Principle 5: Improved Decisions (Item 3)
Effective use of facts, data and knowledge leads to improved decisions.
It is very useful to remember that all strategies are `experiments'
– ie, an approach to reach a desired outcome. No matter how tried
and true it is, who has done it before or where it came from, every
time you apply a strategy, it is an experiment to see if you can reach
your objective. When you think of strategies as experiments, you approach
them differently. You don't cling to them and you want to know if they
are working – are they achieving the results you want? What is
stopping them from working?
Just because something is popular, does not mean it works. Many strategies
do not work, yet people continue to adopt them. For example, eating
carbohydrates to get thin is the strategy most people use following
advice from nutritionists. However, they continue to get fat. Overwhelming
failure has not stopped the experiment.
If what you are doing is not leading you towards your Goal, it is leading
you away from it. Or, it is consuming your scarce resources and stopping
you from working towards your Goal.
Why do you need data and information at all when you make a decision?
Aren't your instincts good enough?
The business world is becoming more scientific as the better companies
take on a more scientific approach to their decision making. Every
strategy you choose, every decision you make, is only an experiment
that you hope will take you towards success. Can you afford to do
the experiment without finding out if it works? Of course not! You must
measure to see if it worked; to find out if you made a successful choice.
In the scientific world, you base your decisions and questions on data.
Which comes first the data or the question you need answered?
In the old thinking of science, the scientist begins by gathering data
– carrying out careful experiments and making useful observations.
These findings would be systematically recorded and published and in
the course of time scientists in that field notice things about the
data. Features would emerge that would let scientists write down hypotheses
— statements of a law like nature that fit all the facts and explain
how they are causally related. The individual scientist would then try
to confirm these hypotheses by finding evidence to support them.
Such verification would prove the new law. And the existing barriers
of our ignorance would gradually move back.
This process is called induction and was the way science was conducted.
It sounds good doesn't it?
It is still how most companies think about gathering and using data.
The trouble is that it does not work.
It is worth having a brief discussion about the word `law', especially
as we are calling these Principles the equivalent of scientific laws.
A law of nature cannot be broken. A law of society, on the other hand,
prescribes what we may or may not do. It can be broken. If we could
not break it, there would be no need to have it. We do not legislate
against someone being in two places at the same time. A law of nature
is not prescriptive but descriptive. It tells us what happens –
for instance that water boils at 100 C. It cannot be broken because
it is not a command: water is not being ordered to boil at 100 C.
For the new thinking scientist, the question comes first. In fact,
you cannot gather any data unless you know which question you are trying
to answer. Do this. "Look out of your window and write down what
you see". How can you possibly do that with any meaning? You cannot
do it unless you know what question you are answering. Should you be
writing about people, buildings, trees, the color of the sky, the clouds.
What was the topic? What question were you tying to answer?
Asking the question before collecting any data is a huge thinking shift.
However, there is an even bigger one. It is about the type of question
you ask. Is it a question to confirm your belief (the old thinking)
or a question to challenge it (the new thinking)?
The breakthrough to the new thinking comes from the realization that
you can never ever confirm your hypothesis.
You can never ever prove it true.
It does not matter how many times you have seen evidence to support
your belief. For example, suppose you have a belief that the sun revolves
around the earth. Every morning you see the sun rise and move across
the sky. This confirms your belief, doesn't it? You can in fact make
tables to predict with great precision exactly when the sun will rise
on each day for the next 10,000 years. Do any of your precise observations
or predictions make the belief true? Even if you see it confirmed every
day? Does the accumulation of thousands of confirmatory observations
(and this may come as something of a shock) increase the probability
of it being true? No way!!
If you cannot prove your hypothesis true, what can you do? You can
try to prove it false. Although scientific laws are not verifiable,
they are falsifiable. This means that scientific laws are testable in
spite of being unprovable. They can be tested by systematic attempts
to refute them.
This new thinking of science has changed our understanding of our physical
world and provided us with leaps in technology.
It is time the business world applied these concepts to the way that
it makes decisions. It certainly indicates a different type of data
collection.
It means that instead of gathering data to confirm your beliefs, you
should be trying to find data that will refute them – to find data
that will prove your beliefs incorrect. It means floating ideas hoping
to have them shot down. It means deliberately testing the edge of what
you know. That is far too challenging for most people. The easier thing
to do is to only look for evidence that confirms your opinion –
ignoring all contra-evidence. Unfortunately, although it is easier it
does not lead forward.
You will have seen people defend their position even when there is
evidence to disprove that what they have proposed. That type of posturing
is extremely wasteful – it holds you in the past. That might have
been good enough in an old business world. It is not good enough any
more. When you have been proved incorrect, build a new hypothesis.
You should formulate your theory as unambiguously as you can, to expose
them clearly to refutation. Another commonly made mistake is to keep
reinterpreting the evidence to make certain it fits with your beliefs.
You should not evade the unpalatable by rewording, redefining or refusing
to accept the reliability of inconvenient information.
But, don't abandon your beliefs too lightly – make certain they
are tested rigorously.
Suppose we believe that "All swans are white". To refute
this, we should be looking for swans that are not white. If you find
just one black swan – easy to do in Australia – you have disproved
the hypothesis. Alternatively, you can go down the unproductive paths
of "that's not a swan" or the common one of "the person
who saw it has no credibility".
Let us look at water boiling at 100C (212F). When we look for instances
where this does not happen we find several – closed vessels, at
altitude. We could modify our law by narrowing it to be more empirical
water boils at 100C (212F) in open vessels at sea-level atmospheric
pressure. In this way, we might see ourselves pinning down increasingly
precisely our knowledge about the boiling point of water. But that leads
only to `descriptive' statements and we would miss the golden opportunities.
Each time we discovered a contradiction (eg, closed vessels, at altitude)
instead of narrowing our theory, we should ask, "why is it so"?
We are now on the threshold of new discovery. Our new hypothesis should
tell us why water boils at 100C (212F) in open vessels and not closed
ones. The richer the hypothesis is, the more it tells us about the boiling
point of water and enables us to calculate different boiling points.
Instead of less empirical content, it should have very much more. We
should be making predictions, devising confrontations between our beliefs
and testable reality.
When we discover that some things we predicted did not occur, this
adds to our knowledge and we should begin all over again building on
what we now know.
We can never accumulate enough evidence to prove that our theory is
true. At no stage can we ever prove what we know to be true. The history
of science is of disproving what was once known to be true.
What we described above is a fundamental shift that happened in science
during last century. The business world has not yet moved to this thinking,
which is largely why it is still swamped in unusable data. It is time
the business world took a scientific approach to the expensive process
of gathering and storing data. And to the very expensive process of
decision making.
Let us make this personal. Suppose for example that you are diagnosed
with cancer (and we apologize in advance to anyone we offend). At that
point, you are probably not an expert in the treatment of cancer. Your
doctor is very likely to recommend a form of treatment – say chemotherapy.
That recommendation is very likely to be only one of a huge number of
options: radiotherapy, herbal medicine, meditation, positive thinking,
etc. Each of these is a strategy to address your cancer. Even within
each strategy, there are several options put forward by various experts.
All claim success. Which do you adopt? As soon as you adopt one by
itself, to some extent you become a research subject for the person
recommending that form of treatment – their guinea pig. Often the
proponent of one strategy will pour scorn on the others – sometimes
to the lengths of belittling you if you should even think of departing
from the true way – theirs. What should you do? Do them all! At
least do all that you can afford. Even to save their life, most people
have limited resources. Even if you had unlimited money and did them
all, there is no guarantee that you will be successful.
Let us assume you are successful. Do you care which one worked? Does
it matter if you can say, this particular thing worked? We doubt it!!!
What does matter? Three things: that you used many strategies (everything
you and your network could think of); that you knew what success looked
like (no cancer); and you kept measuring your progress to success (cancer
going away).
Footnotes
This material draws on the
concepts of Karl Popper. Bryan Magee's book 'Popper' gives a very useful
summary.
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