It’s mid-October, and we are entering peak Autumn. The leaves are on the ground rather than on the trees, the days are getting noticeably shorter, and Saturday night television becomes more and more dance-and-song related.

It’s also the time of year when the weather becomes even more unpredictable than usual. Take today, for example. In London and the Home Counties, it’s going to be warm, sunny, and with a gentle breeze to cool you down. But if you live in Belfast, you’re in for a dose of heavy rain and gales, as the tail end of Hurricane Ophelia hits the western side of the British Isles.

But who knows if this will actually happen! Over the course of my long life I’ve learned not to take the weather forecast too literally. There are simply too many variables.

Michael Fish 

Exactly thirty years ago today, Michael Fish was perceived to be in the wrong – not because he didn’t tell the truth, but because he downplayed the risks. On October 16th, 1987, he announced that a hurricane was not on its way to the UK. Technically, he was right; the wind that hit Britain a few hours later wasn’t quite violent enough to be classed as a hurricane, but it did represent the worst storm to hit South-East England for three hundred years, it did cause record damage, and it did kill 19 people. Michael had been in possession of most of the facts, but not all, so his warning message was not quite explicit enough.

A few hours later, his perspective would have been very different. The closer to the present moment that the weather forecast is, the more likely it is to be correct – a direct result of an accumulation of data and a reduction in the number of variables. (Having said that, it can even be wrong when it’s supposed to be current! Today’s forecast made no mention of this apocalyptic sun, for example. Picture taken this morning.)

Recruitment and weather forecasting

Recruitment is a bit like weather forecasting, in this respect. At the start of the process there is a lot of guesswork, in the form of assumptions based on incomplete information. At every subsequent data-gathering stage of the process, the forecast is refined and the picture becomes clearer. The more data, the better.

When I am planning to go on holiday, I like to check the weather in advance – and these days you can get an estimate of the conditions up to a month before you’re due to go. It’s an imperfect science, and I am well aware that the forecast will change between now and then, but I check it anyway. If the forecast is good, I am happy. If the forecast is bad, I am less likely to believe it, because it doesn’t fit what I want to happen. I am a human being, and my natural reaction is to look for information that backs up what I already think, or want to believe, an effect known as confirmation bias.

The CV is like a long-range weather forecast. How easy it is to make assumptions based on this! It’s very difficult to be completely objective when reading a CV for the first time. For example – you know that X university is less prestigious than university Y, so you assume its graduates to have a particular level of intelligence. You don’t agree with bullet points or some other irrelevant choice of CV presentation, so you dock them a point. Someone who happened to have the same name bullied you at school. You think that working in a role for 3 months means that they are a job hopper. You don’t see a reference to the words ‘client-facing’, so you assume that the candidate can’t hold a conversation.

And our society is very far from eliminating discrimination on the grounds of gender, race, or age, I’m sorry to say, conscious or not.

So, by all means use the CV as a general guide to what the person is like. It’s all the data you have, at this stage. But avoid jumping to conclusions; instead, build the picture by adding other information, for example:

• A telephone conversation
• An interview
• A look at their LinkedIn profile and recommendations
• A search for their other social media profiles
• Personality assessments
• Technical tests
• General intelligence assessments
• Emotional intelligence assessments
• References

Every additional piece of data that is gathered will bring you closer to the truth. But the fact is that you’ll never get there! Unlike the weather, when looking out of the window gives you 100% certainty that it’s currently raining, snowing, or shining an apocalyptic sun, you can never look at a person and claim to know everything about them. Never.

The best you can do is to maximise your chances of making a good recruitment decision, and then to look after that person once they are in place.

Conclusion

My advice, therefore, is to think about Michael Fish in 1987. If we stretch this already thin analogy to the point just before it begins to collapse, we can learn four important lessons from him.

1. Gather as much subjective data as you can before making any firm predictions about what will happen. In fact – don’t make any firm predictions at all until you have complete information. Which, in the case of people, is never quite achievable.  Don’t ignore your gut instinct, but be aware that it can be wrong.
2. If you’re recruiting, use the CV as a guide but bear in mind that it’s a long-range weather forecast, and you shouldn’t make too many plans after reading it.
3. If you’re applying for work, make your CV the best that it can be, because all kinds of unknown assumptions will leap to the mind of the reader, and these assumptions, once established, are difficult to break down because of confirmation bias. Don’t give the reader the chance to make those assumptions if you can avoid it. Explain any gaps in employment, for example.
4. If you’re recruiting, resist confirmation bias, and keep your mind open as much as you can. Don’t make judgements based on a list of past experiences – it’s better to focus on quantifiable results, and potential within your business.

And there’s one final piece of advice, just from me.

The weather looks bad today. Be careful out there!