Making decisions about large, complex engineered systems can be very difficult. Suppose that the University of Gnomon leadership (like the Dean of the College of Engineering or the Provost) asked for help in measuring the quality of instruction from professors in the classroom. Currently the University does end-of-course surveys from students, asking questions about the class. The thought here is that if the student feels like he/she has really learned a lot from the class, then the survey results should indicate that and should therefore be relatively correlated to quality instruction from the teacher. However, remember that correlation doesn’t always mean causation, so student surveys could be misleading. For example, if I never graded homework, gave group tests, graded easily, and gave everyone an “A,” then I may get outstanding reviews on the survey. But in reality, many of you may not have learned a thing in the class. But overall, though, surveys are probably relatively reflective of quality instruction. Explain two other ways you think the University of Dayton could measure the quality of instruction from professors in the classroom. Discuss some pros and cons associated with your measurement, and explain why you think these would help University leadership make better decisions about teachers and classes.
www.insuranceday.com| Wednesday 18 April 2012
You can measure anything
here is one unproductive
and pervasive management
organisations back: the
belief some of the most important
factors in the organisation are
Today’s businesses are likely to
cite quality, reputation or agility as
their key strategic drivers – all supposedly
impossible to measure.
Every day, decision-makers
commit their organisations to
investments, structures and markets
via decisions that supposedly
serve these so-called intangible
qualities yet include no relevant
information. However, anything
that matters can be measured.
What measurement means
A measurement is a quantitative
reduction in uncertainty based on
observation. It does not have to
reduce uncertainty to zero.
Sometimes even a marginal
reduction in uncertainty is helpful
to decision-makers. Furthermore,
major reductions in uncertainty
can be obtained by asking some
In a famous example, the Nobel
prize-winning scientist Enrico
Fermi asked his Chicago students
to estimate the number of piano
tuners in the city.
He showed them by breaking the
problem down into chunks about
which they could make reasonable
estimates, they could come up with
a rough estimate. The students
would come up with answers in the
range of, say, 20 to 200, and Fermi
would reveal the answer was 45. So
they were not far wrong – much
closer than they guessed. We know
more than we think we do.
The ’rule of five’
“We don’t have the data” is a common
reason given for perceived
immeasurability. But if the great
scientists of history were stumped
So think about how you can
actively collect just a random sample
Consider a contemporary classic
of “soft” goals: customer experience.
One way to measure this is to
ask five random customers what
they think of your service.
Not five million. Just five.
Here’s how the “rule of five”
works. Say you want to consider
telecommuting for your business.
Make five random calls to the staff.
Ask them how long their commute
is. Say you get the values 30,
60, 45, 80 and 60 minutes. Take the
highest and lowest numbers in the
sample, which are 30 and 80. Now,
there is a 93.7% chance the median
of the entire workforce is between
those two numbers.
There are four main reasons why
so-called intangibles remain unmeasured.
First, people assume
their problem is unique. But it is
much more likely your problem
has been solved before, perhaps in
another industry. This is why
effect on businesses: it is an opportunity
to transfer solutions across
Second, people think they do not
have the required data. However,
organisations record all kinds of
data that can be relevant to your
needs. Executives have powerful
tools to extract data, they just need
to ask the questions.
you think. The rule of five really
works, even though it is counterintuitive
– even to some statisticians.
You do not need lots of data,
but relevant data. When you know
nothing, you do not need much
additional data to tell you something
you did not know before.
And last, new sources of data are
much more accessible than you
think. Assume there is a simple
means of measurement you can
implement economically and
quickly. It is more a matter of
resourcefulness than any real
obstacle of measurement.
Measuring what we mean
One of the most powerful effects of
changing our attitude to so-called
intangibles is the new light cast on
our business goals. By looking for
meaningful measures, we improve
our understanding of the goals as
Take IT security, for example.
Security is one of those things everyone
wants lots of, but few know
how to measure.
When I work through the concept
of security with organisations,
they invariably identify risk with
some specific list of events which,
in turn, cause loss of data, legal liabilities,
loss of productivity and
more. These effects all have magnitudes
that can be estimated.
For some reason, the “brand
damage” effect of a security breach
is seen to be particularly hard
of brand damage in your industry
is public knowledge and is
reflected in stock prices, market
share or legal cases. Everything is
observable once it is defined.
There is more to measurement
than I can present here, but I hope I
have convinced you that decisionmakers
do not have to fly blind.n
Douglas Hubbard is president of
Hubbard Decision Research
and his website is
One of the most striking books of
the past few years is Douglas Hubbard’s
How to Measure Anything:
Finding the Value of Intangibles in
Business. It is one of those books
that makes you see the world differentlyanditchtoputitsmessage
his ideas in the piece opposite, but
I urge you to read the book.
The insurance industry is built
on the practices Hubbard
describes. Insurance creates
and applies measures for risk, a
a new class of risk, it invents
an elegant means of measuring it.
But we do not seem to apply the
same kind of thinking in our information-management
Historically, the focus of
ACORD has been on enabling the
but ACORD standards
have always played a significant
role in systems integration and
So we are embarking on a
research effort to improve the
metrics we have in place today
and also provide new metrics on
industry standards for decisionmakers.
I see this aspect of our
work growing in importance as
information specialists continue
to improve their business value
and amplify the power of data to
energise the business.
Gregory Maciag can be reached at
firstname.lastname@example.org People think they do
not have the required
organisations record all
kinds of data that can be
relevant to your needs.
powerful tools to extract
data, they just need to
ask the questions
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