We see the world not as it is but as we are – or as we are conditioned to see it. - Stephen R Covey.  It’s well understood that our prejudices colour our views. Perception interferes with objectivity. Behavioural scientists have come to conclude that we see what we believe more often than we believe what we see! Organisations have to contend with this problem every day even as they strive to mobilise the energies of a diverse set of people to achieve a common goal. To solve this issue of differentiating between truth and perception, managers tend to rely on data. This faith in data is deep-set, as evidenced by the overused line, “In God we trust, and for everything else, get the data.” Unfortunately, data does not help differentiate between truth and perception. For instance, two managers might have two different interpretations of the same data, or two opposing views can have fully reliable data supporting them.  So, data, at best, helps validate the existence of an entity, but decisions are made based on the inferences (or deductions) from the data created in the minds of the managers. To illustrate this, let’s look at the specific issue of poor deliveries by a vendor, a common complaint of plant managers at manufacturing companies. It is very likely that an attempt to zero down on the root cause of this problem leads a manager to conclude that the root cause is the vendor’s “bad attitude”. Consequently, the manager may proceed with decisions and actions like counselling the vendor or even replacing him. What is important to note here is that while there is data to support the fact that deliveries were late, the conclusion is purely an inference that  may or may not be accurate. There isn’t much by way of figures or numbers to evidence the “bad attitude”, so the decision was very likely coloured by the manager’s previous experiences either with this or other vendors!  Why are many human decisions thusly, often led by perception-based inferences? Behavioural scientists have pointed out that we can do two kinds of thinking as a species - slow and fast. We are capable of slow, rigorous analytical evaluation. However, since the human species has often had to act quickly in the face of danger, we have also developed the ability to make quick decisions relying on intuition and heuristics. The latter method is obviously not very logical and subject to bias but is sufficient to address immediate dangers.  While such urgent situations and dangers in our environment may have come down, human beings, during decision-making, often prefer taking the ‘lazy’, shorter route to conclusions - hopping on biases and leaping off past experiences. A decision made in a rush can have long-standing consequences for organisations. So, managers must be trained to use a more analytical approach to decision-making. For this, managers must escape the trap of erroneous mental models and biases. The good news is that our minds can be trained to do so if we keep three axioms in mind while analysing problems:1. Organisations are interconnected systemsIn an organisation, issues that crop up are rarely isolated incidents or problems. They may not even have their roots in the same department. Therefore, true power for a decision maker in this context lies in the ability to appreciate the complete pattern of interlinkages in the organisation and the cause-effect connections between its myriad parts. The problem of being able to grasp interconnections becomes even more difficult when cause and effect form non-linear loops, especially when the two are separated by time and space. For example, when a cause in one department has an impact on another, even after a considerable amount of time, the connection between the two can remain undetected.  This knowledge of being able to see interconnections in and of itself is far more valuable than intimate knowledge of the many individual parts of a system. Managers, however, are trained to be specialists in their respective departments and areas of work. Moreover, as bosses of their domains, teams look up to them to rule and protect their turf, to shield them from blame. This culture of protecting self-interest compartmentalises the managers’ thinking and clouds their view of the big picture.  <figure class="image"><img style="aspect-ratio:16668/15082;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//gb-blog-8-img-1-01-1738081276_1777352767.jpg" width="16668" height="15082"></figure>This failure of managers to take a systemic view can be a major hurdle to good decision-making and can hurt not only the department but the whole organisation. So, a simple and effective way to train the mind is to use computer simulators– these can help collapse time and space dimensions (a real-life day can be modelled as a second in computer time and a manager on a simulator can visualise the effects spanning across departments) to make managers aware of the non-linear loops of cause and effect across departments.  2. Along with data, always comes noiseThe other aspect which interferes with clear thinking is ‘noise’. While you need information to make decisions, differentiating between relevant information and irrelevant noise is difficult. Sorting between the two is more complicated because our perception can amplify noise and make it seem relevant based on circumstances. Here’s an example, when you’re running late for a meeting and are slowed down by traffic, you are likely to notice every stop sign and every bad, congested patch on the road. And when you reach the meeting, you will complain about the horrid conditions of the roads. However, had you started early, you would have paid scant attention to these minor obstacles along the way.  Similarly, managers struggling with poor <a target="_blank" rel="noopener noreferrer" href="https://www.vectorconsulting.in/en/research-and-publication/blogs/on-time-delivery-a-misnomer-in-new-product-development-2/">on-time delivery performance</a> could perceive routine issues such as quality problems, absenteeism, machine breakdowns and so on as major roadblocks. When orders are running late, every ‘bump on the road’ created by quality issues, or a machine breakdown can suddenly loom larger. Distracted into trying to solve these noise-level issues can lead to the real problems being ignored – long queues of raw material waiting to be processed and the consequent wastage of buffers. The longer the wait at upstream work centres, the shorter the time available to deal with natural uncertainties in the downstream work centres. It is not that managers are unaware of the problems with waiting time but when making decisions, the real issue often gets drowned out by the amplified noise of the minor ones.  3. Falsification, not verification, is the true test of a hypothesisTaking the system view allows you to develop hypotheses that  span across space and time. Being wary of ‘noise’ can help sort between relevant and irrelevant information that can go into these hypotheses. However, it is also important to be aware of confirmation bias while testing these hypotheses. The best way to avoid it is to take the approach of falsification. The falsification method involves identifying any contrary observation to prove a hypothesis wrong. This is because even if there are many observations that support it, this cannot prove a hypothesis right. So, the best way to check reality is to subject it to tests of failure. If a hypothesis has survived many tests of failure, it can be assumed to be suitable for the time being. As managers, we must develop the ability to attempt and disprove our hypothesis rather than trying to confirm it.  If managers can keep these three axioms in mind when it comes to decision-making, they can anticipate and avoid traps of incorrect causality, amplified noise and confirmation bias. Without appreciating  these principles, data alone can be, at best, a crutch and, at worst, a handicap while trying to decipher between reality and perception. With the right management consulting training to think methodically and analytically, organisations can harness the wealth of intuition in their managers and use available data to provide the right signals.   It is time companies invested in Thoughtware.

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Problem: Managers often struggle to separate fact from perception. Cognitive biases creep in, leading to decisions based on inaccurate inferences. While data is trusted as the safeguard against bias, it can still mislead—since multiple, even conflicting, hypotheses can appear data-backed. Solution: In such cases, managers need a more rigorous analytical approach. This means:<ul style="list-style-type:square;"><li>Mapping interconnections within the organisation to filter out noise in the data.</li><li>Using falsification, not verification, to test hypotheses and counter confirmation bias.</li></ul> Benefits: By adopting a systems-thinking approach, managers can minimise bias, uncover true root causes, and make sharper, data-informed decisions—driving both organisational efficiency and decision accuracy.

Key Takeaways

This insightful article reveals how biases and preconceived notions often colour managerial judgments, overshadowing objective data analysis. It also suggests three steps for more effective decision-making.

Going beyond data for decision-making

We see the world not as it is but as we are – or as we are conditioned to see it. - Stephen R Covey.   It’s well understood that our prejudices colour our views. Perception interferes with objectivity. Behavioural scientists have come to conclude that we see what we believe more often than we believe what we see! Organisations have to contend with this problem every day even as they strive to mobilise the energies of a diverse set of people to achieve a common goal.  To solve this issue of differentiating between truth and perception, managers tend to rely on data. This faith in data is deep-set, as evidenced by the overused line, “In God we trust, and for everything else, get the data.” Unfortunately, data does not help differentiate between truth and perception. For instance, two managers might have two different interpretations of the same data, or two opposing views can have fully reliable data supporting them.   So, data, at best, helps validate the existence of an entity, but decisions are made based on the inferences (or deductions) from the data created in the minds of the managers. To illustrate this, let’s look at the specific issue of poor deliveries by a vendor, a common complaint of plant managers at manufacturing companies. It is very likely that an attempt to zero down on the root cause of this problem leads a manager to conclude that the root cause is the vendor’s “bad attitude”. Consequently, the manager may proceed with decisions and actions like counselling the vendor or even replacing him. What is important to note here is that while there is data to support the fact that deliveries were late, the conclusion is purely an inference that  may or may not be accurate. There isn’t much by way of figures or numbers to evidence the “bad attitude”, so the decision was very likely coloured by the manager’s previous experiences either with this or other vendors!   Why are many human decisions thusly, often led by perception-based inferences? Behavioural scientists have pointed out that we can do two kinds of thinking as a species - slow and fast. We are capable of slow, rigorous analytical evaluation. However, since the human species has often had to act quickly in the face of danger, we have also developed the ability to make quick decisions relying on intuition and heuristics. The latter method is obviously not very logical and subject to bias but is sufficient to address immediate dangers.   While such urgent situations and dangers in our environment may have come down, human beings, during decision-making, often prefer taking the ‘lazy’, shorter route to conclusions - hopping on biases and leaping off past experiences. A decision made in a rush can have long-standing consequences for organisations. So, managers must be trained to use a more analytical approach to decision-making. For this, managers must escape the trap of erroneous mental models and biases. The good news is that our minds can be trained to do so if we keep three axioms in mind while analysing problems: 1. Organisations are interconnected systemsIn an organisation, issues that crop up are rarely isolated incidents or problems. They may not even have their roots in the same department. Therefore, true power for a decision maker in this context lies in the ability to appreciate the complete pattern of interlinkages in the organisation and the cause-effect connections between its myriad parts. The problem of being able to grasp interconnections becomes even more difficult when cause and effect form non-linear loops, especially when the two are separated by time and space. For example, when a cause in one department has an impact on another, even after a considerable amount of time, the connection between the two can remain undetected.   This knowledge of being able to see interconnections in and of itself is far more valuable than intimate knowledge of the many individual parts of a system. Managers, however, are trained to be specialists in their respective departments and areas of work. Moreover, as bosses of their domains, teams look up to them to rule and protect their turf, to shield them from blame. This culture of protecting self-interest compartmentalises the managers’ thinking and clouds their view of the big picture.  <figure class="image"><img style="aspect-ratio:16668/15082;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//gb-blog-8-img-1-01-1738081276_1777352748.jpg" width="16668" height="15082"></figure>This failure of managers to take a systemic view can be a major hurdle to good decision-making and can hurt not only the department but the whole organisation. So, a simple and effective way to train the mind is to use computer simulators– these can help collapse time and space dimensions (a real-life day can be modelled as a second in computer time and a manager on a simulator can visualise the effects spanning across departments) to make managers aware of the non-linear loops of cause and effect across departments.   2. Along with data, always comes noiseThe other aspect which interferes with clear thinking is ‘noise’. While you need information to make decisions, differentiating between relevant information and irrelevant noise is difficult. Sorting between the two is more complicated because our perception can amplify noise and make it seem relevant based on circumstances. Here’s an example, when you’re running late for a meeting and are slowed down by traffic, you are likely to notice every stop sign and every bad, congested patch on the road. And when you reach the meeting, you will complain about the horrid conditions of the roads. However, had you started early, you would have paid scant attention to these minor obstacles along the way.   Similarly, managers struggling with poor <a target="_blank" rel="noopener noreferrer" href="https://www.vectorconsulting.in/en/research-and-publication/blogs/on-time-delivery-a-misnomer-in-new-product-development-2/">on-time delivery performance</a> could perceive routine issues such as quality problems, absenteeism, machine breakdowns and so on as major roadblocks. When orders are running late, every ‘bump on the road’ created by quality issues, or a machine breakdown can suddenly loom larger. Distracted into trying to solve these noise-level issues can lead to the real problems being ignored – long queues of raw material waiting to be processed and the consequent wastage of buffers. The longer the wait at upstream work centres, the shorter the time available to deal with natural uncertainties in the downstream work centres. It is not that managers are unaware of the problems with waiting time but when making decisions, the real issue often gets drowned out by the amplified noise of the minor ones.   3. Falsification, not verification, is the true test of a hypothesisTaking the system view allows you to develop hypotheses that  span across space and time. Being wary of ‘noise’ can help sort between relevant and irrelevant information that can go into these hypotheses. However, it is also important to be aware of confirmation bias while testing these hypotheses. The best way to avoid it is to take the approach of falsification. The falsification method involves identifying any contrary observation to prove a hypothesis wrong. This is because even if there are many observations that support it, this cannot prove a hypothesis right. So, the best way to check reality is to subject it to tests of failure. If a hypothesis has survived many tests of failure, it can be assumed to be suitable for the time being. As managers, we must develop the ability to attempt and disprove our hypothesis rather than trying to confirm it.   If managers can keep these three axioms in mind when it comes to decision-making, they can anticipate and avoid traps of incorrect causality, amplified noise and confirmation bias. Without appreciating  these principles, data alone can be, at best, a crutch and, at worst, a handicap while trying to decipher between reality and perception. With the right management consulting training to think methodically and analytically, organisations can harness the wealth of intuition in their managers and use available data to provide the right signals.    It is time companies invested in Thoughtware.