Three lessons from spaghetti

Sometimes an everyday word becomes a rich metaphor that can teach us a lesson by the visual analogy that it embodies.

Take, for example, the following three lessons we can learn from spaghetti.

Firstly, consider spaghetti communication, that is communication where the source (sauce?) of the communication may be unclear, where different strands may overlap, wrap around each other and double back and become entangled in such confusion that it may be impossible to straighten out the message (somehwat like that sentence!). There may be plenty of color and flavor but not a lot of meat, so you end up being dissatisfied and needing further communication to clear up the confusion, which may then just result in more confusion. In this case, the lesson is to avoid spaghetti to begin with and instead think through the communication clearly and then communicate through a single channel.

Then there is spaghetti code, computer code that has a complex and tangled control structure. This makes it difficult to follow, increases the risk of unforeseen and undesireable side effects and makes it hard to update. However this isn't limited to computer code. We see it in legislation where the use of complex definitions and cross-referencing to other parts of the same legislation may make it difficult to understand and even at times self-contradictory. And it may also occur in the standard procedures used in our businesses where they may have multiple levels of approvals or complex loops through different work units before being resolved. By eliminating this spaghetti we may be able to bring clarity and efficiency to our processes and eliminate waste.

On a more positive note is the lesson we learn from spaghetti sauce is this video presentation by Malcolm Gladwell:



In an earlier post, I mentioned communication differences between different generations (Boomer vs Gen X vs Gen Y) and to a degree this parallels the spaghetti sauce analogy: some people may like their communication to be "extra chunky" (watch the video) whereas others may want it to be smooth, some want it to be thorough and detailed while others may want us to cut to the chase. The lesson here is that instead of pursuing a 'one size fits all' approach, we instead tailor our approach to what best suits our customers.

One food, three lessons. But common to all three is the need for clarity: clarity of intent, clarity of process, clarity in satisfying customer needs.

7 or 6?

Consider the Englsih sentence:
      "This sentence contains at least seven words"
If we wanted to translate this into German then we could say:
       "Dieser Satz enthält mindestens sieben Wörter"
but now a statement that was true in English has become false in German.
Or we could translate it as:
       "Dieser Satz enthält mindestens sechs Wörter" (i.e. "This sentence contains at least six words")
which preserves the truth of the statement but only by translating 7 as 6.

So which is correct? The first translation is literally accurate but at the expense of truth. The second is true but at the expense of accuracy. Which you would use would depend on your purpose.

Sometimes something similar happens with best practice. An attempt may be made to transfer the practice of another organisation point by point to your organisation, based on the idea that we only know it will work in its original form - we don't know what will happen if we modify it. However, the same process that works well in one organisation may fail in another because in the act of "translating" it to a different set of circumstances we have failed to preserve its "truth". So we need to modify it to preserve its truth within the context of our organisation.

Something similar can happen when we attempt to communicate. We can attempt to communicate a consistent message to all of our customers (i.e. use the same form of words) or we can attempt to communicate in a way that ensures each customer receives the same message (i.e. where different words are used to cater for different customer characteristics.)

For instance, in my home state of New South Wales, for people over the age of 18 we have the following breakup by generation:

• 12% Depression/WW2 Generation
• 27% Baby Boomers
• 38% Gen X
• 23% Gen Y

While it may be over-simplifying a little ( different people witin the same generation may respond differently), if we send the same letter to different people, what Boomers may see as professional, Gen Yers may see as patronizing, pompous, pretentious or officious. Alternatively, we could express the same message in different ways targeted to different generations in order to get a similar response from each group.

So we have a choice: we can send a message that is literally consistent or we can send tailored messages that obtain a consistent outcome.

In all of the examples, the difference is between an absolutist perspective in which literal accuracy is a key value and a relativist perspective where context needs to be taken into account. In general, the more nuanced contextual approach will be more effective.

So when you are faced with "translation"-type issues, it may pay to consider wat you want to happen and whether contextual modification is important - sometimes translating "7" as "6" is necessary to preserve the effective essence of a process or communication.

What an average doesn't tell you

Recently, I had a hunch that the average age of males was lower than the average age of females. My logic was that women tend to live longer than men and that slightly more male babies are born than female babies. I wondered if my reasoning was correct so I downloaded the demographic statistics for NSW from the Australian Bureau of Statistics and I found the following:

• There were 4.3% more males than females under 31 years of age
• There were equal numbers of males and females aged 31 years of age
• There were 6.3% more females than males over 31 years of age
• 41.2% of the population were less than 31 years old while 57.4% were more than 31 years old

When I crunched the figures I found that the average male is 1 year 8 months younger than the average female. So my hunch was correct. But the question is: what does this tell us about any given person?

The answer is: nothing!

Averages are about populations not about individuals. So if you were to see a statistic such as "Males are on average 1 year 8 months younger than females", you aren't being given an answer so much as a provocation to ask "Why would this be the case? What does it mean?"

When you are surprised at an average then it may help you to surface your assumptions about whatever it was that was being measured.

For instance, if I were to ask you what the average height for a human being was, the chances are that you would say something like 5'2" or thereabouts. So it would surprise you if I were to tell you that the true figure is under 5'. I will explain this claim in a moment, but first would it surprise you to learn that short people often have lower literacy levels than taller people? Yes?

Well interestingly the reason for both of these statistics is the same: children are human beings too!

Most children are under 5' tall and most childen have lower literacy levels than adults and these two facts result in the given statistics. So if you were surprised at the claims I made, it would be because you assumed that we weren't counting children in the averages. Even if we weren't aware of it, we implicitly assumed that we were talking about adult humans.

This can be true of almost any average. The question we always need to ask is: what is the true population that is being averaged? What assumptions are we making about this population? Are there groups we are excluding which we should be including? Does it make sense to use a single average when you are dealing with a 'mixed' population (e.g. adults vs children, males vs females). Does comparing separately calculated averages lead us to ask new questions about the reasons for any differences? Are the reasons obvious or do we need to dig deeper?

The other question is whether we are comparing the right averages.

In Australia, a serious issue is that average Aboriginal life expectancy is significantly lower than that of other Australians. However is this the right comparison? If we were to segment the two populations by socio-economic level would we find that the issue isn't race but poverty i.e. that people in lower socio-economic groups have much lower life expectancies than those in higher socio-economic groups? In other words, the underlying reason may not be racial but economic. Whatever the answer may be, it determines what sort of strategy you use to approach the issue: do we directly target health services to Aboriginal people or do we adopt a broader strategy of reducing poverty across the board?

I don't know what the answer is, but I raise this issue as just one example where the populations you choose for comparing averages can make a significant difference to what strategy you use to investigate the causes of any differences and the consequential strategies you adopt to deal with such causes.

Averages are one of the simplest statistics to calculate and this is one of the reasons that they are so frequently used. However they may conceal differences and assumptions that need to be surfaced if you truly want to understand what is actually going on.