The country is seemingly awash with entrepreneurs, men and women opening start-ups. There were 35,000 new business owners in 2016 according to an annual Global Entrepreneurship Monitor survey released this month by Enterprise Ireland. If the figures are correct – which I am sure they are – this means that one in every 23 people here is a new business owner.
This start-up rate as revealed by the survey, authored by Paula Fitzsimons of Fitzsimons Consulting and Dr Colm O'Gorman, professor of entrepreneurship Dublin City University Business School, is similar to that in the US and is high compared to European countries,
The Irish survey was in turn compared with similar studies from other countries to allow comparisons to be made. We are in sixth place in the European league table of new business owners, the survey showed, with 63 per cent of the 2016 crop involving male entrepreneurs and 37 per cent females.
While the types of businesses proposed and the services provided vary greatly, every one of these potential start-ups would have created some kind of forecast for how the business was expected to trade and the likely income generated over time. All of the forecasts would have been run past potential investors and lending agencies before any deal could be struck. And lenders and investors clearly responded in a positive way towards our entrepreneurs during 2016 despite the fact that predicting the future is a notoriously difficult and sometimes dangerous business.
Just how tricky this forecasting business can be is discussed in a most entertaining fashion by Paul Goodwin in his new book, Forewarned: A sceptic's Guide to Prediction. We have to make predictions all of the time, in business, in our private lives, changing jobs, getting married, deciding to go for that promotion. And if we feel unable or unwilling to make predictions, we can always rely on "experts" and experienced forecasters to make the decisions for us.
The book is awash with entertaining examples of predictions that were astoundingly accurate and others that were spectacularly wrong. And he points out that success in this fortune-teller game can just as quickly go from amazing success to shocking failure depending on the way the cards fall.
No book on predictions can fail to mention the hapless BBC weatherman Michael Fish who became famous – if not infamous – by dismissing the possibility of a big storm blowing through the UK in October 1987. Needless to say, his forecast neglected to warn of the arrival of the worst storm in 300 years.
More recently we had the failure to predict the UK's Brexit vote or the US election in 2016 that brought Donald Trump to power, and Goodwin identifies many more.
It is worth noting that picking up on examples of failed forecasting becomes easier for Goodwin with the benefit of hindsight, and he has spent a full academic career at the University of Bath in the UK lecturing and researching the forecasting and decision-making processes. Despite his scepticism about the advice given by some who would claim to be an “expert”, a lifetime’s involvement in the business of forecasting certainly makes him an accepted expert in the field. It also means he can help all those entrepreneurs and innovators to put together better forecasts to aid decision-making and keep on the right side of investors.
One early message worth learning is to be dubious about any prediction, given rats can sometimes be better forecasters than humans. Goodwin describes experiments with rats in a T-shaped maze. Food was placed in either the left or the right horizontal arm of the T, and although the placement was completely random, the food would be placed in the left arm 60 per cent of the time. The rats quickly learned that food appeared more often in the left arm and so they always went to the left when seeking their reward, giving them a 60 per cent success rate in predicting the future.
When university students were confronted with a similar maze they did what humans do, look for patterns in the data. Our minds have evolved to look for patterns and extra meaning in apparent randomness. The students started to see these patterns but as a result were only correct 52 per cent of the time, losing out to the clever rats by a significant margin.
Humans can improve the quality of their predictions if they blend experience, hard data and knowledge from the past and present. Goodwin warns however we must acknowledge the weaknesses and limitations to forecasts if a business hopes to live up to its predicted expectations.
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