The web-site is now in readonly mode. Login and registration are disabled. (28 June 2019)

Cloud created by:

Chris Targett
8 October 2016

Making predictions for the future based on Labour Market Information (LMI) which is robust and trustworthy lets us assume that this will lead to a greater chance of individuals finding work, however it is not fool proof. Through some commercial data sets (available at a cost), we have the ability to predict to a certain extent what work will be needed where and how many employees we may need in a given sector. This is not the same as guaranteeing an individual work, but it is a much more accurate guide than some of the data in the public domain which is currently available for free. Trying to make decisions on the likelihood of where the work will be using these free data sets is a struggle, as the sets are often only providing a partial view of the labour market. The students often need help turning the jumble of information into intelligence, which is where career guidance professionals can add real value. To have complete data sets which have real detail and are freely available to students remains an ideal to strive and aim for.

Even though such data sets aren’t yet in the hands of those planning their careers, we can still hope.

Imagine a world where we did have this data. It is almost enough to see such a situation as a utopia or near perfect environment for ensuring everyone could find work and make an informed choice. Yet there is a small catch, and that is the human element, which remains unpredictable and often on the surface, illogical.

Consider a future where everyone has similar skills when they leave school.

We could say this is due to changes in an education system which pushes schools into focusing on the core subjects and reduces their ability to offer the rest of the subjects (to ensure they meet their targets on success and progress). We could say it is because these skills have been focussed on by students consulting the LMI data which, tells them these will be “skills in-demand”.
Now, imagine if all the students were using the same data sets to make their decisions.

All of the students can see shortages in the same areas, so they all take the same subject or suite of subjects to ensure greater chances of work. It could be that someone has told them that studying these subjects to a higher level will increase their chances of earning more money.
This leads to consequences.

A few of the individuals will be able to do jobs which the others can’t, as these jobs require advanced skills, such as the medical professions. These few individuals find work, leaving the other students the remainder of the vacancies.
After their studies, the amount of students who are of average ability, finishing their studies and aiming for the particular option or pathway identified by the data, become huge in number (as they have all been told that this is where the work will be). Perhaps, in such a scenario, we suddenly have more potential employees than there are vacancies for work.
Some of the students see this coming and realise that they aren’t as unique as they thought.

Only those who did more extracurricular activities than the others, to make themselves no longer average, stand out and find work.
Other students who have studied for these opportunities (who don’t stand out), find themselves jobless or in work where very little training is required; they are shocked and disenfranchised from education. How did they end up in low paid or zero hour work?
They are stuck unless the system they are in changes and offers them a chance to retrain and live whilst doing so.
Hmmm, there seems to be a problem if everyone uses the same data and makes decisions the same way. Identifying the same jobs or type of jobs to go for. Let us imagine a second scenario…

In contrast, imagine the student who is just as average in ability; they see an area where there is predicted to be very little work but still some work requiring some skill; for example as a Farrier (which is seen as a declining occupation). No one else hones or develops their skills in this area (everyone else has gone for the popular opportunity due to the data).
When this student finishes their studies or training, they look for work and find work, as there is no competition for the lone job vacancy which arises (no one else has the skills). It is this student who finds gainful and fulfilling employment and not the others.
As we can see data and logic could lead to problems in scenario one, especially if everyone makes the same decisions, using the same steps. Some of you may be thinking that generally we don’t all make the same decisions in the same way. However, when we take a step back and look at the system from a distant perspective. We see many students going to college or university, having made very similar decisions about how to advance their careers. It is because their values, influences and expectations are all very similar. This relates to a career concept called community interaction theory which, looks at how groups of people develop their career ideas.

Some may argue that the first scenario we explored is more fact than fiction when, according to the relatively recent skills survey we have potentially more graduates than graduate jobs available in the UK.

When students make decisions based on ideas which float like memes around schools such as, “go to university to get a good job” these narratives end up forming the basis for a shared logic which everyone pursues blindly. In the future, we may find ourselves applying this same blind logic to the bigger and better data as it becomes more and more accessible.

If the above shows us that making decisions based on data and logic isn’t enough, nor is following the herd, how can students make smart decisions in such a topsy-turvy world?

Perhaps it is by surrendering to chaos and relinquishing the desire to control the future, allowing chance to play its card. It can also be by keeping their choices open and remaining adaptable or, resilient as many have come to remark. Our second scenario also shows us that, there is a benefit in keeping their skills wide by also developing their unique skills and interests, in doing so they may shine brighter than others and be able to do what others can’t. Ultimately, each individual must decide which careers strategy works for them, whether the tried and tested or one which stands seemingly against the odds.

We can give them a path to aim for, but remind them they will be running a race against everyone else taking that same path and that it will be competitive. Interestingly, what our LMI doesn’t currently show us in any depth, is not only how many vacancies there are in any given year but, how many people on average are actively competing for those vacancies in each pathway and their locations (whether Apprenticeship, School Leaver or Graduate Scheme); I would be keen to see what this data might look like and use this with students (to help them be more informed).

We should also be mindful that the competition for places isn’t just against those that graduate with them in the same year, but those that also graduated before who haven’t found a job yet. Whether we are talking about students from plumbing courses at college looking for an apprenticeship to become fully qualified, or graduates from university looking for a graduate placement – the pressures are the same.

So, let’s help our clients hone their skills, whether they are “in demand skills” or “unique skills” or both, as well as coach them to be resilient, adaptable and ready for anything.

Written by: Chris Targett

This article was first published on the CXK website on Friday 3rd June 2016

Extra content

Embedded Content


Contribute to the discussion

Please log in to post a comment. Register here if you haven't signed up yet.