Emotional intelligence in strategic leadership is becoming an important topic in modern management. In the past, leadership was often measured mainly by financial results and decision-making skills. Today, many organizations also look at how leaders manage themselves, connect with their teams, and handle change.
Strategic leaders work in complex environments where they need to align people, resources, and goals. In these situations, technical knowledge alone is not enough. Emotional intelligence helps leaders understand their own emotions, read the emotions of others, and use this awareness to guide better decisions.
For organizations that want to build stronger leadership cultures, understanding the role of emotional intelligence in strategic leadership is becoming increasingly important. This makes emotional intelligence not just a personal trait but a strategic asset: research from the Center for Creative Leadership has found that leaders who show more empathy toward their teams are consistently rated as stronger performers by their own managers.
What Is Emotional Intelligence?
Emotional intelligence is often defined as the ability to recognize, understand, and manage emotions—both in oneself and in others. The concept has become widely used in the fields of psychology, leadership, and organizational behavior, and is now considered an important skill in modern workplaces.
Emotional intelligence is usually described through four main areas. The first is self-awareness, which means understanding one’s own emotions and how they affect behavior. The second is self-management, which is the ability to control emotional reactions, especially in difficult situations.
The third area is social awareness, which involves understanding the emotions and needs of others. The fourth is relationship management, which is the ability to build trust, communicate clearly, and manage conflicts in a healthy way. Together, these four areas form the foundation of emotionally intelligent behavior in the workplace. This four-part structure, often called the Boyatzis-Goleman model, remains one of the most widely applied frameworks for measuring and developing emotional intelligence in professional settings.
Why It Matters for Strategy
Emotional intelligence plays an important role in strategic leadership because strategy is not only about numbers and analysis. It also involves people, relationships, and the ability to guide teams through change. Leaders who understand this dimension can often manage complex situations more effectively.
One area where emotional intelligence supports strategy is decision-making. Strategic leaders often face difficult choices with limited information and high pressure. When they can manage their own stress and stay focused, they may make more balanced decisions instead of reacting emotionally to short-term problems. This connection is supported by recent research: a 2026 study published in Scientific Reports found that emotional-intelligence training measurably improved stress regulation and decision-making performance among professionals in high-pressure roles.
Another important area is leading change. Most strategic initiatives require changes in processes, roles, or culture, and these changes can create resistance among employees. Leaders with strong emotional intelligence are usually better at understanding this resistance, communicating the reasons for change, and building the trust needed to move forward. This is consistent with change management research: Prosci has identified a lack of awareness about why a change is happening as the leading cause of employee resistance, underscoring why clear, empathetic communication from leaders is critical to overcoming it.
How Leaders Can Develop It
Emotional intelligence is not a fixed trait. It is a skill that leaders can build and improve over time through practice and self-reflection. The first step is developing self-awareness. Leaders can do this by asking for feedback from colleagues, keeping a personal journal, or working with a coach who helps them understand their emotional patterns.
The second step is practicing self-management in daily situations. This can include simple habits like pausing before reacting to stressful news, taking time to think before making important decisions, or using techniques such as deep breathing to stay calm during difficult meetings.
The third step is investing in social skills. Leaders can improve their empathy and communication by listening more actively, asking open questions, and paying attention to non-verbal signals during conversations. Regular one-on-one meetings with team members can also help leaders understand different perspectives and build stronger relationships across the organization.
Conclusion
Emotional intelligence is becoming an important skill for strategic leaders in modern organizations. It supports better decision-making, smoother change management, and stronger relationships across teams. Technical knowledge and financial skills remain important, but they are more effective when combined with the ability to understand and manage emotions.
Organizations that invest in developing emotional intelligence at the leadership level are more likely to build cultures where trust, communication, and collaboration support long-term strategic success.
For professionals who want to build the skills needed to lead effectively in complex environments, exploring a certification such as the Certified Strategy and Business Planning Professional program can provide a strong foundation in strategic thinking, leadership, and organizational alignment. These skills are becoming increasingly important in modern strategic leadership environments.
********** Editor’s Note: The article was written by Ms. Sarah Binsaied.
“The greatest danger in performance measurement is not seeing too little, but believing you’ve finally seen everything.”
Nine KPI Lessons, One Underlying Problem
Throughout this series, we have explored what might seem like nine separate problems with measuring performance. Each article examined a different symptom, manifestation, paradox, or unexpected consequence of using KPIs to understand a complex organization. Viewed on their own, these issues seemed only loosely related. When viewed collectively, however, they tell a surprisingly coherent story.
Our journey began with the KPI Theatre, where we observed how the act of measurement changes behaviour. Once individuals know they are being observed, they instinctively begin to focus on what is visible. This is not necessarily because they are engaged in manipulation or deceit, but simply because human attention is finite and people attend to that which attracts attention. Slowly, performance shifts from being good to looking good.
Then, we examined Goodhart’s Law to see what happens when that observation becomes a target.
What began as a useful indicator of performance becomes, over time, the objective itself. People stop asking whether the organization is serving its purpose and start asking if the numbers look OK. The simple act of optimizing has quietly replaced the process of understanding.
It would seem natural that if one metric blinds you, then two will be better, but that then begets something called KPI Saturation – when everything, every function, every initiative, every strategic priority, and every operational process is represented on a dashboard.
Everything becomes so overwhelming that we are blinded by what is actually present.
Information overload has given way to attention scarcity, and when attention becomes a scarce organizational resource, metrics themselves become valuable. In The Politics of KPIs, we examined how indicators gradually evolve beyond measurement tools into instruments of influence. As dashboards grow more central to an organization’s decision-making, knowledge that is difficult to reduce to a number becomes marginalized and increasingly ignored.
Institutional knowledge, relationships, craftsmanship, intuition, judgment, and the trust required for collaboration – all fall off the strategic radar as information that cannot be easily expressed in numerical terms disappears from the conversation, as was illustrated by KPI Memory Loss.
Organizations do not necessarily fail because they lack information. Rather, they fail because certain forms of knowledge no longer receive attention, or, even worse, are actively dismissed by those who treat them as irrelevant data points. The effects of these trends, however, do not remain confined to the organization itself. The final issues that we uncovered in this series impacted the very people who were employed to deliver on those numbers.
In the KPI Identity Trap, we witnessed how those being measured can become so completely identified with the metrics by which they are assessed that they forget to ask whether they are doing worthwhile work and simply focus on what the dashboard or scorecard shows. It could be seen that the last, and perhaps most fundamental point in this series – KPI Blind Spots – is almost a natural consequence. At best, a dashboard tells us something about the reality of our situation and, at worst, it tells us an unintentional lie.
Yet the lie is never told; it simply emanates from what is left unsaid: the part of reality deliberately omitted to focus on what is believed to be most important.
Realistically, none of these were separate problems at all: behaviour, targets, information overload, politics, organizational memory, personal identity, blind spots. These are not independent phenomena, but simply different facets of the same flawed logic. A logic that proceeds on the basis that reality, as it is currently expressed in KPIs, continues to surprise us only because we have not yet measured enough.
This is what can be called the Completeness Fallacy, perhaps the most insidious belief underpinning modern performance measurement – the belief that performance (and measurement) can eventually be made complete.
Why Every Surprise Seems to Demand Another KPI
These are scenes every seasoned executive has lived out.
An unforeseen event derails a critical business process.
A major customer segment starts leaving faster than anticipated.
A manufacturing defect escapes quality control.
A major project comes in late even though all milestone flags are green.
A cyber attack bypasses the security systems that should have prevented it.
A high-performing, tenured employee quits with little notice.
The post-mortem begins, with executives staring at dashboards and data visualizations, trying to pinpoint where the red flags should have popped up and when someone should have noticed that something was going terribly wrong.
One question invariably surfaces: “Didn’t we have a KPI for this?”
Sometimes the answer is yes, and it was simply ignored. Most of the time, more frequently than many organizations are comfortable admitting, the answer is no. Thus, the logical conclusion seems obvious.
“Let’s add one.”
On its face, this is completely reasonable. Every failure is an opportunity to refine the measurement system. If an important early warning was missing, then the dashboard should be augmented to track it. It often is the right solution, truth be told.
The danger emerges when adding a new KPI becomes the default response to unexpected events, because the next time the unexpected happens (and it always does), another KPI is added. Then another and another.
The dashboards get bigger, the reports get longer, the task manager bloats, and the analytical tools become more sophisticated. Still, unexpected events persist doggedly. Each surprise seems to reinforce the idea that something else must still be missing, driving the organization toward the impossible goal of complete measurement.
This is what can be called the Completeness Fallacy.
The Completeness Fallacy is the mistaken belief that all organizational surprises stem from missing dashboard metrics and that simply adding enough KPIs will eliminate uncertainty entirely.
Complex organizations aren’t like simple machines, made of gears & cogs. They are living, breathing systems made up of people, incentives, cultures, relationships, informal networks, dynamic markets, shifting customer expectations, evolving technologies, and countless interactions that cannot be fully predicted.
Every solution creates its very own new problems. Every intervention changes the system it attempts to measure. Having a complete representation of all possible futures is impossible. Ironically, as organizations pursue completeness, they move further away from true understanding because the question asked subtly changes.
We stop asking:
“What have we misunderstood?”
Instead, we start asking:
“What KPI are we missing?”
These two questions sound similar, but there is a profound difference. The first question is about understanding and assuming that the reality of the situation is more nuanced than the dashboard’s representation. The second question focuses on measurement and suggests that the dashboard just needs an additional piece.
The Endless Expansion of the Dashboard
Think about a dashboard that includes 50 carefully chosen KPIs. A few weeks pass, and then a completely unanticipated problem arises that those 50 KPIs couldn’t have foretold.
“We need to add one more KPI!” – Leadership.
The dashboard grows to 51 KPIs. A few months later, an even greater shock arises. A new KPI is added. The dashboard now has 60. Soon 80. Then 100. Eventually, someone gets tired and asks, “If we have 100+ KPIs on our dashboard, how did this still catch us out?“
It’s a strange psychological paradox at work here.
On the one hand, leaders think “everything that is important must be on the dashboard.”
On the other hand, when something isn’t on the dashboard, it is, at least initially, discounted precisely because it is unmeasured.
Whenever reality disappoints, we try to achieve completeness by expanding the dashboard. As the dashboard grows, our confidence in it grows. That, in turn, makes the next surprise that happens all the more baffling. People begin to wonder in dismay how all of this can be happening, since they are measuring everything.
Except they are not. They never can and will never be able to. No dashboard can perfectly mirror reality; reality is always larger than its reflection.
The danger isn’t what dashboards leave out; the danger is that we forget what they have to leave out.
Babies & Video Games: Why More Doesn’t Always Mean Better
The Puzzling Perplexity of Predicting Progeny
The experience of dealing with babies is a universal (if not always enjoyable) one. Say your baby – a perfectly healthy baby, no less – is inconsolable, though not crying as a result of any obvious issue. They have been fed, cleaned, kept comfortable, and healthy, so why do the tears persist?
You hand the child a colorful toy, and for a brief moment, the wailing subsides before resurfacing with renewed vigor.
“Maybe the problem is simply that we don’t have the right toy!” and so you rush out and acquire one, only to have it achieve the same limited result. You then acquire another that sings, and another that flashes. The child continues to cry, and your resolve is steadfast: there has to be one “right” toy out there to soothe their little agitated spirits!
This process seems almost logical, and the conclusion (that another toy is just around the corner) feels almost automatic. After all, if there were a truly suitable toy, the baby would just stop crying. Right? RIGHT?
This assumption misses a crucial detail: the baby wasn’t looking for another toy at all. Maybe they simply wanted to be held, or was bored lying in one position for too long, or maybe they wanted someone to talk to them, or just to feel the comfort of their parents’ closeness. The parents didn’t need a better toy; they needed to understand the baby.
In the course of our lives, we will all learn an invaluable lesson. Sometimes the quickest, easiest path to resolution doesn’t lie in introducing a new component or finding a missing element, but in paying more attention to the subject of our concern.
Organizations are remarkably similar. Organizations often take a very different, and rather analogous, approach. Every unforeseen problem requires a new key performance indicator (KPI); every anomaly requires a new dashboard; every overlooked blind spot demands a new metric.
It’s quite possible that the problem isn’t that another metric is needed. It might be that the organization needs to better understand what it is trying to achieve in the first place. Instead of reading reports, managers might need to speak with their employees more often. Instead of filling out surveys, customers might want to talk to real humans to air their frustrations.
For example, to understand a problem in production, supervisors should walk the factory floor rather than stare at a production-tracking dashboard. While the dashboard asks, “What else can we measure?” reality is asking, “Have you truly understood me?” That distinction is at the heart of the Completeness Fallacy.
The Curious Compulsion to Continue
It plays out just as clearly in an area where organizational management can’t possibly be expected to surface: video games.
Any person who plays role-playing games or massively multiplayer online games knows that optimization quickly becomes a way of life if left unchecked. Let’s assume you load one up, make a character, and it just isn’t putting out the damage-per-second (DPS) that you were aiming for.
The logical first step, you assume, is to get a DPS meter, take some measurements, and look for the source of the deficit.
The meter tells you that you’re falling short of the damage output of everyone else on your team. You now have an answer to your problem. Or do you?
Upon inspecting your equipment, you notice that some items are suboptimal, so you immediately replace them with better ones.
You head back into the game’s content, only to see a minuscule difference in your damage output. There must be some other missing factor, something else you did not account for yet again.
You realize your equipment isn’t enchanted, and so you spend hours painstakingly applying the most potent enchantments possible.
The result is still marginal. You now invest in better gems, talent points, food buffs, consumable potions, and specialization changes. Eventually, you end up with half your screen clogged by meters tracking DPS, timers, combat logs, raid frames, cooldowns, boss warnings, and who knows what else. Still, your character’s damage output does not noticeably improve.
Add-ons are useful, but the most obvious limiting factors, such as positioning, decision-making ability, encounter awareness, or knowing when not to attack, cannot be directly measured by another number or add-on. These qualities must be learned through trial and error and by recognizing patterns. However, the more information cluttering your screen, the easier it becomes to miss the giant boss looming directly in front of you.
The Misguided Mission to Measure More
Organizations can find themselves facing this type of music in exactly the same way. The dashboards continue to grow, the reports become more elaborate, the metrics increase, and the alerts pile up, giving executives an enormous volume of information to analyze.
In practice, the actual amount of real-world understanding often advances at a far slower pace because it becomes so easy to confuse the process of measurement with actual understanding:
You can measure customer satisfaction and learn that it’s trending downwards. However, you can’t measure the sound of disappointment in a customer’s voice on a support call.
You can check a productivity dashboard, which will tell you that the project velocity has decreased, but it won’t reveal the engineer who’s afraid to challenge a deadline they know is unrealistic.
You can monitor employee morale via an engagement survey, but that can’t capture the quiet moment in the hallway, months ago, when confidence eroded just a little bit more.
You can assess sales conversion rates and identify where prospects drop off, but you can’t measure the trust that was lost in a single rushed conversation.
Wisdom doesn’t grow automatically simply from an increasing quantity of observations. Wisdom is built through an interpretation of those observations within the human context in which they occurred.
This is the true danger of the Completeness Fallacy: it persuades leaders to believe that the missing piece of the puzzle is simply another data point, and that there is never a need to stop looking at data and start listening to their people.
From Hospitals to Software: How Industries Share the Completeness Fallacy
The form in which the Completeness Fallacy shows up may vary significantly between industries, but the core principle doesn’t: whenever the world spits out an answer that surprises us, we start hunting around for some new measure, instead of asking a question: “Are we perhaps measuring the wrong thing to begin with?“
Healthcare: Measuring Patients While Missing Care
Modern hospitals amass vast quantities of data: Patient wait times, Bed capacity, Readmission rates, Length of stay, Medication adherence, ED wait times, Length of procedure, and Infection rates, among many others.
They’re all crucial metrics, but none of them fully explains why some patients with similar clinical profiles take weeks to recover while others take days. Many of the things that drive outcomes – whether the patient actually grasps what the doctor just said, has someone to nudge them about medications, or simply trusts their physician with what’s bugging them – are tough to fit into a standard spreadsheet.
We see a bad readmission outcome and naturally reach for a new quality measure. Sometimes that makes sense, but more often than not, we need to make sure the conversation was handled properly, not simply as a way to create another quality benchmark.
Manufacturing: Perfect Machines, Imperfect Systems
Manufacturing organizations usually have extremely detailed operational dashboards: Machine usage, Cycle time, Defect rates, Overall Equipment Effectiveness (OEE), Scrap rates, Downtime, or Energy usage, to name just a few.
When product quality drops inexplicably, the first reflex is often to monitor it even more closely. Very often, the mentality is “time to add another production KPI and slot in another quality check.”
Yet, investigations often find that technical issues are not at the heart of the matter; rather, the heart itself is gone.
Production targets subtly discourage the workforce from signaling minor deviations until they become significant ones.
None of these had anything to do with a lack of data regarding the machinery – these were all human systems impacting technical ones. What was needed wasn’t more sensors but a better understanding of those humans operating them.
Software Development: Measuring Productivity Without Seeing Complexity
Software teams today arguably measure more things than any team has before: Sprint velocity, Story points completed, Lead time, Cycle time, Deployment frequency, PR approvals, Bug counts, Code coverage, or Incident response time.
When the wheels are slowing unexpectedly, leadership frequently adds a new metric for engineering teams to work with.
“Maybe we need more code!”
“Maybe the reviews are taking too long!”
“Maybe the deployments aren’t happening often enough!”
Maybe the real problem might be that a legacy architecture is now extremely difficult to maintain. The team has quietly accumulated technical debt over the years and is now spending much more time understanding systems than developing features, and on top of that, cross-functional communication has broken down.
The biggest thing stopping the team from shipping may not be an absent KPI, but decades of accrued complexity that only a truly seasoned engineer can even see. Such nuance cannot be encapsulated in a dashboard.
Aviation and High-Reliability Organizations: When Safety Lives Between the Metrics
Few industries are more serious about measurement than aviation. Aircraft systems generate vast amounts of operating data, which are measured and remeasured with exceptional rigor.
Flight schedules are closely monitored.
Safety incidents are rigorously documented.
Procedures are formalized.
Performance is routinely assessed.
Nevertheless, aviation professionals know a secret that few leaders recognize: if no safety event has occurred, it doesn’t mean that safety is present.
The organization could show perfectly good safety performance on its metrics, while easily and effectively hiding its own psychological limitations regarding the issue from itself. Pilots may be unwilling to speak about hazards. Maintenance crews might be reluctant to acknowledge near-miss events because they might feel embarrassed or appear incompetent. A Junior team member might have no idea how the process could be made safe and would have no incentive to raise such concerns.
The overall safety indicators might be beautifully and blissfully green right up to the day of a disaster. When such a disaster occurs, the investigators rarely attribute the failure to a single additional KPI or a particular missing index or tool. Instead, they generally attribute the disaster to communication failures or human errors that may have predated the measurement systems.
The organization didn’t lack yet another metric – it lacked a deeper understanding of the system it thought it had measured.
In all 4 of the industries we’ve showcased, the situation repeated itself:
Disasters occurred
Organizations assumed they must be missing a metric
The metrics were expanded
They were surprised again and again
Such scenarios don’t happen because leaders lack the intelligence to be able to design and implement appropriate measurements, but because no complex system, as a matter of principle, can ever be as large as its own representations of itself. No dashboard can become the entity being measured.
When organizations reach that stage, they focus so intently on measurement devices that they neglect the journey and miss where they are going.
Final Thoughts
Looking Through the Windshield Instead of at the Dashboard
Never before has there been so much readily accessible operational information available. Never before has there been a way to see performance across continents in real-time, spot emerging trends within minutes, or turn vast amounts of raw data into simple, intuitive pictures. Organizations have become faster, more coordinated, better informed, and ultimately, more agile.
Problems arise only when dashboards stop being tools for understanding and start becoming substitutes for it. This is, ultimately, what’s dubbed the Completeness Fallacy.
It has nothing to do with whether KPIs are useful or useless, or whether measurement systems need upkeep or not. Rather, it is the false belief that uncertainty can eventually be engineered out of the organization by judiciously selecting enough indicators.
In modern-day organizations, every KPI answers one question but begs another. Every bit of extra visibility alters behaviour, thereby changing what the measurement actually means. Performance measurement isn’t a race towards a state of completeness. It’s a never-ending conversation between what can be measured and what requires only observation, judgment, curiosity, and experience.
Dashboards are the thing that should inform a conversation, never replace it. Think of them as a car windshield. It has never been designed to capture every detail of the road ahead. It won’t reveal what is hiding behind every building, nor will it show every hidden pitfall lurking around the bend. Its purpose is to provide just enough visibility to help us navigate while, at the same time, reminding us that the road itself deserves our attention.
Modern performance dashboards have precisely the same role. They are not the organization, but the windows through which we may view organizations in a richer, more dynamic manner than any collection of KPIs can adequately convey.
If we start to look at the windshield instead of through it, we are liable to lose sight of where we are going, and that may well be the most profound irony of modern performance management:
We have never measured more, and with better tools. At the same time, we have never depended more on the interpretation, conversation, experience, and context provided by the people powering the soul of our workplaces.
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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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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.
Every day, organizations face difficult questions: Which KPIs truly matter? How can strategy move from planning to execution? What practices lead to stronger organizational performance? The articles in this roundup address these and other pressing challenges.
Based on the latest readership data, these are the 10 Performance Magazine articles readers returned to most often so far in 2026.
If you’re looking for practical insights to strengthen strategy, performance measurement, and decision-making, this collection is a good place to start.
1. How Apple Uses the Balanced Scorecard
If your KPIs fail to tell the full story, it may be time to rethink how you measure performance. Learn how Apple uses the Balanced Scorecard to translate strategy into measurable outcomes across the entire organization. Read the article here.
2. 5 Levels of Organizational Maturity in Performance Management
Why does your organization feel busy but still struggle to turn performance data into meaningful results? Discover the 5 levels of organizational maturity—from inconsistent processes and unclear KPIs to an optimized, strategy-aligned system that drives continuous improvement. Check out the article here.
3. KPI of the Day: Utilities: % Electricity supply not restored within 2 hours
Power outages are frustrating—but the real performance issue is how quickly you restore them. This KPI measures the % of electricity interruptions lasting beyond 2 hours, helping utility providers identify restoration bottlenecks, strengthen response strategies, and improve reliability for customers. Find out more about this KPI here.
4. Is Benchmarking Worth a Company’s Investment and Time?
Measure. Compare. Learn. Improve.
Benchmarking transforms isolated performance numbers into meaningful reference points—helping organizations uncover gaps, learn from best-in-class practices, and make smarter improvement decisions. See what our performance management expert says about it here.
5. Southwest Airlines: From Benchmarking to Benchmarked
Your competitors aren’t always your best teachers. Southwest Airlines looked to NASCAR pit crews for lessons in speed, task clarity, and teamwork—then used those insights to transform its turnaround time and become a benchmark itself. Get the details here.
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Whether you’re discovering these articles for the first time or revisiting them for fresh ideas, we hope this collection helps you tackle today’s performance challenges with greater clarity and confidence. If you have any questions or ideas for future articles, don’t hesitate to reach out: [email protected]
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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