Narrative by Numbers

How to Tell Powerful and Purposeful Stories with Data

Sam Knowles

• Publisher: Routledge
• Year: 2018
• ISBN:
  9781351137201
• Edition: 1
• Topic: Business
• Subtopic: Business generale
• Index: Business

Shortlisted for the Business Book of the Year Awards in the Sales and Marketing category.

As jobs become increasingly similar, there are two skills that everyone needs if they're going to thrive. These are the ability to interrogate and make sense of data, and the ability to use insights extracted from data to persuade others to act. Analytics + storytelling = influence.

Humans are hardwired to respond to stories and story structure. Stories are how we make sense of and navigate the world. We respond best to stories that are based on evidence. But storytellers need to use data as the foundation of stories, not as the actual stories themselves. To be truly impactful, rational facts need to be presented with a veneer of emotion.

The Big Data revolution means more data is available than ever. The trouble is, most people aren't very numerate or good at statistics. Many find it hard to look at data and extract insights. Meanwhile, those for whom numbers hold no fear don't always make the best storytellers. They mistakenly believe they need to prove their point by showing their workings.

There are some simple and effective rules of data-driven storytelling that help everyone tell more compelling, evidence-based stories, whoever they need to convince. Narrative by Numbers shows you how.

1
The Two Skills Everyone Needs Today

[Nobel laureate] Lord Rutherford used to tell his staff at the Cavendish Laboratory that if they couldn’t explain their physics to a barmaid, it was bad physics.

David Ogilvy (1963), Confessions of an Advertising Man

First Principles

There are two skills that everyone needs in today’s knowledge economy to thrive and do their jobs most effectively. These are the ability to interrogate, understand, and extract meaning from data and statistics, and the ability to use the insights derived from the data to move people to action. Analytics + storytelling = influence. The purpose of this book is to show you how to excel at both and make the combination worth very much more than the sum of the parts. We’re here to understand how to develop narrative by numbers.

We are all, in the words of U.S. business writer Dan Pink in his book To Sell Is Human, in the “moving business”. And the best way to persuade, inspire, and convince others to do something is to bring together analytics and storytelling: to make data and statistics the foundation stones of the stories you tell. The impact of combining analytics with storytelling holds good for most everyone working in the public or private sector; in commerce, finance, or government; in academia, medicine, or education.

The data generated by and available to everyone in all stripes of organ-isation has grown exponentially in recent years, and the social media revolution means that today many more voices matter in the public domain. In one capacity or another, both formally and informally, more and more individuals have responsibility for speaking for or as an organisation. These trends show little sign of slowing down, which means the ostensibly fire-and-ice ability to tell impactful stories rooted in data and statistics will be everybody’s business within no more than ten years.

In caricature, the data analyst is an introverted, self-reliant number-cruncher who has better relationships with machines than he – and it’s always he in the stereotype – does with other people. He’s got a brain the size of the planet and colleagues consider him to be a social liability to be kept away from clients at all costs, but the insights he can generate with data can help unlock the challenge at hand. There are also often pointed (and usually groundless) snidey sideswipes at the analyst for his attention to personal hygiene, be it showering, shaving, or skincare.

And in caricature, the raconteur is an extraverted, entertaining, empathetic figure who comes alive in a roomful of people and who can use the power of storytelling to convince anyone to do anything. Even Inuit to buy ice, Geordies to buy coal, and Athenians – as the clichés have it – to buy owls. Colleagues and friends talk warmly about storytellers, and while in business their appearance may be protected or rationed – well, you wouldn’t want too much of a good thing, would you? – when the meeting is set, it’s one not to be missed.

The truth about both capabilities and the archetypal individuals who best exemplify them is rather more nuanced and prosaic.

The Trouble with Education

Twenty or thirty years ago, it was fashionable in education systems around the world to classify students as either artists or scientists. I know because – in Britain at least – I was there. It was the done thing to channel people as early as 14 or 15 to pursue one path or the other. This would dictate choices of subjects for the last two years of school, which would in turn dictate choices of degree courses and, inevitably, career trajectory. If you didn’t do chemistry, physics, and maths for your final school exams, it was very hard to see you progressing far in the chemical engineering world.

Unfortunately, it became a badge of honour among many of those artists – who found mathematics to be a challenge and so gave it up as soon as they were not much more than numerate – to happily admit they were “no good” at the subject. Perversely, it even became a little bit cool to say so, too. This problem was first formally identified in 1959 by C.P. Snow in his Rede lecture at Cambridge titled “The Two Cultures”. Snow was both a novelist and a physical chemist – a Renaissance man and data-driven storyteller, if ever there was one – and his lecture proposed that the forced separation of the humanities and the sciences would prevent the world from solving its most pressing challenges. His diagnosis of the challenge is so well expressed, it’s worth repeating this line of argument:

A good many times I have been present at gatherings of people who, by the standards of the traditional culture, are thought highly educated and who have, with considerable gusto, been expressing their incredulity at the illiteracy of scientists. Once or twice I have been provoked and have asked the company how many of them could describe the Second Law of Thermodynamics. The response was cold: it was also negative. Yet I was asking something which is about the scientific equivalent of: “Have you read a work of Shakespeare’s?” I now believe that if I had asked an even simpler question – such as, “What do you mean by mass, or acceleration?”, which is the scientific equivalent of saying, “Can you read?” – not more than one in ten of the highly educated would have felt that I was speaking the same language. So, the great edifice of modern physics goes up, and the majority of the cleverest people in the western world have about as much insight into it as their Neolithic ancestors would have had.

Inspired by Snow’s lecture, early 1960s satirists Michael Flanders and Donald Swann painted a caricatured picture of how those schooled in the humanities need to talk to scientists if they want to make themselves understood. In the introduction to their song The First and Second Law of Thermodynamics, Flanders addresses an imaginary scientist with the line: “Ah, H₂SO₄ Professor. Don’t synthesize anything I wouldn’t synthesize. Oh, and the reciprocal of pi to your good wife.”

Today, fortunately, the impact of early choices is being mitigated to an extent by broader advanced level subject arrays – particularly thanks to innovations such as the International Baccalauréat. It’s encouraging seeing the Arts trying to jimmy themselves among Science, Technology, Engineering, and Maths, and to see that STEM subjects are morphing into STEAM.

The fact that jobs are, indeed, becoming more similar also helps. Because, increasingly, we are all in the moving business, to thrive in this new world order we all need to master the skills of analytics and storytelling.

The Trouble with Psychology

Psychology also needs to shoulder some of the blame for the misperception that analytics and storytelling are not easy bedfellows – or at least the wilful misinterpretation of some influential psychological research. It is still widely believed, for instance, that these two core skills are mediated by different hemispheres or sides of the brain.

Humans are simple creatures, albeit simple creatures in possession of the most powerful supercomputer yet devised or discovered: the human brain. I say we are simple creatures because we tend to look for simple, elegant, and reductive solutions to the challenges that face us. We also use a wide array of shortcuts – technically known as cognitive heuristics – to try to solve these challenges. While heuristics enable us to make decisions when confronted with mountains of data, they often lead us to make very predictable mistakes in data processing and decision-making under pressure or uncertainty. This has been characterised as System 1 thinking by the psychologist Daniel Kahneman in his popular 2011 book Thinking, Fast and Slow, in contrast to more deliberative, considered, and slower System 2 thinking. That book summarises decades of Kahneman’s research, including the award-winning experiments he ran with his long-time collaborator, Amos Tversky.

A good example of this process in action is the universal human desire to favour single-factor solutions – solutions that say that “the Gulf War was about oil”, that “Leicester City won the premiership because of Claudio Ranieri’s leadership”, or that “Trump won the 2016 election because of fake news”. When we’re generalists looking into a specialist field, as most of us are most of the time considering most issues, we find it very difficult to consider the interaction of multiple factors working together. Factors like: Ranieri’s management style, plus Vardy, Mahrez, and Kanté all peaking in the same team at the same time, plus the Premiership’s Big Six clubs all underperforming for different reasons in the 2015/16 season, plus Jose Mourinho imploding and being sacked by Chelsea, plus the impact of the Sky billions on smaller clubs’ playing budgets, plus media momentum, plus bookmakers’ commentary, plus, plus, plus …

When it comes to popular neuroscience – a dangerous oxymoron if ever there was one – the left brain/right brain, analytical/intuitive, sciences/arts, rational/emotional dichotomy has proved to be one of the most stubborn and pervasive and inaccurate separations of function yet perpetrated by psychology on its lay readers. It’s a complete caricature, and a convenient single-factor explanation of the ultimate supercomputer that is the human brain. It is, in the handle of one of my favourite Twitter feeds, total @neurobollocks. And it’s been popularised at every turn by the reductionist, popular media.

Yes, it’s true that certain functions more connected with analytical processing have been identified as generating more left than right brain activity. But to ascribe this function to a single hemisphere and to categorise individuals as left- or right-brained on this basis is to display gross ignorance of the finer-grained nature of the brain.

Computer/brain analogies are always imperfect. This is because the billions of neurons and junctions between them – the synapses – not to mention the hundreds of different neurotransmitters at work simultaneously, independently and on each other, are generations more complex than any computer made by humans to date. Or for the foreseeable future.

Talking about the impact of brain damage on brain function, the psychologist Richard Gregory¹ drew a famous analogy: “If I remove a transistor from a radio and the result is that the only sound that I can get out of a radio is a howl, I am not entitled to conclude that the function of the transistor in the intact radio is as a howl suppressor.”

Just as a transistor is not a howl suppressor, so the left hemisphere is not responsible for analytics nor the right brain storytelling. Complex brain function like analytics requires the simultaneous and sequential firing and interaction of hundreds or more of interconnected functional units controlling discrete subroutines. These exist across both hemispheres. It would be convenient if the generalist, lay public could understand the left brain as the analytical part of the brain and the right as the storytelling part, but only convenient because it would tell a simple, reductionist story. And as Steven Pinker frustratingly concludes in his 1997 book, How the Mind Works, as creatures we lack the cognitive architecture to understand the promise of the title of his book. Frustratingly, but – it appears – quite correctly.

The other glaring error of the left brain/right brain, analytics/storytelling division of both function and types of people is that it assumes that the other hemisphere (and functionality) is inactive. So, analysts can’t communicate, and communicators can’t analyse. While it’s true that some people are naturally better at analysis than others, and others are naturally better storytellers, as jobs in the knowledge economy converge and as we all gravitate towards the “moving business”, we are all required to excel across both domains. And the motive of this drive is a little word with a big impact on all our lives. Data.

The Rise and Rise of Data

Data has grown so fast and to such an extent that it’s rarely talked about these days as just plain old data. Today it’s usually big data. And though English resists the temptation to follow its Germanic cousins and capital-ise words or phrases other than names, countries, or brands, big data is also very often Big Data. Perhaps it’s grown so much, it’s already acquired titular or nation status.

It’s hard to keep a handle on how much data individuals, businesses, and nations produce, and many find the sheer volume of data available today to be overpowering – threatening, even. In Big Data: A Revolution That Will Transform How We Live, Work, and Think, Viktor Mayer-Schönberger and Kenneth Cukier calculated that, by the end of 2013, there were an estimated 1,200 exabytes (EB) of data stored on earth. 1EB is 10^18bytes. Or 1bn GB, enough to fill 40bn, 32GB iPads, which would stretch from the Earth to the moon. And we produced the same volume of data again in 2014. If, in a single year, we produced as much data as had ever been produced in the 574 years since Gutenberg’s first printing press, it’s clear that the overwhelming majority of all data produced has been produced in the past few years. The graph is only going to become ever-more asymptotic.

Data is getting bigger everywhere, in every aspect of our lives. Cars produce and record details about every trip you take, from fuel economy to average tyre pressure as speed and temperatures change. Every phone call you make generates permanent records – about your location, the person you called, for how long, what your talked about. Personal fitness devices from Apple Watches to Fitbits record every heartbeat, as well as exercise and sleep patterns, and then give you a nudge when you haven’t been for a run for a few days or been mindful for a few hours. And conversations on social media reveal what people think – perhaps particularly vocal people in the early days of social, but today much closer to representative samples – about products, brands, personalities, and politicians.

The pace with which data is growing shows no signs of slowing down – if anything, it’s accelerating – and there are two interrelated factors to support this assertion (a bit of data-driven storytelling, if you will). One is that Moore’s Law of exponential growth continues apace. Gordon Moore, who cofounded two pioneering silicon chip businesses in the 1960s – Fairchild Semiconductor and Intel – observed in a seminal paper in 1965 that the number of transistors on dense, integrated circuits doubles every year or so. By 1975, Moore revised this down to every two years. What we’ve seen on average since 1965 is in fact a doubling about every 18 months.

Twice as many transistors in the same space every 18 months means cheaper and faster computer chips, both memory and processing chips. They’re cheaper because they take up less physical space and use less of the precious material silicon. They’re faster because electrons representing the ones and zeroes of digital data processing have progressively shorter distances to travel. As a result, computers continue to get faster and cheaper and storage capacity increases. Because it doubles every 18 months, this represents exponential growth according to a geometric rather than an arithmetic progr...