gets tricky, though, when in the interest of transparency the hospital doesn’t just fire or retrain incompetent surgeons which it identifies based on such statistics, but when it “publishes” the patient mortality data. (I use quotes here because this does not necessarily mean sharing it with the general public, but perhaps sharing it with all of the surgical staff.) At that stage, an individual surgeon’s rank in the statistics will become at least a matter of pride, status, and prestige, even if it’s not otherwise rewarded in any way, nor seen as a precondition for continued employment.
This, then, will incentivize surgeons to avoid taking on risky surgeries where there is a significant chance of the patient dying mid-surgery — surgeries typically attempted in the first place to save the patient’s life, in the course of an immediate major emergency. Thus, Dr. Alpher who only ever treats torn knee ligaments might look better in the ranking than Dr. Bethe the polytrauma specialist, or Dr. Gamow the neurosurgeon who specializes in particularly challenging malignant brain tumor removal. If there is a non-negligeable risk of intraoperative death for a particular brain cancer patient and such an event would be bad for Dr. Gamow’s ranking, then Dr. Gamow might have an incentive to declare that patient inoperable — and as a result the patient would certainly die, just not in surgery.3
Although less well known than Goodhart’s law, Campbell’s law is closely related and, in my humble opinion, just as important.
Donald T. Campbell, a U.S.-based social scientist, wrote in 1976, on the subject of standardized testing in education:
The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.
Achievement tests may well be valuable indicators of general school achievement under conditions of normal teaching aimed at general competence. But when test scores become the goal of the teaching process, they both lose their value as indicators of educational status and distort the educational process in undesirable ways.
— Donald T. Campbell, “Assessing the impact of planned social change” (1976)
In other words, if you conduct a one-time evaluation of student achievement across many students in multiple schools, then the fact that the test is standardized might help in achieving comparable results. However, as soon as you make the tests a repeat occurrence, and tie students’ test results to school funding allocations, teacher salaries, or even just school prestige, you’re undermining their original purpose: teachers will now spend a significant portion of their time and effort to ensure that students score well on the test, rather than build the competence that the test was originally designed to measure.
This is an example of allocating resources (teacher and student time and effort) to an activity with no inherent value (taking a standardized test) just to improve a measurement (the test score). And since the resources are finite, spending them on the activity with no inherent value (test-taking) makes less of them available to the inherently valuable activity the indicator is intended to assess (teaching and learning). This is the “corruption and distortion” Campbell talks about.
The McNamara Fallacy, and the Yankelovich Ladder
Closely related to Goodhart’s, Strathern’s and Campbell’s observations is something called the McNamara Fallacy.
Robert McNamara, U.S. Secretary of Defense during much of the Vietnam war, infamously believed that he could scientifically measure the progress of the war by quantitative indicators alone. One of his favourites was body count, the number of enemy personnel killed, in comparison to friendly casualties. The rationale appears to have been, whatever other factors (qualitative or quantitative) are in play, whichever side kills more of the other wins the war. Indeed he seems to have been inclined towards ignoring all non-quantitative indicators of how the war was going.
An anecdote told by U.S. Air Force general Edward Lansdale alleges that he (Lansdale) pointed out to McNamara in a briefing that McNamara, when assessing the progress of the war, failed to take into account the feelings of the common rural Vietnamese people. McNamara then allegedly wrote an item saying “feelings of the Vietnamese people” on his list of things to keep track of in pencil, pondered it for a moment, and then erased it — reasoning to Lansdale that feelings cannot be measured, thus must not be important.4
This is step 3 on a progressive scale social scientist Daniel Yankelovich described a few years later:
The first step is to measure whatever can be easily measured. This is OK as far as it goes.
The second step is to disregard that which can’t be easily measured or to give it an arbitrary quantitative value. This is artificial and misleading.
The third step is to presume that what can’t be measured easily really isn’t important. This is blindness.
The fourth step is to say that what can’t be easily measured really doesn’t exist. This is suicide.
— Daniel Yankelovich, “Corporate Priorities: A continuing study of the new demands on business” (1972).
And it’s somewhat remarkable just how often businesses and organizations fall into this trap, fifty years later. They might not end up at step 4, but falling for step 2 or 3 is bad enough.
An applied example
Let’s now turn to an example from our industry. Something that’s so important, evidently, that it has given rise to a whole discipline in our field: site reliability.
Now it’s perhaps a bit amusing that although you can find myriads of articles describing what site reliability engineering (SRE) is, a definition of “site reliability” lives only in a small footnote of the Google SRE Book:
For our purposes, reliability is “The probability that [a system] will perform a required function without failure under stated conditions for a stated period of time”.
— Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Murphy, “Site Reliability Engineering: How Google Runs Production Systems” (2017)5
But, at least there is a definition, which is good. Now I think it’s reasonable to say that the following two statements about site reliability are probably true:
In keeping with SRE reflecting a holistic approach to engineering, trying to unify a multitude of considerations, site reliability is not something we can judge by a single, numerical, universal, and useful metric. You can’t measure a single “site reliability score”, and then compare hundreds of platforms based on that.6
Whatever site reliability is as a whole, it certainly includes a site’s ability to process your data and not mangle it. So, if you upload your data into a platform, you want to be able to do something useful with it.
SRE tends to rely on service level indicators (SLIs) to measure compliance with service level agreements (SLAs), manage error budgets, and generally keep track of what shape the site/platform is in.
So, let’s compare two indicators that differ greatly in their measurability.
Availability is exceptionally easy to measure for, say, a REST API. You send a request with a defined payload, you measure the time it takes to serve your request, you check the status code, you check whether the response contains what you expect, and you record a data point.
Durability is much more difficult to measure at any given point in time. Effectively, to properly take a data point for durability at the same time as getting one for availability, you’d have to read back some data you wrote, say, a year ago, and check its content against something like a known hash.7 And also write some data now, travel a year into the future, read it back at that point, travel back into the present,8 and record your data point.
Now before I continue I’d like to inject another thought to the issue of data durability: not every platform is a storage solution. In other words, you don’t always have the option of reading your data back verbatim. Say for example you’re feeding an inordinate number of data points into a platform that ingests and aggregates them. You may not even be interested in the original data some months or years down the road, so it might be acceptable (and even necessary, as dictated by cost concerns) to discard the original data immediately after it has been processed. And that rules out the possibility (or necessity) to ever read it back exactly as it went in. But you will be interested in the statistics that you generate based on the aggregated data.
And now suppose there is a subtle bug in the implementation of the aggregation algorithm. As in, the algorithm itself is perfectly fine, but there’s a flaw in the implementation. That, too, may render part of your data unusable or outright invalid, violating data integrity and durability.
But the tricky part here is that availability is easy to measure. Data durability isn’t. Therefore, availability lends itself to becoming a target (hello, Professor Strathern), and durability tends to be seen as difficult to measure and hence less important (hello, Secretary McNamara).
So now, if you find yourself in charge of a system that you suspect has started to corrupt a significant fraction of customer data, data which customers are pouring into it at an alarming rate, what do you do? You’re not sure whether there’s actual corruption yet. The proper thing to do, if it’s impossible to rule out or fix the data corruption problem immediately,9 is probably to stop intake, and also ensure that no requests are served that may touch potentially corrupted data — that is, shut the service down even before you’ve ascertained corruption. But if you suspect that your next bonus payout or promotion may rely on you meeting your availability goals, and you know you’re already shaving it close with your availability error budget, would you really be inclined to do that?
“You can’t manage what you don’t measure”
There’s a popular saying in management circles that takes one of the following forms:
- “If you can’t measure it, you can’t manage it.”
- “You can’t manage what you don’t measure.”
- “You can’t manage what you can’t measure.”
Whichever variant you discuss, it is commonly attributed to either Austrian-American management thinker Peter Drucker, or to American engineer and statistician William Edwards Deming. Drucker is seen by many as highly influential in management theory, Deming developed groundbreaking sampling techniques used on the massive scale of the United States census. So either of them would be an authority on management and measurement, lending high credibility to the statement.
There’s just a small problem: neither of them appears to ever have said or written anything to that effect.
The closest that one of them, Deming, ever wrote was:
It is wrong to suppose that if you can’t measure it, you can’t manage it — a costly myth.
— W. Edwards Deming, “The New Economics for Industry, Government, Education” (1993)
In case you didn’t notice, the point this makes is the exact opposite of the popular version of the quote. It’s so wrong that it comes close to the corruption of the Seneca lament, “non vitæ sed scholæ discimus”, “we learn not for life but for school,” of which you surely learned the inverse… in school.
Metrics-obsessed managers often take the misquote for gospel. So much so that they frequently see issues where a qualitative approach is obviously necessary, and they still try to apply quantification.
My standard example for this are employee satisfaction surveys.
Ultimately, what leadership should be interested in learning from those surveys is how good people feel about working in the company. There are a number of factors that contribute to this: are they overloaded, well utilized, or bored? Are people treating each other with respect and kindness, or malice and contempt? Does everyone feel that they are doing something meaningful, or do they all hate their work and are solely in for the money? All these things are inherently qualitative. And the company could do a great job by hiring a person trained in sociology or psychology, who sits down with people for confidential qualitative interviews, and then prepares a research report with findings and recommendations that management can act on.
But no, we have to measure. Make everyone take an online survey where they rate everything on a scale of 1 to 5. Do you know what that is? Exactly, step 2 on the Yankelovich ladder. Give that what can’t easily be measured an arbitrary quantitative value — because that’s what it is, arbitrary. People from different cultures won’t agree even on what a simple 5-step scale really means.
And depending on what version of the faux quote they adhere to, a manager may even be farther up the ladder:
If they say “you can’t manage what you don’t measure” (with the translation being “I won’t concern myself with anything for which I don’t have quantitative data”): that’s step 3, blindness, that which isn’t measured isn’t important.
If they insist that “you can’t manage what you can’t measure” (with the translation being “I won’t concern myself with anything that isn’t quantifiable”): that’s step 4 (suicide), that which isn’t measured doesn’t exist.
So, what now?
Every article and book on bad metrics ends on a positive note, giving you suggestions for “good” metrics: for example, make them hard to game, make sure they are defined by competent experts, ensure that they are in line with inherent ideas of respectability and professionalism. Honestly, I’ve yet to come across a metric that ticks all these boxes.10
So, I am aware that if you are running a platform under an existing SLA, you will be running under some metrics of questionable utility that you cannot get rid of — just because they happen to be industry standards.
However, instead of expanding metrics obsession to your entire organization by introducing ever more counterproductive metrics, I want to propose a different approach:
Whatever you measure, make the marginal cost of a measurement negligeable.12 The cost of adding a new metric should be practically zero. The moment someone has to repeatedly spend time on collecting and compiling the data, they can’t spend that time on doing productive work (and Campbell says hi), so you want to avoid that.
This effectively means that all the systems you care about (machines, services, applications) should generate collectable data points, everywhere, all the time.11 And you probably won’t be collecting metrics from anything else. In other words, you are just measuring that which is easily measurable, and you keep aware that there a lot of things you don’t measure that are just as important. You stay on step one of the Yankelovich ladder.
Now, I’d propose you make the data thus ingested available throughout your organization, in machine-readable form and using standardized APIs. You want people to actually discover things from your data.
Encourage people to use real, scientific, statistical methods to figure out statistical regularities (“indicators”). Offer statistics training to people who are interested.
Once someone identifies a statistical regularity, encourage them to form an opinion of whether it would be beneficial for it to go up or down, formulate a hypothesis on what change to your system would have the desired effect, and conduct an experiment. If the experiment has no effect, roll back the change and proceed with the next hypothesis. If it has an adverse effect, roll back and try the opposite. If it has the desired effect, keep the change. Move on to discovering the next regularity. Resist the urge to make the discovery a target. (Otherwise, Strathern will drop by.)
Constantly observe and identify things that are important, but not measurable. Apply qualitative analysis, emotion, and empathy. (Otherwise, McNamara will introduce himself.)
So, is there anything inherently wrong with measuring or measurements? Nope. But making them targets, introducing arbitrary quantifiers, and ignoring everything else is.
The reason the condensed version is called “Goodhart’s Law” and not “Strathern’s Law” is apparently due to a coinage by British researcher Keith Hoskin, who wrote a year prior to Strathern, in a paper she cited:
“Goodhart’s Law” — that every measure which becomes a target becomes a bad measure — is inexorably, if ruefully, becoming recognized as one of the overriding laws of our time.
— Keith Hoskin, “The ‘awful idea of accountability’: inscribing people into the measurement of objects” (1996)
If you think that term sounds a bit odd, I’d agree. I guess it comes from the idea of milking a cow and then skimming only the cream, discarding the rest. ↩
The Lansdale/McNamara anecdote is paraphrased from the Wikipedia article on the McNamara Fallacy, which in turn cites Rufus Phillips and Richard Holbrooke, “Why Vietnam Matters: An Eyewitness Account of Lessons Not Learned” (2008) as its source. ↩
It should be noted that the SRE book is itself quoting a definition of reliability found in Patrick P. O’Connor & Andre Kleyner, “Practical Reliability Engineering” (2012). ↩
The irony is not lost on me that by the definition quoted in the SRE book, such a score absolutely should exist if its definition of reliability were adequate: it claims to be a probability. Probabilities go from 0 to 1. That would make site reliability a dimensionless quantity between 0 and 1, end of story. But it goes without saying that such a score would be “an arbitrary quantitative value”, which would put it on step 2 of the Yankelovich ladder. ↩
That hash would have to be separately stored outside the system. If the hash is stored alongside the data whose integrity it’s meant to protect, then it only guards against unintentional data corruption, but not against deliberate manipulation. ↩
I wish to point out that the only bit that’s impossible here is the backwards time travel. The forwards time travel is fine, we all travel forwards in time all the time, just at a constant rate of one second per second. ↩
I’ve run into a few issues of suspected silent data corruption in my career and I’ve never been in the situation where a reliable fix was available immediately. ↩
In particular, pretty much any real-world metric fails the “hard to game” test. Said Lukas Grossar on Twitter: “It always amazes me that people don’t believe that slapping a KPI onto something won’t lead to people gaming that KPI. We’re engineers for God sake, making broken stuff work in our favor is basically our job description.” ↩
I’d argue that this requires strong privacy guarantees for your users/customers. Effectively, just don’t collect data that’s none of your business. ↩
Emphasis on marginal. It’s obvious that the fixed cost of building and maintaining an instrumentation platform and metric system is nonzero. But once you’ve got it set up, the cost of adding a new metric should be substantially zero. ↩