1.1 Introduction to Understanding Well-being Data

This book seeks to advance understanding of the role of well-being in social and cultural policy, politics and research. It does this by focussing on ideas, concepts and uses of well-being, as well as differences in types of well-being data. It was written primarily to offer practitioners a view ‘under-the-bonnet’ of data collection, analyses and uses to see how they actually operate, as well as what happens as a result of their very existence. Its accessible style aims to include students and a more general audience in discussions about data and those about well-being as two crucial issues of our time.

Understanding Well-being Data uses real-life examples, paying particular attention to the ways data are generated, analysed and used, to demonstrate how data practices respond to, and how they shape, society, culture, politics and policy. Its short and longer case studies make this an accessible learning curve, and one that is applicable to experts and novices of all sorts in all our everyday lives. The book focuses on uses of data in culture and society, and how they work as social policy, so that comparisons and contradictions are easy to see.

‘Following the data’ is a now familiar phrase in the UK from its significant role in government communications about COVID-19. The phrase is important, because it demonstrates that the very idea of data is used to justify decisions and policies for the nation’s health and well-being. Many across the UK watched various press conferences in 2020 in which its prime minister and other advisors would refer to ‘the data’ as an objective thing that they were following, rather than various types of data and information that people learn how to use, deliberately collect and generate, and that they interpret and analyse.

The government broadcasts on managing the COVID-19 crisis also included graphs and other data visualisations. Some of these were designed to show a comparison across areas of the country to justify which were under restrictions and which were not. They were badly labelled, making them hard to interpret by those who are data literate, let alone ‘the public’ being broadcast to. Most people felt more alienated by these uses of data than comforted that they understood what the government was doing—and why. The last one of these press conferences that I personally saw, before finishing this book, was a few days before I was supposed to travel to spend Christmas with loved ones. The whole nation was told that this was no longer to be possible. We were told that the government had followed the data, but that the ‘science had changed’.

Of course, ‘the science’ had not changed at all. Instead, the decisions made, based on human interpretations of data about COVID-19, and other data about the economy and mental health, about schools and universities, about the inequalities of those who can work safely, and those who cannot, were all in a melting pot of pressures involved in decision-making at this level. It was policy that had to change, not the science that had changed, and suddenly one set of data seemed more important than another to those in charge.

So, here we can clearly see that it is not that there is ‘the data’ as one indisputable thing, but these data are not neutral. By which we mean the data are not unbiased, nor impartial. They are collected, read, interpreted and presented and these processes involve many decisions. But, how can data themselves be biased? A good example of bias in data lies in the recent increase in algorithms that are trained using data to automate certain digital processes. Algorithms have actually been with us for centuries (an eighteenth-century happiness algorithm appears in Chap. 2). The word still refers to any form of automated instruction. The majority of algorithms are simpler than most people think and can be a single ‘if something is this, then do that’ statement that can then be actioned. Contemporary algorithms tend to be long sequences of these instructions. As you can imagine, with these many instructions and decisions, bias is likely to creep in.

One of the starkest instances of bias can be found in the search engine, which most of us now use all the time. It is a mundane part of our everyday lives that we don’t often think about. Search engines have been designed to learn to second guess what we are looking for, as they have a record of, or they ‘know’ all the searches we have made before this one, alongside all of everyone else’s searches.Footnote 1 Safia Noble (2018) revealed how these guesses are biased in dangerous ways that are both racist and sexist. As recently as 2011, the first thing that would appear in searches with the term ‘black girls’ was a link to hardcore porn. You may try and explain this away as an algorithm prioritising some ads over others. Explaining these things away may be—in fact—a part of the problem, of course, when it comes to bias, sexism and racism. It therefore very much deserves attention.

Noble provides much more evidence than this example above, though. Noble shows a variety of ways that the search engine predicted the searcher was looking for derogatory images of black women, even apes, as well as pejorative character traits. Noble ‘followed the data’ to reveal how data practices are biased, but also revealed our own biases to us. People were shocked when Noble’s revelations were published. This shows us that not only are the search engines biased, but that we are. People are biased, in the way some want to believe that we live in a ‘post-racial’ society, and that we do not need to worry about racism any longer, when actually they are blinded to the fact they are consuming culture, through data, that are both biased and racist.

Data play a large role in society. Critical data studies, like Noble’s and throughout this book, where we ‘follow the data’ to see how it works in context, reveal truths about both data and society. We need to learn from these revelations about data to improve well-being and society.

1.1.1 Subjective and Objective Data

But what if we return to data used by politicians, surely this does not contain evidence of the same biases? A good example is ‘the poverty line’. When a politician talks about ‘the poverty line’, we think that this is an absolute thing. Not necessarily a real thing, like picturing people living under a power line, but that the line represents a measure from data which are objective.

Objective measures of poverty are objective by name, but they are not entirely neutral. So, does that mean they are actually objective? There is no measure of poverty that is conclusive: while it means not having enough resources to cover essential needs, this is a subjective valuation of the words ‘essential’ and ‘enough’. The subjective nature of the word essential has also gained prominence in the UK, as politicians have used it to avoid making clear decisions on what COVID-19 restrictions should entail—despite their data expertise. Instead, people are forced into making their own evaluations on what counts as ‘essential’ travel, work or food, and therefore what is lawful behaviour under parallel lockdown restrictions in different areas of the UK at different points in time.

Returning to the issue of poverty, in the UK and in most countries, ‘enough for essential’ tends to mean around 60% of the nation’s median income (Francis-Devine 2020). This is classed as ‘relative poverty’, and it fluctuates. Absolute poverty is adjusted in line with inflation, rather than average living standards. These two different metrics can be used to paint two pictures of the same story, as a topical case demonstrates in Prime Minister’s Questions in UK Parliament.

The UK government refused to commit continued support of free school meals in the 2020 summer holidays. This policy decision about children’s well-being led to a high-profile campaign and a U-turn (that was repeated again in the Autumn). This controversy and debate included a wider discussion of the current government’s impact on child poverty. The leader of the opposition cited that 600,000 more children were living in relative poverty than in 2012 (UK Parliament 2020). Given that the Conservative Coalition took office in 2010, the implication here was that the Conservative governments of the last decade are responsible, and with serious negative effects. The prime minister retorted, ‘There are 400,000 fewer families living in poverty now than there were in 2010’ (UK Parliament 2020). How can one politician use poverty data to make a claim and the other use poverty data to claim the opposite?

How can data on poverty from the same time period, and cited in such an important setting as parliament, paint such contrary pictures? Each party leader chose slightly different timeframes within this ten-year period and they chose different poverty data. The leader of the opposition chose the poverty data and timeframe that told a story of the greatest negative impact, while the prime minister is thought to have possibly chosen a different timeframe and the other index to argue the exact oppositeFootnote 2 (BBC 2020). These different indices aren’t intended to be fiddled with by politicians, but, actually, some measures will subjectively suit some arguments more than others. This does not mean that they cannot offer a more objective appraisal in other contexts, but as you can see, expert judgements can be subjective when deciding which objective data to use about people’s well-being, and in which context.

This use of poverty data is a good example of how well-being data have been used for centuries. Their collection and analysis are motivated by the need to track the health and wealth of society and evaluate the success and progress of social projects and policies. Indeed, these underlying assumptions have been the backbone of social science, statistical and policy work for the last 200 years. Yet, these data are not neutral or entirely objective. They can be used and misused as evidence in forums in which important decisions are made, and yet, we do not often ‘follow the data’ to appreciate these inconsistencies ourselves.

Understanding well-being data means looking at instances and inconsistencies of their use. It is generated to inform decision-making, which also means it can be used to hold others—particularly those in power—to account. It is also gathered on far smaller scales to appreciate the impact of aspects of society on us: our weight, our work, our children and their schooling. Major events, such as COVID-19, enable the power of well-being data to come to the fore. But these are data about us and are used to evaluate what to do next in a crisis. That is why everyone should feel able to access tools to help them better understand how this all works in society, should they want to; that is why this book tries to offer something for everyone.

1.2 Who Is This Book for?

For people who work in social and cultural policy and charities, this book offers lots of context to the data they use every day and aims to help everyday usage of data in practice. It hopes to speak to people who think they can’t do numbers at all. This includes those who think they do not understand the numerical aspects of arguments that use data. It also includes understanding the arguments themselves and potentially their limits.

Capability , capacity and confidence with data are issues for researchers and practitioners working in cultural policy and the sector (DC Research 2017; Oman 2019a, b). Organisations and individuals are affected differently by data-related issues, depending on various matters, including who funds them, how large and ‘professionalised’ the organisations are, for example (Oman 2019a, b, 2020). Despite increasing emphasis on the importance of data in social policy and cultural policy practice and research, capability, capacity and confidence have not received much attention.Footnote 3

Alongside some evidence of data gaps in social and cultural policy, there is anecdotal evidence that key arguments relating to the value of particular social policy areas remain obscure to some working within them, because of the way data are expressed. For social and cultural policy researchers and students, who are not comfortable with numeric data and the way they are presented, this book aims to open the black box and shed light on what is happening. Looking under the bonnet of data means peering under the cover of the workings, the arguments made, the evidence used and the connection between them and data. Looking at all these components together helps us better understand well-being and data at the same time.

For readers who are happy with analysing data and reading statistics, the book reveals some of the social or political ramifications of data and their uses. How governments ‘follow the data’ as a way of justifying policy decisions has been foregrounded in COVID-19 times. Revealing the implications of using the idea of data to justify bad, even dangerous decisions, does not mean all is fixed, however. The enduring presence of the pandemic should be the motivation to ask more questions about policy decisions that claim to be fair and equitable based on evidence using specific data, but which are often just the opposite. Understanding well-being data in these broader contexts is therefore critical.

1.3 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 any way. 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.

1.4 Why Well-being Data?

Well-being data can be about individuals, such as Fitbit data, or population data, such as the census . They include health data and poverty data; information on how we feel, on how we live and how long we live. This book focusses on well-being data for a number of reasons. Firstly, it is easy to assume that well-being data are similar in some way, because they are about the ‘same thing’: we will look at how diverse well-being data are. It is also through trying to understand ‘well-being data’ as a thing that I came to know data in general.

To come to know well-being data, I had to spend years trawling through books from within and beyond economics, psychology, statistics, policy, politics and philosophy. This was a slow process, and an uncertain process, which fuelled my feelings of imposter syndrome. All these different disciplines used different language that I had to be familiar with. Or worse, the same words to mean different things, which I try to overcome as much as possible in this book. It was years before I slowly gained confidence in my own common sense when reading about either well-being or data. The very idea of data and academic or policy language means we stop trusting our own common sense. We shouldn’t. To be honest, some academics do too. They also shouldn’t.

Secondly, well-being ‘as the aim of all policy-making’ (we’ll come to this in the next chapter) has unique relevance for areas of social and cultural policy. This is because—in common-sense terms—culture and society are undisputedly about people, and those working in these policy domains often aim to either improve people’s quality of life or interrogate what improving lives might actually involve! Unlike other aims of policy, well-being, as a concept, makes sense to those working in it and those affected by it—which is everyone.

Thirdly, well-being is about experience. Some people find it hard enough to explain how they feel with words, let alone using the same words. It is even harder to capture experience with numbers. I mean, for thousands of years, people haven’t even agreed on what well-being is exactly and statisticians also admit it’s impossible to agree on a definition, even, as we shall see! How do you know what you are measuring when you don’t know what it is? We’ll find out how people have tried and why they have tried.

Fourth, we all have a sense of what well-being is. We also have a sense of doing what is good for us and knowing what has been bad for us or others. We all make decisions daily that are well-being related—that balance of going to the pub versus going to the gym. Maybe it’s not getting takeaway coffees and sandwiches for a month to save for a holiday. These decisions we make are based on pleasure and purpose at different moments in time, that’s all well-being. We are all well-being experts and we all ignore the evidence (except that app that told me I was happiest in a beer garden with my friends; I listened to that and return to it in Chap. 5).

Fifth, it is also all too easy to forget that not everyone has the same idea of well-being: what makes some people feel better can actually be bad for others.Footnote 4 For example, not all religions and cultures will feel as at home in a British pub as I do on a sunny day: not all activities are available or desirable to everyone. Even formal well-being advice from governments and the media in the pandemic has routinely forgotten you can’t go for a walk to make you feel better if: you are home alone with three kids, are in the middle of a long shift or are indeed unable to walk. It is important to remember that exposure to well-being solutions is a reminder of what is not available for some, which is inevitably bad for their well-being. We also need to be mindful of when ignoring ‘evidence’ is better for well-being and that universal solutions do not work.

Lastly, data affect people’s well-being. As I’ve already said, it may seem like data are neutral, but they are used to inform decisions that are political because they affect people—and some people more than others. I ask my students to think about good data and data for good. Good data might be thought of as an issue of quality. In the case of statistics, this means they ‘fit their intended use, are based on appropriate data and methods and are not materially misleading’ according to the government statistical service (GSS n.d.). The GSS also state that their statistics ‘serve the public good’, not only because they capture aspects of society, but because they are shared. So, how data and the information they are capable of providing are shared is implicit in an idea of ‘good data’. However, more attention should be paid to how this is shared understanding (which is where we shall conclude this book).

1.5 How Are Data Cultural?

Popular culture is constituted by data about popular culture. (Beer and Burrows 2013: 56)

Data issues are bigger than well-being and bigger than social and cultural policy. As we have seen they affect much of how we experience society. In 2015, Helen Kennedy asked ‘is data culture?’ (2015), ultimately answering yes. We interpret data through journalism and visualisations like graphs, which change the way we understand the world. Data also change the way that we consume the arts and culture.

We might think that data can tell us facts about popular culture, but as Beer and Burrows argued in 2013, data don’t just capture culture. In actual fact, data feed back into popular culture, again changing how we feel about things and the decisions we make. Beer and Burrows were diagnosing the digital consumption of music, and the ‘digital traces’ these processes create. This has been proved empirically in a number of cultural forms,Footnote 5 and what they describe is relevant of culture more generally. In other words, they argue that data shape and define culture and have a hand in making culture: they change what we do with our lives in ways we may not notice.

What we listen to, or what we watch, is tracked and stored as data. These data are used to suggest to us what to watch or listen to next (by way of what is called a recommender system). As you might imagine, this then changes what shows are thought popular, which are commissioned and recommissioned, the actors in them and who becomes a star. Therefore, data can change what is valuable and this is another obvious way in which we can see some of the biases described by Virginia Noble (2018). What is happening in the virtual world, or how we move around the online world, therefore changes what happens in our offline social world. We saw this relationship play out in the call centre in the opening to this book. What we do, and when, generates data that do more than help us decide what we might want to watch. These data can restrict our behaviour in more sinister ways.

Thus, data are cultural in that they shape our social values and ways of living. They can also shape how we feel, even our access to healthcare or welfare support. Yet, the way we are taught to live with numbers and data in school, and throughout our lives, does not account for these realities. This is why everyday data literacy and comfort with numbers is a social issue, and one that is increasingly acknowledged by government. Not just the parts of government that care about statistics like the GSS (as mentioned in previous section), but data and the data strategy are now the responsibility of the Department for Digital, Culture, Media & Sport. ‘Creating a fairer society for all’ is one of the key aims of the strategy, which is ‘underpinned by public trust’, according to the Secretary of State (DCMS 2020).

There has been a lot written about ‘trust in numbers’ (Porter 1996), but also, trust in how data are used. We trust certain institutions to use data well, while others use them badly; yet trust other institutions, again, to report data honestly and transparently (Steedman et al. 2020; Kennedy et al. 2020). We have already seen how an idea of a poverty rate can be manipulated by politicians to suit their own ends. While politicians themselves exclaim it is only others’ numbers we cannot trust. Donald Trump claims that ‘negative polls are fake news’ (Batchelor 2017) and the UK is told that it has ‘had enough of experts’ (Gove 2016).

COVID-19 management has resulted in governments telling us how important it is to trust data, but to trust in their interpretations of data. People in authority are now dictating how we should feel about numbers (and showing us which numbers they want us to feel safe or terrified as a result of). Running in parallel to this rollercoaster of data and trust is the disproportionate faith that we have in the numbers we read on Facebook and other social media. Which presentations of COVID-related deaths do we believe? What makes one more believable than another? Missing from many analyses and discussions of trust and data is how it came to pass that despite the fact that data are everywhere, we do not trust ourselves to use and read data.

Why don’t we (the general public) feel able to trust ourselves to understand data and numbers? Are there particular parts of society who feel at greatest disadvantage from this lack of faith in ourselves? Many were taught at school that numbers offer some sort of objective truth: that there is a purity to numbers. We leave school with the feeling that if we don’t get them, that’s because we won’t get them. In fact, as you can hopefully see more clearly, all sorts of numbers, statistics and graphs are misused all the time. Sometimes this is to deliberately mislead people, others it is not. Quite frequently, in terms of well-being data though, numbers only suggest what is going on, and they can be interpreted in different ways, if truth be told.

It is hard to navigate which numbers to trust in our everyday lives, but what about the numbers we may use in our working lives—or, as a student writing an essay? For most people, these are not numbers we will have been involved in generating. Even academics, experts and statisticians probably refer to more data generated or analysed by others, than those they may have had a hand in. Instead we all use data to justify our positions, whether that’s down the pub to argue about the football, how many man-hours are needed to fix a leaking roof, or for how much, or to a funder for the value of the work we do.

How do we trust which numbers to use in our working lives? Perhaps we trust those that appear in a policy document or from something else we think is a reputable news source. Does citing a published academic paper make us feel like the numbers should be okay, even if we suspect something feels fishy about them? In this book we’ll look at how you can better trust yourself with numbers—by feeling more confident in the signs that the numbers are good and not bad. This involves knowing where the data came from, how well explained the approaches to analysing the data are and looking at how it’s presented.

1.6 How Should I Use This Book?

The simple answer here is that, like with any book, you should use it how you want. What I wanted to say is that although there is a logical order to this book, which we go into next, not everyone will find all of it useful or interesting. So, as much as this book is about feeling confident in your judgement about data, you should feel confident that if you are not interested in a section of this book, you should feel you can read the next section.

Because this book aims to explain a lot of background detail to give contextual information for different types of data, or ideas about well-being and society, not everything will feel relevant to everyone. For example, you may be interested in the history of well-being data in a general sense (Chap. 2), but feel like you do not have a need to read about the history of decisions behind the OECD well-being indicators in particular (at the end of Chap. 3). If you are that reader, then feel you can skip a section and move onto the shiny new chapter about the recent history of happiness as a new science (Chap. 4) or Big Data (Chap. 5).

Similarly, you may be interested in the first section about well-being data, but less interested in the specific case studies in social and cultural policy. So, why not skim or skip those and jump to the conclusion—where you may find you want to refer back to specific points in previous chapters any way. This book is designed to hopefully allow you to feel confident to read the whole thing in order, like a novel, or refer to sections. It is designed for you to use it how you like.

There are boxes scattered throughout (that you will find after the list of figures). These are used in different ways. Sometimes the material in the box elaborates on the main text and can be skipped if you are not interested. It is often definitional, explaining the difference between two types of economics, or what a variable is, for example. Sometimes a box might present example data, as with the case of some tweets in Chap. 5. Sometimes, reading it will help contextualise what is happening next. Again, the boxes are meant to make it easy to decide whether you want this detail or not.

1.7 Why Is the Book Written in This Order?

This book is a game of two halves, with a post-match pint to digest what we have just watched: the performance of the players and those calls which are on the edge of the rules of the game. The first half is about how different kinds of well-being data (data about well-being) came about. It begins with the historical traditions of philosophy, governance and social science that led to ‘well-being data’ becoming a thing that is useful and looks at the methods, innovations, contexts and limitations of these.

The second half looks at how well-being data are relied on as evidence in social and cultural policy, also how they are used to answer questions beyond the contexts they were collected in. Ideas of a cultural society as a good society have long-shaped social policy and informed future philosophy. We look at how this enabled cultural policy to become an aspect of social policy, before presenting a number of case studies on the relationship between well-being and culture that I have elsewhere (Oman 2015a, 2015b) called the culture–well-being relationship.

The conclusion aims to be a sort of post-match pint down the pub. It reflects on moments of tension, recapping on what has happened and reflecting on how these might be understood from a different position. We end with trying to understand ‘understanding’ in a number of ways. First, as the ways we understand the world, through data, information, knowledge and wisdom.Footnote 6 Second, as a reflection on the work that needs to be done towards a shared understanding of data. Third, how in using well-being data, we may become more understanding of each other.

1.7.1 The First Half

We start by setting up some of the background story to well-being data. Chapter 2, ‘Knowing Well-being: A History of Data’, puts the concerns of this book into context, these contexts being historical, political and technical. There are different theories of well-being from different times and places, and how these are understood today by researchers, national statisticians and policy-makers affect what data are collected to understand well-being.

We look at the project of measuring well-being as one that wanted to understand how to improve human welfare. We also consider well-being as a tool of policy, as the very idea of it is used to make arguments for one policy decision over another. Or in more real terms, to fund one social project over another. This is deeply connected with developments in national politics and governance, which changed and increased the role of economics in auditing, efficiency and valuation. We consider how these processes led to not only more well-being data, but more well-being data practices. In other words, more uses of more data. This chapter will help the reader think more critically about why and how well-being became such a default ‘good idea’—and some of the issues at play here. It will also help think about how striving for a good society became inextricably linked with well-being data.

Chapter 3, ‘Looking at Well-being Data in Context’, moves more specifically into thinking about the uses of data and measurement in policy, practice and research. The previous chapter’s historical focus on measurement as an expression of objectivity and governance is extended here. This chapter is a more focussed appraisal of contexts in which data are collected and used. We think about the role of methods and methodology (explaining what this word means). We look at specific examples of how well-being is measured and how that maps onto philosophical accounts of well-being. This is not a methods textbook, as there are plenty out there that do this job. Instead, this chapter’s focus on context, difference and limitations across mundane, critical and authoritative contexts aims to help us think about how we might understand well-being better, or differently.

Therefore, we think about the implications of different kinds of data, starting with how they are collected. Well-being data can be collected in various ways: through administrative processes, such as the recording of births, marriages and deaths, or crime-rates. These data will be used as quantitative data, to understand and develop measures we see in the press, like ‘mortality rate’. Quantitative data can also be collected using surveys that allow understanding of more complex aspects of people’s lives. Asking people questions means you can know how long it is since they visited their GP (general practitioner), for example, or how far they have to walk to their nearest children’s play area. These data are easily turned into numbers to give a picture of how people’s lives compare, or how we are doing overall, and can help governments make decisions about how to allocate resources.

Data collected in questionnaires and online surveys can also be qualitative, as can interviews, diaries and observations. Qualitative data are most generally text-based, and so are good to understand how people have described their experiences or opinions; although can also involve image or sound, for that matter. Using qualitative data can allow researchers to understand the complexities of a situation and the specificities of people’s personal lives. While quantitative and qualitative approaches tend to be discussed separately, some data collection methods, such as surveys and questionnaires , collect both quantitative data (by ticking a box) and qualitative data (by a free text field), so surveys are able to gather data that offer a bigger picture and more detail at the same time.

Qualitative data often have lots of rich detail about few people in a specific context that have to be interpreted by the person analysing it. Quantitative data will have been collected so they can be quantified, removing contextual detail for analysis using numbers and comparison across a population. Somewhat confusingly, if you have enough qualitative data, you can quantify them, but this is less common and we look at how and why that can be useful sometimes. While quantitative data also require interpretation, there are standardised mathematical approaches, usually drawing on statistical methods to support these decisions and analyses. This means quantitative approaches are considered to be more neutral and objective. But as we shall see, lots of decisions are needed, and this poses key questions about the idea of objectivity in the data used to make statements about what is good for society and to make arguments that one thing over another will improve well-being.

Chapter 3 is the first chapter where we start to look under the bonnet of well-being data. At some points we get up-close to specific research examples and ideas, including quotes from focus groups and examples of well-being survey questions in an imagined context of evaluating a local community event. We also look at so-called objective well-being indicators (e.g. mortality rate) that feature in well-being metrics, like the OECD’s Better Life Index. We ‘follow these data’ using qualitative data in reports to think about how objective these measures really are. We will reflect on the distinction between objective well-being, as something experts decide is important to well-being, such as an aspect of health, and subjective well-being measures which involved asking people how they feel. All data and ways of using them have pros and cons, which is why context is important. Understanding how different data work in different contexts is key to well-being data and key to data for well-being.

‘Discovering “the New Science of Happiness” and Subjective Well-being’ is the title of Chap. 4. Here we consider the formation of happiness as something that can be measured. Happiness is part of a broader academic concept called ‘subjective well-being’—as an idea of how well-being is felt. Subjective well-being becomes extremely influential in the well-being agenda and we look at the role that these new measures hold. The chapter begins by describing how ‘happiness’ became a ‘new science’ including the different academics, politicians and fields of study involved. It describes the evolution of positive psychology and happiness economics and their influence in the realm of policy-making.

Disciplines like psychology and economics often group subjective well-being data into different types. They refer to evaluation, experience and eudaimonicFootnote 7 measures. This chapter does the same to explore what these mean in practice, and how they are used or useful to understand specific aspects of the human experience, which is then used in evidence for policy-making. Again, specific examples of the contexts in which these sorts of data are collected and used reveal their limits, as well as contradictions in their use. We then focus on subjective well-being measures in the UK and the Office for National Statistics’ (ONS’ ) Measuring National Well-being Programme.

Looking at the invention of subjective well-being measures in the UK offers context behind the ubiquity of well-being measurement practices. Understanding the recent history behind, and breaking down the different ways of measuring a particular idea of well-being, is vital to appreciate the limitations of such projects. While the innovations and limitations of well-being data remain unaddressed, their positive contribution for society can never be fully realised. This chapter’s comprehensive survey and critical lens aim to offer tools to promote better understanding of subjective well-being and happiness data, their capacity to change culture and society, and the limits of their application in areas of social and cultural policy and practice.

Chapter 5 looks at Big Data, which is an enormous topic to try and cover in one chapter. ‘Getting a Sense of Big Data and Well-being’ asks many questions, beginning with: what do we even mean by the term?—how are data big? The amount of data on individuals that is now collected is quite simply mind-boggling. The International Data Corporation (IDC) predicts that by 2025, the total amount of digital data created worldwide will rise to 163 zettabytes (Coughlin 2018). That is 1021 (1,000,000,000,000,000,000,000 bytes) or one trillion gigabytes. The European Commission forecasted the European ‘data market’ to be worth as much as €106.8 billion by 2020 (Ram and Murgia 2019). We can therefore see that not only have the amounts of data increased, but their economic value has as well. It is, therefore, even harder to maintain that all uses of well-being data enable neutral decisions about how society is managed, when it is being called ‘the new oil’ (The Economist 2017).

We begin by asking the question: ‘What even is Big Data?’ We look at what the term means, as well as what Big Data are and what they can do, including how as soon as someone tries to define it, somehow that definition is not quite right. Emergent technologies from all walks of life are producing and collecting and analysing data about us as we move about the online and offline world. This means that more can be known about people—which we discover means that data are a double-edged sword for well-being.

Big Data are often attributed with much power—by those in favour of their use, and those who actively work to limit the negative possibilities of these new data and how they are used. The chapter demystifies Big Data by putting them into historical and a number of practical contexts. For example, smaller organisations, in the arts and social sector, use data mining in small, mundane and often unobtrusive ways (Kennedy 2016; Oman 2013). It is possible to use data in research like this in a way that is ethical and without much software skill or financial resource. We revisit a practical example of a manageable project I undertook to reanalyse Twitter data using a hashtag that was started by a Mass Observation projectFootnote 8 to understand what makes people happy. As a spoiler, there are many cats.

Mass Observation was a project originally established by an anthropologist, a poet and a filmmaker in 1937Footnote 9 who wanted to record everyday life in Britain. The project emerged at a time where there was a desire for more detail in data, and around the same time as social surveys were becoming more complex to understand more detail about people’s everyday lives, particularly around World War II. More data were wanted to understand quality of life and manage populations beyond the administrative data collected on mass-scale, like the census.

Most countries now undertake a census of sorts, and in the UK, the ONS have collected its census data every ten years since 1801. The new ‘enthusiasm for numbers’ in the early to mid-nineteenth century (Hacking 1991, 186; Porter 1986, 1996) coincided with a growing infrastructure to collect and analyse data. This desire for numbers, and the data processes that were required to provide them, led to the ‘great explosion of numbers that made the term statistics’ (Porter 1986, 11). In this ‘avalanche of numbers’, ‘nation-states classified, counted and tabulated their subjects anew’ (Hacking 1990, 2; 1991, 186). Censuses date back far farther, of course, and the ONS’ website offers an interesting history of censuses in the UK, back to the Domesday book ordered by the Norman (French) King, William the Conqueror in 1086 (ONS 2016). Again, censuses precede these European data moments by some 4000 years in both Egypt and China, who recorded who lived where how wealthy they were. The Romans held regular censuses to keep track of their expanding—and then contracting—empire. Further back still, the clay tablets of Sumerian script (Harford 2017) might be considered a dataset of Big Data from 6000 years ago. The promise of Big Data is therefore not new.

We look at the promise of Big Data to predict a pandemic, reflecting on the obvious failings of Big Data to forecast COVID-19’s impact in a way that could have averted international crisis. We also look at a company that claims to have predicted the pandemic, yet failed to stop it: is it possible that the commercial value of the intelligence they had was a barrier to more effective global prevention? We start some years before that, in 2009 with the failings of Google Flu Trends (GFT ), which promised to beat the slow infrastructures of health services and testing in the US. GFT analysed what people searched for on Google, analysing what, where and when people typed symptoms into the search. Yet, this did not work for a number of reasons tied to a lack of capacity to understand context.

Back in the UK, I took part in a home testing programme that the media said would ‘clear up [the] “Wild West” of Covid-19 estimates’ (Devlin 2020). In what has been called the ‘largest testing study for Coronavirus’ (Ipsos Mori 2020), tests were posted to you, using the UK’s traditional Royal Mail postal system. That all worked fine for me, but there were a series of steps registering different barcodes and I found myself wondering how accessible this was for everyone (when I say everyone, I often think of my once tech-savvy Dad, who’d have been bewildered at this whole process). As a result of these steps, a courier was ordered to collect the test, but failed after three attempts (that I describe in more detail in the chapter). A neighbour told me in passing that this particular courier company was infamous for not bothering to try and collect from my high-rise flats, probably because the buzzer has never worked and it can take too long for a resident to come down. This looks bad for the drivers’ performance data, which are meant to encourage them to make as many deliveries and pick-ups as possible.

In my case, while some aspects of the traditional data infrastructure (the post) worked fine for this COVID-19 data collection research, they didn’t necessarily all work together as they might. This meant that my test remained uncollected; therefore my data became ‘missing data’. Thinking about the contexts in which data are collected (or not) can be both extraordinary and mundane, and we often don’t hear of these stories—when they work, and the odd occasion when they don’t, and what that might mean for the data.

We follow other case studies of data from mobile phone usage, social media data and tracking apps, for example. We, again, ‘follow the data’ and how they are used to interpret whether these data projects are primarily concerned with improving human well-being, or with refining data practice. It is crucial to problematise the ethics of Big Data for well-being, particularly their commercial aspects, rooting these in the larger questions of what data can do more generally and the limits of data for understanding well-being or improving well-being.

1.7.2 Half Time

The data we look at in the first half of this book are either all collected to better understand people or society, or have been analysed to do so to enable a government or a company to make better decisions. There is a sense that these data are all neutral—they are not affected by bias and can all be treated as fact. These chapters reveal the fragilities in the assumptions behind these kinds of data. When you consider the hypothetical and real-world examples, you can see lots of humans mainly doing their best to work with data. We can also see mistakes in the systems and analysis, and therefore, some of the data-driven decisions we live with are not the best decisions they are assumed to be.

The fact that data have real-world impacts and implications is not something that is often made clear by those who use data, or advocate data-driven decision-making. The impact of Big Data has seen an increase in those considering their social effects. Consequently, the negative aspects of data are an issue of government agendas with new emphases (DCMS 2020). However, the ways that data about people make the problems of society legible are not necessarily new, and neither are the problems. Data on residents, together with a map produced by the City Office of Statistics of Amsterdam , enabled the rounding up of the city’s Jewish population under Nazi occupation in 1941 (Scott 1998, 77). Yet, the same techniques of mapping people and personal data about them also led epidemiologists to identify how the AIDS pandemic was spreading and of course the current COVID-19 crisis.

We need context to understand data practices and the possible ramifications of their social effects. They have their own ‘social life’ (Beer and Burrows 2013; Oman n.d.), meaning they might be thought of as living in that they act on the world as much as humans do. Data and numbers ‘make up’ people (Hacking [1983] 2002) and tese later theorists enable us to think. Decisions are made about our lives without asking us, but looking at how we are represented by data. Data decide whether you will get a commercial loan or access to financial support by the state. Postcode data in the UK will decide how you will receive medical treatment and what drugs you are entitled to. Data hold much power through metrics (Beer 2016) and algorithms (Kennedy 2015). But also, the very idea of data is powerful; it affects our day-to-day behaviour. Crucially, however, it is also in the desire for data where its power lies.

1.7.3 The Second Half

We ‘switch ends’ in the second half. The goal instead is thinking more about how society has increasingly required well-being data. So, while we do not entirely leave thinking about contexts of data collection, we think more about the contexts in which they are used. We continue to focus on how society works, its relationship to governance and decision-making, and the role of data in this. Given that data are social and cultural, we will, therefore, look at areas of social policy, focussing on cultural policy in particular to make comparisons more readily across some simple arguments about well-being that use data. To be truthful, it is also in looking at data in the cultural sector and in cultural policy that I came to understand data, and is my natural data habitat.

Chapter 6, ‘Well-being, Values, Culture and Society’, provides an overview of how cultural policy became a form of social policy, specifically looking at the role of well-being. The chapter historicises the idea that particular aspects of culture have a social role and are good for well-being using accessible interpretations of key philosophers from Aristotle to Kant. We reflect on the fact that much like population data, the arts have an honourable and dishonourable history (Belfiore and Bennett 2008), as both have been co-opted for political projects, such as fascism: that didn’t just damage well-being, but were almost indescribably catastrophic for people and society. The chapter brings these empirical accounts of uses of culture into play with social theory from cultural studies scholars, including Raymond Williams ([1961] 1971, 1977, [1958] 1989a, [1968] 1989b). These later theorists enable us to think through some assumptions around the role of culture, even what gets to be called culture, and why that is a problem for cultural and social policy. In turn, we are in a position to contextualise how the institutions and historical assumptions that decide what is good culture, and manage cultural policy, are not so different from thinking about the institutions that manage data and the way we work with and understand data. These overlaps are rarely acknowledged.

We reflect on a genealogy of the idea that culture (broadly defined) is good for well-being (broadly defined); how that has been naturalised over time and then popularised. By this I mean, there is a generally accepted view that culture is good for well-being, and we look at the lineage of this idea as something that began with philosophers and is now common sense. We will then investigate how this relationship has been instrumentalised as a form of social policy. This involves looking at how culture is used as a means or ‘instrument’ for attaining goals in other areas of society. Examples of this can be found in policy documents, research agendas and in practitioner movements including ‘arts in health’ (ACE 2007; AHRC n.d.; AHSW 2019) or the use of culture in urban regeneration projects (DCMS 2004; LGA 2020; UNESCO 2018). The idea that the arts can be used to directly address societal problems has led to arguments that culture is—in fact—instrumental to these social policy areas.

The idea that arts are instrumental in delivering broader social projects and improving social infrastructure has been operationalised to advocate for funds for the arts. We have, therefore, witnessed changes in the value of culture from something belonging to everyone (Hall 1977; Keynes 1945), to how much social impact it can demonstrate, or indeed financial estimates of the creative industries (Campbell 2019; DCMS 2011). In return for advocating the value of culture, the sector is increasingly required to evaluate how much of this value it has generated in response to funding, or to argue for more funds.

This has also seen the slippery nature of culture and its definitions be instrumentalised in arguments, where one meaning of culture is used to justify another aspect of it. The benefits of culture as something more everyday (Williams [1958] 1989a) are used to justify the funding of artforms which are considered the opposite of commonplace in that they are elitist, with often small numbers of people interested in participating (opera being the default perpetrator in this argument). This slippery effect is also used when it comes to ‘creativity’ and arguments surrounding the economic impact of the arts, where ‘the arts’ become ‘the creative industries’, including some professions in IT, which in many cases do not seem to be very creative at all—in the way we would normally use the word.

We have, therefore, seen a process in which the culture–well-being relationship is theorised (through philosophers) and become naturalised in people’s day-to-day thinking: making it common sense. Figure 1.1 shows the full journey of processes described in the chapter. The common-sense nature of the relationship is operationalised in policy and instrumentalised to argue the value of the arts and culture to other areas of social policy. This process, however, has led to the cultural sector finding itself in a bind to the burden of proof. It has to evidence the social impact of the work it does, which is a costly exercise of data production and analysis.

Fig. 1.1
figure 1

The culture–well-being relationship

Full size image

These shifts in the culture–well-being relationship have seen the value of data increase and become capitalised on (Oman and Taylor 2018). The increase in funding saw an upturn in evaluations required to report back to funders. With this came demand for data and data practices that are often outside of the skills and confidence of many working in the cultural sector, and broader areas of social policy. These skills therefore often need buying in from elsewhere. With the newer forms of well-being data introduced in the first half of this book, come new metrics and valuation tools, which are presented as a solution to issues of advocacy and proof in the sector. They also perpetuate this cycle of funding and evaluation, which preserve this process of instrumentalising, operationalising and capitalising on the culture–well-being relationship. We will therefore look at some examples of how well-being data are used to make arguments about culture—and we will follow the data in different ways to see how they work.

Chapters 7 and 8 draw from the framing in Chap. 6 to look at how the culture–well-being relationship has been operationalised in research to provide proof. Chapter 7 is called ‘Evidencing Culture for Policy’. It takes three fundamental arguments about the culture–well-being relationshipthat are used in advocacy and looks at them more closely. The first is that culture warrants funding, because it is good for well-being. We look at a number of different examples of data to establish if a relationship between public funding and well-being can be found. Again, through investigating the contexts of data collection and analyses, we are able to think about the limits of what can be known using these data.

Why are well-being data in demand to understand some relationships and not others? Despite the naturalised belief that we should invest in culture for its well-being benefits? There is little research which explores whether a pattern can be established between increased funding and well-being. Why are some questions repeatedly asked and not others? Is this a matter of the data (what can be known) or the limits of what people want to know?

We look at the question of ‘how much is culture good for well-being’ in more detail. The chapter considers two pieces of research which investigate the well-being of cultural practitioners and creative professionals who are often presented as similar, even the same, population. The two studies ostensibly use the same approach to analyse survey data to understand this culture–well-being relationship. In comparing these two cases, we unpack differing findings and look at limitations of data, in categories, populations and analyses, and question how they help us understand well-being in this instance. Crucially, this is not necessarily a case of comparing studies to see if one is better than the other. Instead, we look at how asking (at least superficially) the same question using similar data about similar people at comparable points in time does not present the same results. So what does this mean for ideas of evidence?

The final section looks at a piece of research that is found in important and high-profile reports as evidence that culture is good for well-being. The article uses what it calls ‘data mining’ to understand ‘cultural access’. We look under the bonnet of this idea of cultural access and the data that have been used to measure it. We also follow the authors’ data mining practices and analyses to find combined variables which change the meaning of the category ‘cultural access’, resulting in an inflated outcome.

Unpacking the different ways that culture has been packaged as something that is good for people and society is important. In this chapter we discover how particular findings become popularised as ‘common knowledge’ and how they then become operationalised in reports, the media and policy documents. This is crucial to grasping the idea that the relationship between data and evidence is cultural, and relies on practices, understandings and meanings.

Once we begin to question the social value of generating evidence in this way, the economic value of contracting in well-being data and research practices warrants investigation. In Chap. 8, ‘Talking Different Languages of Value’, we follow a piece of research that was commissioned to help with advocacy for the arts. The commissioners were an organisation called the Happy Museum, and the research was funded by Arts Council England. Building on the work we have done in previous chapters to understand how data work in contexts (see also Oman n.d.), we look at how culture and well-being are operationalised in this study, and walk through the processes, step by step.

The chapter opens with this idea that this book seeks to challenge: that the arts and data speak different languages. Breaking down what is happening, we follow the data in various ways. There is a description of how the data were collected in a national-level survey. We look at the questions, as they appear in a survey, because it can be hard to imagine the mundane contexts that data originate from, when you are looking at the complex results. We follow the data forward, to see how key findings are interpreted by the world. This allows us to ask questions like: what does research do? How does it affect the world or change things?

We follow the conceptual work behind what is being measured before reflecting on some of the steps in the analysis. There was another way that these data were followed, as I was part of a research project to reproduce findings, using details on the processes and the data available. Crucially, the second piece of research arrived at different conclusions from the first. What does that mean for the very idea of ‘evidence’?

How does commissioning well-being data analysis to support the arguments people want to make change the nature and role of evidence in different social policy areas? How does this affect overall knowledge of ‘what works for well-being’ in terms of social policy? Importantly, how does ‘capitalising’ on well-being data affect their capacity to do social good or to be good data? Do the economic value of data and their analysis change the relationship between well-being data and a good society? We have found indications that this is the case with COVID-19, but is this more generalisable?

Chapters 7 and 8 break down various aspects of how data are used in cultural policy to communicate quantitative expressions of well-being to people who lack confidence in these areas. Crucially, this will enable readers to think about how something that is described as culture or cultural is said to impact on well-being, whilst also looking at the limits of the data we have to make such claims. These chapters aim to encourage you to make your own mind up (with a little help) as to whether everything adds up (not just the numbers). Do the arguments make logical sense based on the evidence we actually have, rather than what we are told we have? How can considering the contexts of data help those working in data and working in social policy do more good with data? History tells us the dangers of ignoring the good and the bad that can be done with data, and that how it is used is a matter of culture.

The final chapter is simply called ‘Understanding’. Here we will reflect on different ways of understanding well-being and different ways of interpreting data. We will look back on how well-being and data are related by way of policy and politics. We consider the relationship between evidence and policy, and the politics of data. How do these conflicting ideas work together when the aim of the game is well-being?

We reflect on how understanding contexts of data helps us better understand the politics of data and evidence for policy. We look at the limitations of well-being data that we have explored in terms of claims that can be made and we look at their limitations when it comes to calling data objective. The huge amounts of decisions involved in establishing the well-being measures in Chaps. 3 and 4 show these are not neutral decisions. Furthermore, Chaps. 7 and 8 reveal the decisions made in modelling: what data to clean, weights and adaptations to valuation techniques when well-being data are used to make arguments about value.

We think about what understanding means. It means understanding as knowledge, shared understanding of how something works and being understanding, or having empathy. Well-being data promise information that leads to knowledge and wisdom, but these do not currently lead to a shared understanding. Research is commissioned for the cultural sector and presented in ways preoccupied with proof, rather than communicating findings with those who work in the sector.

The concluding chapter presents a case study of how people crave understanding of why they are being asked certain questions on equality monitoring forms, what will happen to and with the data they offer. Yet, it is not common practice to share understanding of how and why different data are valuable. There is much room for understanding and empathy in approaches to inequality and well-being data, and this is currently overlooked in most projects that work with these data in the name of social justice.

The ‘social life of methods’ is a body of research proposing that methods are not neutral ways of capturing an objective reality, but have their own social effects; in fact, changing the reality they claim to capture. Data: how it is collected, shared, analysed and where the results are published are a fundamental part of this. We have looked at how data are cultural, in that they change culture, making new cultures, and we look at the implications of these social effects. Those who are campaigning for data rights are very focussed on what can be known about people from data. However, this is often framed as an issue of privacy as an abstract human right or as an issue of social justice, as the effects of data-driven decision-making disproportionately affect marginalised groups. This, of course, is an important ethical question.

A broader question, however, is what can these data actually tell us about people? There are limitations to most data when it comes to what we can actually understand about society that are not always taken into consideration. Crucially, the question we must ask ourselves at this moment is how can we also rethink questions of what can be known about people from data to incorporate data’s limits, as well as their power? How might well-being data improve well-being? Can we be better at moving from understanding people as units of analysis to becoming more understanding in the way we collect and use data?

These are the provocations this book leaves us with and I hope to continue to do work that not only tries to answer these questions, but which goes about changing things. This book is set up so that we can look at the work that well-being does in policy and practice contexts for social and cultural policy, for third sector organisations and arts managers, for charities. Most of all this book is meant to help us all have a better grasp of ideas of well-being and ideas of data, how they work in different contexts and how they are used and manipulated for different ends. Neither are neutral. They are imposed by historical traditions which say what works and what doesn’t. They are imbued with values—and I hope this book will help you value your own judgement to decide what they mean for you.