819Yl2J9DZL._SL1449_Back in the early Nineties, I was working on a Ph.D applying a tool called a Geographic Information System (GIS) to the challenge of modelling archaeological deposits under cities. For those of us worrying about these things, Mark Monmonier‘s then-newly published first edition of How to Lie with Maps was required reading.

It wasn’t so much a handbook for the nefarious, as a primer for those who wished to understand – or avoid – the traps and pitfalls so easily baked into both physical and digital maps. A slight change in colour palette, a shift of projection, an emphasis of this over that and a superficially factual and accurate map flips from portraying one truth to suggesting (or trumpeting) a very different one. Sometimes it’s deliberate. Sometimes it’s a (hopefully!) unfortunate accident.

It’s time, I think, for How to Lie with Data. There are plenty of books telling data scientists (whatever they are) and others how to visualise data, how to tell stories, and how to persuade. The great Edward Tufte‘s earlier books were doing the rounds alongside Monmonier’s guide, and did much to expunge the cartographic equivalent of ‘chartjunk‘ from the creations of those of us sometimes tempted to over-do the computer-enabled excesses of cartography.

But there’s a real gap in need of filling today; a short book to illustrate some of the easy and obvious pitfalls as we rush to make everything data-based, data-supported, and data-rich. It would be a book written to open the eyes of those approaching the data visualisations of others with a little too much credulous naivety. It would also be a book written to remind the enthusiastic visualisers of data to think a little harder. Just because you can use that particularly jarring juxtaposition of scarlet and puce in your palette, maybe the perception of your data would be more balanced if you didn’t. And we know that the data looks more convincing if you flip the axes and start counting from 1,000 instead of 0… but wouldn’t it be more professional to make it clear what you’ve done?

We have beacons of good practice like Information is Beautiful (plus book) and Flowing Data. Now, though, let’s have Information can be really ugly when put in the hands of bad people and those with more data than skill.

A bit of a mouthful? Then How to Lie with Data will do nicely.

So. Who’s going to write it? I’ll be at the front of the queue, recommending it to an awful lot of people who really should know better…