A flashlight has a most mundane but curious property: the moment you flip the switch, the room becomes both brighter and darker. Wherever the light beam hits, details are rendered in sharp focus, and objects you hadn’t realized were there become clearly visible. At the same time, the opposite occurs everywhere: the rest of the room not illuminated by the flashlight beam falls into deeper shadow. The darkness is not a flaw of the flashlight; it is a necessary trade-off for the light it produces. The same is true of performance measurement. Each KPI illuminates one dimension of a company’s operations, whether that be revenue growth, customer satisfaction, employee productivity, inventory costs, or operational efficiency. KPIs make it easier for executives to understand, analyze, and (one hopes) improve organizations, converting complexity into data points that facilitate decisions, rather than gut feeling or anecdote alone. For decades, the standard solution has been the same: if one KPI helps uncover something important, perhaps 10 KPIs can provide a better understanding, and 100 can illuminate everything clearly. That’s the rationale behind the modern dashboard: displays of gauges and graphs, crammed with performance indicators, accompanied by meetings to debate the resulting data, all aimed at dispelling ambiguity.
For all intents and purposes, this is a completely reasonable yet equally impossible aspiration. That may sound startling given how much more access we have to data than ever before. We store it on massive servers at minimal cost. We use sophisticated analytics and AI to glean insights from terabytes of information. It seems that if we just gather enough data and track enough metrics, we should eventually be able to eliminate the shadows completely, but we never do. This isn’t because organizations aren’t diligent, or their dashboards are poorly designed, or due to management failing to pick the right KPIs. It is because measurement always has its limitations. The first step in any measurement is deciding what we want to measure, a step often so mundane that it slips below notice.
Someone first decided that customer retention was a worthwhile thing to track.
Someone first determined that employee productivity could be represented and measured in an operational way.
Someone decided to define and track inventory turnover.
While individually unremarkable, these choices coalesce to determine how an organization understands itself, and this is where a conversation about management science turns toward a more ancient philosophical inquiry: is it possible to represent all aspects of reality? As the history of thought suggests, the answer is no. All representations of something omit something from it. Every descriptive attempt leaves something else out. A given perspective must, by its very essence, exclude other perspectives. The limitation of KPIs is not that they don’t capture enough, but that they cannot capture everything simultaneously…and maybe they shouldn’t. Imagine that a cartographer were asked to draw a complete map of a country, including every road, every river, every building, every tree, every shifting cloud, and every stone on its surface. If the map were rendered with perfect accuracy down to the atomic level, it would no longer function as a map – it would be indistinguishable from the country itself. In that case, its utility would depend entirely on what it had omitted.
The Argentine author Jorge Luis Borges nailed this with his short tale “On Exactitude in Science.” The empire there had grown so hung up on perfection that the imperial cartographers had produced a map so detailed and at precisely the same scale that it mirrored the entire empire. This was an incredible (and utterly useless) feat of accuracy.
A map as large as reality provides nothing useful because it has forfeited the very abstraction that made maps useful.
Organizations seem to be striving toward the same ambition when they set out to create dashboards. Each new initiative seems to yield yet another metric. Each new blind spot feels solvable if we can just add one more indicator. Along the way, however, we realize that the dashboard has ceased to represent reality by abstracting from it and has begun to become reality itself by trying to represent every aspect of it.
Rather than helping us understand the world by reducing its complexity to a set of meaningful patterns, the dashboard simply introduces its own brand of complexity. It becomes another source competing for our attention, rather than helping to direct it. The ultimate irony, of course, is that by attempting to eliminate uncertainty, we’ve somehow succeeded in regenerating it.
Therein lies the discomfort. Performance measurement has never been about completeness. It has always been about selection. The pertinent question isn’t whether or not our dashboards contain blind spots. They always will – that is a foregone conclusion. The truly germane question is this:
On which blind spots have we collectively and knowingly chosen to focus, and what price does this quietly cost us?
Every Map Leaves Something Out
Abstraction is the very reason measurement exists.
1933: a philosopher and scientist named Alfred Korzybski made a statement that has endured as one of the most profound observations about the nature of human knowledge: “The map is not the territory.” Maps work precisely because they are incomplete.
A road map omits soil conditions.
A geological map omits speed limits.
A weather map omits property boundaries.
A subway map omits actual geographic distances.
A political map omits mountains and rivers.
None of these maps is wrong per se; they simply emphasize certain features by ignoring others. In other words, every map imposes an opportunity cost.
By helping you see one thing more clearly, it forces you, however temporarily, to stop looking at countless others. The same subtle truth underpins every single KPI we have ever created.
Imagine a manufacturing plant decides to elevate production speed to the top of its list of indicators. Almost immediately, the company begins to view itself through that lens. Conversations about throughput take center stage, and managers trumpet short cycle times. None of that is necessarily bad in itself, but the problem lies elsewhere. As the spotlight shines brighter on the speed-of-production-indicator, other valuable activities start to fall into the shadows. Craftsmanship becomes hard to recognize because it never moves at maximum velocity.
Careful experimentation with new processes is slowed by the urgency to produce.
Mentoring inexperienced workers becomes harder to justify because it doesn’t contribute directly to immediate output.
Knowledge sharing is quietly abandoned because documenting lessons learned doesn’t increase this month’s production figures.
Preventive maintenance suddenly feels like a costly delay rather than a wise investment.
None of those things become any less valuable; they just become less visible, and that is a critical distinction.
Organizations don’t typically abandon what matters because they consciously decide they don’t care about it. More often, they abandon it because attention shifts. Like a river changing its course, attention reinforces whichever pathway it flows through, gradually starving its adjacent tributaries of life. Every KPI generates the current:
Measure costs mercilessly, and resilience is slowly yielding ground to pure efficiency.
Measure speed aggressively, and craftsmanship begins to politely negotiate for a little bit of air to breathe.
Measure customer acquisition relentlessly, and customer loyalty quietly slips into the background.
Measure individual performance exclusively, and collaboration starts competing for recognition.
Measure short-term results obsessively, and long-term capability becomes an investment nobody feels they can afford.
Measure productivity and some amount of creativity has become the opportunity cost.
These aren’t implementation problems but a natural consequence of choosing one map over another. No organization, however sophisticated, escapes this inherent trade-off. The only question is whether it acknowledges it.
To believe otherwise is to believe that light can exist without shadow, or a river can flow down all of its tributaries simultaneously. Neither is possible regardless of how much wishful thinking we may engage in.
Self-reflection: What parts of your organization exist only because they were left off the map? What have your dashboards quietly trained you to stop seeing?
The Things We Know but Cannot Measure
A lot of an organization’s most significant strengths never make their way into a spreadsheet or a performance dashboard. That doesn’t mean they’re worthless. It simply means they’re unquantifiable.
There was once an old tale about a master luthier. Years ago, his apprentice learned how to master every single measurable aspect of the luthier’s art. They learned the ideal wood thickness, neck angle, and sound box dimensions. They learned precise moisture levels for every species of wood. They learned about ratios honed by centuries of master violin makers.
One afternoon, after completing their masterpiece, a violin so technically perfect that it was a work of art, the apprentice presented it to the master. The old craftsman peered at the instrument, ran his hand over its smooth, polished surface, and asked the apprentice one simple question: “Did you listen to the wood?” The apprentice stared at him, confused. He’d measured everything to a T, but he’d never thought to listen. “What does it even mean to listen to wood?”
The story may be apocryphal, but the phenomenon it describes is undeniably real. There’s a form of knowledge that cannot be expressed in mathematical equations or codified in best practices manuals. However, we can recognize it immediately when we see it, though we have difficulty pinpointing its nature.
The experienced doctor whose intuition alerts them to a problem that’s not yet showing up on the medical monitors.
The teacher who somehow senses that a perfectly attentive student with straight A’s is secretly struggling.
The firefighter who somehow knows when a building’s imminent collapse.
The negotiator who intuitively understands when utter silence will be more effective than a persuasive argument.
Ask any of these individuals how they knew, and you’re likely to get equally unsatisfying answers: “It just didn’t feel right.” / “Something was off.” / “You get a feel for it.“
From the perspective of someone seeking concrete data, these explanations can feel maddeningly elusive. Nevertheless, organizations implicitly rely on such judgment calls all day long.
For example, most of Michael Polanyi’s thought process was organized around that observation. He had the famous concept, “We know more than we can tell,” as a challenge to the notion that any valid knowledge eventually would be captured, measured, standardized, and written down. Some knowledge can easily live in a spreadsheet, yet other knowledge lives in people. They accumulate it from experience and mistakes, from gut feeling and intuition, and the kind of pattern recognition and observation which is rarely explicit enough to measure, which Polanyi referred to as tacit knowledge.
Maybe one of the best illustrations comes from something as simple as bicycle riding.
All but the most clumsy can ride a bike, hardly thinking, balancing, managing pressure and momentum, timing the minute variations in the bars, and coordinating muscles all at once. Now try asking someone to describe every single detail needed to balance, and you get a clear sense of just how much their knowledge lies beyond their words.
Knowing how differs from knowing about.Organizations have vast storehouses of this tacit knowledge:
The repair specialist who can listen to a car engine and sense what needs repair down the line.
The customer service rep who detects someone’s incipient dissatisfaction long before the complaint is lodged.
The project leader who picks up on tensions in a team meeting long before the employees are even conscious of it, or the survey forms do.
The production supervisor who notices a subtle change in a machine’s rhythm before any sensor or maintenance report flags an issue.
The sales manager who recognizes that a long-standing client is preparing to leave, not because of declining revenue, but because of a slight shift in tone during routine conversations.
All those things, those pieces of organizational know-how, don’t fit into a nicely curated dashboard, but that doesn’t make them less true. Unfortunately, just because they don’t fit, that can be an excuse to ignore them, since what can be measured tends to take precedence over what cannot.
It is here that another philosopher enters the picture, not to tell anyone how to run a meeting, but because he wrote so clearly about human thought: Nobel laureate and psychologist Daniel Kahneman, who coined the acronym:
WYSIATI –“What You See Is All There Is.”
His point was simple: human beings naturally construct stories based on the information available to them in the moment. We seldom consider what’s missing. This makes our lives easier in countless day-to-day decisions. Within organizations, it quietly sculpts our culture.
Imagine two leadership meetings.
The first features executives spending an hour analyzing revenue trends, customer acquisition costs, production efficiency, and employee utilization.
All the charts are ready. All the numbers are current.
The second meeting begins with a question that causes a flicker of discomfort: “How much institutional knowledge have we lost this year?”
Now, everyone is hushed, almost deathly silent. This is not because the question is irrelevant, but because no one has a chart tracking decades of experience retiring with long-time employees. No dashboard shows the slow erosion of mentorship. No KPI reports on the silent confidence a junior engineer accumulates watching a senior colleague solve tough problems over half a decade. Thus, the conversation inevitably circles back to the numbers, not necessarily because they are more important, but because they are present.
This is the subtle peril Kahneman described. Visibility masquerades as importance so powerfully that the longer a metric appears on a dashboard, the more likely we are to assume it deserves our attention. Soon enough, the organization behaves as though the measurable world is indistinguishable from the genuinely important world. Hardly. We don’t see trust appear on a dashboard. Nor does curiosity, judgment, wisdom, humility, psychological safety, institutional memory, craftsmanship, talent, or brilliance.
These qualities do not diminish in value because they defy quantification; they simply attract less attention, and attention (arguably more than money or time) is an organization’s most scarce resource. Consider the origin of a great river. Initially, it has countless tributaries – some narrow and tortuous, others wide and majestic. It cannot flow down all of them simultaneously, and once it commits to one path, the others quickly recede. Attention works the same way.
Every meeting agenda, every dashboard, every quarterly objective, every KPI selects one path for the organization’s energy and focus, while the rest begin to fade into the background. This is the opportunity cost of our dashboards, which we seldom discuss. When leaders choose to measure productivity, they aren’t simply choosing to observe productivity; they’re choosing to commit meetings, incentives, conversations, budgets, promotions, and intellectual energy to it.
Something else will inevitably receive less attention: maybe it’s creativity, mentoring, experimentation, or reflection. What is certain is they won’t vanish overnight, but, like an abandoned riverbed, they’ll receive a little less water each season until we eventually wonder what happened to the current. Organizations often assume culture changes because people change. In fact, culture sometimes changes simply because attention changes. The dashboard didn’t tell employees to stop mentoring one another; it simply stopped reminding them to do so. This is the quiet paradox of measurement: not that it tells us what to value, but that it gently nudges us to value what it tells us.
It is perhaps why the most enduring qualities within an organization often remain nearly invisible: the quiet conversations after meetings end, the intuitive grasp that builds over decades, the acts of kindness that, while never appearing on a quarterly report, fundamentally shape the workplace over many years.
No dashboard will ever fully capture them, and maybe it is for the better that none should ever try. After all, the purpose of a map is not to be the territory itself, but to guide our journey through it, without allowing us to forget that the territory is always infinitely richer than the paper upon which it has been sketched. Self-reflection: If your dashboard vanished tomorrow, what knowledge within your organization would still be accessible? What invaluable capabilities might have quietly receded, not for want of value, but for want of attention?
When Measuring Changes Reality
As soon as a metric becomes important, people will start to restructure their lives and their behaviour around it, and, in doing so, the organization has quietly transformed into something new.
Imagine you take a walk through a forest and carry a compass. When you are in wild country, confused as to which way to proceed, it gives you absolute assurance.
It consistently points north regardless of which direction any of the trees are oriented, and no matter how uniform all the trees look. Despite all of this, the compass does not tell you about cliffs. It says absolutely nothing to you about riverbanks or unstable ground, about poison berries or an oncoming storm. It performs perfectly for the set purpose, and it says nothing to you at all about most of the rest of the landscape.
We know that this instrument exists for the sake of asking one particular question, and not for the asking of all these questions. The problem arises when we start treating an instrument as the territory itself. It is on this basis, among others, that KPIs may have gotten themselves into trouble.
When they are first instituted, they do not, at the start, look so obviously bad as things become. What they are expected to do is help people find the way: help us see where things stand, where we are with regard to the world around us. Over time, however, they morph into something rather different.
Instead of helping people make sense of the world, they actually start to shape and create it.
What people ask no longer comes out as: “How can I do something that will help me add value over the longer term?” Instead, people begin to ask: “How can I do something to make the number for this month better?” That is not a good change at all, and neither is its effect.
This is a thought that spills over beyond the confines of business and out to the philosophers again.
German philosopher Martin Heidegger argues that technology does more than simply provide us with tools to perform useful tasks. Technology can also fundamentally change how we see the world. Heidegger’s notion of “enframing” or “Gestell” means, more simply, our tendency to see the world only in relation to how we have divided and framed it for organizing purposes.
The forest can be many things depending on how you see it:
A painter’s inspiration
An adventure park for a child
A natural ecosystem of incredible complexity to a biologist
A resource for a timber company, ripe for extraction
At its core, the forest remains unchanged. The lens through which we view it shifts.
The same phenomenon often occurs in performance management across an organization:
One manager might view an employee just as an 87% score on one dashboard.
A second manager, however, may believe this employee is the lynchpin holding a team together and should be regarded as an experienced mentor.
A third might see them as an untapped potential who will eventually revolutionize company culture.
A fourth could turn to them when a critical problem has no documented solution.
The person hasn’t altered, only their apparent visibility, and this is why the dashboards you install to “measure performance” may end up having a much more profound impact: they do not just capture a representation of an organization – they actively teach that organization what it should consider meaningful.
Take, for example, a call center team for which “Average Handle Time” is the primary KPI. At the outset, this is an appropriate metric. Nobody wants their time on hold, nor for calls to drag on indefinitely, so reduced handling times should, in principle, improve the customer experience. After a period of months and a growing emphasis on meeting the metric, you might see some subtle shifts:
Customers might find their calls cut short, and complex queries are often quickly passed on to someone else.
Calls requiring additional customer support may be concluded sooner than necessary to avoid negatively affecting the metric.
Eventually, employees might be actively encouraged to make calls as brief as possible, even when there is a clear need to spend more time with an individual. No one asked or directed the staff to stop being caring, but they learned that caring did not reflect well in the KPI. This effect isn’t isolated to call centers.
We’ve seen it time and again in organizations:
Hospitals boost patient throughput, but the time available for each patient to connect with their nurse decreases.
Universities champion graduation rates, but in practice, they have reduced the number of required in-person teaching hours to free up resources to process more students.
Software companies are on track to close out their backlog, but accumulate huge amounts of technical debt in the process, which will be handed on to someone else down the line.
Retail chains are promising ever-faster delivery times but work their warehouse employees to the point of burnout during peak periods.
Banks reduce average loan processing times, but the depth of conversations needed to truly understand a customer’s financial situation becomes increasingly rare.
Construction companies meet aggressive project deadlines, but quality inspections become compressed, allowing small defects to accumulate into larger problems later.
The KPI is a success. Reality simply adjusted to accommodate it and, in doing so, became the embodiment of Goodhart’s Law. However, this doesn’t mean people are gaming a metric. We are observing the metric changing the environment, which it was always meant to reflect.
Imagine you put a large rock in a river. ↩️
The river doesn’t stop; instead, it has to reconfigure itself around the obstruction. The water flow is altered, new streams emerge, and debris begins to accumulate in various places. The river becomes something new as a result of a piece of infrastructure that wasn’t built to redefine its flow, but that had that very effect nonetheless.
↪️ KPIs are much the same.
If you introduce a KPI within an organization, it naturally triggers a cascade of reconfigurations. Budgets change, conversation topics shift, job titles are reassessed, and career progression criteria implicitly shift as people respond to whatever behaviour is sanctioned or rewarded. None of this happens as the result of deliberate manipulation; it simply emerges from the fact that people, just like rivers, respond to their environment and incentives in quite natural ways.
Nassim Nicholas Taleb can help us understand why. His career has been dedicated to distinguishing between systems that appear efficient and those that are actually resilient.
Picture a bridge for which a designer aiming to optimize for efficiency might shed every pound of weight considered extraneous. The structure is lighter, streamlined, less costly to build, and mathematically perfect. In theory, it is nothing short of an engineering masterpiece…until an earthquake shakes its foundation or heavy traffic grinds it mercilessly. Suddenly, what was so-called “excess” turns out to be strength. It was resilience, which to an inexperienced eye looked like inefficiency.
Organizations do the same thing every single day.
A business that meticulously limits its inventory appears brilliantly efficient-until supply chain networks fail.
A company paring down its staff to achieve peak productivity appears financially responsible until demand surges, and there’s no one around to respond.
A factory delaying routine maintenance to keep machinery humming may look great on utilization metrics-until an easily avoidable mechanical failure brings everything to a standstill.
An organization minimizing cybersecurity spending appears fiscally disciplined until a single breach costs more than years of preventive investment.
Every optimization quietly borrows against resilience, and every optimization comes with a price tag paid in foregone opportunities. The problem is that resilience is silent until it is needed. It’s like the unseen roots of a wise old tree, readily ignored as long as the wind doesn’t howl, yet absolutely essential once it does.
Perhaps this is why we so often hail visible efficiency while neglecting invisible capability.
Resilience is expensive, slack is wasteful, redundancy feels inefficient, curiosity feels unproductive, reflection is just a delay, until uncertainty strikes and the very things we criticized for slowing down progress become the reason progress remains possible.
This isn’t a case against optimization; it is rather an argument against ignoring its cost. Every optimization narrows the river, forsaking countless tributaries. Every intensification of a beam of light plunges another part of the field into shadow. The practice of leadership is therefore not merely about pursuing improved performance against metrics. It is about the disciplined recall of all that this pursuit inadvertently leaves in the shadows.
Self-reflection: If the uncertainty we know is lurking should arrive tomorrow, what might your dashboard wish it had kept safe? What unseen resilience has it already surrendered in favour of something far more tangible?
The Blind Spots We Choose
Leadership is not about the search for perfection or visibility. It is the wisdom to choose which shadows you can live with.
If there is one temptation that has been with us in every civilization, in every scientific breakthrough, it is the notion that the next tool will at last allow us to see it all: a better telescope, a more detailed microscope, a faster computer, a larger database, a smarter algorithm, a more inclusive dashboard. With each passing generation comes this same silent belief: this time, maybe this time, the blind spots will be gone. Inevitably, history shows a different pattern. Every innovation expands our view only to make evident what we had not yet seen.
The telescope opened the sky only to reveal a far larger universe than we had ever imagined.
The microscope unveiled worlds unseen, only to reveal how much more complex life was than we had ever known.
The process of discovery is the same again and again. The more we illuminate, the more we realize what remains to be illuminated, and businesses are no different when it comes to this topic.
Every metric answers a question but raises ten more. Every dashboard reduces uncertainty in one area while allowing for endless uncertainty to persist elsewhere. The goal, then, was never to create a dashboard that had no blind spots (Borges’ perfect map). The goal, instead, was something far humbler & more valuable: to understand what blind spots we have accepted.
Herbert Simon offers another key insight here. He famously noted: “A wealth of information creates a poverty of attention.”
Businesses today, almost without exception, do not lack information. Quite the contrary, they have way too much of it. Every department, every software system, every meeting, every team, every person – everyonepours information ceaselessly, splitting attention like atoms.
Yet, attention is a very limited resource; like sunlight, it brightens the spots where it lands but does little elsewhere.
Every meeting on one topic takes time that might otherwise have been devoted to another.
Every incentive reinforces one behaviour and subtly undermines another.
Every promotion tells employees (intentionally or unintentionally) what is valued.
Every promotion carries an opportunity cost, just as surely as a cash purchase.
It may also explain how an organization seems to lose characteristics it never deliberately gave up:
Curiosity gives way to Conviction ➔ Thought becomes Action ➔ Action becomes the new Thought ➔ Reflection cedes to Urgency ➔ Long-term Thinking collapses under the weight of Quarterly Performance Reviews.
No one sets out to make a career of being certain or impatient. The river simply shifts its course: a bit more attention to one side, a bit less to the other, and so it continues, day by day, until the landscape has changed beyond recognition. That may be the paradox of measurement.
When we measure, people move in the direction we point the lens. They engage with what is presented in meetings and what leaders routinely ask about. Everything else slowly slips out of view, not for lack of value, but because it has fallen out of organizational focus.
Thus, we arrive at the point that measurement always requires humility. Humility reminds us that no dashboard, however powerful, is the absolute truth of the world. Every measure is a perspective, and every perspective is incomplete. The question is not to eliminate our blind spots, but to come back to them again and again and ask:
What have we stopped noticing?
What assumptions have become so deeply embedded that they no longer warrant questioning?
What capabilities have we quietly allowed to atrophy because they didn’t make their way into a report?
These questions are important because organizations are dynamic systems in constant flux.
There’s a famous observation attributed to the ancient philosopher Heraclitus: “No one steps into the same river twice.”
The person has not changed, but the river has moved on. An organization is similar in this regard: it itself might not have changed, but the markets it operates in, the customers it serves, the technology it uses, and the culture it promotes have changed.
Even if a KPI reads the same numerically, the underlying reality it represents may have morphed beneath the surface. An 85% customer satisfaction rating now may not reflect the same customer expectations as five years ago. A current employee engagement survey, using the same wording as previous surveys, may be interpreting an evolving sense of what meaningful work means today.
The numbers endure, but their meaning shifts, which is why our dashboards can never be sacred. The minute we cease to scrutinize our metrics, we cease to scrutinize the reality they represent. It may be that the best leaders aren’t the ones with the most sophisticated dashboards or who track the most metrics. Maybe the best leaders are those who:
Never mistake the map for the territory
Remember that each illuminated beam also casts a shadow
Know that every river in the organization might have flowed somewhere else
Have the insight to put down the dashboard now and again, and wonder what it cannot show us
Final Thoughts
Learning to Respect the Shadows
Every photographer chooses a frame. Every sculptor removes stone to reveal a statue. Every author leaves unwritten pages behind. Every traveler follows one road while countless others disappear beyond the horizon. Every act of creation is also an act of exclusion. Every KPI is a decision about what deserves to be seen. Every dashboard is a statement about what an organization believes is worth discussing. Every target shapes behaviour long before it records it. Every number carries an opportunity cost that cannot be eliminated, only accepted. The problem has never been the existence of these tools, but more so forgetting that they are tools in the first place. A map is invaluable precisely because it is not the territory. A flashlight is useful precisely because we understand it cannot illuminate the entire room. Likewise, a KPI is powerful precisely because it simplifies reality enough for us to act, yet that simplification comes at a great cost:
It purchases clarity with incompleteness
It exchanges breadth for focus
It gains certainty by accepting blindness elsewhere
This is the opportunity cost of knowledge itself.
Therefore, we can infer that the purpose of performance management is neither to eliminate uncertainty nor to measure everything that matters, but to consciously and deliberately choose where we wish to shine the light, and to remember that, somewhere just beyond its edge, the rest of reality patiently waits in the shadows.
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For centuries, professions carried meanings far removed from any actual job descriptions. Teachers were nurturers and enablers. Engineers were architects and makers. Doctors were caregivers and saviours. Public servants were literally people serving the public.
Performance was certainly expected, but mostly measured through the lens of the work itself. An experienced engineer didn’t measure their success in equipment uptime. A doctor didn’t go home tallying percentages of discharged patients. A policy analyst didn’t define a productive day by the number of reports finished.
Their work was full of moments that could never be fully captured on a spreadsheet, but many of those unmeasured moments were precisely what got them into the profession in the first place. Modern organizations, however, slowly began to break that link. As performance management became more sophisticated and measurements more granular, organizations increasingly embraced the idea of constant reporting.
Now, this in and of itself is not a bad thing. Organizations require clarity in their operations, and performance metrics can be incredibly powerful tools for linking strategy to measurable outcomes. The real problem isn’t felt until later when things get overdone.
Evaluating behaviour has undergone such tremendous uprooting that, in many cases, behaviour is being shaped, not merely measured. Repeated behavioural reinforcement, over time, becomes a habit, leading to changed priorities that, in turn, become a core aspect of professional identity. “This is how my work is measured” gradually shifts to “This is what good work looks like” and finally “This is who I am.”
Organizations have, perhaps subtly, started to train people to tie their self-worth to the numbers. Organizational psychologists have long noted how external reward for an action can gradually supplant intrinsic motivation. This is known as the Overjustification Effect.
In a similar vein, Self-Determination Theory explains that people feel better about life when they have some control over their tasks, can be competent in them, and have a sense of purpose, but that this motivation is diminished if externally imposed controls take over those inner drivers.
Of all the effects of performance management, this may be the most underdiscussed. Organizations are terrified of employees “gaming” KPIs, picking the “wrong” indicators, or measuring far too many things. Few, however, seem to pay attention to the inevitable process by which their employees learn to tie their identities to the very metrics the organization uses to monitor their performance.
Ultimately, dashboards are less likely to function as a mirror onto professional performance and more as a lens through which individuals see themselves. They are measuring a proxy for success rather than success itself, focusing on green indicators, favorable trends, and a solid quarterly report, rather than the substance of the value created. So the question quietly changes from “Did I do something valuable today?” to “Did my numbers stay green?“. In fact, at that point, organizations aren’t managing performance anymore; they are shaping professional identity.
Government Performance: When Public Servants Become Compliance Managers
The government is just one sector that vividly illustrates such a shift. Over the decades, public agencies have developed performance measurement in an effort to improve accountability. The use of taxpayer money requires public officials to be transparent; elected officials need proof that government policies work, and citizens demand that governments provide demonstrable results rather than empty promises.
Due to these demands, KPIs have become ubiquitous across the public sector, ranging from case-close times and permit-approval processing times to budget execution, customer satisfaction, inspection volume, and compliance rates.
None of these measures is inherently flawed, and many of them have contributed to operational discipline and better response times. The problems occur when these measures ultimately alter what it means to be an effective government employee.
Take, for example, a regulatory inspector whose job it is to enforce environmental regulations.
Historically, their career trajectory is oriented toward a mission-based approach – reducing the risk of harm to public health and the environment and educating the public on environmental policy.
These tasks require careful decision-making, dialogue, and sometimes extended investigation, and many have helped prevent harmful activities. If the job is reorganized so that employees are evaluated based upon the number of inspections conducted in a month, subtle changes in employee behaviour can emerge.
We go from ➛ “Which of these inspections will contribute the most to environmental safety?“
To ➛ “How many inspections can I complete by month’s end?”
Case-related activities are put on the back burner in favour of routine, less time-consuming inspections. A pursuit of efficient throughput displaces professional judgment. While the individual has become more productive and efficient in executing their tasks, their sense of professional purpose and impact has eroded.
As noted by researchers in behavioral psychology and organizational design, “individuals will repeat those behaviours for which they are consistently rewarded” (e.g., in organizational performance systems, rewards can be symbolic recognition, financial, fiduciary, or simple acknowledgment by managers). Public organizations don’t often explicitly encourage their employees to prioritize measurement over meaning, but performance management systems do just that.
A similar shift in identity can be observed in public policy formulation. For instance, consider a public policy analyst whose mission is to develop policies to enhance economic opportunity in their region.
Good policy development requires innovation, open-mindedness, questioning established assumptions, extensive stakeholder engagement, willingness to reconsider original proposals, and the capacity to admit when a given approach may not achieve its intended outcome. The work itself involves considerable ambiguity and is inherently risky.
However, many policy departments measure success based upon report completions, consultations held, milestone completion times, and the number of projects delivered on time and on budget. These metrics are no indicators of the quality of public policy produced. Over time, analysts may begin to view themselves as administrators rather than problem-solvers; the number of reports produced takes on a higher significance than their contribution to informed policy decision-making. Taking the time to gather compelling evidence and debate potentially controversial findings would put the policy writer at performance risk that would not be faced by a policy writer producing a report more promptly.
The outcome is a less effective public policy in exchange for a more efficient output of report production.
Perhaps the starkest example of this unintended side effect of performance measurement is evident in the government’s citizen-facing services.
Most public agencies track average times to complete tasks such as issuing permits, licenses, processing benefits, or answering queries. While there’s no denying that swift processing saves valuable time and resources and should indeed be encouraged where it represents efficient, effective government service, speed itself has acquired an almost sacred status.
Case completion times may even be viewed as reflecting the personal capability of the individual responsible for completing them, regardless of whether these cases require significant social service provision or legal deliberation.
A given employee might have two applications come in the same morning:
The first can be addressed quickly within twenty minutes.
The second is complex, requires a telephone consultation with other government bodies and a deep analysis of relevant legislation, and will likely take several days to complete.
If time to completion is the measure that determines success, then the employee will be motivated to expedite the first and avoid engaging with the second’s complexity so that their numbers continue to look strong on the monthly performance review.
While the manager has not instructed anyone to shy away from difficult or time-consuming cases, the organization’s reporting systems have implicitly taught employees to prioritize quantifiable output over the quality of public service delivery. This insidious transformation of identity within public organizations has been one of the most staggering and, in some respects, the most challenging consequences of the modern emphasis on performance measurement.
Governments invest tremendous resources in these measures to improve responsiveness and effectiveness, but as measurement becomes more sophisticated and all-encompassing, there is a danger of narrowing the definition of a good public servant. Over time, public sector employees who originally joined the government seeking to solve complex societal problems and make a tangible contribution to public well-being may find that they have become focused on improving their scores on a dashboard.
The mission has not disappeared; it has just been translated into the language of numbers, and eventually it can start to look as if those numbers really are the mission itself.
Oil & Gas: When Safety Professionals Become Incident Managers
The oil and gas industry is arguably the one in which performance measurement has been implemented more widely and integrated more deeply than in any other. Given the sheer scale of operations, the hazardous environments, and the enormous potential cost of failure, measurement is an operational imperative.
Industries within the oil and gas sector have implemented sophisticated tracking systems for a variety of performance indicators, including equipment availability, maintenance compliance, production efficiency, process safety, environmental impact, workforce competency, and dozens of safety-related measures. To a significant degree, these metrics have made the industry demonstrably safer than it was several decades ago.
Therefore, precisely because performance measurement has been implemented so widely, the oil and gas industry is a perfect example of how metrics gradually influence the way professionals see themselves over time.
Let’s examine three common areas where the practice of measurement shapes occupational identity.
I. Safety culture
When an occupational health and safety, environmental, and risk (HSER) manager starts their career, the driving mission is clear and singular: everyone should be safe.
Their day-to-day work is devoted to identifying hazards, empowering their colleagues to speak up, investigating near-miss incidents, and instilling a culture in which reporting mistakes is natural and welcome before they become accidents. For a long time, performance measures have supported this work; they provide a framework that encourages reducing harm and helps pinpoint areas where improvements are needed.
Over time, however, one number in particular often becomes pervasive within organizations – and often it’s the lost-time injury frequency rate (LTIFR) or another similar type of incident-based statistic.
Typically, a low number means a safe place. Yet, once all-in discussions revolve solely around achieving a “zero” number, a subtle shift may start to occur: employees can start to associate achieving performance with maintaining the number, rather than with creating and maintaining the culture that is the reason the number is low.
A supervisor may no longer log a minor “reportable” incident for fear of the “statistics.”
A worker may fail to mention a minor, workplace-acquired cut because they don’t want to negatively impact their team’s “record.”
Managers may end up debating whether an incident meets the reporting criteria, rather than focusing on understanding why it occurred and taking steps to prevent it.
The organization begins to protect the number that is supposed to reflect risk reduction.
The paradox here is that some of the safest workplaces are those where employees feel psychologically safe enough to report issues, even if it initially results in the numbers reflecting an issue.
II. Maintenance and asset integrity
Maintenance engineers tend to be the sort of folks who love taking apart machines and putting them back together – complex systems and machinery excite them. They have the technical acumen to anticipate how machines can and will fail, often before any visible symptoms emerge. For them, this involves everything from proactive inspection to deep-level repair and understanding system dynamics.
However, many maintenance functions are measured by a variety of KPIs that can shape professional identity in interesting ways. These can include planned maintenance compliance, backlogs reduction, equipment availability and uptime, and on-time execution. Such measurements are essential, without a doubt, but engineers may end up seeing their work less as optimizing a system for safety and reliability and more as “keeping the indicators green.”
For example, imagine that during routine maintenance, a qualified engineer determines that an asset requires additional inspection and maintenance outside the regularly scheduled process. In a best-practice scenario, this should be handled in accordance with established procedures, focusing on what’s necessary to ensure long-term reliability and safety.
Instead, they run into the issue that taking an asset offline for non-scheduled work may negatively affect overall plant availability metrics and other performance measures.
No one explicitly says to the engineer “ignore it,” but they can feel internal pressure, as the responsibility for demonstrating effectiveness shifts:
We go from ➛ “I am an engineer who understands this equipment and should fix it as needed”
To ➛ “I am an engineer who is measured on availability, and taking this offline will not be a good thing for the company.”
Over time, this constant exposure to such incentives may nudge professional focus away from what is best for the system toward what’s needed to present good numbers. Maintenance excellence can inadvertently become about reporting excellence.
III. Safety leadership
Most leaders of safety organizations can articulate (and strongly believe in) the importance of building a “learning culture” or “reporting culture” – one in which reporting of near-miss incidents is not seen as problematic, but as an opportunity to learn and prevent future accidents.
This bears an unintended psychological impact: long-running streaks of incident-free operations. The longer the company maintains this “record,” the more everyone feels attached to it. Workers feel it. Frontline supervisors feel it. Upper management feels it. The entire workplace feels it, and, before you know it, a perfect safety record moves from being a performance indicator to a key aspect of the company’s identity.
When an organization proudly states, “We had a great run of X years without a recordable,” it transforms the act of reporting an incident from an opportunity to improve into an attempt to end a celebrated achievement.
Psychology teaches us that identities are reinforced by repetition and social validation. As employees are repeatedly told, “We are the guys who run our operations without an accident,” those values can quickly become the workforce’s identity. Ironically, this desire to protect the identity may inhibit the learning culture that is needed to actually keep people safe over the long term. The industry is slowly waking up to this reality by introducing leading indicators that complement its long-standing practice of lagging performance measures.
These leading indicators (such as learning observations, behavioural interventions, hazard identification programs, and proactive risk assessments) signal a shift from seeing professional work as the guard duty of a number to viewing it as the responsible management of risk and the continuous improvement of an organization’s capabilities.
Construction: When Project Managers Become Schedule Protectors
Construction is run under a constant stream of fire under its proverbial behind.
The budget is sealed.
The timeline is out there for everyone to see.
Clients demand sure things.
Investors demand visible returns.
Managing performance becomes paramount.
Companies keep tabs on the budget variance, the on-time completion rates, productivity, rework, safety incidents, equipment use, procurement cycles – everything it takes to keep even the largest construction projects from spiraling out of control. For project managers, these metrics provide crucial visibility into what’s happening. Over time, though, they also subtly change what it means to succeed.
Let’s look at schedule, probably the most obvious metric in construction.
We all know we need to deliver on time; delays cost time, money, opportunities, and jobs for everyone from the subcontractor to the end user to the neighborhood around the site. We also know that schedules are living things, not etched-in-stone laws of nature.
Construction hits unexpected bedrock.
The weather shifts.
The supply chain falters.
The designs need to be altered.
Good project managers understand these realities, and their job involves judgment & flexibility about changes. Yet when on-time completion is the ultimate benchmark for success, that can lead to a different, more limited role: “I’m responsible for protecting the schedule.”
“I’m responsible for delivering the best possible project” is no longer behind the steering wheel; it’s not even part of the conversation occurring in the car. That’s critically important for a few reasons.
Suppose you discover halfway through construction that a minor modification will dramatically increase a building’s resilience for decades to come.
From an engineering standpoint, it’s a clear improvement. From a KPIs standpoint, the redesign adds a few weeks to the project. The project manager must decide whether they are a builder or a schedule guardian, and by the time organizations reinforce decisions with KPIs year after year, the answer is often obvious.
A similar phenomenon occurs with productivity measures.
We track labour rates, equipment efficiency, and throughput to ensure work gets done efficiently, because construction is, after all, an incredibly resource-intensive endeavour. However, productivity measures tend to focus on visible activity and output rather than on the thoughtful prep work that enables high-quality execution.
Picture two supervisors:
The first, realizing the potential for confusion, takes a couple of hours before his crews begin installation to coordinate subcontractor schedules and clear potential conflicts.
The second supervises his crews’ continuous activity despite having a pile of unresolved questions.
According to the day’s dashboard, the second supervisor clearly had a more productive day. Activity continued, and the work output was impressive. Weeks down the line, the project suffers costly delays due to rework because all that prep didn’t happen. The first supervisor was building for tomorrow, and the second was optimizing for today.
Systems that measure performance rarely reward proactive problem-solving that may never actually surface as a problem. Over time, people come to believe that active work and output are rewarded more highly than proactive avoidance of potential problems.
Nothing demonstrates this shift more profoundly than safety leadership.
Companies monitor incident rates, safety inspections, safety briefings, and compliance actions. The problem arises when safety, which should be an expression of craft, becomes just another task, and when safety documentation is perceived as equivalent to actual safety.
Completing checklists and filing reports can be perceived as being productive because they generate readily quantifiable results. Conducting tough conversations about safety hazards, patiently coaching an inexperienced team member, or stopping work to address an immediate hazard requires more patience and is harder to quantify. In these situations, too, the role silently transforms.
Over time, the job becomes less about building something well and more about managing reports, schedules, charts, graphs, and other metrics. Construction has long been a trade driven by craft, ingenuity, the willingness to confront ambiguity, and the determination to adapt when circumstances change…qualities that rarely fit smoothly into KPIs.
The risk isn’t that KPIs will put an end to what it means to craft and be part of it; the danger is that they will fundamentally reshape what craft looks like. When the metrics for success are matters like schedule adherence, people will still build projects, but they may begin to lose the very identity that motivated them to become builders in the first place.
Transportation: When Operators Become Punctuality Optimizers
Of all the industries we could choose to observe, transportation is probably one of the most externally visible ones.
The numbers generated each day by every airline, train company, logistics firm, port, and public transit authority are mind-boggling. These organizations are producing vast quantities of data to inform decisions. The on-time performance (OTP), expected delivery window, vehicle utilization rates, fuel efficiency, wait time per passenger, load factors, vehicle turnarounds, and customer satisfaction scores indicate whether the system is working well.
The organizations whose work depends on millions of people moving from place to place at specific times rely heavily on this information to coordinate operations across enormous networks. Nevertheless, of all the Industries that have so much data, transportation perhaps best captures the danger of equating operational excellence with numerical excellence.
Let’s imagine a very sharp airline operations manager. What drives them into the aviation business in the first place?
Passion, enthusiasm, dedication, joy, a fascination with aviation, logistics, and engineering, or, most importantly, a commitment to transporting people safely and efficiently to where they need to go. Maybe all of these, at once, all the time.
So, then, what is the most often discussed single data point for most airline operation teams?
It’s on-time performance!
Now, now, lower your pitchforks, please. On-time performance is an incredibly valuable metric. Customers, connecting flights, operational costs, and more are all negatively affected when delays occur. No one is claiming it is not important. However, when punctuality ceases to be a goal and becomes everything that person ever was, professionally, that is when the trouble begins.
I) Consider the scenario of our aforementioned airline operations manager, and let’s ideate 2 scenarios for them:
Scenario 1 – Think about the operational and behavioural consequences of a departure in which the aircraft pulls back on time but leaves with a cabin issue that significantly increases stress for both cabin crew and passengers.
Scenario 2 – Now compare this to another departure where the aircraft leaves several minutes later because the team used those extra minutes to resolve an issue with a piece of equipment, assist an elderly passenger, address a situation with a screaming baby, or ensure all the correct bags were loaded, etc.
From a dashboard’s perspective, the first option wins hands down. For a professional, however, the second might reflect much better operational judgment.
As with every system and human, that very act of monitoring the “right things” and offering them as rewards in the system for a sufficient period of time conditions people to pursue the number rather than the purpose behind it.
Teams slowly begin to ask, “Did we protect our OTP?” instead of “Did we perform well?” This is subtle, and the behavioural consequences can be significant. We see this phenomenon mirrored across other major industries within transportation, like logistics and supply chain.
Organizations across these sectors rely heavily on the delivery performance indicator. Customers now expect increasingly tight and precise delivery windows, and most companies have developed highly sophisticated methods for monitoring these metrics. While logistics coordinators, drivers, and dispatchers all recognize this importance, their continued exposure to metrics (even when used appropriately) will influence how they see and interpret their own roles.
II) This brings us round to another scenario – the delivery driver!
Imagine a delivery driver approaching the time and weather is deteriorating. They can either:
Scenario 1 – Slow down and potentially find a safe haven
Scenario 2 – Push through and hope for the best
Given our present climate change woes, where the weather is impossibly unpredictable at times, professional judgment will suggest to slow down and accept that some of these delivery times may be adjusted later in the day, when their performance report will, unfortunately, highlight poor on-time performance.
The delivery driver is unlikely to endanger their life or that of others to make it on time; nevertheless, their frame of reference will likely shift to see their poor OTP score as an indictment of their ability rather than an acceptable outcome given unforeseen circumstances.
The majority will continue to prioritize safety, but their sense of professional accomplishment may begin to shift: rather than being seen as professionals who reliably and safely deliver people and goods, their internal professional identity may become that of a protector of delivery performance. Their professional identity now incorporates the KPI.
III) In the case of public transportation, we will find a remarkably similar story.
Scenario 1 – Should a professional working in the public service move people around a city comfortably and efficiently?
Scenario 2 – Should they become a performance enforcer, responsible for protecting the schedule at all costs?
Transit agencies will monitor many things to ensure reliability and efficiency: passenger volumes, schedule adherence, average boarding time per passenger, on-time departure rates, average dwell time at stations, and the like.
All the above indicators will no doubt lead to better overall system performance, but public transportation is ultimately a human system. Yes, throughput is important, but moving people in comfort & safety, from point A to B, is even more so.
Not every passenger fits a standard profile. There are elderly travelers, people with disabilities, young children, and individuals who cannot rush and require extra assistance. When, as part of that performance management regime, we focus on schedule performance, we may create an identity conflict.
This problem won’t arise if performance metrics are set and monitored to manage the system, but the minute employees’ identities manage it, the problems can begin. At least the irony is that no one goes into the transportation business to maximize on-time performance. They are in business to provide safe, reliable service to meet people’s travel needs. Punctuality is only one component of delivering that service.
Healthcare: When Clinicians Become Throughput Managers
Few fields may demonstrate how performance measurement can transform professional identity more starkly than health care because more is at stake than the success of an organization – human lives themselves.
Modern health care depends heavily on measurement, and for good reason. Hospitals measure Emergency Department (ED) waiting times, bed days, readmission rates, numbers of surgeries performed, infection rates, patient satisfaction levels, care timelines, staffing levels, and hundreds of other metrics that can guide quality improvement and ensure accountability. Without such measurement, health care leaders could not know where to direct resources or pinpoint areas for systemic improvement and patient benefit.
Yet health care has always been about more than just that, more than just metrics. At heart, it is a vocation centered on discretion, compassion, and personalized attention. Performance systems, of course, oversimplify such complexity.
ED Wait Times
Let us look, for example, at health care performance metrics for emergency care waiting times. While minimizing unnecessary delays is a worthwhile pursuit, with benefits for patients and the health care system alike, in practice, ED work does not proceed in a straight line defined by average times.
Patients arrive with all manner of urgency: some require but ten minutes, others six hours; one family conversation might stave off a formal complaint, while another requires as much reassurance as a prescription. Often, these acts of service represent some of the finest clinical practice and are difficult to quantify. When clinicians are rewarded for shaving off waiting times, it can prompt a subtle shift in their identity: “Did I treat the patient optimally?” or “Did I move the patient through the department most rapidly?”
The two objectives are hardly mutually exclusive, but when one becomes visible and the other remains unseen, the visible outcome naturally receives higher priority. We see a similar shift occur for decisions about hospital patient discharges.
Length of Stay
Another frequently used measure is the length of stay, used in part to help hospitals manage their capacity and avoid inappropriate admissions.
Again, a valid measure that serves a real purpose. However, decisions to discharge a patient are often complex and not purely dictated by strict, measurable clinical criteria. A patient may, on paper, be technically “discharge ready” but feel nervous about managing at home. Another might benefit from a few extra days of observation for reassurance, even if there is no immediately pressing medical need for more intensive care.
Clinicians make such judgments day by day. However, when, time and again, our hospitals focus on patient throughput, our beds, and our flow, the length of stay increasingly gains psychological weight over its functional meaning. Delayed discharges may begin to feel like interruptions of flow and barriers to organizational performance rather than clinical considerations, not by design, but because our performance systems encourage such reactions. This specific detail – performance systems affecting the identity of health professionals themselves – is perhaps the most pervasive of all.
Identity of Choice
Few people choose careers as doctors, nurses, technicians, or paramedics simply because they find delight in improving dashboards or making progress toward organizational targets. Most enter the field out of a deep desire to care, to heal, to solve complex problems, and to improve the lives of others. It is this inherent motivation, which organizational psychologists have shown to be the strongest long-term predictor of job engagement, that pulls these individuals through many a night of emotional hardship.
It is these motivation systems that are most endangered by an organizational environment that overemphasizes measured outcomes. When the organization’s performance metric takes precedence over that purpose, something begins to change, even if the clinician does not abandon their care for patients.
Instead, they increasingly begin to experience their work as defined by outputs: numbers of patients seen, numbers of procedures performed, number of appointments scheduled, number of metrics met. Caring does not vanish – it is simply diminished in what counts as success.
Many health organizations are aware of this threat and are attempting to broaden their focus toward patient experience, interprofessional collaboration, the development of learning cultures, staff well-being, and psychological safety. All of these efforts acknowledge that great health care depends not just on measurable efficiencies but also on protecting the fundamental reasons people came to the profession.
At the end of a stay, most patients don’t remember how well their care improved the hospital’s quarterly reports, but rather that someone listened, someone cared, and someone acknowledged their unique and individual story.
Final Thoughts
The major benefit of KPIs is focusing attention. They assist organizations in transforming a big ambition into a concrete goal, creating common alignment among teams, and providing evidence of whether progress is being made.
Without the metrics, strategy is little more than aspiration, and within any set of metrics lies another, much more subtle force that most observers do not know: measurement not only shapes decisions, it also shapes people.
Behavioural psychologists demonstrated long ago that habit, and eventually identity, are forged through repeated reinforcement. Therefore, performance systems teach not just process but also employees the objects of attention, the feelings of success, the rewarded behaviours and what they’re meant to turn into, ideally, professionally.
Most often, no one sets out to prioritize their dashboards over their mission. Yet, through a gradual sequence of performance reviews, recognition meetings, promotion opportunities, and the green indicators turning, professionals begin to intuitively protect the measures originally developed to do little more than guide their performance. None of this implies an organization should drop KPIs – au contraire, metrics remain necessary.
In their place, the best question any leader can pose may well be less, “What will this KPI motivate?” – a question extensively addressed over time – and more, “What type of professionals will this KPI eventually build?.”
After all, organizations, over time, perform as well as their people are made, not because they measure well, but because they are well.
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Every organization remembers its numbers: revenue, profit margins, cost of customer acquisition, employee utilization, defect rates, NPS scores, or average resolution times.
Pull open any dashboard, and you’ll see hundreds of highly selective data points meticulously tracking almost everything happening within the business. Organizations today are astoundingly adept at capturing data. However, they often can’t answer simpler questions.
Why is this team performing so well when they are tracking only average productivity metrics?
Why are customers loyal to this account manager?
Why did our innovation efforts grind to a halt when our key engineer left, even though the KPIs remained unchanged?
What, beyond hitting deadlines, contributed to that project’s success?
Some of the most crucial assets any organization holds cannot be conveniently pinned on a dashboard. As conversations naturally become centered around measurable outputs, organizations gradually risk developing a kind of “KPI memory loss” – an inability to recall the details that fail to fit within a given metric.
This is not a criticism of KPIs. Not at all, quite the contrary! Businesses must have these metrics to measure performance, diagnose issues, understand thresholds, and make decisions. The issue starts when metrics become less tools for observing the world and increasingly the world itself.
When Metrics Become Memory
Picture a brand-new manager being hired to run a thriving customer service department. They’ve taken over a fantastic dashboard, their average response time has dropped, customer tickets are being resolved faster than ever, and overall productivity is growing each month. From their perspective, they’ve inherited a picture-perfect operation.
Six months down the line, customer churn is on the rise.
But why? What gives?
After interviewing veteran staff members, the manager learns that agents have stopped investing a few extra moments to build rapport with their customers. All the targets were being met; everything looked fantastic on the dashboard, but they had slowly let the human side of it all slide: those little interactions that helped customers feel like they mattered. There was nothing in the dashboard to indicate this.
Nothing in the dashboard was accounting for this. This is one of the greatest strengths (and biggest weaknesses) of performance measurement: KPIs can highlight things we would otherwise never know, yet they also narrow our attention to an unhealthy degree, turning focus into horse blinders.
As much as an organization obsesses over what it can measure, it begins to overlook everything that it cannot. When people start aiming for a metric specifically, that metric eventually fails to reflect what it was intended to reflect.
You’ve almost certainly seen this play out in a large organization:
A sales team prioritizes quick-close deals over long-term customer value because its quarterly target emphasizes sales volume.
A call center has reduced the Average Handle Time (AHT) by ending calls abruptly, leading to more inbound repeat calls from irate customers.
A software team has achieved a high number of resolved tickets while allowing technical debt to fester in the codebase silently.
An HR team fills open positions faster by prioritizing speed-to-hire, but the quality of new hires drops, leading to higher turnover within the first year.
A manufacturing plant reduces production costs by using cheaper materials, only to see warranty claims and customer complaints increase months later.
The metrics look good, but the underlying reality does not. This often has nothing to do with bad motives or intentions, but more with incentives.
Incentives have been at the basis of human behaviour since the dawn of time. Therefore, if success is defined by what appears on a dashboard, people will focus their attention there. Over time, companies develop excellent memories for metrics but an almost complete memory loss for everything else.
What Gets Left Behind?
Try to think of the best colleague you’ve ever had. What was it about them that made them excellent? Were they the emergency adult everyone called to soothe volatile clients before a situation erupted? Maybe they instinctively knew when a project was careening off course. Perhaps they just knew which other departments would be required long before an issue was apparent, or they just knew how to mentor a junior person in the office from scratch.
Knowing all these aspects, we are posed with a series of questions:
How could you quantify these skills?
How could you put a number on them?
How would they feature on a spreadsheet?
It might not be possible. Is it possible? Is it feasible? Now we are left with more questions than we had before we knew about the aforementioned series!
Let’s take a look at a different example.
What about a company trying to build itself on measurable, trackable KPIs and not much else? We have a massive body of work in knowledge management that draws a line between the explicit and the tacit: the former can be written down and shared, the latter can only be understood and absorbed through experience and judgment, in context, through interaction.
There are numerous studies that indicate that organizations that have solely relied on measurable performance-only systems fail to capture value and knowledge, even in areas that are absolutely critical to long-term organizational success.
Interestingly, it’s often the people who do not appear on many charts in any system, or who have nothing visible to put on a spreadsheet, who make the organization successful.
The experienced cardiac nurse may have noticed subtle changes in the patients’ physical condition much earlier than the monitors do.
The savvy machinist may hear an anomaly in the noise from an old tool and just know the machine requires maintenance.
The proficient project manager might have noticed the relationship between two key stakeholder groups deteriorating well before the tangible signs of breakdown were evident.
The well-versed account manager may recognize that a client is quietly disengaging long before declining renewal rates or negative feedback makes it obvious.
These can be moments where organizational failures are averted long before anyone even sees an indicator on a dashboard. These are moments that create and deliver value to an organization every day, yet remain invisible to most of its people and many of its systems.
The Things Dashboards Cannot Remember
The majority of businesses believe their decisions are based on facts. In reality, they generally base their choices on whatever facts happen to be quantifiable. Culture is one of the clearest examples of such behaviour.
Companies commonly try to measure culture through surveys, retention data, absence rates, and employee satisfaction scores. While such information is useful, culture itself is not a figure. It is actually the unwritten principles and practices that establish whether workers report errors early or cover them up. It’s that thing that makes junior employees feel empowered to question those higher up. It’s that je ne sais quoi that leads groups to readily volunteer their expertise rather than guard it or choose to assist their colleagues, even when no one is watching.
Boiling these activities down to a handful of quarterly data points has the threat of mistaking the map for the land. The same is true of reliance on craftsmanship, mentorship, interest, durability, and expert judgment. Organizations seldom lose these features overnight. Rather, they simply fail to mention them because they stop measuring them and ultimately stop noticing them.
As soon as something is missing from the discussion, it tends to be absent from decisions on the whole. That is possibly the major peril of KPI memory loss: organizations do not intentionally cease caring about what is most important; they become so adept at remembering their numbers that they fail to remember everything those numbers can not tell them.
The Hidden Costs of Measuring Everything
Most companies do not wake up one morning deciding to disregard culture, relationships, or craft. It happens more subtly, often barely perceptible to the senses.
A new dashboard gets added.
An additional KPI arrives.
Quarterly reviews become more number-focused.
Charts, scorecards, graphs, and trendlines support decisions.
Conversations turn to the question of what we can measure versus what we ought to be asking.
It appears to be a reasonable transition. At the end of the day, numbers are objective, are they not? They establish commonalities and help control a complicated organization. However, numbers are also a source of our most profound blind spots.
Think of onboarding. Think really well. While it seems prudent for a company to track the number of days before a new employee reaches full productivity, there are typically no measures around building trust with other staff, the organization’s unspoken rules, or the logic behind past decisions. This results, six months and two seasons later, in a productive individual who, by all accounts, repeatedly makes the exact same mistakes the company had already overcome a decade earlier.
The knowledge had existed, scribbled on meeting minutes or stored in the heads of long-serving staff or within an unheard conversation, but it had never reached the recipient in need. This tendency pervades almost every field of work.
An oil and gas operation may monitor equipment uptime and production volumes with remarkable precision, while overlooking the field operator whose practical experience prevents a minor anomaly from escalating into a costly shutdown.
A government agency can report on service delivery targets and policy milestones with detailed dashboards, yet fail to recognize the informal relationships between departments that quietly determine whether complex initiatives succeed or stall.
A real estate firm may measure listings closed and average time on market with ease, while overlooking the seasoned agent whose local knowledge and trusted network resolve problems before they jeopardize a sale.
A hospital may monitor how long patients wait with a stop clock, yet it would struggle to assess the level of trust a pair of experienced nurses builds.
A legal firm could chart the time partners log on individual cases with great precision, while ignoring the unstructured mentoring that cultivates new associates from rookies to confidants.
A manufacturing operation can track its output by the hour, but may miss the insight of the retired engineer who stops a press before it breaks down, preventing a sensor from triggering.
With all of these cases, tangible output may increase; however, the intangible abilities that support that output go largely unnoticed until they can no longer be ignored.
When Efficiency Begins Replacing Craftsmanship
Nowhere may the dichotomy be stronger than in craft. Craft isn’t limited to woodworkers and machinists – there’s an equivalent for every role. A software engineer’s craftsmanship might not be about delivering features as quickly as possible but rather about writing testable and maintainable code. A customer success manager’s craftsmanship might be recalling some tiny, human detail from a conversation with a customer and using it to make them feel deeply seen. These are habits you practice into being, not lessons you teach into being.
Picture two identical table factories.
One rewards everyone for output alone (units per shift). The other one measures output AND craft (the ability of seasoned employees to mentor and teach the younger ones). Thus, the most experienced artisans have time to think of better ways to practice their craft, and they reject pieces they deem inadequate, even if it slows output, while prepping a new generation that comes after. One year in, the output factory is ahead.
Five years later, the craft factory might have developed an entire workforce capable of creating not just more output, but better & smarter output without sacrificing quality or values. Their competitive advantage wasn’t about today’s output; it was about tomorrow’s capabilities, and quarterly KPIs don’t easily capture them.
It grows over years so subtly you usually only realize it’s gone after you notice its absence.
The Things Employees Stop Doing
Not only do metrics influence what employees do, but they also influence what employees quietly stop doing. Take a veteran project manager who routinely spends their Friday afternoons working through colleagues’ complex, messy projects. There is no metric for mentoring, no dashboard tracking generosity, and no quarterly goal to help other departments meet their targets.
Nevertheless, when the company adopts a utilization rate that values nearly all hours spent on billable activity, the manager is never explicitly asked to halt his mentoring, only that “we’d love for you to be 100% utilization and work your shift’s duration on billable projects”. Over time, the manager has trouble justifying mentoring anyone.
Then, in an instant, poof, it’s gone!
The company gets 3% points of utilization and a loss of something far harder to repair. Moreover, those who, at this point, would be tempted to say “it’s just an individual matter” should remember that a company is made up of hundreds to thousands of living, breathing individuals. It’s not so much that one person stops functioning; entire departments stop sharing knowledge, because collaboration time could be allocated to departmental goals. Managers stop coaching team members because getting stuff out the door right now takes precedence over people’s development and future growth. Employees hesitate to try innovative projects because failed attempts have consequences for their personal evaluations. These things are not deliberate managerial decisions; these are inevitable responses to organizational cues and the incentives we keep mentioning.
Peter Drucker observed well: “What gets measured gets managed.” Yet what is not measured will be ignored, seldom discussed, forgotten, and will surface as unforeseen consequences later on.
When Good KPIs Produce Bad Decisions
The KPIs themselves may not be wrong; they’re just limited. A good metric can become a bad one when it shifts from a guidepost to the destination itself. Organizations of all shapes and sizes have had the same experience.
Software Development
For many years, developers were measured by the lines of code they wrote. On the surface, the logic seemed fine – the more code written, the more productive the developer. Unfortunately, developers were incentivized to write more code, not better code – ye’ ol’ quantity-over-quality shenanigan. Conversely, modern software engineering holds that good solutions often involve writing less code.
Healthcare
Patient throughput in the emergency room is routinely monitored for a range of reasons, not least to reduce wait times and improve access to care.
This metric is clearly important, but clinicians are aware that meaningful conversations, nuanced observations, and shared decision-making cannot always be neatly slotted into pre-set time boxes. Hospitals that focus solely on speed do so at the risk of missing key aspects of care.
Aviation
Even in this highly quantitative field, there is an understanding that not every important thing can be represented by a number.
Commercial airlines meticulously monitor thousands of variables, from fuel efficiency to maintenance schedules. Nevertheless, they spend a considerable amount of time and resources on developing Crew Resource Management (CRM), an approach focused on building communication skills, mutual trust, leadership, and a safe psychological environment within the cockpit. These aspects are not ignored because they are hard to measure. They are carefully nurtured because, as history shows, they save lives.
Automotive
Perhaps one of the most widely known examples in the business world comes from Toyota, the Japanese automaker. The Toyota Production System (TPS) is well known for its metrics and continuous improvement methodology. Concurrently, it also strongly emphasizes people development, encourages employees to halt the line if they detect quality issues, and views improvement as a collective learning process rather than a numbers game. In essence, the numbers do matter, but so do the conversations that occur around them, and that can be easy to miss.
Companies struggling with KPI memory loss tend to assume that if a metric is not displayed on the dashboard, it cannot be strategically important. The healthiest companies take the opposite approach. They understand that the dashboard offers only a partial picture of the organization’s health.
Some of its most vital components – trustworthiness, judgment, craftsmanship, curiosity, mentorship, and shared experience – remain alive, regardless of whether they are measured. The real problem is not whether to rely on numbers or intuition, but rather the failure to remember that one can never replace the other.
What High-Performing Organizations Choose Not to Measure
That raises an interesting question: if some of the organization’s greatest capabilities are elusive to measure, what do the best organizations in the world do?
They can’t just abandon performance measures, right? RIGHT?
Right, they don’t. In many cases, high performers recognize that measurement has its limits.
Take a look at Pixar. For years, the animation studio has turned out films that win hearts and minds and create core childhood memories for parents and children alike. Of course, Pixar monitors budgets, schedules, and production milestones. Yet some of the real magic happens because the company is willing to make room for what can’t be quantified by a KPI: candid dialogue.
One of the most widely discussed Pixar traditions is the Braintrust, a circle of seasoned directors and writers who regularly gather to roast works in progress.
No scores, no charts, no dashboards, no key performance indicators. What matters is genuine feedback, a psychological safety net, and a willingness to push ideas (not people) to their breaking point. The organization creates room for judgment.
Now let’s go back to Toyota for a second.
Not everything gets translated into a number. The famous Toyota Production System may be well known for its metrics and focus on continuous improvement, but one of the company’s enduring guiding principles is respect for people.
Its workers feel empowered to halt a production line when they spot a flaw not because a performance measure mandates it, but because their judgment is trusted and valued.
This doesn’t mean that Toyota avoids measuring. It has more to do with the fact that it appreciates that its greatest assets reside alongside its measurements, not within them. That theme will appear time and time again across top-tier companies.
Experienced executives don’t just ask, “What should we measure?” ❌
They ask, “What do we need to keep talking about even if we can’t measure it perfectly?” ✅
Beyond Dashboards: Remembering the “Why“
One theme that echoes throughout the literature on organizational memory is that organizations are pretty good at recording what happened. They’re a whole lot worse at remembering why it happened.
Minutes of meetings show what was decided, project plans show when the decision was made, dashboards show what the result was; however, even with all that, the reasoning behind the decision (the trade-offs it required, the alternatives it rejected, the hunches it relied on) often remains elusive.
Think about walking into a company where the same customer policy has been in effect for a decade. Everyone adheres to it, but nobody knows why. Its memory has been lost among dusty desks and cramped file cabinets. A manager suggests tweaking it, as it seems stale and no longer aligns with the organization’s current state. Their peer protests that “it’s always been done this way,” yet none of them can tap the original logic behind it all. It’s not just that information is missing. The entire context for the origin of the information is missing.
This is the plight of most KPIs as well.
We recall that our customer satisfaction score dropped four points, and not that our recent reorganization had frayed our client relationships months prior.
We recall that productivity grew by 12%, and not that our employees started shunning one another to get there.
We recall that costs declined, but not which abilities those reductions simultaneously hobbled.
We recall that revenue exceeded its target, and not that a handful of unsustainably large discounts drove it.
We recall that safety incidents declined, and not that workers had become increasingly reluctant to report near misses.
Numbers capture results or the end product. Stories capture context or the journey to said end product. The best companies value both.
Building Organizations That Remember More Than Numbers
None of that is to say that companies shouldn’t measure less. Often, they should probably measure better. A balanced performance system understands that metrics are evidence, not adjudication.
When your engagement metric drops, it should start a conversation, not conclude it.
When your productivity metric improves, you should question your leaders: “What did you change? What may have suffered as a consequence?”
In the same way, when there’s an unexpectedly great result, don’t just look at it on a celebratory dashboard and gloat to everyone near & dear. Dig into it: What did we do differently to get here? Was it more collaboration? Did a senior, intuitive employee make a gut call at just the right moment? Did the team have enough faith in each other to say, “Hey, this isn’t working?”
Some companies consciously strive to keep institutional memory alive through mentoring, after-action reviews, storytelling, communities of practice, intergroup collaboration, and discussions focused on reflecting on the past. These are more than just tools for transferring knowledge. They are tools for transferring judgment because, as the adage goes, judgment doesn’t live in the data alone. It lives from person to person, conversation by conversation.
Final Thoughts
Performance management has revolutionized modern management. Organizations would have a hard time understanding performance, gauging results and failures, allocating resources, or identifying potential risks without KPIs. The use of metrics remains the strongest lever available to leaders. However, every tool has its limitations.
A map shows us the path around a city; it’s not the city itself. Likewise, a dashboard illustrates organizational performance; it’s not organizational performance itself. Organizational performance is much more than just mere engagement numbers; leadership is much more than productivity metrics; organizational innovation is much more than just the number of ideas spewed forth by lateral thinkers; organizational customer loyalty is much more than Net Promoter Scores, and our organization’s memory is much richer than any data we collect in reports and dashboards.
The single largest risk may be that we measure too much, rather than recognizing that there are more ways than measurement alone can provide. Organizations do not become exceptional by quantifying everything; they become exceptional by discerning what must be quantified and what must be conversational, observant, coached, and trusted.
Numbers tell us what happened; people explain to us why the numbers happened.
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The Politics of KPIs: Why Metrics Are Never Truly Neutral
If you present two experienced chief executive officers with precisely the same digital dashboard, one will zero in on cash flow and return on investment.
The other will bypass them and ask to know customer retention, employee engagement, and product penetration rates. Both will be right, and both might even become brilliant leaders, but before they make any decisions, the CEOs will have implicitly expressed what success will look like for them.
It is an issue that organizations seldom concede. Organizations want to pretend that the Key Performance Indicators (KPIs) on any dashboard are impartial, that they are simply observers of what is really going on. People put their faith in dashboards because numbers are perceived to be objective.
However, KPIs do not exist to be discovered. They were invented. Someone had to choose what mattered most; someone defined success, agreed on thresholds for it, frequency of assessment, and selection of appropriate measures. Long before any of these figures had been generated, human beings had to decide how a compelling narrative might unfold for these numbers.
This should not discredit the utility of the KPI; rather, it should humanize it. The more organizations grasp the reality that the KPI is a deliberate fabrication, the more successful they will be in designing performance management that aligns with strategic intent rather than covert assumptions.
The Myth of “Letting the Data Decide“
Modern organizations commonly describe themselves as data-driven. It is a buzzword you’ll find in strategy documents & presentations from a wide range of industries. Underlying all of these instances is the notion that data ought to inform decisions instead of instinct.
From a general perspective, it is the perfect path. From a narrow perspective, problems start to creep in, though, when data is automatically presumed to be objective. Data rarely makes its journey to us in a void. Any dataset exists for the simple reason that someone thought that it would be valuable. Any KPI exists because someone has decided that it represents a significant area of performance.
Take, for example, a company that has decided to quantify customer service performance.
A first step might be fairly easy to determine. Develop some KPI’s, for example.Yet, which one to select?
To be able to respond within a few moments?
Achieve first-contact resolution?
CSAT?
NPS?
Reduce the number of customers lost?
Reduce the number of complaints received?
Each data set offers a different perspective; it supports a distinct way of working and motivates employees to focus on a unique perspective within customer support. The data alone is not the most important priority. It is the people who comprise the organization. Even to opt not to make a measurement constitutes a decision in its own right.
By omitting the monitoring of employee happiness from the leadership dashboard, the company has subtly signaled to staff what needs more focus and consideration. Therefore, in this respect, the dashboard is not only a descriptor of the actual organizational state, but it also creates the organization itself.
Every KPI Reflects A Worldview
The single biggest misunderstanding about performance measurement is the idea that KPIs exist in a vacuum, devoid of the people who build them. KPI “1” doesn’t just describe reality; it defines it. Consider two almost identical manufacturers:
Company 1. A CEO who spent 20 years in finance leads them.
Everything in the quarterly board report is financial: Opex, Inventory Turnover, EBITDA, Working Capital. Conversations naturally center around efficiency and the bottom line.
Company 2. An operations engineer leads them.
The dashboard’s a completely different beast.
Quality, uptime, scrap rates, Overall Equipment Effectiveness – because in their eyes, quality is what drives the company forward. Two similar companies, two different roles, but one set of dashboards tells a completely different story about what’s worth talking about, purely as a consequence of the personal histories of the leaders.
It’s a pattern that repeats everywhere. An operations person in a hospital might focus on utilization rates and lengths of stay to better predict capacity requirements, while a clinician with a patient focus might emphasize read rates and post-treatment outcomes. It’s not about which is “right.”
Both have value. Both obscure other important things, because the difficult but useful reality is this: KPIs don’t just measure your priorities, they display them.
Measurement Is Also An Exercise In Omission
When organizations talk KPIs, there’s a lot of discussion about what we should measure. There’s very little discussion about what we’re not going to measure.
Every dashboard has limited real estate. Every organization has finite analytical resources. Selecting one KPI often means leaving another behind. That act of omission defines behaviour just as much as what lands on the dashboard.
Consider a software company focused on frequent feature releases. They emphasize deployment frequency, development speed, feature usage time, and release cadence. This company becomes an expert at pushing features out the door, yet if it doesn’t emphasize measuring feature adoption with similar prominence, it might find itself continuing to crank out features with no clear signal of whether anyone is using them. The KPI’s they picked are perfectly fine, just incomplete.
This applies well outside of software.
Retailers may perfect sales efficiency and miss out on customer lifetime value.
Universities can increase graduation rates at the expense of the actual quality of education.
Healthcare providers may decrease patient wait times, but at the expense of staff burnout.
Manufacturing may maximize production output, but at the expense of product quality and defect rates.
Customer support teams may reduce average handling time, but at the expense of customer satisfaction and first-contact resolution.
Logistics companies may optimize delivery speed, but at the expense of delivery accuracy and package condition.
Marketing agencies may increase campaign volume, but at the expense of campaign effectiveness and client ROI.
Hospitality businesses may maximize room occupancy at the expense of the guest experience and repeat bookings.
Call centers may focus on the number of calls handled, but at the expense of actually resolving customer issues.
Construction firms may prioritize finishing projects on schedule, but at the expense of workmanship quality and long-term durability.
Organizations don’t disregard these outcomes because they don’t want to. Typically, they simply aren’t being measured with equal visibility. Whatever we’re paying attention to gets better. Whatever we’re not tends to atrophy when competing for attention and resources. That’s why KPI design is a strategic exercise, even though many companies treat it as a math exercise.
KPIs Shape Organizations Long Before They Measure Them
Maybe the most fundamental transformation executives need to make in their approach to KPIs is understanding that they aren’t just retrofitting performance to evaluate what has already occurred. They are prospectively influencing what occurs. When an organization rolls out a new KPI, the company’s behaviour shifts almost immediately:
Managers start deploying resources differently.
Employees prioritize differently.
Different departments redefine what constitutes success.
Investment decisions shift.
Performance evaluations change.
The language used in internal meeting rooms shifts.
In short, KPIs do not just observe an organization from afar. They are involved in building an organization, and this is precisely why discussions of performance metrics can sometimes turn emotional.
Outwardly, business leaders appear to be arguing over numbers. Internally, what they’re actually arguing over is something more fundamental:
What kind of organization are we aspiring to be?
One finance executive might argue that, given the economic environment, profitability must be given greater visibility.
An HR executive might emphasize that employee retention provides an early signal of long-term viability.
A third executive overseeing customer experience might contend that retention needs to be emphasized just as much because losing today’s most loyal customers will cause problems for tomorrow’s financials.
Each executive can generate persuasive data and can construct a well-reasoned business case. However, under each case, there lies an underlying question no scorecard can answer directly:
Which version of the organization’s success should we endeavour to achieve?
That is why discussions about performance measurement seldom stop at purely technical questions about methodology. Instead, they are discussions about priorities, strategy, goals, objectives, vision, and identity.
That makes them inherently political (though not necessarily in the partisan sense of the term, but rather in the political sense of negotiation and compromise among stakeholders). Acknowledging this is not a weakness in performance management, but the first prerequisite for more mindful use of KPIs.
Who Defines Success?
If every KPI is rooted in a human choice, a much larger question arises:
Who gets to make that choice?
On its surface, it may seem simple. Leadership sets out the organizational strategy and KPIs, then monitors progress toward those goals. However, that rarely pans out in practice.
Companies are divided into departments with different competencies, skills, values, and views of what success looks like. Finance, Operations, HR, Marketing, Sales, Customer Success, IT: each looks at the business from a distinct vantage point. None is correct, none is wrong, yet most importantly, none is sufficient alone.
It’s not so much that leaders lack consensus on what may be the best path for performance management; it is much more so that they lack consensus on which performance metrics matter most.
At that point, KPIs become subtle tools of governance. Choosing a metric becomes about who we want to have in leadership conversations, which projects are funded, and which team members are celebrated. Ultimately, each KPI is a person’s priority amplified.
Different Backgrounds create Different Dashboards
One might be inclined to think that executives in identical positions build similar performance dashboards, but our experience with real-world examples suggests otherwise. Two leaders can arrive to manage the same organization, with the same market dynamics and strategic ambitions, yet still focus on completely different sets of key performance indicators.
What influences them most is often shaped long before the executive suite was within reach.
Scenario 1
Suppose a retail company appoints a new CEO.
One candidate had twenty years as a Chief Financial Officer. Unsurprisingly, the dashboard the executive team reviews focuses on gross margins, operating costs, inventory turns, and the cash cycle. The conversations always start with financial discipline, simply because the executive has been speaking that language for their entire career.
Scenario 2
The company promotes the former Chief Customer Officer.
Suddenly, the dashboard is dramatically reframed. The customer lifetime value (CLV), the customer repeat purchase rate, the Net Promoter Score (NPS), and the customer retention rate are the focus of the top portion of all reports. Financial metrics matter, sure, but they are now a consequence of customer experience.
Neither CEO is being obtuse – they are just asking different first questions, and this is the pattern we see across many different kinds of companies.
Let’s say a manufacturing organization promoted an engineering executive who has great rigour in monitoring the defect rate, equipment reliability, production throughput, on-time delivery performance, and OEE.
Now, change the chief executive to a commercial executive, and one begins to see a shift in the focus of reports towards the delivery performance, market share, customer demand, and revenues. Both individuals want the organization to do well, but they have different visions of how to achieve that.
KPI Ownership Isn’t About Control – It’s About Influence
The politics of KPIs seldom originates from people fudging their numbers. More frequently, the politics are generated because all functions honestly believe that their numbers need a higher profile than everyone else’s numbers. Picture a leadership discussion where next year’s executive dashboard is being developed.
Finance will argue that increasing cash conversion and profit is more important than any other objective during economic instability.
Sales will argue that pipeline value and revenue growth need greater focus, since future profits depend upon present revenue.
HR will claim that employee turnover is high and that replacing talented staff is becoming extremely expensive.
Customer support will show that reducing churn delivers far greater lifetime value than acquiring new business.
Operations will insist on focus and delivery – the ultimate success factor.
Everyone has credible evidence and data to present. Everyone has good reasons to make their case. No one is trying to pull the wool over anyone’s eyes; rather, each party is conducting a negotiation based on diverse viewpoints.
This is why it can be so hard for some companies to construct a dashboard that represents the whole organization – there are distinct differences in perspective shaped by individual professional experience. The end product of all this discussion is, in effect, a collection of priorities.
The Same Role Doesn’t Always Produce The Same Priorities
The clearest evidence that KPIs are far from neutral becomes apparent when leaders with almost identical roles and completely contrasting career backgrounds are considered.
Healthcare
– On the one hand, a hospital CEO, who was formerly a doctor, is prone to prioritizing indicators such as patient outcomes, hospital readmission rates, quality of treatment, clinical safety, etc. Hence, the quality of care to the patient would naturally be the most obvious indicator of how well the organization is doing.
– On the other hand, a CEO who has risen through the ranks in operations within the hospital may be more concerned with ED wait times, bed occupancy rates, resource utilization, patient throughput, and similar measures that help ensure more patients are treated at the earliest possible moment.
Of course, this doesn’t mean they have ignored the other aspect; rather, they will attain the same objective through different pathways.
Education
– In the sphere of higher education, a president of the university who was an academic before taking on the administrative role will place maximum importance on indicators of research productivity, faculty career growth, scholarly publications, and university reputation.
– Conversely, if the president came from a background of business or financial administration, then greater significance would be given to indicators of student retention, enrollment growth, student graduation rate, and institutional financial viability.
While both care for a high-quality education, there would be differing views regarding which parameters would truly signify the accomplishment of this goal.
Technology
– In the arena of growing technology ventures, a CEO-founder with an engineering background would focus primarily on system availability, product robustness, deployment speed, and system stability as performance indicators.
– Alternatively, if the founder has a background in marketing, then parameters such as customer acquisition cost (CAC), conversion rates, brand visibility, and market presence will receive equal and immediate attention.
There is no one viewpoint that is more correct than the others; they simply demonstrate how one interprets the areas that demand attention earliest in any given organization.
Why These Differences Matter More Than We Think
These examples may seem like just leadership personal preferences, but these actions have powerful consequences throughout an entire organization. What a leader chooses to measure shapes what his managers focus on. What the managers focus on shapes how their teams choose to spend their time. Over time, the patterns of decision-making based on these priorities create a corporate culture.
Think of two organizations in the same business, with nearly the same model.
In one company, excellence is defined by efficient operations. Employees come to see that improving productivity, cutting costs, and eliminating waste are a fast track to success and promotions.
In the other company, excellence is defined by a drive for innovation. Employees are recognized and promoted for learning rapidly from failures and for innovation.
These organizations aren’t giving out explicit instructions about what employees should think about the nature of the organization. They’re telling their employees that through the metrics they display.
This is why companies tend to resemble the scorecards that they create. Employees are not merely reacting to incentives. People learn what the organization truly cares about from the metrics executives most often refer to.
Your mission statement may claim your organization stands for several values: innovation, collaboration, sustainability, and customer satisfaction. However, the numbers on a scorecard tell the story of your true priorities in brutally honest terms. If there’s a metric you see repeated again and again in executive committee meetings, influencing bonuses and being factored into strategic decisions, it gradually becomes very important to employees. Other metrics can begin to seem less so.
This is why a discussion of who owns the company dashboard can never be a conversation simply about accounting software and spreadsheets. That conversation always evolves into one about the core of the organization’s identity because deciding what to measure, in the end, is another way of asking what winning looks like.
When Metrics Become Power: How KPIs Shape Organizational Behaviour
By the time a KPI lands on an executive dashboard, it has endured endless discussions. Someone proposed it. Someone challenged it. Someone defended it. Finally, it becomes part of the organization’s definition of success. Yet the journey is far from over.
When the KPI is tied to performance reviews, incentives, promotions, budgets, or strategic decisions, it ceases to be an innocent indicator of an organization’s health. It becomes an incentive, and people do have an outrageously uncanny ability to respond to incentives.
This natural predilection has nothing to do with 200-IQ deceitfulness, but rather with the fact that any organization inherently sets its employees up for success through the rules it puts in place. People will, predictably, focus on metrics they are being judged by. The question remains whether they are improving what they said they were improving by driving the metric.
From Measuring Behaviour to Driving Behaviour
Businesses often view KPIs like rear-view mirrors: they only provide a snapshot of what the business did, when in fact they’re a lot more like steering wheels – after you set an organization on the road with any particular metric, everyone begins to steer by it.
Think of your customer support organization with average ticket resolution time as its leading indicator of success.
On the face of it, a logical target, customers prefer quicker support. Nevertheless, what happens when they all start steering toward that target? Well, over time, things begin to get murky and odd incentives sprout up. Your support team begins to know that they’re being rewarded for quickly closing tickets. They pass on the tougher tickets to the next available team; they begin closing tickets before customers feel resolved, and their post-support phone calls and emails become more concise. They all look like they’re performing well, but the customer experience continues to deteriorate as the focus shifts from quality to speed.
Nothing is being falsified, and nobody is breaking the law. Everyone’s just doing what they’re incentivized to do. The metric is doing what it was built to do: driving behaviour, but everyone assumed the metric was the behaviour itself.
When the Measure becomes the Target
This effect has been seen across industries for years and can often be boiled down to a well-worn observation: “When a measure becomes a target, it ceases to be a good measure.”
What that means is that when people know their performance will be judged based on a particular number, their incentives are immediately aligned to achieve that number. That behaviour is not always aligned with the leaders’ expectations for why they implemented that measurement in the first place.
Just ask teachers or healthcare providers, for example.
If educational performance hinges solely on standardized tests, then those teachers are likely to spend a large chunk of their precious time training students to beat those tests.
If hospital management puts tremendous pressure to reduce ED wait times, departments will likely find ways to shorten wait times without increasing throughput or improving patient health.
The KPI improves, but what about the actual outcome? What comes out at the end of the entire process?
It is important to note that we are not recommending against setting and measuring performance targets in the first place. However, we should be aware that every indicator of success will drive behaviour in unintended ways.
Something we don’t talk enough about when it comes to performance management is the fact that the KPIs themselves are typically negotiated. KPIs seldom spring fully formed out of some vacuum. Instead, they’re born from a series of debates: between departments on what’s achievable, between executives on the relative merits of optimism and pragmatism, between finance on the financial case for making some improvement, between operations on practical constraints, between managers on what’s realistic for their people.
At a macro level, these debates are about numbers, but at a micro level, they’re debates about risk and accountability, expectations and aspirations.
The sales director suggests that the department should aim for 25% revenue growth next year.
The marketing director argues that brand awareness can’t deliver that without more investment in brand building.
The operations director points out that capacity constraints might emerge.
Finance expresses doubt whether the forecasts could hold up given the prevailing market conditions.
Through several meetings, a figure between 15 and 20% is eventually agreed upon.
Was politics the issue in determining the KPI?
Yes, yes it was.
Should it have been a problem?
No. A certain level of politicization is necessary in business to enable us to accommodate the various conflicting, yet valid, points of view we have to wrestle with. We risk deluding ourselves about how our systems work by denying that these exchanges exist. That’s where the problem lies, not in the exchanges themselves.
The KPIs That Get Attention Usually Get Resources
At the end of the day, organizations spend money, talent, and time in ways that leadership consistently prioritizes.
Think about two organizations dealing with exactly the same challenges.
At Company A, sustainability is a topic of every executive meeting. Financials and carbon emissions are both represented on board meeting agendas, as are renewable energy and suppliers.
In Company B, sustainability is addressed annually.
Who do you think will be investing more in the environment? Who do you think will be drilling down into those numbers in leadership meetings? Who do you think will feel that their remit includes managing this initiative rather than just reacting to a mandate?
It’s got less to do with values than visibility. Ultimately, on the outside, any leader or organization will invest resources in the things their senior leaders talk about and pay close attention to.
The same is true on the inside. If the leadership team shows new product revenue on their dashboards, experimentation metrics for product teams, and measures related to idea generation or the marketing pipeline, people quickly understand that innovation is not just an aspiration; it’s a priority. The same is not true if the topic comes up during a leader’s quarterly inspirational talk but not during any other type of review meeting or dashboard report.
It has become almost a cliché that experienced executives tell people that organizations become incredibly good at whatever it is they measure. It has little to do with other aspects being irrelevant; rather, it’s more about the fact that everyone’s attention span is limited, especially nowadays.
Metrics Also Shape Organizational Narratives
Beyond incentives and resource allocation, in some environments, KPIs can have an even more insidious effect. They dictate the narratives a company tells itself.
Consider a firm with decelerating revenue growth. For a CEO who is solely obsessed with profitability, flat performance can be cast as evidence of fiscal prudence: margins are expanding, the cost structure is well contained, free cash flow is improving, and cash reserves are strengthening. The narrative is one of resilience.
Now, let us behold a similar firm, but helmed by a CEO with a penchant for tracking customers. A similar result – growth sputtering – might be spun as a signal that it’s time for urgency, highlighting fading customer momentum and the increasing threat from rivals.
The same result but different interpretation, and therein lies the critical point about performance management: KPIs don’t simply convey information; they shape perception.
While executives may have the best intentions, most aren’t actively seeking to mislead their organization when constructing executive dashboards. Rather, they generally do genuinely want to point to the most significant signs of organizational progress.
Still, all dashboards, however well-intentioned, carry narratives, and each begins by making choices about what matters most. That’s why the debates over a particular metric or target can sometimes become so emotionally charged. They’re not just arguing over numbers but over the story of where they’re going – the future that begins to take shape once the figures enter the conversation. KPIs reflect reality in so much as they also make it.
Better KPI Governance Starts With Better Questions
You may be inclined to arrive at an uncomfortable conclusion if you have read up to this point: if every KPI represents a human choice, organizational bias, or a competing perspective, do truly objective measurements even exist?
Well, no, and in fact, embracing this fact is one of the most beneficial mental models for an organization to adopt. Human judgment being present in KPI formulas is not the issue. The issue is that it pretends not to exist in the first place.
Organizations spend an enormous amount of time fussing over formulas, fiddling with calculations, optimizing data quality, and investing in increasingly complex dashboard infrastructure. These are valuable pursuits but may mask the illusion that better analytics invariably yield better decisions.
An impeccably precise KPI might still measure the wrong thing. A well-designed dashboard may reinforce old habits of organizational thinking. In reality, the real task at hand is not to eradicate subjectivity; it is to expose it.
Organizations that are at the forefront of the analytics field understand that their KPIs aren’t sacred scriptures. They understand that KPIs represent a decision to measure something, a choice that must change as the business context changes. This outlook changes the dialogue at leadership levels entirely.
Rather than asking “Is this KPI accurate?”, leaders ask far more pragmatic questions: “What am I measuring this for? What are its downstream impacts on behaviour? What crucial outcome may be getting missed?”
The Best Dashboards invite Discussion, not Blind Agreement
Perhaps the most damaging fallacy regarding executive dashboards is that they should somehow negate debate. Actually, great dashboards inspire much better debates. Picture showing the same performance metrics report to a group of executives from finance, operations, HR, product management, and customer service.
If they don’t even argue the numbers in front of them, it may be a cause for concern.
People come at things from different angles for good reasons in an organization; organizations are complex systems.
The CFO sees declining margins as a clear risk
The CHRO may detect employee burnout lurking below deceptively positive productivity figures
The head of operations is aware of production capacity constraints before they show up on financial statements
The customer service executive notes the first whispers of declining satisfaction before customer revenue has been negatively affected
Healthy companies view these different lenses not as conflicting interpretations of truth but rather as complementary observations that together yield a far richer view of reality. There isn’t a single “right” way to measure a company’s health; rather, a high-performing system must measure many facets that determine a system’s health.
Just as a doctor doesn’t look only at a patient’s blood pressure, nor does an airline pilot fly a plane only by watching the fuel gauge, an organization can’t monitor only one dimension to determine overall health.
Good KPI Governance Means Challenging Your Own Assumptions
Perhaps the best leadership habit an executive team can build is to occasionally challenge the KPIs to which they’ve become so accustomed, because they have realized that the very essence of their business has changed.
The market changes.
The expectations of the customers change.
Technology disrupts industries.
The strategy changes.
Yet somehow, too many businesses will go on reporting on the same KPIs as last year, or the year before that, simply because that’s what they’ve always done. Dashboards simply become a habit.
Organizations start asking whether they are measuring what is important and begin talking about whether they hit last year’s target, leaving out the fact that some of the most important leadership conversations aren’t about performance – they’re about whether they are even asking the right question.
Think of a business that has long since learned that office space usage, in-person collaboration, physical footprint, and building occupancy are metrics. Those numbers may have seemed to make sense until remote and hybrid work totally upended the way that we collaborate.
This organization now needs a new way to conceptualize performance altogether. The business that survived didn’t necessarily have the best-looking metrics. They simply weren’t afraid to ask whether their assumptions about what constitutes performance were out of sync with market reality.
Transparency Builds Stronger KPIs
If KPIs are truly strategic choices, organizations should disclose them. This doesn’t mean offering an executive summary of each KPI – it simply means being able to articulate, clearly, why a particular KPI exists in the first place.
Who asked for it?
What strategic goal does it serve?
Why this metric over other potential measures?
What shortcomings should the decision-maker consider?
These dialogues might seem shockingly mundane, but they’re profound at a human level.
Just picture a new exec joining a company. Instead of receiving a dashboard of meaningless numbers, they may have a conversation with leadership about why KPIs such as customer retention, investment in employee development, and innovation play such a central role in current operations. This conversation turns the dashboard into a mirror of the organization’s underlying strategy. Even more importantly, the practice of transparency helps with subsequent evolution.
Once the rationale behind a KPI is clear, the organizational decision process shifts to the question of whether that metric still applies or if a different measure might serve the organization’s goals more effectively. The distinction between defending an individual metric and defending a KPI’s intent may be subtle and unimportant to the inexperienced, but vital to those with many winters over their brows.
Final Thoughts
Organizations have spent decades trying to refine how they measure performance.
✔️ We’ve improved the richness of dashboards. ✔️ We’ve elucidated data so that it is more readily available. ✔️ We’ve made analytics quicker and richer. ✔️ We’ve enhanced the systems to be more intelligent through artificial intelligence.
Yet there’s one factor that’s remained constant: the human decision-making power behind every KPI. Humans decide what needs to be considered, what needs to be celebrated, which numbers make it to the boardroom, and which ones never even make the dashboard. This is a natural outcome of leading human beings with varied experiences, areas of expertise, priorities, and roles.
KPIs being a thing isn’t an error – we as a species have loved numbers, measurements, comparisons, and benchmarks since we had the mental capacity to engage with these matters. The error lies in the expectation that all of these things we love are entirely objective.
Great organizations accept, confront, and consciously bring other points of view to the discussion because effective conversations about performance indicators usually start with a question, not a spreadsheet: Why are we even tracking this?
Companies rarely achieve what they declare is important to them; they reflect what they measure. The truest measure of an organization’s ability to govern its work, lead with conviction, nurture with care, and innovate for impact is not a KPI on its dashboard; it is its commitment to challenging its time-honored metrics.
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In most enterprises, there is at least one, often multiple, dashboards. These glowing arrays of data, updating at lightning speed, are producing weekly reports that land in everyone’s inbox, from the CEO down to the floor supervisor.
The platforms used to gather such metrics are significant investments in configuration, and continued investment is required to extract this information and make it consumable by diverse stakeholders.
The problem is that, in many of these enterprises, the same metrics haven’t influenced a key decision for months. The underlying paradox of measurement within most of our current organizations is that the more we attempt to quantify something, the less effective we seem to become at deriving meaning and insight.
As the volume of available information proliferates, it seems increasingly challenging to discern which actions are necessary, let alone effective. Measuring for progress now seems to have morphed into measuring for mere participation.
Measurement in excess has transformed the metrics themselves into little more than participation indicators that have little to no genuine impact, when, really, you should measure only what can be measured well.
The Measurement Maximalism Trap: How Organizations Got Addicted to Data
The Dashboard That Glows But Doesn’t Guide
The dashboards originally designed for illumination, not for eye-popping designs, have become the organizational equivalent of corporate wallpaper. Visually engaging complexity, not driven to a specific action. This isn’t the flaw in the underlying technology. It’s the flaw in how you’ve defined what is worth measuring.
Today’s analytic toolset makes it easy to record virtually anything. Coupling the ease with an organization whose desire for control equals its belief in the power of more information to be that, led to the phenomenon of what you can call dashboard inflation: metrics accumulate until they crowd out the signal. When a dashboard reports 40 or 50 different metrics, there isn’t the capacity to read them deeply enough to explore anomalies and derive decisive strategic conclusions.
The organization skips and skims through. It glances over, looking for simple, overt, bright, shining red flags. Then it checks the numbers that matter and moves on. What was initially intended to drive action ends up as a task to be filed away as evidence.
The Math That Should Make Any Leader Nervous
Here’s a calculation that is great at cutting conversations short. Imagine an organization with six to ten departments or business units. Give each department an average of 8-10 KPIs to follow, which seems perfectly logical if you think about the unique needs of a single department alone.
Multiply the two, and you’re looking at 48-100 specific measurements the company is technically keeping an eye on.
Next, ask the questions that should follow:
Of all these metrics, how many are really key?
How many truly have an impact on whether or not the company is winning or losing against its highest strategic objectives?
How many exist only because someone decided back then that, since the system made them cheap and easy to generate, someone someday might even use them?
For most companies, the number of strategic indicators is considerably smaller than the hundreds one typically sees on a dashboard.
All of the others survive simply as products of inertia, and each of those hundred “key performance indicators” consumes time and brain space that should instead be devoted to the small subset of data that is truly indicative of performance.
If we had 100 “key” indicators, “key” would essentially become a meaningless word.
The “If We Can Measure It, We Should” Fallacy
The measurement maximalism trap is, at its core, a confusion between capability and wisdom. Modern data infrastructure has given organizations the ability to track almost anything in near real-time. However, capability and strategic judgment are not the same thing, and treating them as equivalent is exactly where organizations start losing the plot.
Just because something can be measured doesn’t mean it has strategic value.
Just because a platform supports 200 custom metrics doesn’t mean you need 200 metrics.
Just because data is available doesn’t mean adding it to your dashboard brings you closer to understanding your business.
What it typically brings instead is noise – an ever-growing collection of numbers that require time to maintain, energy to interpret, and attention that could otherwise go toward the things that genuinely drive performance. Gradually, the act of measurement begins to crowd out the act of improvement. Teams work harder at tracking their progress than actually making any. The organization becomes, in a very specific and avoidable way, busy without being productive.
KPI vs. PI: The Critical Distinction Most Organizations Have Forgotten
Not Every Metric Earns the Word “Key”
About 90% of what we call “KPIs” within organizations are not actually KPIs. They are PIs – performance indicators – and the difference is far more material than many admit.
A performance indicator measures something that occurs within an organization: ticket resolution times, report generation volumes, packing efficiency rates, or training completion percentages. These are all valuable numbers, certainly. They offer an operational view to the teams that own the associated processes and help them establish benchmarks for quality. Yet, they are not necessarily key.
A true KPI, used to the full potential of its name, links directly to a strategic objective.
The organization aims to increase its market share > the strategic KPI is market share.
The organization is trying to retain customers > the strategic KPI is customer retention.
It tells leadership at the highest level whether the organization is winning the game it believes it’s playing: revenue growth, net margin, profitability per customer, market share – these are the top-tier measures. Everything else is essentially noise or in service of those top-tier measures.
We have blurred the lines as organizations have grown, technology has become ubiquitous, and we’ve democratized metric-taking across teams.
It’s easy for each department or business unit to grab hold of operational metrics and dub them “KPIs” without asking whether they actually contribute to high-level organizational strategic outcomes.
Dashboards have become cluttered with PIs dressed up as strategic goals. Nobody realized they were promoted; they just sort of got there.
The 40,000-Foot View vs. Getting Lost in the Weeds So, what does a CEO actually need to know about the state of his company at any moment?
He really doesn’t care about the rate at which the warehouse packs goods unless that number affects a critical cost and/or the customer experience of the company as a whole. What he cares about are a couple of well-communicated indicators:
Is the company growing?
Is it making money?
Are customers staying?
Are we executing on the strategy we agreed we would execute upon? At that 40,000-foot level, you typically need at most six to ten really important indicators to accurately describe what’s happening. Everything else, all the departmental and operational process-level metrics, all the ratios that management needs to manage the day-to-day functions, is nobody else’s business in any of these discussions.
The weakness of measurement maximalism is, to some extent, the weakness of the hierarchy: not being able to sort and separate strategic vs operational measurements.
When you start bubbling up all the individual and departmental PIs to an organizational review meeting, the view gets blurred; the managers are going over meeting click-through rates and newsletter open rates rather than the items that are important and telling.
When KPIs Stop Driving Behaviour and Start Decorating Reports
There is a simple test that measures whether a metric warrants the label of KPI:
Does it change the way people behave?
It should detect issues early and clarify what success looks like and what’s left to do: in short, it should help teams focus.
Once a metric accomplishes these 3 things, it may deserve the “KPI” tag. When it doesn’t change behaviour or decision-making, it’s decorative.
If an organization suffers from KPI proliferation, it probably has too many decorative KPIs. Most of their metrics have gradually become decorative out of mere habit or routine report filler. They have ceased to prove anything useful. Those metrics may simply fill the gaps where data collection and reporting are requested, but without providing insights or stimulating any form of change in people’s work or behaviour. When a good KPI changes the organization’s functioning, a bad KPI or one of 50 other KPIs simply doesn’t.
Any company unable to differentiate between them faces a measurement challenge that is beyond the reach of mere dashboard adjustments.
The Real Cost of KPI Overload: Cognitive Fatigue, Decision Paralysis, and Teams That Stop Thinking
When More Data Produces Fewer Decisions
The hypothesis on which measurement maximalism operates is that more data equates to better decisions. It seems sound and scientific. It’s also quite wrong.
When executives see the dashboard, they usually don’t feel their decisions are being enhanced; the opposite generally occurs. Productivity may have increased, but consumer satisfaction has fallen. Moreover, 40 separate indicators are currently being updated simultaneously. As a result, decision makers delay while scheduling another round of meetings and trying to determine the scale of the real threat.
This is what experts call decision-making paralysis, a symptom of excessive reliance on indicators. Whenever individuals’ capacity to process additional input is overwhelmed, they automatically delay making decisions until a broader range of statistics is available. As more information is considered and more individuals are involved, the perceived uncertainty also grows, and, meanwhile, whatever issue the metrics were intended to uncover deteriorates.
The tragic reality is that these measurement frameworks, which have been used to accelerate decision-making, only lead to a stagnation of decision-making.
Hitting the Metric While Missing the Mission
Here’s a situation that illustrates the danger of mismatched KPIs more effectively than almost any theoretical statement we could make.
A company sees that its Net Promoter Score, its gauge of customer loyalty and enthusiasm, is falling. They identify a solution. Compensation and enticements are provided when feedback is being collected.
NPS rises. The number looks healthy in the next quarter’s report. The actual causes for the customers’ discontent – the friction or the failure of the product – were not, however, altered in the slightest. Many organizations develop this practice, often unwittingly, as they learn to prioritize the score rather than the outcome for which the score was originally developed.
Organizational staff who primarily gain recognition for meeting KPIs develop mechanisms to meet them.
It’s not pessimism – but rather human conduct in reaction to incentive design. Colleagues can tell which things are tested, observed, noticed, and rewarded. Individuals shift accordingly. They obtain experience in offering the appearance of efficiency, but not necessarily the efficiency itself. This is called “conquering the score while neglecting the objective” – one of the most costly forms of failing a company may encounter, since it is quite hard to spot in retrospect.
The dashboard seems all right, and values develop from the correct orientation. However, real life is slowly being corroded.
The Quiet Epidemic of Reporting Fatigue
There’s another cost of KPI saturation that doesn’t show up on any dashboard but that everyone inside organizations swimming in it can feel: the sheer time it takes to feed the beast that is the measurement system.
Getting a hundred different KPIs to tick and tock requires somebody or somebodies to collect, validate, refresh, format, and disseminate that data, frequently, sometimes weekly or monthly.
To a mid-size company, for example, those hours pile up in a hurry. Its analysts produce reports, managers pour over numbers they sort of get, and department heads struggle to fill out the same old forms with numbers only slightly different from last quarter.
That’s time spent feeding the system rather than fixing what’s broken, creating what’s needed, improving customers’ lives, helping their team develop, or making whatever executive decision they truly need to make. It’s the modern corporate bureaucracy, disguised as diligent management work. Over time, a unique, unspoken kind of demoralization seeps into these companies. The people who signed up to build things or help people come in and feel as though they’re spending an outsized fraction of their time on activities that yield little beyond raw data.
The link between their efforts and actual business outcomes begins to blur. Engagement lags persistently, subtly, maybe not to a critical degree, maybe, and not all at once, but to devastating effect down the line. The chosen metrics were designed to empower them to do more. They’re instead burning the fuel that could help them do so.
Signal-to-Noise Collapse: When Reporting Becomes the Work
Vanity Metrics and the Illusion of Progress
There’s an all-too-common disease lurking in metrics-driven workplaces: the proliferation of what could be described as “vanity metrics,” a collection of figures that make a report or a presentation look good but have little or no connection to real business performance: total hits to our website, our number of Facebook followers, the number of features we shipped this week, the amount of customer support tickets we logged, and so on.
These are all relatively easy to produce and easy to feel positive about, and, in most cases, have nothing at all to do with the really important questions like “Are we growing the right way?” or “Are customers truly getting value from what we produce?“
Vanity metrics are seductive because the directionality is right when you simply add more effort.
Your number of social followers will increase as you post more social updates. Your output quantity will increase as you produce more output. Your customer activity will rise when you do more of it. However, the link to a positive result is nonexistent.
Worse, vanity metrics muddy the waters. Genuine metrics like customer retention and the quality of outcomes you help users achieve are harder to work with than tracking output, and they rely much more on human interpretation than the former does.
It’s therefore all too easy to focus on those and neglect to measure those that might not look as good in a report but do provide far more actionable information.
The Bureaucratization of Measurement
There comes a scale at which KPI culture transforms from managing performance to compliance. It’s where measurement has become bureaucracy, full stop. You know when you’re getting there through certain signs.
Measures with no clear strategic explanation, but which were introduced into the monthly report so recently that it feels too dangerous to try to take them out again.
KPI meetings in which nothing much gets followed up afterward.
Reports that are seen, signed, stamped, and stored away.
People who know the targets they have to meet, but don’t know how they relate to anything else that the company does or wants to achieve.
As soon as measurement has begun to develop a life of its own, it loses any reason for it to have had one in the first place: to promote action that enhances performance.
Its purpose becomes simply to ensure the self-preservation of a framework for producing reports that prove reports are being produced in a seemingly organized way.
As the system appears to be very active, the system is also largely uncontested – the dashboard is refreshing, and the monthly reports are being circulated; therefore, it is clear that something is being controlled.
How Good Metrics Quietly Become Bad Incentives
The most insidious part about KPI saturation, perhaps, is what it does to our behaviour in the long run, even when the metrics were a well-intentioned effort to start with. Every single metric, the second that it’s tied to an evaluation, begins to drive behaviour. That is, after all, its job, but that’s different from improving the system the metric was designed to measure.
People get good at gaming the system to produce the number. We optimize around a specific KPI. We cut corners to hit the number. Risk-aversion increases because a miss, however minor, kills the score, and suddenly we have a workplace perfectly optimized for the appearance of performance while the real work goes unimproved.
The issue isn’t individual greed or lazy employees. It’s the predictable consequence of over-measuring and under-trusting. It seems like, by now, we’d have learned that when we tie evaluation to everything, the only smart move is to play the game, not do the work. Metrics were intended to indicate how we could improve things. In systems of over-measurement, we have replaced honest indicators with carefully managed signs of activity: noise dressed up as useful data.
From Measurement Maximalism to Measurement Intelligence: How to Build a Leaner, Smarter System
Start With the Question, Not the Dashboard
The antidote to measurement maximalism, to this idea of just measuring more and more, doesn’t actually mean “measure less for the sake of measuring less.” It means “measure with intent,” and to do that, we need to start with the most important thing, and most organizations get this part wrong more often than they get it right.
What typically happens, if you look at most organizations’ KPI frameworks and dashboards, is that they approach the creation of those frameworks based on the answer to “What’s out there for me to measure?”
Therefore, we assess what data we have, what the analytics tool can tell us, and what can fit on the dashboard, and we build the dashboard from what’s available. That feels pragmatic, and the dashboard ends up looking great, and, by and large, we’ve approached the problem backward.
The real right thing to do is actually to come back to another question: “What decision am I trying to use this metric to inform?”
If the question of what decision I’m trying to make doesn’t have a really, truly clear answer, then maybe that metric shouldn’t be on the dashboard. Maybe that KPI, that part that I’m measuring, just doesn’t belong on the dashboard unless there’s a decision tied to it, a meaningful way for me as a decision-maker to consume it.
If it’s not driving a decision for someone, it won’t serve as a helpful management tool; it will become a floating, isolated data point, and that is the real distinction between measurement intelligence and measurement maximalism. It’s not “how much do we measure” but “how focused do we measure,” which ultimately comes down to a company having more than just an objective in mind.
It should have a specific decision for every metric or set of metrics, a specific type of decision it’s going to serve, and a specific accountable owner who is expected to act as a consequence of observing the metric. If any of those three requirements are not met, perhaps that metric should be dropped.
The KPI Audit: A Framework for Cutting Without Going Blind
The practical dilemma for most people is not a lack of clarity regarding having too many KPIs; rather, it’s determining how to reduce them while maintaining visibility on the really important items. The best tool for identifying which KPIs to prune and which to keep is a system, and it does not have to be overly complicated.
For every measurement indicator on every dashboard, ask:
Does this measurement relate directly to a company’s strategic goal (the ones that really are on there)?
Does it drive a decision and prompt a quick response as the score fluctuates?
Can an informed employee explain what the measurement is tracking and why it’s important?
Would a person use this measurement to make better decisions than they otherwise would without the measure?
If the answer to any of the questions is a simple no, it means the measurement warrants significant evaluation. If you answer simply no to two or more of these questions, you could also answer simply no, since it means your measurement is unlikely to be as productive as you had initially hoped.
In addition to asking these three questions, there is a logical framework for determining how many KPIs the organization requires at different levels, which may serve as initial talking points among interested parties.
Disclaimer:The following numbers are not set in stone and are not end-all be-all guidelines; they should serve only as a starting point for a theoretical discussion on cutting down KPIs in an environment that sprouted so many of them that you don’t even know what each one tracks. They do not represent a cookie-cutter suggestion or a golden standard – they are merely the beginning of a conversation. Each company and industry is different and requires distinct efforts to maximize the use of KPIs.
For practical guidelines or a detailed plan, tailored to a specific organizational situation, get in touch with us here: https://kpiinstitute.org/contact-us
With that out of the way:
A) At the individual employee level, there is some empirical support for certain limits. Often, that revolves around the idea that one person should own no more than three KPIs at any level. If that exceeds 3, few owners can dedicate time to it, and possession skews towards fiction rather than fact.
B) At the team level, you may set up team dashboards of about ten to fifteen KPIs, as long as every measure in the dashboard is genuinely owned, assigned to a goal, and the KPIs are regularly and critically examined and are not simply noted and filed away.
C) At the organizational level, usually around no more than six to ten genuinely strategic KPIs are to be shown to executives, not out of some arbitrary constraint but out of an awareness of the cognitive limits of human beings when focusing on complex and interrelated decisions. Numbers greater than 6 – 10 make it more of a data repository than a viable system. People start focusing on quantity rather than quality.
Treat Metrics as Signals, Not Verdicts
The cultural shift that distinguishes high-performing organizations from those drowning in the metric-maximalist world is this: high-maturity organizations do not use metrics to replace judgment; they use them to inform judgment. In a KPI filled organization, metrics are always verdicts. If a metric is green, things are okay. If it’s red, someone is failing.
Teams spend their time explaining away numbers rather than understanding the system that generated them. Leaders look at averages and move forward regardless of what the averages mean; anomalies are not explored because there are too many data points to examine. In a measurement-intelligent organization, those same numbers initiate dialogue rather than conclude one. An unusual move in a metric isn’t a verdict; it’s a prompt to go deeper and inquire further, to understand what caused the movement.
Why has a number moved?
What is it signaling about the underlying system?
Is it still the thing being measured?
What action is really indicated?
Qualitative insight is as important as quantitative data, not less. The number may signal a shift, but it usually doesn’t say what to do about it. The judgments of people closest to the work, who understand the context far better than any dashboard could, are considered insights, not distractions.
Moreover, accountability in a measurement intelligence organization remains human. Decisions don’t get handed off to dashboards. They are held by humans, with dashboards as backup.
If It Doesn’t Drive a Decision, It Doesn’t Belong
The simplest reframing any organization can adopt for serious measurement culture improvements is this: a KPI that doesn’t inform a decision is not a KPI – it’s noise.
Much like writers kill their darlings when they remove words, sentences, paragraphs, or entire chapters, businesses should do the same with KPIs. Not every metric residing in your data system today will survive or should survive.
Some will be metrics that only made sense three years ago when an entirely different priority was at play. Others will be internal departmental KPIs quietly slipped into the executive dashboards. Many are vanity metrics that are too vain to keep. Pruning these will enable you to stop operating blind and start to see clearly for the first time in what feels like a long time.
Final Thoughts
Measurement is not evil.
Measuring things up is a response that makes complete sense – that impulse to understand if whatever you are up to is actually happening, to detect what might soon become an acute problem, to gauge what may be a slept-on trend, and to want to hold people to account for results.
Yet, when measurement takes on a life of its own, it seems more crucial to do measurement for its own sake. Dashboards become more of a concern than just tools that support decision-making. Teams devote so much effort to feeding some form of measurement tool to demonstrate progress toward the desired end that the effort shifts away from running the business to the business of measuring the business.
Metrics are powerful tools. Used with intention, they drive the kind of accountability that genuinely changes things. However, they are terrible masters, and the organizations that remember the difference (that keep humans in charge of judgment while using data to sharpen it) are the ones that turn performance measurement into a real competitive advantage.
Measure less to understand more to decide better.
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Looking to strengthen your expertise in KPI design and performance measurement? Gain practical knowledge, proven methodologies, and globally recognized certification through The KPI Institute’s Online Certified KPI Professional.
