**cover picture:An insight is something that is NOT derived from current data; it is not a conclusion one arrives at based on past experience; one sees connections and relations before these are proven to be theoretically correct afterwards. (Pixabay)**

## Introduction

In trying to save a business or operation that has problems, instead of trying to solve the problems by analyzing the current situation, the insightful thinker directly sees an ideal model (M) for the business/operation. He then insightfully recognizes (1) THE metric (m, be it profit or efficiency) which can best measure the success (S) of this ideal model; (2) the different factors that contribute or constitute towards S. (A contributing factor, say x, is such that S can be expressed (in terms of m) as a function of x. Constituting factors, say a, b and c, can be positive or negative. S is the sum of a, b and c or their multiples.) Together, the contributing and constituting factors are called Factors; (3) the connections (the connectors) among these Factors, if any; and (4) the Basic Building Blocks (BBB’s, the most basic set of structures in a system on which the whole system can be built). If the system is a segmented one, then there is a different set of BBB’s for each segment. If the segments, in some ways, are related to each other, this means they share some common BBB’s. But the segments, if they are segments, do not have identical sets of BBB’s.

Together (the metric, the Factors, the connectors and BBB’s) make up the supporting elements of M.

## The relations among the supporting elements

For any given system, there is only one set of BBB’s (segmented or not). This is what makes the system itself.

For a given system, we can measure it in any metric we want, although it is meaningless to measure something by a metric that is irrelevant. For any chosen metric, there are its Factors and the Factors’ connectors (if any).

For the insightful thinker, once he sees, by intuition, his ideal model, he needs to recognize, by intuition, a metric what is most relevant in measuring the success of his model. With this metric set, he must be able to recognize, by intuition, the metric’s Factors and their connectors. If he can also recognize, by intuition, the BBB’s of his ideal model, the BBB’s will help him realize internal relations within the model. Knowing the internal relations within his model will help him come to grip of the Factors and connectors more readily.

## The nature of insights

The important point here, for this insightful approach towards problem solving, is that the thinker insightfully sees the ideal model just by taking a look at the problematic model. While the truth of the matter is that for any model that makes sense (coherent within itself), there have to be a best answer to what the supporting elements are, besides insightfully seeing the ideal model, the insightful thinker’s talent is to also insightfully recognize what the supporting elements are (the metric, its Factors and connectors and, hopefully, the BBB’s too). This is because later on when M is built to operate for real, the building is based on the supporting elements.

The whole insightful thinking approach is top-down: one already has the answer or the solution (the ideal model) by intuition. He may even insightfully recognize all the supporting elements that uphold this model. Then, if his insights are correct, two things need to happen: (1) a theoretical analysis of the model WILL show that the supporting elements of it are those insightfully seen by the thinker; (2) the ideal model, when implemented correctly in accordance to the supporting elements, WILL solve the customer’s problem in the sense it will give optimal results in terms of m.

Another important (and final) part of solving business problems by insightful thinking is to actually build the proposed ideal model. But, if the thinker already has his answers in what the metric, Factors, connectors and BBB’s of his model are, the building of this model is relatively simple.

One cannot jump steps in insightful thinking. For example, although it is obvious that profit is most likely to be ultimate metric to measure the success of a business, if the insightful thinker’s first M is “the shop should upgrade its décor”, then the metric will be something like the “number of customer visits” not profit. The shop décor may eventually impact the profit, but the relationship between décor and profit is complicated: neither can be expressed in terms of the other. Thus, we need to problem-solve by steps: once we have built a model that has helped improve customer visit, we need another model that can optimize profit with customer visit as one of the Factors towards profit.

The other thing that can happen is that it is the customer who specifies the metric, in which case, the process of insightful thinking is the same.

The word “insight” simply refers to a conclusion that one comes up to without first having to go through the normal (logical) process of reasoning including analysis of current data from the existing model. But we must note that in the end, behind whatever we insightfully see, these must be connected theoretically. Nothing is random. And if they are, there is no much point in talking about them in one conversation. It is just that one comes to a conclusion on these relations by insights, not by derivation from current data, nor from one’s accumulated experiences.

Please note that what is written in this article applies equally well to both the business and operational models. In fact, it applies to any situation when one needs to solve a muddled up situation. For simplicity sake, only the word “business” alone is used in my writing here.

## Understanding the root of insightful thinking

There are two kinds of judgment: analytic and synthetic (Kant):

- Analytic: what the conclusion (the judgment) says is already contained in the initial condition: for example, “when hit by a heavier brick, it hurts more”. The fact that “the brick hurts more” is already contained in the initial condition that the brick is heavier. Analytic judgments are necessarily so: a brick with more mass necessarily hurts more. Therefore there is no new knowledge added to the world with an analytic judgment because the judgment is only a derivation from the initial condition.
- Synthetic: two initially not-connected events are synthesized together into one judgment; example: “the apple falls because there is gravity”. The apple’s falling, in itself, is in no way related to whether there is gravity. That there is gravity is an a priori claim. To recognize that there is a connection (synthesis) between “apple falling” and “there is gravity” and then to make it into one judgment (“the apple falls because there is gravity”) is a synthetic judgment. Synthetic judgment gives new knowledge to the world: these are called synthetic a priori knowledge. Eventually, nothing is randomly connected. The common ground-theory behind “apple falling” and “gravity” is the physics law that there is force of attraction between masses. But to realize there is such a force and to connect “apple falling” with it are synthetic judgments.

The word “a priori”:

- Literally it means “before experience” as opposed to “a posteriori” (a knowledge claim that is based on the evidence of the senses or from experience).
- Note that a posteriori knowledge is not the same as analytic judgment. The latter is necessarily so as what is suggested in the judgment is already contained in the initial condition. But a posteriori knowledge (knowledge derived from experience), by itself, is only empirical knowledge which does not have to be necessarily so. An example for a posteriori knowledge is “the sun rises from the East” because I have observed for the last 100 days that it did that. This knowledge, by itself, is not a necessity, i.e. the Sun DOES NOT HAVE to rise from the East on the 101st day unless this claim is backed up by further scientific findings of some causality relations such as the Earth self-rotates on its axis from West to East.
- A priori claims:

o Are NOT derived from experience or experiments

o DO NOT need to be proven empirically

o CANNOT be proven empirically

o But must be able to stand up to experience or experiments

o For a claim that is a law of nature (physical law) or a mathematical model, eventually, one must be able to prove it theoretically.

o Anyone who challenges this claim must be able to provide a counter-example in experience

o These are very powerful claims as they are universal (applicable to all), absolute (there is no other way things can be) and necessary (will definitely happen).

- – Examples of a priori claims are:

o “All men are in search of highest happiness (i.e. what is best to them) in life”

o “God exists” (although the fourth condition that the claim “must stand up to experience” is not acceptable for all of us. However, on the other hand, a counter example that “God does not exist” also cannot be established).

## The word “insight” or “intuition”:

- This refers to the fact that such a claim (the insight or intuition) is NOT a result of the process of reasoning, meaning it is a synthetic judgment. It is, in theory, a priori in nature meaning the insight is not derived from experience or experiment, needs not to be proven to be true experientially (empirically) though it must be able to stand up to attestation in experience and it is universal/absolute/necessary although this is only confined to the realm of the business involved.
- We usually talk about “scientific intuitions” or “business insights”. While a more experienced person is more likely to be insightful (sees in things the contributing/constituting factors and connections among them), a particular insight itself is NOT a result of enough observations nor is it derived from actual data. Almost all of the new scientific inventions begin with a scientific intuition. A scientific intuition (which would have made itself into a hypothesis by the time testing is pending), if correct, has to be empirically attested and proven to theoretically correct afterwards. A business insight, if right, when things are put into implementation accordingly, has to solve the particular business problem that is given.
- No insight or intuition is empirically derived: that is, insights and intuitions cannot be derived or concluded from existing data, nor is it what one concludes from his past experiences.
- To be insightful is a talent. But the following things will help one come to grip of an insight faster:

o Have enough experience but this is only in the sense of being able to notice things in general; nothing comes from nothing and nothing happens without a cause or an association with something else.

o Always think of things in terms of their root causes. For any occasion, good or crappy, think of it in terms of its constituting or contributing factors (positive and negative) and its Basic Building Blocks (BBB’s). The former are necessary factors that make up or destroy the occasion as it is. The later is that set of required structures that is necessary and sufficient to build up the system as what it is. For example, the BBB for a democratic election is that the participation of each voter weighs equally towards the result of the election. This principle alone is enough to build a true universal suffrage system or to verify if an election is such. A universal suffrage can come in different forms of implementation. But the true one, when theoretically analyzed, will show each voter’s vote weighs the same towards the result of the election. Therefore, to measure the success of an election by, say, the number of voters is not a measurement of democracy.

o The point about this is that next time you are asked to think about how to save a crappy business or simply to improve it, think what are the relevant Factors and BBB’s for this kind of business. The ideal model is likely to be built on some of these Factors and BBB’s. An insightful thinker’s talent is to recognize the ideal model, the model’s most relevant metric, the metric’s Factors and connectors and the model’s BBB’s. Furthermore, if the thinker can also recognize the non-Factors, false connections and the non-BBB’s, he can avoid to use them in building the operations of his ideal model.

o An insight is NOT a miracle (something happens out of nothing) nor is it a blind guess. It is someone being able to connect things from the unobvious and to recognize the BBB’s on which alone the whole system can be built. At the bottom, things have to connect for any system to be functional. When broken up, any system can be rebuilt entirely from the set of BBB’s alone though the set can be big in size or segmented. It is just that the insightful person is able to connect things and recognize the BBB’s without first having to go through and prove them to himself. As a very simple example of this, two happenings with overlapping Factors are likely to be connected without first having to understand in what ways they are connected. For two systems sharing some common BBB’s, one of them can be partially rebuilt from the other (the reverse may not be true). And if an insight is correct, all things forced connected in the insight will prove to be connected later on under the scrutiny of reason as well. And if the set of BBB’s is correct for a system, the authenticity of this system can be definitively identified upon analysis of the system in terms of the BBB’s.

## Conclusion

Once you think you have an insight, you do not need to positively prove that you are right from existing data. You cannot. The more different your insight is from the current existing business model, the more you cannot prove your insight from the current data. If your insight is 180 degree opposite to the current crappy business model, an analysis of the actual data, which has to be in support of the existing crappy business model (because these data come from that crappy model), can only prove itself to be contrary to your insight. But, unfortunately, in most cases, your insight is sort of different (but not 180 degree different) from the current crappy model. In that case, your analysis would not be conclusive in any useful way.

The other thing that can happen is that what you are trying to analyze from the current data (hoping to draw some useful conclusions from it) are only around some irrelevant factors, i.e. these are not contributing or constituting factors towards the model built on your insight. They may not even the Factors for the failure of the existing crappy model. Or if your analysis is not scrutinizing the business (ideal or current) in terms of its BBB’s, but you are just focusing on any one piece of data in it, such analysis, even if correctly done, are useless and non-conclusive.

The next challenge once you have your insight is to actually build a business model in accordance to your insight. For this, again, thinking around the Factors and BBB’s helps. And, as mentioned above, if along with the ideal model, you also have insight about the Factors and the BBB’s, the building of the actual business model in accordance to the insight is relatively easy.

Each business problem can have many insightful claims that can help improve the problem. (Though for any given business problem, there can be only one insightful claim that can absolutely optimize the business as the customer wishes in terms of the metric specified.) For each insightful claim, absolute or not, identify its contributing/constituting factors and BBB’s and build the new business model around them.

For each desired business with a definite metric set ahead, there exists a set of contributing/constituting factors (variables that can affect the outcome of the business as measured against the set metric). For example, a Factor in one business case can be the shop décor, in another can be number of stores…But the point here is be able to spot THE Factors for your business case. And when the BBB’s for this system is identified, building a system using no more and no less of the BBB’s will give you nothing else but the desired system. And if you can also identify the contributing/constituting factors towards the failure of the crappy business model, whatever insight you come up with, it needs to avoid these factors.

Insightful thinking is synthetic judgment. It takes courage to connect two seemingly unconnected events. However, insightful thinking alone can give you hope to solve a muddled situation because such a solution cannot come from analyzing or derivations from the current data. The fact that you call the situation muddled is exactly because you cannot find the heads and tails of things. Then what is there to analyze? Any analysis starting from a random end, even when the analysis itself is carefully and correctly done, will only lead you to the other end of this random end. It leads you from nowhere to another nowhere.

An insight is something that is NOT derived from current data; it is not a conclusion one arrives at based on past experience; one sees connections and relations before these are proven to be theoretically correct afterwards.