Author: mhannagan

This article discusses the Prussian, or Clausewitz, Quadrant which was meant to be an army personnel classification method, putting people into four categories based on their levels of intelligence and industriousness.

Kurt von Hammerstein-Equord

Helmuth von Moltke the Elder

The two fearsome-looking gentlemen (hover over the pictures to see their names) above are also credited with the invention of the quadrant. See the end of this article for links to Wikipedia articles. I do prefer the picture of Clausewitz, he looks very flamboyant.

I have read about this quadrant in many books and articles, but I have not been bothered to verify its provenance. I still can’t be bothered, by the way, but feel free to confirm or deny its provenance if you must.

Over my career, I’ve had the chance to test the effectiveness of this classification method and have sometimes evaluated my own performance and decision-making based on those criteria.

The thing about quadrants is that they are easy to understand. Being easy, they tend to be a blunt instrument and lacking in depth. Making important decisions on what you see in a quadrant comes with a healthy dose of risk.

Please note that in this article, I am putting my own spin on the quadrant, based on my experience of people encountered throughout my working life.

I’ll explain the categories over the next few paragraphs, but let’s start by looking at the Quadrant:

You can see that there are two axes – the horizontal one evaluates industriousness going from lazy to industrious, whilst the vertical one illustrates intelligence ranging from stupid to clever. Because we like simple models, we only plot four areas in the quadrant (hence the name), and we evaluate people based on the quadrant to which they belong.

I’ll mention a key weakness of the quadrant approach in the conclusion to this article.

Now, let’s examine the first category:

The Stupid and Lazy category can be used for people who are not necessarily stupid per se, but are absolutely not engaged and will not invest any of their resources in the organisation in which they participate.

The Prussians found a use for these persons where they would be given repetitive, simple tasks that needed to be done but would frustrate a more capable or invested individual.

Modern organisations may still need such people but automation and AI are definitely in the frame to replace them.

Next, we have:

Being Clever and Industrious is an absolute gift to an organisation. Complex tasks can be given to these people, and they will thrive on the challenge.

Furthermore, they are keen to take on more work but run the risk of burning out.

The astute leader will know how hard to drive and challenge these valuable resources to obtain the best sustainable yield.

Clever and Industrious people can often over-complicate things and will design processes and systems that may challenge those who do not have their capacity for thought and work. They may demand of others a yield that, to them, seems achievable, but would have  less endowed individuals working at full throttle permanently. That doesn’t usually end well.

My favourite category is next:

Belonging to the Clever and Lazy category is an attribute that should be valued providing an organisation is not entirely composed of such persons.

People who seek to get the best yield out of the lowest amount of work possible will look for efficiency and simplicity in order to get the tasks done. They are often good delegators.

Clever and Lazy people think laterally and can reach innovative solutions which would elude their more industrious, focused peers.

They need to be led and motivated to keep the lazy side of their nature under control. Promotion is often a method used to get the best out of this category. Being lazy does not imply a lack of ambition and a desire to succeed.

I have left the most dangerous category for last:

One suggestion I have read (not mine, although…) for such people is that they should be shot on sight.

This may be a bit harsh, but there’s no understating the damage that can be caused by someone who is capable of stupidity at scale.

On a less dramatic level, such people could be the ones who follow dogma to the letter and are unable to adapt to unusual or changing situations. They are often a cause of friction and stress when the need arises to be agile or decisive. Another instance of this behaviour can be shown at the higher echelons of leadership where lack of experience or foresight when making decisions can have unforeseen and disastrous consequences.

Such attributes can be self-correcting, in that organisations should eventually divest themselves of such people as the impact of their actions rarely go unnoticed. And sometimes, lack of experience can be mistaken for stupidity. Providing the incidents are survivable and the individual teachable, it should be possible to evolve out of this category. So all is not lost.

To conclude this article:

Classification methods are often unsatisfactory. It seems convenient to put people in neat little boxes, because it allows organisations to devise processes and policies which can be standardised.

I was alluding to a flaw in the quadrant method, in that it permits the presentation of subjective and poorly curated data about a collection of subjects as an absolute and inevitable truth. It is an impressive consulting tool, but the decision maker must drill into the collection and classification methods to assess their validity.

Nevertheless, I like the Prussian Quadrant because, empirically, I have found this classification to be accurate, even if it is incomplete. It is also not a predictor of success, but describes a simple set of behaviours and attributes which can be the starting point to evaluate desirable personnel profiles.

When it comes to success in an organisation, some of the factors I have observed, beyond capability, have been:

-An ability to be relevant to the leaders

-A way of being credible and trusted by leaders and followers

-Skilled in managing upwards while keeping a professional distance

-Delivering results reliably and repeatedly

Plus, really, an ineffable capacity to be heard, to influence thinking and to make sound judgement calls in uncertain circumstances.

You could argue that these capabilities can exist independently from intelligence and industriousness, and may even be present when one or both are lacking. It all depends on the organisation – like people, they tend to be quite individual, despite our efforts to classify them.

I hope you enjoyed this article. I list below a couple of links to find out more (all hail Wikipedia !).

Wikipedia page about Carl von Clausewitz

Wikipedia page about Helmuth von Moltke

Wikipedia page about Kurt_von_Hammerstein-Equord

And a link to one of many articles discussing the origin of the quadrant:

Quote Investigator: Clever-Lazy

…but it seems that you can get away with committing crime, with very little risk of ending up in prison (unless you’re a shoplifter, more about this in a following post).

I’ve been looking at the crime data from February 2018 to February 2019. It’s been fun enriching the data and building the metrics I needed, and discovering the limits of working on a laptop as opposed to the big servers that I normally work on.

Fun up to the point where, to verify what the Police Commissioner for the Metropolitan Police said in her article (click here), I wanted to see what the crime outcomes were for one of the more serious categories: Violence and Sexual Offences.

I am using the data from data.police.uk, publicly available.

My data is restricted to four towns in the Herts/Beds/Bucks counties: Milton Keynes, Luton, Dacorum (Hemel Hempstead) and Watford. I have visualised the data on  an Esri map with two layers. The area layer shows the deprivation (darker is more deprived, lighter is less deprived) and the crime point layer shows the crimes where they have occurred.

So here we go:

Violence and Sexual crimes reported. Quite a lot over a year.

Out of these crimes, which were the ones for which no suspect was identified ?

And then, when was a suspect identified but couldn’t be prosecuted ?

So the final question, for now, is who got sent to prison?

Not that I’m hell bent on sending people to prison, for various reasons beyond the scope of this post to explain. But still, the offences seem serious enough that you’d hope someone would get caught and punished for it. Yet the data seems to confirm the Commissioner’s assertion that crime solving is woefully low.

And solving this is beyond the scope of this article, and my competence. Although…

One of the inconveniences of being fascinated by data is that there are many domains of great interest. I have focused for a while on social deprivation, crime and election results – Brexit has been a goldmine of insights, for instance.

Trawling the ONS for some interesting data, I stumbled upon an article on human capital. The link to is here. It’s most interesting because at a time where assets are being dematerialised, and information is as valuable as gold, placing a value on people seems at the same time worrying (are we to be bought and sold like chattels) and reassuring (people can be valued, in the appreciative sense of the term).

An initial reading was eyebrow-raising : Young people were more valuable than older people, and men more valuable than women. Reducing this apparently complex field to such a provocative and simple sentence is lazy, and dangerous. Nevertheless, I have seen grids of data that make such a statement.

The key thing is: why ? At a time where an ageing population inflicts an ever-increasing cost on a healthcare system, and where the disparity of pay between men and women is frequently  in the news, it might be useful to delve into the reasons that lead to this valuation disparity between groups in our society.

It’s important to do so because measures from the ONS inform government policy, and are also used to justify why some groups receive more attention and benefits than others.

I will also, where possible, make statement that challenge the reasoning behind the valuation disparities, and propose ways of increasing the value of different population categories: since the value of our population seems to have an impact on our future welfare and prosperity, it might be a good thing to do so.

And, of course, i will do my best to visualise this data in a pleasing way using my MicroStrategy citizen data scientist toolkit. What else ?

Let me steal the definition of human capital from the Office of National Statistics:

Human capital has been defined as “the knowledge, skills, competencies and attributes embodied in individuals that facilitate the creation of personal, social and economic well-being”. It sees education and training as investment into individuals’ value and how much potential they gain.

And, if you want to know more about the mechanics of it, there’s a United Nations definition:

UN pdf on Human Capital

(not light reading, but essential as a primer on the topic).

Update: I am seeking to delegate research on this topic, in particular why it seems to be that women appear to be valued less than men. I say it seems because I have not had time to investigate this, and I have probably fallen foul of the “jumping to conclusions because I am too lazy to read on” syndrome. However, I sense that this might be key in unlocking true gender equality if a fundamental economic measure, that shapes policy, can be rewritten so that the true value of women can be recognised in hard financial terms. Everything else on this topic might be dependent on this.

As you know, this is a professional blog. And as my profession is to be a consultant for MicroStrategy, it’s only natural that I present a use case for MicroStrategy which is not obvious at first sight.

I have to do some data modelling in my day to day job. Steeped in over 30 years of getting to grips with data in all its forms, I tend to do it almost without thinking and I sometimes model things I don’t need to, because, primarily, it’s a sign that I am vaguely on the spectrum (together with insisting that my locker numbers at the gym or swimming pool must be a multiple of three, otherwise the world will end).

What I have noticed, however, is that data models are extremely difficult to communicate – it’s almost a write-only discipline. When I show an entity-relationship diagram to a customer, I usually get the ‘it’s very nice and complex’ remark, and it makes me look very clever. It’s not a great tool for checking that I have got a requirement right, although there are some – rare – Alan Turing-like people out there who can get the diagram in one go and construct a logical or physical schema from it.

Such complexity was fine when BI was an arcane science, cosy in the realm of IT where its mysteries could be communicated amongst the computer science priesthood.

Nowadays, though, BI is done by everyone, including people who have no formal grounding in the subject – the famous Citizen Data Scientists, who have engaged with BI via spreadsheets and cute visualisation tools which I shall not mention here. So good luck with your complex data model, and getting it across to those users when you are building a self-service solution for them.

However, modelling is what you must do when building a system that has to answer a number of questions. You can muddle along without it but you are likely to end up with a construct held together by gaffer tape, impossible to maintain and scale.

There, your skill as a consultant comes in to play. If you are lucky, you have a set of outputs which are reasonably well defined, like some sample reports or dashboard mock-ups (don’t assume the thinking is complete, I find that even with this there are hidden complexities that the customer has simply not thought about). If not, you’re going to have to insist on asking what the essential outputs will be – the universal BI system that answers all questions has eluded me throughout my years of practice. Maybe I’m just unlucky.

I have a small example – a sales order system. The report set consists of a sales order, and lists of customers, suppliers and products. I am keeping it simple – in reality you’re going to have a multitude of outputs. The trick there is to work out what elements (attributes and facts) will need to be modelled to satisfy the requirement. I’m using Excel to list the entities:

That’s a good start but it’s not the finished article. I could simply go down the list and create all the attributes and facts I need, but it would be riddled with errors because I am not highlighting the common elements – the joins – between the different reports.

This is where MicroStrategy comes in. I can read this list into a Network Visualisation, which will then look like this:

This diagram shows the common elements in all the reports. Thus you can see that the attributes and facts that will need modelling. It has some misleading features – the address for the supplier and customer is unlikely to be really common, and might come from different tables – but it’s a much clearer way of transforming a set of requirements into an intermediate form that you can review with the customer. For instance:

In the revised table above, the elements in bold are new. The fact that there are a number of order lines per order is recognised, as well as the supplier and customer addresses being different. After a refresh of the visualisation:

You can then refine the model and refresh the MicroStrategy Dossier. With Vitara Charts, you can build the logical model – the schema – and visualise it using the Org Chart visualisation (image coming soon, I need to refresh the vitara license on my setup).

I can then build a similar table with the elements, and the columns from the physical tables that contain the data. Similarly, I can visualise it with a Network Diagram, and I can iterate this process until the model reaches completion (if not perfection, there’s always refinements and unforeseen challenges).

Finally, within the same dossier, I can have a set of visualisations and lists which document the transformation from reporting elements to schema, in an easily shareable and transmissible format.

I find it elegant to use MicroStrategy to create the constructs that will configure MicroStrategy so that the customer’s requirement can be implemented. It is this completeness of vision, the whole journey without dead ends, which I find so compelling with this product.

Why don’t you have a go ?

I wanted to acknowledge my colleague Sachit Vinod, who came up with this visualisation idea when we worked together on a Business Objects conversion exercise. The use of the network chart, for me, was a revelation, and I am very grateful to him for the insight.

The business world is full of contrived metaphors. Maybe this is done to give sparkle to topics which can be a bit dry or complex to get across. Over the last few weeks I have started using analogies from the world of transport to put across concepts that were not embedding themselves easily enough in the minds of my audience.

This article stems from reflections I had when thinking about the environment, or forces, that act upon any project or endeavour which involves change or innovation (pretty much everything I am involved in these days).

I do love aeroplanes. They are one of our most wondrous achievements, in my opinion. In particular, I love the Spitfire. It is difficult for me, when seeing them in flight (Duxford Air Show ! You must go !), to repress a shudder of delight as they fly overhead, with the outraged howl of the Rolls Royce Merlin engine.

You cannot love aeroplanes without knowing about the physics that permit them to fly. My understanding is that there are four forces that play together to give the plane motion and control:

• Thrust (provided by the engine)
• Drag (Resistance to the passage of the plane through the atmosphere)
• Lift (a force generated by the shape of the wings, caused by pressure differentials underneath and above the wings)
• Gravity (Our dear planet’s attempt to keep things close to her)

The modulation of  these forces through control surfaces and power input results in the aeroplane performing as the pilot desires. Losing control of one or more of these forces tends to lead to undesirable outcomes.

Looking at projects, or any endeavour that requires changing from one state to another, I came to the realisation that you could model the forces effecting the progress of a project in a very similar way to those acting on an aeroplane. Thus, substitute:

• Thrust with Innovation ( the desire to change or improve something).
• Drag with Resistance to Change ( the work you have to do to bring others along with you )
• Lift with Agility (the ability your organisation has to effect that change rapidly)
• Gravity with Governance (the function that controls and governs the rate of change so that it fits with statutory or strategic goals)

The role of the aeroplane’s pilot is similar to that of the project manager, who has to have knowledge of the extent and nature of the forces assisting or impeding the endeavour to lead it to its satisfactory conclusion. The four forces exist in varying quantity and quality from organisation to organisation, and are mainly an outcome of the blend of leadership, regulatory or competitive forces and established practices.

It’s clear that if these forces are out of balance, a supplementary input of resource will be necessary for the project to reach its conclusion. For instance, an excess of resistance to change will need to be countered by an increase in the outreach and mitigation of this resistance. It will take more time and cost to get to the destination. Similarly, an excess of governance will cause friction in the project’s progress, whilst uncontrolled innovation may not be beneficial to an organisation’s overall strategy.

Like most business metaphors, this one will not stand the scrutiny of an aeronautics engineer as there are clearly many other factors that affect the performance of an aeroplane. I may even have got the physics wrong… But I am pretty certain that an astute project manager, who understands the extent and nature of the forces that influence the progress and outcome of an endeavour, stands a far better chance of leading it to its conclusion.

It is also down to the leadership of organisations to get a grip on the supply and nature of these forces, and to put in place the right balance so that a majority of projects succeed in reaching their destination.

Some work-related things

The last few weeks have been a mix of upgrade projects, professional development, a force 9 gale, 8-hour sea journey to Ireland and turbulent air travel from America, with a pinch of jet lag. Hence the slightly reduced output as far as this blog is concerned.

This is a bit of a teaser post, just to let you know that I am working on presenting – at some point –  some advances in machine learning and AI that my company are working on – but first, I need to actually do the work so that I can report an authentic experience.

I’ll simply hint that I have heard of a ML use case at scale with promising results, using some pretty powerful methods, and that I know of another use case that could possibly be as interesting.

I’ll repeat what I said in a previous post: I feel that with the maturity of data provision (good warehouses, faster methods, better understanding, better tools) we are now in a position to deploy new and interesting software to add predictive and deep learning capabilities to our existing BI systems. The move from forensic reporting to AI-based forecasting, scoring and automation is afoot, and I hope to report back on this more detail in Q1 2019.

More thoughts about Cynthia

Enough about work – I have also been working on my Cynthia concept, which is a set of models that will interlock to form a synthetic personality. I made a post about Cynthia (article on LinkedIN – Introducing Cynthia) where I describe a basic personality framework.

I have also made an attempt at modelling an influencer mechanism, which would use strategy to bring about an outcome. It may be an application that interfaces between Cynthia and a human, or it may evolve into a survival mechanism for an AI such as Cynthia to overcome obstacles and achieve goals.

I show the high level concepts for this influencer mechanism which I have called Machiavelli, for obvious reasons.

Worth labouring the point: The interesting thing about the model above is that it can be instantiated in different ways: As a human-based system, as a hybrid of humans with application assistance, as a stand-alone goal seeking system or as a component of an aggregation of such modules which would eventually become Cynthia. What will happen will heavily depend on time and possibly funding.

That’s it for now – it’s going to continue being busy so I may not be back for a few weeks – but I’ll try and make it  interesting when I do return.