Understanding Well-being Data


This website features the book Understanding Well-being Data, reproduced in smaller, more digestible chunks for accessibility, readability and to make it more interactive. Each subsection of a chapter, table and graph has its own URL, so that individual case studies can be used for ease in teaching – or whatever you might want! The site also holds details of related resources, like podcasts, animations, videos of presentations, articles I have written, as well as things others have written about the book and these related media.

This website is also designed to GROW. It will continue to evolve as I learn how people might want to interact with these ideas, issues and provocations in ways that help their work, study, or day-to-day interest in the topic. I would therefore love any feedback or comments you might have.

Well-being data have become integral to the evaluation of public policies and to the tracking of behaviour in the age of surveillance capital. Susan Oman breaks open the methods, assumptions and contexts that shape how well-being is constructed as an object, bringing clarity and sharp critical insight to an increasingly important field of expertise.

Prof. William Davies, author of The Happiness Industry
(read more endorsements of the book)

The book itself addresses the need to think about well-being data: how well-being is understood and the ways evidence is ostensibly used to improve society. Well-being data is pivotal in decisions that affect our life chances, livelihoods and quality of life. It is increasingly valuable to companies with their eyes on profit, organisations looking to make a social impact, and governments focussed on societal problems. ‘Following the data’ is a now-familiar phrase in Covid-19 policy communications. This book follows well-being data back centuries, showing it has long been used to track the health and wealth of society. It questions assumptions that have underpinned over 200 years of social science, statistical and policy work.

Understanding Well-being Data is a readable, introductory book with real-life examples. It offers practitioners an accessible view ‘under-the-bonnet’ of data collection, analyses and uses to see how they actually operate and what they enact. Understanding the contexts of data and decision-making are critical for policy, practice and research that aims to do good, or at least avoid harm. While the limitations of well-being data remain unaddressed, its positive contribution for society can never be fully realised. Through its comprehensive survey and critical lens, this book provides tools to promote better understanding of the power and potential of well-being data, and the limits of their application.


I wanted to write this book for previous versions of me and other people I have met over the years, who had questions about well-being and / or data that they found hard to find answers to. Even when I / they did find answers, these weren’t always presented in a way that was easy to understand, or that gave background to the answers. So, I wanted to find a way to help people grasp some of the things I struggled to grasp for a long time.

Issues of well-being concern many, many disciplines and fields of work, but are discussed differently. They are understood differently, sometimes measured, or perhaps not measured at all, as a point of principle. So, people also have different relationships to ideas of data and well-being. 

I wanted to write a book to help people answer questions they have, when the answers they need aren’t easily available to them, because: they’re not academics, they are academics, but in a different field, they’re students, or just anyone who didn’t know where to start.


When I left school and wasn’t sure that I knew what I was doing with my life, I worked in a call centre. So, when I read Dave Beer introduce his book on the power of data (2016) with his recollections of working in a call centre in the mid- to late nineties, memories came flooding back. When you logged on to start taking calls, and how many calls you were taking, even when you went to the loo and for a cigarette break and had lunch, were some ways data about you were collected. This data, or these data((A note on data as singular or plural. Most of the time, people talk of data as one thing. Actually, in this book we are going to use data as a plural, as data are rarely one thing, but lots and lots of small things.)), were used to indicate how well you were doing at your job. They enabled people to make judgements about you.

Crucially, I didn’t feel like I knew what I was doing with my life, but as a result of the data collected on me at work, others knew exactly what I was doing with moments in my life. Dave Beer’s account is an important part of an increasing body of research critiquing the use of data as a form of surveillance. Using data in this way is changing workplace cultures and breaking codes of privacy((Legislation is beginning to address these issues. GDPR is an example that offers greater protection, but is currently flawed and cumbersome. My research has found that GDPR in some ways complicates how we experience data, rather than making it better.)) in broader everyday life that are seen as part of

our societal values. It also changes how we feel in day-to-day life in ways we may not immediately recognise.

Using data to monitor people is also referred to as a ‘data practice’. These data practices have been shown to make people feel uncomfortable, as they sense they are being watched. In turn, this increases stress and anxiety. These feelings are understandable: these data are about you, but out of your control, and clearly enabling someone else greater control over what you do. The existence of these data changes people’s experience of work; it can make them apprehensive of how long they spend going to the toilet or eating a sandwich. Also, despite data’s capacity to capture these mundane aspects of your life, these data, and what they look like, remain abstract, somewhat bewildering and hard to grasp(( I now research how people experience data on a project called Living With Data.)).

Moving forward ten years to the mid- to late ‘noughties’, I found myself working with data in a very different way. I was working in a university that trained students in various aspects of theatre and the performing arts. Part of my job was trying to argue the value and impact of the students’ work. This task was bigger than that, really; it was to argue the value of training that these students were receiving at the university I worked in, precisely for the impact it had on students and the impact they had on society. A part of making this happen was to ‘find data’—often data that could tell a story about well-being. ‘We need data to evidence these claims’, I was often told.

I believed in the work of the cultural producers I was working for, and with, all those years ago. I was just not so sure about the data and statistics cited in the policy documents that I was being asked to find for funding applications and evaluations. These numbers and the way they are used in arguments about society, culture and value sat uneasily with me. The ones I was borrowing from policy documents didn’t make sense to me in a common-sense way, but I also simply didn’t quite understand them well enough to feel confident that they were evidence. I was also worried about the quality of the data I was collecting myself and their limits. Was I really sure that graduates from creative courses were contributing millions to the economy through the soft skills they gain in their training? Was I really sure that by simply attending a theatre-in-education workshop that the children involved would experience an improvement in their well-being?

It turned out I wasn’t sure enough to feel confident using evidence in ways that were demanded for funding bids and evaluations. It also turned out that it did not necessarily matter, as the fact I cited data as evidence was all that mattered to those who expected numbers in return for funding. The anxiety I felt about the quality of data and evidence I had to use, and the slightly absurd realisation that no one else seemed to care, led me on a journey: leaving this job for another university to become a student myself.

Understanding cultural policy through my masters, I hoped, would help me recognise how I might feel confident in using data and evidence—particularly to make the arguments I’d been making. I hoped it would help me overcome the barriers between me and the numbers—and the policy documents – that had increasingly become the backbone of my day-to-day job. In actual fact, all that extra critical thinking meant I became even less sure and less trustful of data and evidence as they are often used to argue social aims in policy. 

It also made me less sure that cultural policy means or should mean culture as the arts. Instead it made me more sure that society is far more cultural than what is limited by the category ‘the arts’. So, I proposed a PhD on well-being data, policy, culture and society (which also didn’t help me feel reassured in how evidence and data are used)((My PhD (Oman 2017) was attached to the AHRC-funded project called ‘Understanding Everyday Participation: Articulating Cultural Values’, 2012–2017 [AH/J005401/1]. This was funded by Arts and Humanities Research Council’s Connected Communities Large Project funding. Orthodox models of culture and the creative economy are based on a narrow definition of participation: one that captures engagement with traditional institutions such as museums and galleries but overlooks more informal activities such as community festivals and hobbies. The project aimed to paint a broader picture of how people make their lives through culture and in particular how communities are formed and connected through participation.)) After which I took two academic fellowships to improve data and data practices in the cultural sector((This research project, initially called ‘Social Mobility: The Case of the Arts’ was supported by two AHRC-funded projects: Data, Diversity and Inequality in the Creative Industries (or DDI) and What Constitutes ‘Good Data’ in the Creative Economy? (or Good Data) ran from January to August 2018, January to July in 2019, respectively. Both were funded by the Arts and Humanities Research Council’s Creative Economy Engagement Fellowship Scheme (or AHRC CEEF).)), to now find myself as a Lecturer in Data, AI and Society, as of 2020.

So, this book is written for the me in 2010: the me who was reading the Labour Party’s cultural manifesto and cutting and pasting arguments with a sick feeling that I didn’t know what I was doing, but I did know it felt a bit wrong. It is also written for the me in 2015 as a PhD student editing a conference presentation, and someone looked over my shoulder at an equation I had copy and pasted for a PowerPoint slide to tell me that the equation did not make sense to them – it wasn’t talking their language. I turned and laughed and said: ‘I thought it just didn’t make sense to me’.

This book is for so many of the people I have met in the last ten years, who have said, ‘I hadn’t thought of that’ or ‘I didn’t know that’—when these ‘thats’ can often be simply explained, but never are. Or maybe they are indeed amazed when they have understood something they thought they could not. It is also for the many people who have to use data in their day-to-day jobs, but feel a bit anxious about it—even if they are unsure why.

This book is also for the me in the 1990s call centre who knew I was being watched at work in some way, and it changed my behaviour. Yet I did not really think of this as anything to do with data at all—which all happened somewhere in sci-fi land. It is for all the people who are maybe interested in how data are such a big part of our lives and our way of being. Whether this is experiencing call centres in the 1990s to Fitbits of the 2010s, the management of resources in World War II or the use of data in the battle against COVID-19.

This book is also for my friends – they WILL read it – who send me links to online articles about data that are misleading or misrepresentative. Or, worse, fill their feeds with shared Facebook posts about ways to happiness and well-being (my pet hate). It is for those I don’t know, but who aren’t sure about how data about us are used: it isn’t all 

Alexa and deliberating the latest Bill Gates conspiracy theory. In fact, data about us have been used for thousands of years in ways we don’t hear about. Even when we know about data collection, as with the UK Census 2021, do we really think about what data they are collecting and why? Who is it for? What do they actually do?

This book is for my current Data Science students. Last term one of them told me that ‘people don’t care about other people’s well-being’, while another said, ‘I really liked the idea of thinking about data with a human element and not just as something a machine would produce’. For those who can do great things with data, how much do we know about whether they think about the people involved? What do people’s data help us understand about them? Can it help us be more understanding as a society?

This book is for all my previous students who care so much about their work improving other people’s well-being or society in some way. They were often hindered by anxiety surrounding their own research 

skills and data comprehension. There is often an unacknowledged cultural gap between data and well-being, despite the proliferation of well-being data. This needs addressing.

This book has an agenda for improved data literacy and data competency to address this gap. The book therefore reflects on how understanding well-being data use might help us become a society that is more understanding of each other. The fact that most of the people I list I am writing for are people I have met also means the book retains a mainly UK-specific focus. Perhaps in another ten years, I will be writing about these issues from a different place again. 

For now, this book is a personal endeavour to reflect on how I have come to understand the issues and to address data literacy in two main ways: 

1, in research on, in and with cultural and social policy sectors and, 
2, in the social aspects of data science and data studies. 

More simply, this might be explained as teaching ‘culture and society people’ about data and teaching ‘data people’ about culture and society

As this review of sociology (as the study of social life and society), points out, everyone has to interpret research in their lives by way of the 

media, but few of us produce it:

to consider more seriously the relationship between research literacy and research competency. All students of sociology at whatever ‘levels’ and in whatever institutional settings will become long-term consumers of research, but very few of them indeed will ever become producers of it apart from in undergraduate classrooms. In our view, most textbooks (and with honourable exceptions) over-emphasise teaching students competency skills, and considerably underemphasize giving them the literacy skills to read, unpack, interpret and evaluate research and the conclusions drawn from it. (Wise and Stanley 2003)

This book is no textbook, but an overview of how we are equipped to understand data in society and how that helps us understand well-being. The book offers many examples of data collection, and some examples of analysis, that can improve your research skills, should you so need them. However, it was not necessarily written to help people understand how to do research, but how to understand data in research. Therefore, it aims to improve understanding of how others use data—and how data can be used. This means we can better appreciate the limits and benefits of assertions regarding what we can understand of people, well-being and data.

This book is for those who feel uncomfortable with data to feel more comfortable with its collection, its expression (basically, those tables and statistics and sometimes squiggly lines) and the language of data. Even for those people who undertake research, or work with data in some way, the language of data can feel so different and alien that this is a barrier to engaging with data. I have found that this is the case with cultural and social policy practitioners, and as we shall see, this affects how people engage with evidence and arguments.

This book is also for people who feel confident with data, but have perhaps been trained to think of data as objective and neutral and to be read as fact. Consequently, the prospect of considering the social contexts of data may feel odd. It is, therefore, also for those who feel comfortable with data to be able to imagine the uncomfortable aspects of data. These include the various questions we should ask about contexts of the data used: where they have come from? Have they already undergone some kind of analysis or cleaning? How they will be used? Context is key to considering the limits to claims made from data about well-being, and, perhaps, even more importantly, how does ‘what we do with data’ (that we call data practices) affect a person’s well-being, or does it have broader negative social impacts?

Caring about well-being doesn’t necessarily mean people consider data issues. As I have described, the same is true the other way around: people who care about data don’t necessarily consider well-being. It is critical that this book does not reinforce a line of clichés of those who do and do not care about one thing or another, and those who are good at data and those who are not. Rather, there is a culture of misunderstanding that this book aims to help address. 

This book tackles this gap from the standpoint that just because things are not readily understandable to all does not mean they are hard to understand. Crucial to overcoming this is making it easier to feel more confident that if something about data is incomprehensible, then that may be because the way the data are used is bad, rather than you are not able to grasp what is going on.

As I have discovered a number of times in ten years’ researching well-being data, the way data have been used to describe society may not be robust. Also, they may be used to make claims of improving society in some way, when in fact these may not be true. Similarly, the negative social and cultural effects of how data are used to manage and monitor people and society may not be considered. 

We do not all need to be able to look under the bonnet like a trained mechanic to understand well-being data, but being able to peer in with some confidence may be enough to help us grasp the limits of what we are looking at. Only then can we—as a society—better understand well-being and data: how well-being is captured as data and how data affect well-being.