Jul 21, 2021 | Leadership
There is a huge hole in every ‘top 10 characteristics of a great leader’ list I have seen.
They all list approximately the same things, by different names, and in different order.
Honesty, integrity, transparency, vulnerability, vision, creativity, dependability, empathy, and so on.
In none of them have I seen the one characteristic that to me just sticks out like a beacon.
Compounding.
Compounding is as Einstein pointed out, ‘The most potent force in the universe‘
Leaders who actually lead, do so not because they demand to be leaders, but because they invest, tiny bits, over a long period. in the individuals around them. Each investment (read interaction) may be small, but it compounds on the last one, and the one before, where they counselled, advised, observed, offered praise, stepped back when praise was offered from elsewhere, and provided cover by taking responsibility when there was a cock up.
Compounding good leadership practices breeds great leaders.
Jul 19, 2021 | Change, Customers, Innovation
Edges are often fuzzy, but are where the action happens, in nature and in business.
At the edge, there is less homogeneity, more opportunity for the different and interesting to be seen, trialled, and if successful take hold. By contrast, in the ‘middle’ there is little but homogeneity. It is why large businesses have trouble with innovation, their model is to do the same thing repeatedly, optimising it continuously, removing the opportunity for the unusual and unexpected to influence the way things are done.
If you think about where the ‘edges’ are in your business, they seem to fall into three categories:
The technology edge: where the existing technical status quo bumps up against development happening elsewhere. These days this is remarkably common. I once found a simple Bill of Materials program based on MS Access for a client. It successfully managed his inventory, costs, and associated information in the form of a program designed to manage the recipes and inventory in a restaurant. It worked perfectly well in an entirely different environment; the names just needed some changing.
The customer edge. The point at which you initially interact with your new customers and engage with potential customers is an edge. The interpretation of your value proposition changes depending on the context, and the challenges faced by people inhabiting a niche you may not have seen or considered relevant.
The core/non-core edge. This is an ‘internal’ edge. What is seen by the leaders as core and what is non-core to a business. The debate about what is core, and what is non-core capabilities, and competitive advantage started by the outsourcing movement 30 years ago remains. Enterprises seek operational excellence and differentiation by innovation at the same time. Often these are mutually exclusive objectives. I have seen businesses move one way and then another, as the competitive environment around them evolves. It can be argued that we are on a significant inflection point in the core/non-core debate currently. Supply chains are being disrupted by climate change, Covid, increasing complexity and the resulting reduction of item invoice price as the determining factor, and the growing awareness of the value of sovereignty.
To find an ‘edge’ opportunity, ask yourself four simple questions, continuously, during the strategy development and review processes:
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- What are the challenges our different types of customers face?
- What could or should our solution include?
- Which of our capabilities may be useful elsewhere, and by who?
- Which of our assets would others value, and why?
You might uncover something surprising that delivers a new lease on life.
Jul 16, 2021 | Change, Collaboration, Operations
Nothing these days is done in one place, by one person, beginning to end. There is always a process in place, a chain of events that has to all work together in a co-ordinated manner to optimise the outcome.
We all know that old cliché, a chain is only as strong as its weakest link.
This is how it is with any process; it is limited in output by its weakest link.
Therefore, rather than spending resources in vain attempts to boost process performance by doubling down on the obvious bits that work well, find the weak link, fix it, then move on.
Eli Goldratt, the brain behind the Theory of Constraints, wrote a book called “The Goal” to articulate his theories in simple form. Boiled down in the book is a story of reverse engineering the process chain in a mythical factory. The management identifies the weakest link, works with it until it is no longer the weakest link, then moves on to the next identified target, now the weakest link in an improved process chain. This is an ongoing process of continuous improvement.
As Aiden Kavanagh, one of the best ‘Lean Thinking’ implementers I have seen in my travels put it succinctly in a comment on a previous post: ‘Tune the system to the pace of the bottle neck and make sure everything else has capacity to make sure the bottle neck never stops’
Is this how your improvement initiatives work, or are you continually making investments in new shiny things that always seem unable to deliver the promised outcomes?
Header photo courtesy of Daniel Stojanovic
Jul 14, 2021 | Analytics, Management
The ‘Standard Error’ is another of those confusing statistical terms marketers need to understand. It is often confused with, and is as misunderstood as ‘Standard Deviation’. While they are related, and the Standard Deviation calculation is used in the calculation of the Standard Error, they tell entirely different stories.
The standard error calculates how accurate the mean of any sample from a population is likely to be, compared to the true mean of the total population.
An increase in the standard error means that the means of varying samples of data are spread out, so it becomes more unlikely that any mean of a sample will be an accurate reflection of the true population mean. The higher the standard error, the more spread out will be the population around the mean. Conversely, a low standard error indicates a closely distributed data set, and so is more likely to be representative of the population.
To continue the example in the earlier post explaining Standard Deviation. If you were planning to improve Sydney’s terrible road congestion, it would be valuable to know how representative of the total commuting population of Sydney the mean of your trips from Artarmon to the CBD of 30 minutes was.
To do that, you would do a wider study of the whole population, and calculate the mean, and standard deviation. You would then apply the Standard Error formula to calculate the standard error of the Artarmon sample, compared to the mean of the whole Sydney population.
The standard error is the standard deviation divided by the square root of the sample size. It therefore tells you the accuracy of a sample mean by measuring its variability from the known mean of the total sample.
Header illustration courtesy Wikipedia.
PS. I guess the government could have done such a exercise in parking lots, swimming pools, women’s change rooms, and all the rest. Perhaps they do not understand real statistics when disconnected from political statistics?
Jul 12, 2021 | Analytics, Management, Marketing
Marketers often hear the term ‘Standard Deviation’ during research debriefs, and conversations with operational personnel managing quality. Many do not know what the term means, and in what context to use it.
Standard Deviation is a statistical term that measures the variation in a set of values from the mean, or average of that set of values. The greater the standard deviation, the greater will be the spread of the data from its mean. In effect, it gives you a level of confidence in the conclusions drawn from the data.
Those values can be anything, from the time it takes for you to travel to work each day, to the variation in the size or weight of a widget coming off a production line, or indeed, from any individual part of that production line.
Take your pre-covid commute as an example. You live in Artarmon, 10km’s from your Sydney CBD office. The drive can take anything from 15 minutes to 110 minutes. If you recorded the time taken for a period, say 3 months, assuming you worked every weekday, you would have 130 data points of the time it took to make the commute, to and from work. Assume you took an average of those times, and it was 30 minutes. The Standard Deviation calculation ‘translated’ means that in 68.2% of the commutes, your travel time would be within one standard deviation of the mean of 30 minutes, and 95.4% of the commutes would be within two standard deviations of the mean of 30 minutes.
Let us assume the distribution of the data points led to a calculation of one standard deviation being 7 minutes. In other words, 68.2% of the time you would complete the trip between 23 and 37 minutes. That calculation also results in 2 standard deviations being 17 minutes, meaning that 95.4% of the time you made the commute between 47 and 13 minutes.
Those ‘outliers’ falling outside two standard deviations will be unusual situations. You went into work at 2.00am for a conference call overseas, and you got to the office in 10 minutes, and one morning, there was a ‘prang’ on the bridge, and it took over 2 hours to make the journey. These would be the journeys that made up the very unusual data points in the set, out at 3 standard deviations, within which 99.7% of journeys fell, or further.
This might seem a bit quantitative for many marketers, but if you are to be taken seriously in the boardroom, you need to be able to speak ‘Data’ the language of the boardroom. The typical marketing type assurances based on opinion and theory must be at least partly replaced by the quantitative language of the boardroom.
For those looking for a bit more, there are plenty of resources on the web, and there is a SD formula in Excel which leads you through the steps to do the calculation. However, in principle, the calculation has a few steps:
- Calculate the square of the differences between all the data points, and the mean, then add them up.
- Divide that sum by the sample size minus1, which gives you the variance. The variance is a statistical picture of how spread out the data points in the set are.
- Calculate the square root of the variance, to give the Standard deviation.
As a marketer, you do not have to know the formula, but you absolutely must understand what the term ‘standard deviation’ means, and where it is best used. It might be useful to ‘fiddle’ with the formula in Excel.
Header graph from Wikipedia.
