Accountants are all torque
At first glance there may not seem much in common between Formula One racing and chartered accountancy. So the news today that KPMG have teamed up with a division of the McLaren racing team is both unexpected and exciting. McLaren will give KPMG access to the methodology they use to process the amounts of ‘big-data’ such as the information they collect as the racing car speeds round the track so that it can be used to help them make predicative decisions such as when to bring a car in for a pit stop. KPMG intend to use this information in a number of ways, most commonly to help them identify (predict) future problems and issues when they are doing audit work, rather than allowing the audit simply to be a backward looking view of a corporate body. Part of the deal is that KPMG will become one of the sponsors of the McLaren team – it is good to see that both parties used their negotiating skills to make a good trade.
All exciting stuff. Of course the concept of using historical data to help predict future events is not new at all, but technology to make it easier and quicker to do, and perhaps more reliable as well, is welcome. One can see that this might produce insights into future frauds and deceits within organizations which up to now have simply happened in ignorance of management and have only been revealed retrospectively as the result of whistle-blowing.
Big-data - the collection and analysis of mega amounts of historical information - is becoming increasingly important in future decision making, not least in the commercial operations of most organizations. Analytical personality profiles are very common within procurement teams and stock market traders. And the results of big-data analysis can be very surprising.
The two Freakonomics books by Levitt and Dubner demonstrate this. They take examples where there is a counterintuitive fact – for example that most drug dealers live with their mothers, rather than lording it in expensive mansions of their own. They then apply big-data analysis and demonstrate that there are perfectly logical reasons why this is so –the majority of drug dealers by number are small time street traders who never make any real money – there are relatively few drug lords and they are the ones who make the fortunes.
Negotiators also find big-data increasingly useful – in fact more and more commercial negotiations are primarily data-driven. An often asked question over the years from participants on our courses has been about information imbalance – ‘how should I deal with counterparties who know much more than I do?’ Ultimately the only answer is ‘catch up’! But data-driven negotiating can become just a mathematical exercise, and the result is that sometimes common sense, and sometimes ethical considerations get forgotten. Good negotiators need to add intuition, lateral thinking and a moral compass to their skills-set, and sometimes analytical thinkers find that difficult to do.
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