Soft landing

Muhammad Tahir Rabbani risked poking the bear on LinkedIn when he asked the Learning & Development Professionals Club:

How can we differentiate between hard and soft skills?

His question was serendipitous because I had been giving that very question some serious thought, and where I landed was that the successful application of hard skills can be measured definitively. For example, the code you write works as intended, or you arrive at the correct mathematical solution. Hence the metric is a hard number, or a binary yes or no (1 or 0).

On the other side of the coin are soft skills, so named because they are not hard skills – a bit like the white rhino and the black rhino, or hard dollars and soft dollars. Successful application of these skills less obviously boils down to a number. For example, how do you measure your communication skills or your relationship management prowess? The number of emails you send or the size of your network both miss the point. One approach might be to measure the skill indirectly, perhaps in terms of employee engagement or volume of sales.

I was comfortable with my position until I listened to a podcast by David James in which Guy Wallace quotes Joe Harless: “Soft skills is a euphemism for hard skills which we have not worked hard enough yet to define.” In other words, Guy explains, we typically don’t begin with the end in mind – that is to say, terminal performance.

And this made sense to me. I realised we could measure an executive’s communication skills by monitoring his target audience’s actions in response to the key messages in his memo; and we could measure a line manager’s provision of feedback by calculating her team member’s subsequent uptick in performance of a task. Thus, if we factor in terminal performance, a business metric emerges.

However, as Guy also explains, this is all highly dependent on the intended outcome in the context of the individual’s role, which makes it challenging to quantify at scale. Yet I also see how, just as we standardise the outputs of hard skills via acceptance criteria, we can do the same for soft skills. For example, we could use a rubric to assess whether the feedback that the manager provided clarified the situation, described the behaviour observed, and explained the impact. In this way, we “harden” the skill.

Complicating matters is the fact that some folks take umbrage at the word “soft” because it conveys the impression that they’re easy or weak. I consider this a misinterpretation, but I also acknowledge that perception is reality. Thus, countless peers have proffered alternative adjectives such as “business”, “professional”, “power”, “behavioral”, “employability”, “core”; or one that I’ve used myself in the context of a skills-based learning strategy, “transferable”.

The problem with all these labels, in my view, is that they don’t satisfactorily differentiate hard skills from soft. For example, is a hard skill such as data analysis not also a business skill or a power skill? It’s certainly transferable.

Wikipedia describes soft skills as “psychosocial”, and I feel this hits the mark closer than most. Intrapersonal skills that are exercised inside your head – such as creative thinking and resilience – are psychological, while interpersonal skills that are exercised with other people – such as communication and relationship management – are social. Unfortunately Wikipedia goes on to declare that hard skills are specific to individual professions, which is demonstrably false.

Another popular way to differentiate hard skills from soft is with the labels “technical” and “non-technical”. However Josh Bersin argues that soft skills are not soft because they’re highly complex, take years to learn, and are always changing in their scope. That sounds a lot like technical skills to me. In Figure 1 of his article he also combines core/technical skills! I wouldn’t dare suggest he was wrong; it’s just a matter of personal proclivity.

Harking back to my answer to Muhammad’s question, my own proclivity is to use the labels “objective” and “subjective”. Hard skills such as computer programming and data analysis are measured quantitatively; hence they are objective skills. In contrast, while the measurement of soft skills such as communication and relationship management can be rendered objective, we tend not to and so they remain qualitative in nature; in which case they are subjective skills.

Having said that, perhaps all this linguistic gymnastics is just an academic sideshow. Our audience doesn’t much care for it, and for them I suggest it would make more sense to repackage “digital” skills (working with data, working with technology) and “people” skills (working with humans, working with yourself). These complement “role-specific” skills (pertaining to accounting or derivatives trading, for example) and of course “compliance” skills (such as privacy and AML), both of which might be better pitched as competencies.

I also suggest that however you slice and dice skills, it’s always going to be a little bit wrong. There will inevitably be exceptions, cross-categorisations and dependencies. Frankly, it will be a marriage of convenience.

And that’s OK, because whatever you call them, what really matters is that we develop them to improve our performance.

The equation for change

Guns don’t kill people. People do.

It’s a well-worn saying that Americans in particular know only too well.

And of course it’s technically correct. I don’t fear a gun on the table, but I do fear someone might pick it up and pull the trigger. That’s why I don’t want a gun on the table.

It’s a subtle yet powerful distinction that occurred to me as I absorbed the core reading for Week 1 of The University of Edinburgh’s E-learning and Digital Cultures course; namely Daniel Chandler’s Technological or Media Determinism.

Technological determinism is a philosophy that has implications for e-learning professionals as we grapple with technologies such as smartphones, tablets, ebooks, gamification, QR codes, augmented reality, the cloud, telepresence, ADDIE, SAM, and of course, MOOCs.

Chandler explains that “hard” technological determinism holds technology as the driver of change in society. Certain consequences are seen as “inevitable” or at least “highly probable” when a technology is unleashed on the masses. It’s how a lot of people view Apple products for example, and it’s extremist.

Like most extremism, however, it’s an absurd construct. Any given technology – whether it be a tool, a gadget or a methodology – is merely a thing. It can not do anything until people use it. Otherwise it’s just a box of wires or a figment of someone’s imagination.

Taking this rationale a step further, people won’t use a particular technology unless a socio-historical force is driving their behaviour to do so. History is littered with inventions that failed to take off because no one had any need for them.

Consider the fall of Aztec empire in the 16th Century. Sailing ships, armour, cannons, swords, horse bridles etc didn’t cause the conquistadors to catastrophically impact an ancient society. In the socio-historical context of the times, their demand for gold and glory drove them to exploit the technologies that were available to them. In other words, technology enabled the outcome.

At the other end of the spectrum, technological denial is just as absurd. The view that technology does not drive social change is plainly wrong, as we can demonstrate by flipping the Aztec scenario: if sailing ships, armour etc were not available to the conquistadors, the outcome would have been very different. They wouldn’t have been able to get to the new world, let alone destroy it.

Of course, the truth lies somewhere in between. Technology is a driver of change in society, but not always, and never by itself. In other words, technology can change society when combined with social demand. It is only one component of the equation for change:

Technology + Demand = Change

In terms of e-learning, this “softer” view of technological determinism is a timely theoretical lens through which to see the MOOC phenomenon. Video, the Internet and Web 2.0 didn’t conspire to spellbind people into undertaking massive open online courses. In the socio-historical context of our time, the demand that providers have for altruism? corporate citizenship? branding? profit? (not yet) drives them to leverage these technologies in the form of MOOCs. Concurrently, a thirst for knowledge, the need for quality content, and the yearning for collaboration drives millions of students worldwide to sign up.

MOOCs won’t revolutionise education; after all, they are just strings of code sitting on a server somewhere. But millions of people using MOOCs to learn? That will shake the tree.

So the practical message I draw from the theory of technological determinism is that to change your society – be it a classroom, an organisation, or even a country – there’s no point implementing a technology just for the sake of it. You first need to know your audience and understand the demands they have that drive their behaviour. Only then will you know which technology to deploy, if any at all.

As far as gun control in the US is concerned, that’s a matter for the Americans. I only hope they learn from their ineffective war on drugs: enforcement is vital, but it’s only half the equation. The other half is demand.