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:

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 progression.

I’m much less interested in the data privacy or security or Big Brother aspects of the Big Data revolution. That’s not to say these aren’t important issues for the world to consider and agree on; they are. It’s just that there are plenty of people and resources more knowledgeable about those areas than me. I’m interested in the potential that the explosion of data offers for better, fact-based, evidence-driven storytelling. This is because spotting insights and patterns and trends in data is one of the keys to better storytelling – storytelling that’s rooted in human truths we couldn’t record or observe or report on before, but that are now little more than a few clicks away for even entry-level users.

The second factor supporting the assertion that the Big Data revolution shows no sign of slowing down concerns the tools developed to harness, manage, and make sense of data. Just as there’s more data available about almost everything that’s going on in the world, so the tools for analysing and visualising Bigger Data sets are getting better and simpler and more straightforward to use. Tools like IBM Watson. Tools like Tableau. Tools like Brandwatch.

The real challenge of Big Data for storytellers is finding and isolating that little corner of it – Little Big Data, or maybe little big data – that’s relevant to the story you want to tell. And then analysing it and extracting meaning from it. But the real power of using relevant, little big data sets as the foundation for better storytelling is that it’s true. This is a theme that I and my co-presenters Neville Hobson and Thomas Stoeckle return to in every episode of our Small Data Forum podcast (see www.smalldataforum.com or iTunes).

As I’ll explore in the last chapter of this book, my contention is that we don’t live in a post-truth era, whatever the Goves and Trumps and Bannons of this world would have you believe. We live in a more open and data-rich world in which anyone can cross-check what anyone else says. And for brands and corporations, politicians and personalities looking to grow and sustain loyal audiences – audiences who can become, through social media, their very advocates – truth and authenticity have never been more important.

The Power and Impact of Storytelling

As cognitive creatures, humans are hardwired to respond to stories and story structure. Stories are how we make sense of and navigate the world. We pay attention to stories, we are persuaded by stories, and we react to them. To do something different, or to carry on doing the same thing our families have been doing for generations. In March 2017, the English sociologist and broadcaster Tom Shakespeare quoted novelist Philip Pullman to Radio 4’s The Power and Peril of Stories: “After nourishment, shelter and companionship, stories are the things we need most in the world.”

Story structure was first identified – or at least first codified – by Ancient Greek philosopher Aristotle in his elegantly brief, fourth-century BC work, The Poetics. Considering the art forms of the day – principally epic poetry, tragedy, and comedy – he showed that people respond best to stories with a three-act structure; a beginning, a middle, and an end; a thesis (proposition), an antithesis (an opposing view or plot), and a synthesis (a bringing together). Academics and practitioners since have developed this beguilingly simple structure, labelling the first act the set-up, the second act the confrontation, and the third act the resolution. Indeed, Hollywood scriptwriters and screenwriters have also contributed much theory and practice to our understanding of story structure.

American scholar Joseph Campbell identified a 12-stage narrative pattern which he called the hero’s journey and is the narrative structure many epic stories of trial and redemption follow. Common elements include the call to adventure, meeting and being trained by a mentor, tests and trials in a world different from the world inhabited by the hero, rewards for overcoming adversity, and a return to the world where the story started. Just consider Star Wars and Harry Potter, The Odyssey and The Aeneid, Little Red Riding Hood and The Very Hungry Caterpillar. Not to mention episodes, series, and entire boxed-set narrative arcs of The Sopranos, Breaking Bad, and Modern Family.

The reason these stories resonate so well with us is because they are based on universal principles of storytelling. The three-, 12-, and other numbered-step models – identified and expounded by academics and practitioners from Aristotle to Robert McKee – are designed to draw out universal rules from stories that resonate. Stories about people like us who have to experience something extraordinary in order to move on with their lives. We are able to make our own decisions in our much more mundane, slower-paced lives by reference to those stories that chime best with us. We may not be a king or a queen, we may not be dealing with murderous plots of evil dictators, but the power of great story, well told, is our ability to learn from it because it feels authentic and like something we could go through. Our lives are all reflected through the prism of Game of Thrones.

The literature goes on to suggest that we respond best to stories that are based on reality which itself depends on experience and evidence: stories that are rooted in genuine, data-driven insights that explain an aspect of the human condition and are wrapped in a veneer of emotion. As it is my intention to demonstrate in this book – through both theory and practice – modern-day organisational storytellers need to use data, facts, and evidence as the foundation of their stories. Not instead of stories or as the stories themselves.

The economist Steve Levitt and the journalist Stephen Dubner have proved themselves to be the masters of data-driven storytelling through their Freakonomics franchise – the books, the (partially sanctioned) film, and the podcast. At time of writing, Freakonomics Radio had just published its 300th weekly episode. Think Like a Freak is the closest thing they’ve come to writing an instructional manual, and it distils their learnings into a very readable how-to guide. They conclude this book with the line: “Stories stick with us; they move us; they persuade us to consider the constancy and frailties of the human experience.”

We’re All Storytellers Now

Before the Big Data and social media revolutions, communication was a closed shop and a restricted discipline. In corporations and governments, in the private and the public sector, communication was done by communications professionals – an ironic soubriquet, given that both communications departments and communications agencies are populated by amateur generalists. Most often, these people were arts graduates, except in unusual cases that required specialist technical or biomedical knowledge, such as science or law or medicine.

Communication was a mediated monologue, with communications departments and/or agencies issuing statements, and media outlets using that information to report on what the organization did and said. They didn’t blindly report, copy-and-paste style, what the PR teams put out. Not all the time, at any rate. For investigative journalism has a rich history, from Watergate to News International’s phone hacking; from Woodward and Bernstein to Nick Davies. But very few individuals – particularly in listed businesses or positions of public authority and power – were mandated or even allowed to speak.

How different the world is today. Not only do most people who work in any category of organisation have personal social media profiles on which they share details of their lives outside the office (Twitter, Face-book, YouTube, Instagram), but many also use these channels and others besides (personal or company blogs, LinkedIn, Reddit) to comment and share their opinions on issues of direct relevance to the businesses they represent. They are involved in a process of impressionistic or pointillist corporate image building, adding their opinion to that of colleagues and competitors.

Today, many more voices matter. Anyone with a smartphone and a Wi-Fi connection has as much right and as much chance of being heard as anyone else. They have a chance to get their voice out there, the same chance as anyone else on social media sites – bar celebrities and high-profile commentators to whom a different set of rules apply. Only ten years ago, that would not have been possible. Influential bloggers start trends, celebrity chefs cause products or ingredients to sell out, and doctors – particularly in the U.S. – can lead unsafe drugs to be recalled from the market by regulatory authorities.

In this world – and it’s a world that’s not going away or slowing down any time soon – everyone in an organisation has the potential to be a storyteller. For themselves in the context of their employment, of course – such as when experts in a business attend conferences. But also on behalf of the organisation they work for. Again, for the purposes of this book, I’m less interested in the ethical or governance issues raised by the democratisation of corporate and brand storytelling. That’s a matter for legal teams and counsel inside organisations, and there are plenty of good resources about how to get your employees to be a social media asset and not a liability, starting with “don’t tweet drunk”. What I’m keen to explore is how, given the reality of a world in which many more voices matter, we can all make better use of data and statistics to tell better, more convincing, more impactful stories.

Telling Stories with Data and Statistics

However specialised workers in the knowledge economy become – as researchers, managers, or technicians; as scientists, data analysts, or consultants – there truly are today two core skills everyone needs: analytics and storytelling. The higher up an organisation we go, the further we’re likely to get from the raw data itself. But to tell stories authentically and with impact, we need to understand not just what the data show but also why and how. This is the sense in which everyone’s jobs are becoming increasingly similar.

So, this book is designed to help many different people in many different types of organisation in their quest to become better, data-driven storytellers.

They might work in a communications agency – advertising, PR, or digital – and need to create a new idea for their client’s product, brand, or service; for a company, an NGO, or a charity. This is the world I know best, but narrative by numbers isn’t just about communications agencies. It’s about communications for and by everyone.

They might have discovered something through empirical, academic, or market research and want to share what they’ve found out with others, to get them to see the world from their point of view, and to share the real-world impact of their discovery.

They might need to motivate others to do something differently from how they do it today. To start doing something (take up exercise, say). To stop doing something (smoking). To do more (eating fruit and veg) or less (drinking alcohol) of something. Or to do something for the first time and adopt a new habit (wear a smart watch and integrate it into their lifestyle).

They might work in an organisation on a mission and want to recruit others to their cause.

They might be tendering or pitching for a new contract and need to make a compelling case for why their bid is best for the prospective client or customer.

Or they might work in an organisation and need to persuade colleagues to adopt a new strategy. To buy from a new supplier. Or to adopt a new policy.

In each one of these instances, taking an analytical and data-driven approach to storytelling will make the story better, stronger, and more impactful. It will help a wide variety of different people, in different types of organisation and at different levels of seniority, with the task of convincing people to support them. It will assist them in the “moving business”.

There are plenty of excellent books and online resources and tools to enable you to choose how best to visualise the stories you tell with data. Former Googler Cole Nussbaumer-Knaflic’s Storytelling with Data: A Data Visualization Guide for Business is one of the best, most accessible, and most practical guides to show you how to communicate data to tell a story. She shows very clearly how different types of charts or particular elements of a graphic can distract the eye or get in the way of telling an impactful, data-driven story. She uses some of the principles of storytelling at the heart of her process, and I learned a lot from reading her book. What it’s all about is given away by the subtitle after the colon. You’ll find it great too, and should buy it. But this book does something different.

There are also plenty of resources extolling the virtues of taking a storytelling approach to business communications – particularly the ubiquitous and frequently pernicious PowerPoint presentation – and Anthony Tasgal’s Storytelling Book is one of the most instructive here. If you can get past his love of the pun – something I share with him, so it didn’t hold me up – there are 24 great tips that will help you “find the golden thread in your presentations”. I learned a lot from reading his book, as I’m sure you will too. I’ve also enjoyed hearing him speak at Market Research Society events, though I think in person he slightly overdoes the argument that storytelling needs to move away from numbers and back to story, but different points of view are healthy. Add his book to your Amazon order. But again, this book does something different.

Technology, data, and the fact that many more people fulfil the role of storyteller than ten or even five years ago doesn’t necessarily mean everyone does it well. The ability to look at, interrogate, and understand data sets, and then to extract only those elements of the data that you need to tell a convincing and compelling story – that takes real skill. Colleagues and bosses and clients don’t necessarily need to know how you got there or to see your workings out. In fact, this is usually a distraction or irritant at best, but can be confusing and undermining to the point you’re trying to make at worst. It can, indeed, be counter-productive.

This book sets out a series of five simple rules that will empower and enable any storyteller in any organisation tell better, data-driven stories no matter whom they need to convince. By sharing the pitfalls and the pratfalls I’ve suffered over nearly 30 years advising companies on how to tell better stories, my aim is to help the current and future generations of data-driven storytellers do their job brilliantly. And remember, we’re all storytellers now.

We’ll see how you can keep your storytelling simple. How to find and use only relevant data, detecting genuine signals from the siren call of noise and avoiding false positives and spurious correlations. We’ll learn to beware the Curse of Knowledge and drive genuine engagement in data-driven storytelling through a combination of energy, empathy, and emotion. Data-driven storytelling is very definitely NOT all about facts, facts, facts. It’s much more about knowing your audience – understanding whom you’re trying to convince to do what – and then talking human. Even though companies are abstract concepts without the power of speech, it’s terrifying how un-human corporate-speak can be, even though it has to be uttered or written by a person.

There. If you were in a hurry, those last two paragraphs have covered what we’re going to go through in detail over the coming chapters. We’ll look at each of these rules, in theory and in practice. What’s more, each chapter will present a practical exercise to bring different elements of data-driven storytelling to life. Each chapter also features an example of a truly great data-driven story that has lived in the public domain, and we’ll analyse what it was about these stories’ use of data that made them work so well.

As legendary screenwriting coach Robert McKee is fond of saying, “a business leader should think like an author about their brand.” As we’ll see in the pages to follow, this is particularly true when an organisation is using data and statistics to inform their narrative and shape their storytelling.

I trust you’re up for the ride.

Summing Up

David Ogilvy’s assertion that physics was bad if Lord Rutherford couldn’t explain it to a barmaid is outdated, and in the twenty-teens we’d say “member of bar staff”, though hopefully not “waitron”. But the principle is spot on. Everything is explicable.

Analytics and storytelling are the two core skills for most people working in the knowledge economy.

Educational systems and theory around the world have pigeon-holed people as “artists” or “scientists”. This has been unhelpful.

Psychology should also shoulder some of the blame in keeping analytics and storytelling apart, particularly the caricature of the left brain/right brain dichotomy.

People like simple, single-factor explanations of phenomena, but the world is often more nuanced than that.

The exponential growth in data means we have never had more opportunity to use information as the basis for observations and ultimately insights that reveal deep human truths as the foundation of our storytelling.

The growth in the availability of data has been mirrored by the number of tools – often free – to crunch enormous volumes of data into much more manageable summaries from which narratives can be developed.

As creatures, humans are hardwired to respond to stories and story structure. It’s how we make sense of the world. This was first observed by Aristotle in his Poetics, 2,400 years ago, but the principles of the three-act structure are eternal and eternally appealing, including as the underpinning of Hollywood screenplays, short stories, novels, and multi-series TV epics.

The rise in data has been paralleled by the explosion in social media, which has provided many more individuals in all kinds of organisations with the potential to speak as and on behalf of their employers. Today, many more voices matter.

Telling stories with data and statistics is relevant right across the knowledge economy – public and private sector, profit-making and not-for-profit.

While there are plenty of books that talk about data visualisation or storytelling, there’s nothing that brings these two disciplines together – until now, and until this book.

Give It a Go: The Five “Whys?”

When someone presents you with a set of data or a data-rich presentation, become a four-year-old again. Ask “Why?” whenever they draw a conclusion from data. And then ask “Why?” again. Spice it up and vary it by asking “For what purpose?” And if you work for the Royal Shakespeare Company, you could even sprinkle in the odd “Wherefore?” Do this five times on a single argument a colleague is trying to make using a key data point, a killer stat, or a critical finding from their research or data set.

It may be best let them know that you’re doing this because you want to get to the bottom of whether the data really does matter – really does tell a transformational story. Because if they don’t know what you’re up to, they might – just might – get annoyed with you.

Build this technique into your repertoire of enquiry and interrogation whenever data is presented. It’ll sort the spurious correlations from the organisation-changing data sets.

Data-Driven Stories