Data and AI

How to Call B.S. on Big Data: A Practical Guide

It is – or should be – well known that 82.63% of statistics are made up. Apparent precision gives an authority to numbers which is sometimes earned, but sometimes completely spurious. More generally, this short article argues that humans have long experiences of detecting verbal nonsense, but are much less adept at spotting nonsense conveyed through numbers – and suggests a few rules of thumb for reducing the risk of being caught out.

Much of the advice offered isn’t specific to the big data of the title – but it does as an aside offer a neat encapsulation of one of the very real risks of processes based on algorithms, that of assuming that conclusions founded on data are objective and neutral, “machines are as fallible as the people who program them—and they can’t be shamed into better behaviour”.

Michelle Nijhuis – the New Yorker

Future of work Social and economic change

Chill: Robots Won’t Take All Our Jobs

All the signs that you would expect to see in the labour market and wider economy if robots were displacing jobs are absent: productivity is not growing rapidly, labour turnover is not going up, and employment remains high.

That’s not to say, of course, that automation isn’t happening – and Surowiecki is careful not to say it – or that what has happened up to now is an infallible guide to what will happen in the future. But this article does contribute to the recognition that technological progress, the social and economic adoption of that progress, and the wider impact of that adoption are all very different things, potentially with very significant lags between them. That perspective is now coming through more strongly elsewhere as well – which should mean that the debate can be more balanced.

James Surowiecki – Wired

Organisational change Service design

Why Amazon is eating the world

The success of Amazon has been told many times and in many ways. This is one of the less obvious and more compelling versions, focusing on the power of treating its internal systems and relationships as if they were external  If they are good enough for other people to want to use them, that’s a good sign that they are good enough for Amazon to use for themselves.

It’s clearly worked pretty powerfully for Amazon. That’s interesting in its own right, but it also raises some important and difficult questions for organisations which are not Amazon, perhaps in particular for governments, which are quite heavily insulated from the consequences of customer satisfaction. Government is not the next Amazon, nor should it be, but it’s worth reflecting on whether there is a similar process by which a drive to quality improvement could be designed into processes and systems.


Zack Kanter – Tech Crunch