My experience with a large Rio Tinto building asset database led me to develop new directions of thought about uncertainty and risk management in data quality for asset management.

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Risk Management and Statistics: Reflections on
a Large Asset Portfolio
Hein Aucamp
WA Integrated Asset Management
WA Integrated Asset Management is an
Infrastructure Asset Management company
serving Local Government and the mining
sector based in Perth, Western Australia.
www.waiam.com.au
Photo by Carlos Muza on Unsplash; other photos from pixabay.
Acknowledgement
Thanks to Rio Tinto Iron Ore Core Services for their kind permission to present this material. I
managed an SPM system containing their residential and commercial building portfolio. I agreed to
refrain from a level of detail which would reveal sensitive information.
Background
This article is based on a presentation I delivered at the IPWEA International Conference in Perth,
Australia, on 23 August 2017.

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The SPM database I managed for Rio Tinto contained building assets of more than a million
components. We can visualise the large scale of the portfolio, which is several times as big as a Perth
inner city council, in this way:
My experience with the Rio Tinto building assets led me to develop new directions of thought. The
large scale of the database introduced an inherent uncertainty into the data. This made me think more
broadly about uncertainty and risk management in data quality for asset management.
Einstein’s definition of insanity
Let’s start with a statement attributed to Einstein, which appears in management books.
Insanity.
Doing the same things
over and over again and
expecting different
results.
This quotation is used to caution managers against desperately repeating past actions that have never
worked in the hope that they will begin to work.
There is, however, one area of human experience where we expect that the same actions will produce
different results. This is the area of uncertainty and probability.
Dealing with uncertainty
When we are dealing with uncertainty, we expect a range of different outcomes for the same action or
the same situation. A series of identical outcomes would be suspicious, and advance knowledge of
these outcomes would be highly suspicious – the kind of instances where sports betting firms would
be investigated.
Here is an anecdotal example of this kind of suspicious regularity:
One day in Naples the reverend Galiani saw a man from the
Basilicata who, shaking three dice in a cup, wagered to

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throw three sixes; and, in fact, he got three sixes right away.
Such luck is possible, you say. Yet the man succeeded a
second time, and the bet was repeated. He put back the dice
in the cup, three, four, five times, and each time he produced
three sixes.
George Polya, Patterns of Plausible Inference, p74
The probability of the dice doing this randomly is less than 1 in 470 trillion. Moreover, the man’s
prior confidence makes us willing to consider alternative explanations for his success – even ones that
reflect on his integrity.
Uncertainty covers events that might or might not happen. Many of these events are neutral
alternatives. But some of these events are adverse: they cause loss, injury, or death. When the
uncertain events which we are trying to manage are adverse, we are in the domain of risk
management.
Prioritisation and kinds of risk
To prioritise our management of risk events, we must determine the impacts of these risks. There are
2 ways to do this:
• Quantitative risk analysis, where the risks are specified as dollar values.
• Qualitative risk analysis, where measures or categories are applied to determine the final risk
ratings. (Users of the NAMS.PLUS toolset will recognise this approach, which is supported
by standards such as ISO 31000:2009.)
When it comes to the kind of risk, it is useful to distinguish between pure risk and business risk. Pure
risk is only adverse, whereas business risk includes favourable as well as unfavourable uncertainties.
The tornado diagram shown below is a useful representation of business risks, which have upsides as
well as downsides.
It is also useful to note the difference between project risk and operational risk. Project risk is
associated with introducing a new situation, whereas operational risk is associated with the situation
after it has been introduced.
A structural analogy of project risk is the stresses in a precast concrete beam while it is being lifted by
crane it to its place in the final structure. On the other hand, operational risk corresponds to the
stresses in the beam when it is functioning in the completed structure.

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A dart and dartboard analogy for risk management
Imagine a situation where a stream of darts is coming towards us. The darts are events. Most are
neutral or good, but some are adverse. We don’t know which darts will come to us. They travel
towards us in mystery boxes, and when they arrive, the boxes open: only at that stage do we discover
whether they spell trouble or not. Waiting for an individual dart can be a nerve-wracking business.
But there is another aspect to this: the dartboard side. This is where all the individual darts materialise.
The essential thing to note about the dartboard is that in effect it receives an aggregate average dart,
which is much better than receiving an adverse individual dart. Because the dartboard receives all
events, its owner experiences an average event. In the diagram above, the multi-colouring of the dart
on the right shows its average nature; its larger size shows its aggregate nature.
As one simple example of this analogy applied to risk management, consider the insurance industry.
The insurer owns the dartboard which receives many darts, whereas you will receive only one. (Your
dart might be a crash or non-crash event relating to your car.) The insurer accepts your dart in advance
(without knowing whether it will be good or bad) and sells you an average dart.

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So whether or not you experience a severe loss, you pay for the smaller average loss. You also pay a
mark-up to make it a viable business proposition for the insurer. And there are safeguards such as an
insurance excess to curb your temptation to irresponsibility – known as moral hazard in the insurance
industry.
The insurer on the other hand receives enough money to pay for the loss of all the adverse events, and
a bit more to make a profit.
Sampling
To use the language of the dartboard analogy: how many darts are required to understand the
dartboard? At one stage the life assurance industry would undertake sampling by noting the birth and
death dates on tombstones to quantify life expectancies and to be able to sell products.
We use sampling for 3 reasons:
• When there are too many items to measure them all.
• When it is too difficult to measure them all.
• When it is impossible to measure them all: e.g. the reliability of an explosives detonator can
be measured only destructively: if you measured them all, you would destroy them all.
When drawing conclusions about a population based on a sample, you have to consider certain things
(this is a bit of a simplification):
• The effort of sampling must balance the risk of drawing the wrong conclusion.
• An alpha error (false positive) is when your sample leads you to accept something as being
true of the total batch which is in fact false.
• A beta error (false negative) is when your sample leads you to reject something as being true
of the total batch which is in fact true.
• A p value reveals the confidence you have in your conclusions based on sampling.
The science of statistics has a well-defined and rigorous body of knowledge for hypothesis testing and
sampling.
Charles Babbage once used his detailed knowledge based on sampling to challenge a scientific
inaccuracy in a poem by Alfred Tennyson (account from Simon Singh, The Code Book, p77).
Alfred Tennyson had written this:
Every moment dies a man / Every moment one is born
Charles Babbage felt the need to challenge this statement. He wrote to Alfred Tennyson, suggesting
an amendment and clarifying it with a parenthetical explanation:
Every moment dies a man / Every moment 11
/16 is born
(The actual figure is so long I cannot get it onto a line, but I
believe the figure 11
/16 will be sufficiently accurate for
poetry.)

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(From my vague memories of things such as iambic pentameter from sonnet analysis in my high
school English literature, I couldn’t help thinking that such an amendment might be hard to fit into a
poetic rhyme scheme.)
Self-referential statistical techniques
When you are dealing with large datasets it is useful to have techniques which allow you to draw
conclusions about the quality and risk profile of your data simply by examining the dataset itself. This
is especially true when you don’t have external benchmarks.
There are simple and more sophisticated self-referential techniques: some of my other LinkedIn
articles provide additional information:
• Simple sampling, where we keep 2 totals: items examined, and items defective.
• Double sampling: where we keep independent totals of defective items and use a Bayes
calculation to estimate total defects. Please see my SlideShare articles on LinkedIn if you
would like more detail on the combinatorial mathematics involved.1
• Seeding, which I mention in my articles covering double sampling.
• Frequency analysis, where we can determine whether a characteristic is continuing or ending.
Please see my LinkedIn article entitled Federer, Nadal, Djokovic and Frequency Analysis in
Asset Management Quality Assurance for more details.
Varying formality of statistical techniques
It is also useful to have scalable forms of our statistical techniques. We don’t always have the luxury
of time or of precise data to apply rigorous analyses.
• At the formal extreme, we would process the data and analyse the characteristics in Excel or
MiniTab.
• The next step down is an approximate analysis where we have an idea about the scale of a
measure. Perhaps we know that the average is probably in a band, which allows us to make a
quick calculation of an upper and lower bound.
• And the most informal approach is measures in the crude form of simple questions. I
discovered this myself: a series of simple questions allowed us to decide the likelihood that
we had encountered the last show-stopping issue in a dataset. Please see my LinkedIn article
entitled Federer, Nadal, Djokovic and Frequency Analysis in Asset Management Quality
Assurance for more details.
I found this informal approach confirmed during my recent re-reading of the Theory of Constraints:
That’s how Jonah knew. He was using the measurements in
the crude form of simple questions to see if his hunch about
the robots was correct…
Eliyahu Goldratt, The Goal, p66
1
https://www.slideshare.net/HeinAucamp/estimating-the-total-defects-in-a-software-project-before-competion
and https://www.slideshare.net/HeinAucamp/maths-background-to-estimating-total-defects-in-a-software-
project.

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Einstein’s definition of insanity?
As a final ironical underscoring of the uncertainty of our subject, there is considerable doubt whether
Einstein ever coined this definition of insanity.
??????
Insanity.
Doing the same things
over and over again
and expecting different
results.
??????
Data Science informational freebie
At conferences I like to provide the audience with something useful for their own information or
development. This time I mentioned the Microsoft Business technology stack, which has some freely
downloadable tools, and which supports data science, data mining, and smart reporting. As you might
know, a technology stack is an assembly of technologies to provide an end-to-end solution.
Power BI Desktop, for example, is a free download and an excellent visualisation tool. The image
below shows Power BI using sample demonstration data provided by obviEnce ©.

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A final question about casinos
At the end of my presentation, someone asked a light-hearted question about how we could apply this
information to casinos. (He asked this because the conference dinner was at a casino: he wanted to
remind us not to go to the wrong venue.)
For many reasons, risk avoidance is the best form of risk management for the uncertainties inherent in
the casino business. The owners of the dartboard are generating the darts; you are voluntarily
exposing yourself to the darts. This is different from the insurance industry, which protects you from
darts which you would have to face anyway, at greater risk on your own.
But my response to the question about casinos was something I believe was said by the late Ronnie
Corbett, of the comedy show The Two Ronnies:
There is a foolproof way of coming out of a casino with a
small fortune: you go in a with a large one.