Chapter 1 Introducing well-being data
What is this book trying to do?
It’s just really hard when you’re bogged down in numbers and reports, and you’ve got a deadline looming, to be sure to know that the statistics you use are correct, or that you’re even reading a graph properly.
Someone who uses data all the time said this to me a few years ago. This person’s confession in an interview chimed with me and my own imposter syndrome. How can we feel reassured in the data we use and the way others use data? How can we begin to trust ourselves more to know when to trust others?
This whole book reflects on my realisation that—without training and familiarity (and sometimes even with this stuff)—it is really hard to be sure to know that the statistics you cite reflect the ‘real world’ in some way or that you are interpreting a graph or data visualisation properly. This feels all the more important when these data and arguments are related to people’s well-being or social justice. This is the main justification for the value of data in social and cultural policy. Yet data are undervalued at the same time, in that while the importance of data is an absolute, less attention is paid to the data itself: where they are from, who they are about, how they are used. Are well-being data being used appropriately?
Most importantly, the book aims to tell those of you that think you are inherently bad at numbers, that you are not, and this goes for reading graphs or policy documents. Instead, more often than not, it is how these are presented that are flawed or lacking in various ways. People who do research are not always good at communicating it. This is probably, to be honest, mainly because the authors had their own deadline looming, rather than necessarily any immoral practices. But also, sometimes, it can be that people report on their findings without thinking about how to make their findings accessible. This is—of course—why it is important for people who are confident with data to consider those who are not.
There are times, however, when you encounter a bad statistic: one that is misleading or misused. We encounter them all the time in the press and in parliament—and we’ll encounter many throughout this book that are linked to well-being. This book might encourage you to realise that you are fully equipped to look for alternative statistics, or to look through the headline findings to understand the data better, and why that statistic sounds inflated or confusing. We have lost confidence in our common sense, which affects confidence in critical thinking and our own resourcefulness to see through the ways that data are used. This book hopes to increase confidence in looking beyond a presented statistic: to look (or at least peer) underneath the bonnet ourselves.
Data of some sort are a vital part of our daily lives, now. Whether we are writing a report with numbers in it, filling in a ‘well-being at work’ survey or having our BMI measured by our doctor. We have all spent time in COVID-19 working with the data we were given to decide whether our trip to the supermarket was essential enough. We are all living and working with data and in contexts that need data. Well-being data are often our data, in that they are personal data about us—and their collection requires our time and consideration.
When thinking about data, we need to remember the version of us— yes that’s you—that encounters data daily. The version of us that ignores those emails asking for our opinions or asking after our well-being because we are too busy, or we feel that whoever is asking for these data don’t really care anyway. We need to remember that we (well, we here is actually me) will always give an Uber driver 5 stars, irrespective of how safe we felt or kind they were. We need to remember that time we went to a capital city and the highest-rated restaurant was McDonald’s. We need to think about whether those numbers represent our understanding of the world or not, and if not, then, why not? In a book about well-being data we need to be pragmatic about how different official well-being data are from these more familiar data contexts.
Every day, we interact unthinkingly with metrics, statistics, numbers and data collection all the time. We make common sense, snap judgements that enable us to dismiss them as useful or not to us. What is so different about statistics in a book or in our jobs—or even in research published in reports? Why is it that some people’s use of numbers feels incontestable? What is it that means we do not even think to question numbers and their uses? It is a sense of authority and context. So, I hope that with more personal authority and greater appreciation of context gained through reading this book, maybe we can feel more like engaging in and with, not only data as numbers, but ideas of data.
More specifically, this book has six key aims:
- one, to explain the history, politics and contexts of data produced that might be called well-being data;
- two, to explain some of the limitations of these data and the research and policy that have used them;
- three, to describe how changing uses of data have changed how we live in various ways;
- four, to present real-life examples of presentations of data and statistics, to break down how they have been ‘made’;
- five, to show how numbers can be misrepresentative, why this is a problem and how you should be able to feel confident challenging them; and
- six, to show that data do not capture reality neutrally, but are used to create realities through public decision-making that directly affects personal, community and national well-being
The examples chosen have been accumulated from my experience of learning to feel more confident with different kinds of data and numbers. They come from my own moments of head scratching and the lost hours on the internet trying to understand why things don’t quite seem right; all those times I have asked someone else ‘does this make sense?’—to which the other person has sometimes looked puzzled and said, ‘actually, no’.
This book also emerges from my feeling uncomfortable with what I was asked to do with data and comfortable to question the status quo. I found myself in a situation where there was an assumption that only numeric data are evidence and that somehow all numeric data were assumed to be evidence. I felt able to challenge the idea that just because data are in a formal-looking report, it is not necessarily ‘good data’, or factual.
This book also emerges from my realisation that just because things are not readily understandable to all does not mean they are hard to understand. For example, this book also developed from collaborations with academic colleagues who do use data well to understand culture and well-being. It also emerges from working in a sector-based data network with colleagues who collect data on what the cultural sector and creative industries are well-known for, as well as what they are less well-known for.
So, let’s shake this identity that arts can’t do numbers—a phrase I’ve heard too much. Let’s shake this idea that one of my Data Science students shared, that people who do data don’t care about well-being. Let’s also make sure that the claims made using well-being data in cultural and social research and policy can be substantiated and understood.