Data are an important part of my job as an economist. I use it as a core part of my role when talking to customers, but it's only a part of what I do and think about.
But for executives looking to take advantage of the transition to a digital economy, it’s clear data are increasingly critical for success.
When looking across sectors, it’s hard to define which businesses are doing data ‘better’ and who isn’t. Everybody's on a different data journey. With that in mind, I think we can always learn something about how to leverage data better.
Below, I'm going to talk about eight key things I've learnt about data, as an economist, over the journey. These rules have shaped how I think about data and use it in my work – although to quote Captain Barbossa, they’re more like guidelines than actual rules.
Rule number one is what I call ‘bend It like Beckham’.
I recently came across a report online which covered some of the important statistical ideas of the last half century. At 21 pages it was riveting stuff (!). But do you need to study like this to work with data?
David Beckham was an enormously successful footballer who found global fame. But the truth is – at the risk of offending some of the audience - Beckham was not a great footballer in the strictest sense of the term. It’s just some of his specific skills - free kicks the obvious one – lifted him above his contemporaries.
Beckham showed to be a great sportsperson you don't have to have every skill in the game. In the same way, not everybody who wants to work with data needs the skills of a scientist. You just need to play to your speciality.
In business, everyone's game is different. It's about finding a level of technical expertise that suits you, your skillset and your role.
Rule number two is data are not like Coca-Cola. Bear with me here.
It’s easy to become very certain when first using data. ‘We have the numbers’, we say. Things become quite binary; true or false. We tend to adopt higher levels of conviction around things we think we can model.
There’s a book on modern or contemporary art called Why Your Five-Year Old Could Not Have Done That. I know very little about art but, before I read that book, was probably guilty of thinking my five-year-old self could have replicated the occasional piece of modern art. Why Your Five-Year Old changed my mind.
What separates modern from classic art is you can't appreciate it purely for the technical skill involved. The power of modern art is the symbolism if offers. You need to exercise your brain to understand it. You need to think about what the artist is trying to tell you.
Andy Warhol - a very famous contemporary artist - used Coca-Cola imagery quite a lot. In his book The Philosophy of Andy Warhol: From A to B and Back Again, Warhol wrote about the levelling nature of the drink in the US; a shared experience from the man on the street to the President or movie stars.
“A Coke is a Coke, and no amount of money can get you a better Coke,” he wrote. “All the Cokes are the same and all the Cokes are good.”
Data are not coke. There are tendencies to truth, but there is no universal one. In many ways data analysis is an art, not a science. You are trying to both discover new things and communicate and tell a story.
But art also doesn't mean easy. It doesn't mean undisciplined. Just because the final product looks straightforward, it’s still powerful.
Rule number three: data only measures what the data measures.
There’s a chapter in Caroline Criado Perez’ Invisible Women: Exposing Data Bias in a World Designed for Men called Drugs Don't Work. It focusses on the idea many clinical studies for health treatment include only men, but the results are then applied to the broader population.
As an example, she wrote of how only one in eight women who has a heart attack report the classic mild symptoms of chest pain. For one half of the population, this common inference is simply useless.
When we look at data samples we infer from the specific to the general. We love to infer - it's what our brains do. And what gives data power is inference. That’s kind of is the whole point.
But we need to be thoughtful and careful about the data we're using and the inferences we're making. Try and stay disciplined. Try not to make big leaps of logic - even though it can be tempting.
Rule number four: make the familiar, strange.
When I was young my dad would encourage me to ask silly questions. It used to drive me bonkers.
Journalist Gillian Tett joined the Financial Times in the early 1990s with a background in anthropology - the scientific study of human behaviour. As a writer she predicted the 2008 global financial crisis – at least in part, she claims, due to her anthropological skills.
One of the objectives of anthropology, she says, is to make the strange familiar. Before the GFC, Tett made the strange familiar by really seeking to understand the basics and foundations of the financial system and from that she concluded the edifice was not stable.
Making the strange familiar refers to a genuine effort to understand and empathise with those who think, act, and appear differently to you.
In her writing, Tett also encourages people to make the familiar, strange. Everyone agrees X, Y and Z is true – but is it really?
Everyone says the most successful businesses do A, B and C. But does that still apply? In a world that has been changed so much with the digital revolution and COVID-19?
We increasingly have the data to answer those sorts of questions. But the data are useless if you're not asking the right questions. And sometimes, a silly question is the right one.
Rule number five is one I like to call Frankenstein.
In Merchants of Truth by Jill Abramson, the author outlines how a handful of different media organisations handled the transition to the online new world. While the book itself found some controversy post publication, it’s still an enlightening read.
At its heart the book is essentially about change. Old news organisations were steeped in tradition and had a sense journalism was done a certain way. The business side of the organisation was very separate from the part that wrote the stories.
But the online world upended that model. Now it’s clear you need to conflate those two things. You have to try to marry your purpose with commercial imperatives.
It’s the same with data. A business’ credibility as a peddler of expertise – be it in products or services - comes from your data being in some sense accurate or truthful. But you can't pretend you don't have a commercial objective as well.
In business, we need to ensure the data resonates with the audience. Analysis without considering your audience is not commercially viable.
Rule number six is to seek out bad weather.
Many of my points so far have been focused on what to do and not to do. But once you decide to do, how do you go about doing it? The answer is: with creativity.
There are, of course, no rules about creativity. But John Hegarty's Hegarty on Creativity makes some interesting points around a generalised approach.
Hegarty argues creativity flourishes in adversity - and suggests creating some by seeking out bad weather.
“I've got nothing against sunny locations,” he writes. “Sydney, for instance, is one of my favourite cities but is not, in my view, a creative centre.”
“The problem with Sydney is it’s weather. It’s just too good. In comparison, London always ranks as one of the world's great creative centres. Why? Because the weather is [rubbish]. Rain is London's creative trump card.”
You can get creative with data, too. If you can do so successfully, it will resonate more with yourself, as well as your audience.
Rule number seven is pasta should be al dente.
Okay, so now you understand data are a tool you need to explore, and you’re working on your creativity.
How do you successfully express the complex ideas you can get from data to an audience?
In 1936 Dale Carnegie published a book called How to Win Friends and Influence People. The emphasis of the book is always to put yourself in the other person's shoes. It’s a valuable lesson for applying data. But how does this relate to pasta?
There’s a widely held misconception spaghetti is cooked when you can throw at the wall and it sticks. This is, of course, a crime against food. Pasta should be al dente - firm to the bite, and not anywhere near the wall.
The same goes for data. You can’t expect data analysis to resonate if you just throw it all at the audience and see what sticks.
Even if the best insights are in there, it can be overwhelming. Skilled operators can distil the data and present the right insights in the right way.
This can be a difficult approach in some risk-averse businesses as there’s an instinct to give the customer everything and let them choose what they want. But that's almost a guaranteed way to fail. You're much better off working hard on the messaging.
Empathise with the customer. Put yourself in their shoes. Perspective is particularly relevant when you have a genuinely revolutionary idea that challenges someone's worldview - or view of their business.
The more challenging the implications of your data work, the harder you need to think about how you get the customer to understand and accept it.
Rule number eight is: use the force, Luke.
James Surowiecki’s The Wisdom of Crowds is a famous book that explains the way groups of people are often smarter than just a few experts. Under the right circumstances, crowds are remarkably intelligent - often moreso than the smartest people in the group.
Two implications on data flow from the wisdom of crowds. One is, when you're a bit stuck, throw it put to the group. Use the collective intelligence of others to help you out.
The other one is a bit harder, but just as important: don't dismiss out of hand input from people that don't have any obvious subject matter expertise.
People tell me about the economy all the time. It's hard sometimes to listen. But often people with a fresh perspective have something valuable to offer. Make sure you're open to those ideas from people that don't obviously seem like experts.
Data are a creative endeavour. Throw it out to other people if you are struggling or worried that you might be missing something.
Richard Yetsenga is Chief Economist at ANZ
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