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A hospital tracks wait times, and the wait times improve on paper. A school tracks test scores, and teaching narrows to the test. A fitness app tracks steps, and people pace their kitchens at 11 p.m. to hit a target. In each case the number went up while the thing it was meant to represent stayed flat or got worse.
This article is about that gap. It uses a working idea we’ll call beyond numbers disquantified thinking: knowing when a metric has stopped describing reality and started replacing it. “Disquantified” is not a standard dictionary word, and no academic field owns it. Here it means something specific, defined below, and the rest of the piece builds a practical toolkit around it.
What “Disquantified” Means
A working definition
Something is quantified when you express it as a number. It is unquantified when nobody has measured it yet. Something becomes disquantified when a measurement exists but has lost its connection to the thing it was supposed to track, so the number is still reported while the reality behind it has drifted away.
In this article, beyond numbers disquantified describes the habit of looking past a distorted metric to the underlying reality, and deciding what to do about it. It is not a rejection of data. It is a way of asking what the number is actually telling you.
Quantified vs. unquantified vs. disquantified
| Term | What it means | Example | Main risk |
|---|---|---|---|
| Quantified | Measured and expressed as a number, with the number still tracking reality | Blood pressure readings at a checkup | Over-trusting a single reading |
| Unquantified | Real but not measured | How supported a team feels | Being ignored because it’s invisible |
| Disquantified | Measured, but the metric has detached from the goal | A call center rated on call length while customers’ problems go unsolved | Decisions based on a false signal |
The third row is the dangerous one. An unquantified factor at least looks like a gap. A disquantified metric looks like knowledge. finance article disquantified
Why Measurement Breaks
Goodhart’s Law and Campbell’s Law
Two principles explain most disquantified metrics.
- Goodhart’s Law comes from British economist Charles Goodhart. It is usually summarized as: when a measure becomes a target, it ceases to be a good measure.
- Campbell’s Law comes from social scientist Donald Campbell. The more a quantitative indicator is used for decision-making, the more it will be pushed to corrupt the process it was meant to monitor.
The mechanism is the same in both. A metric begins as a rough proxy for something you care about. Once people are rewarded or punished based on the proxy, they optimize the proxy directly, and the link to the real goal weakens.
The McNamara Fallacy
Sociologist Daniel Yankelovich described a four-step trap, often named after Robert McNamara and the use of body counts to judge progress in the Vietnam War:
- Measure whatever is easy to measure.
- Disregard what can’t be measured easily.
- Presume that what can’t be measured easily isn’t important.
- Conclude that what can’t be measured doesn’t exist.
Steps three and four are where good analysts go wrong. Hard-to-measure things like trust, judgment, and craftsmanship are often the factors that determine outcomes.
Metric fixation
Historian Jerry Muller gave the pattern a name in his 2018 book The Tyranny of Metrics. He calls it metric fixation and lists its recurring features:
- A belief that numbers can replace judgment
- A belief that publicizing performance data produces accountability
- A belief that the right rewards are tied to measured performance
Muller’s argument is not that measurement is bad. His point is that measurement works best when it informs the judgment of people with experience, rather than replacing them.
Five US Cases Where the Number Won and Reality Lost
Wells Fargo sales quotas
Wells Fargo pushed aggressive cross-selling targets across its branches. Employees opened accounts customers never asked for to hit their numbers. In 2016 the bank agreed to pay $185 million in fines, and roughly 5,300 employees were fired over the practices. The metric (accounts per customer) rose. The goal it stood for (customers who actually wanted more products) was not being met.
Atlanta Public Schools
Test score targets created pressure to show gains. A state investigation found cheating at dozens of Atlanta schools, with educators changing student answers. In 2015, eleven educators were convicted. The scores looked like progress, and the students had not learned what the scores claimed.
VA wait times
In 2014, reports from the Phoenix VA health system alleged that schedulers kept secret lists to hide long waits for veterans. The wait-time measure was shaping how appointments were recorded rather than how quickly patients were seen. The scandal led to the resignation of VA Secretary Eric Shinseki.
College rankings
In 2022, Columbia University admitted that some data it had submitted for the U.S. News rankings was inaccurate, after a faculty member publicly questioned the figures. Columbia’s ranking then fell sharply. When a single composite number drives applications and prestige, institutions face strong incentives to manage the inputs.
The 10,000 steps goal
The 10,000-step target is often traced to a 1960s Japanese pedometer marketing campaign rather than to clinical research. A 2019 study led by I-Min Lee of Harvard, published in JAMA Internal Medicine, followed older women and found that mortality benefits leveled off at around 7,500 steps per day. The round number stuck because it was easy to remember, not because it was precisely right.
What these cases share
| Case | Metric | What people optimized | What got lost |
|---|---|---|---|
| Wells Fargo | Accounts per customer | Account openings | Real customer demand |
| Atlanta schools | Test score gains | Score reports | Actual learning |
| VA | Appointment wait time | Recorded dates | Patient access |
| College rankings | Composite rank | Submitted data | Honest institutional picture |
| Step counts | Daily steps | Hitting 10,000 | Overall health |
Every row is a failure of beyond numbers disquantified judgment: someone saw the metric move and assumed reality had moved with it.
Where This Shows Up in Daily Life
At work
Common disquantified workplace metrics include:
- Lines of code as a proxy for developer productivity, which rewards bloat
- Average handle time in support, which rewards ending calls rather than solving problems
- Meetings attended or emails sent as proxies for engagement
- Employee engagement scores gathered in surveys people don’t trust to be anonymous
A manager who practices a beyond numbers disquantified approach pairs each metric with a question: what would someone do to improve this number that wouldn’t improve the outcome?
In school
Standardized test scores, GPA, and class rank are useful signals. They also reward test strategy and grade negotiation over curiosity and retention. Students learn quickly what is counted.
In health and wellness
Sleep scores, readiness scores, and calorie counts can help people notice patterns. They can also create anxiety about a number that wearable sensors estimate imperfectly. Consumer devices infer sleep from movement and heart rate, not from brain activity, so treating the score as a medical measurement overstates what it can tell you.
In algorithms and AI scoring
Credit scores, hiring filters, and risk assessments compress people into a number. The model may be accurate on average and still wrong for individuals, and the person scored often can’t see how the number was built. Researchers in organization studies have pointed out that numbers reveal some things and hide others, and that the rationale behind many metrics stays inaccessible to the people they govern.
A Practical Framework for Beyond Numbers Disquantified Decisions
The five-question test
Run any metric through these questions before you build a decision around it:
- What is this number supposed to represent? Write it as one sentence. If you can’t, the metric is already floating.
- Who gains if the number goes up, regardless of reality? This identifies the incentive to game it.
- What is the cheapest way to move the number without improving the goal? If the answer is easy, expect it to happen.
- What does this number leave out? List at least two things that matter and aren’t captured.
- What would I see if the number were wrong? Pick a qualitative signal, like customer complaints or teacher observations, that would contradict it.
When to measure, and when to leave it alone
| Situation | Lean toward measuring | Lean toward judgment |
|---|---|---|
| The goal is clear and narrow | Yes | |
| Outcomes are easy to observe directly | Yes | |
| The metric will be tied to pay or punishment | Yes, or use several metrics | |
| The goal is creative, relational, or ethical | Yes | |
| People can’t see how the number is calculated | Yes | |
| Stakes are low and reversible | Yes |
Rules of thumb that hold up
- Use several metrics that pull against each other, such as speed and quality, so that optimizing one exposes the other.
- Treat a metric as a question starter, not a verdict.
- Rotate or retire metrics before people learn to game them.
- Keep the person closest to the work in the room when you interpret the data.
Qualitative Evidence That Works Alongside Numbers
Moving beyond a broken metric does not mean moving to guesswork. Several methods give you evidence that numbers can’t:
- Structured interviews: Ask the same open questions of 8 to 12 people and look for repeated themes.
- Direct observation: Watch the work happen. Spend an hour at a support desk before judging its handle time.
- Case reviews: Examine five good outcomes and five bad ones in detail rather than averaging them.
- Open-text analysis: Read the comments attached to survey scores, since they often explain the number.
- Pre-mortems: Before launching a metric, ask the team how it could be gamed.
Mixed methods are the standard answer here. Numbers tell you how much and how often. Qualitative evidence tells you why. A beyond numbers disquantified practice uses both and checks each against the other.
Mistakes to Avoid
- Throwing out data entirely. The lesson of the cases above is not that measurement fails. It is that unexamined measurement fails.
- Adding more metrics without removing any. A dashboard of 40 indicators is usually just a larger place to hide a distorted one.
- Blaming individuals. In most of these cases, the incentive design produced the behavior. Fix the design first.
- Treating qualitative evidence as automatically more honest. Interviews and anecdotes have their own biases, so check them against numbers too.
- Assuming a stable metric is a valid one. A number that never moves may be measuring habit, not performance.
Conclusion
Metrics are useful tools that people mistake for facts. The Wells Fargo accounts, the Atlanta test scores, the VA wait lists, and the 10,000 steps all show the same pattern: a number that began as an honest proxy and ended up standing in for the truth.
Seeing beyond numbers disquantified means keeping two questions open at once. Is this number accurate, and is it still connected to what I care about? If you can answer both, use the metric. If you can only answer the first, you are holding a precise measurement of the wrong thing.
Start small. Pick one metric you rely on this week, run it through the five-question test, and note what it leaves out. That one exercise is the first step in building a beyond numbers disquantified habit into how you work, teach, or manage.
Frequently Asked Questions
What does beyond numbers disquantified mean?
It is a working term for looking past a metric that has detached from its goal. A disquantified metric still produces numbers, but the numbers no longer reflect what they were meant to track.
Is “disquantified” an official word?
No. It is not in standard dictionaries. This article uses it as a coined label for a metric that has lost meaning.
How is disquantified different from unquantified?
Unquantified means nothing has been measured. Disquantified means something has been measured, but the measure is misleading.
What is Goodhart’s Law in plain terms?
When people are judged by a number, they start optimizing the number instead of the thing it was supposed to represent.
Does this mean metrics are bad?
No. Metrics are essential when they are tied to clear goals and checked against direct evidence. The problem is blind reliance on a single number.
How can I tell if a metric has become disquantified?
Ask whether the number could improve while the real outcome gets worse. If yes, and incentives exist to make that happen, it probably has.
What is a better alternative to relying on one metric?
Use a small set of balanced metrics that pull against each other, and add qualitative evidence like observation or interviews.
Where can I learn more about the research behind this?
Start with Jerry Muller’s The Tyranny of Metrics, and look at the “Beyond Measure” special issue of Ephemera (2020), which examines how quantification shapes management, organization, and politics.
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