9.1 Understanding, Well-being and Data

We started this book with a preface: a personal note on why and how it came about. This included reflections on some of my experiences of coming to understand data and well-being—not only my direct experiences, of course, but my observations of people I know and have met, and how they interact with data issues and well-being issues. I argued this book was for friends, family and acquaintances on Facebook. For my students from courses across theatre studies to data sciences to social policy. For the data practitioners I work with in the cultural sector and for the hundreds of people I have spoken to about their well-being and/or their data in my research.

Given that most of these people are people I have met, the preface also points to how this book is based on my understanding of these issues. It often uses UK cases and relates them to more general problems, international contexts, lessons learnt and some of those that remain. Perhaps in another ten years, I will be writing about these issues from a different place again. I have been honest about how I came to know data and theories about well-being. I found it hard to find all I needed in order to be confident that I understood what I needed to understand.

Because there is a need to understand well-being and data together across many areas of society, this book is written for anyone. You are told to write a book with a specific reader in mind, but this is hard when you are writing about big problems and your audiences are multiple. Given the aims of this book, it had to address everyone, but knowingly; aware that not all its parts are everybody’s cup of tea. It is therefore up to the reader which bits they want to read, and what they wish to pass on. All I could do was write for what I understood to be (1) the needs and (2) the desires to grasp these issues better or, indeed, differently. But the needs and issues are various, so one size can’t always fit all if you want to address understanding of the broader concerns. So, to return to all the people I wrote this book for, I want it to be clear that everyone can contribute to how we could understand the issues, and differently. What if we looked at the issues from someone else’s perspective, or approached them in an alternative way?

I want to close this book by focussing on understanding for all these reasons, and more. The title Understanding Well-being Data might imply that we were simply going to try and understand well-being, data and ‘well-being data’. Its subtitle ‘improving social and cultural policy, practice and research’ implies, of course, that I aspire for this book to change big things in society for the better. Really, this book has more modest aspirations to improve understanding in small ways—and who knows, perhaps these small ways can make their own differences. Whether it enables anyone who reads it to think about things they had not thought of before, or from a different perspective.

I have been talking about understanding in relation to data in a few ways for a while now (i.e. Oman 2019a, b): first, as in how we acquire knowledge; second, as how we share understanding; third, how these work with becoming a more understanding society. When I have summarised my findings on how people understand data, I have also suggested that we might think of this on a scale of knowing at one end, and feelings on the other (Oman 2019b, c). These qualities of understanding are in essence what well-being data should be about. Collecting data to inform how we might be more understanding of people’s needs and experiences to do better for them and society.

How do data, well-being—and data about well-being, help us with these two concerns of understanding? In many ways, that is what this book is about. We are going to touch on what understanding means, explaining it through another case study: this time one of my own experiences of watching people try and understand what data are doing. But more generally, how the social sciences can adopt a more understanding position.

9.2 Meanings of Understanding

understanding | ʌndəˈstandɪŋ |.

noun [mass noun].

1 the ability to understand something; comprehension: foreign visitors with little understanding of English.

• the power of abstract thought; intellect: a child of sufficient intelligence and understanding.

• an individual’s perception or judgement of a situation: my understanding was that he would find a new supplier.

2 sympathetic awareness or tolerance: he wrote with understanding and affection of the people of Dent.

[count noun] an informal or unspoken agreement or arrangement: he and I have an understanding | he had only been allowed to come on the understanding that he would be on his best behaviour

adjective

1 sympathetically aware of other people’s feelings; tolerant and forgiving: a kind and understanding man | people expect their doctor to be understanding.

2 archaic having insight or good judgement. (Oxford Lexico n.d. [bold and italics in original])

Sympathy This was first used to express ‘understanding between people’; it came via Latin from Greek sumpathés (from sun- ‘with’ and pathos ‘feeling’). (Cresswell 2010, 432 [bold and italics in original])

Now that I spend some of my time in academic research meetings, I am party to conversations on how we understand what understanding means. As you can see above, people who write dictionaries also think about these things. Ironically, people talk about academics living in ivory towers—not caring about what people think and feel; but for some of us, that is so much of what we think about. For example, I am a co-investigator on a research project called Living With Data (n.d.).Footnote 1 In project meetings (perhaps you can picture it?), we academics have spent quite a lot of time discussing what we mean by understanding and knowing. How they differ and overlap and how our understanding may be different from people in their day-to-day lives. This was also a conversation point in a recent project meeting with our Advisory GroupFootnote 2 made up of experts from across public sector, civil society, advocacy and research.

One of the experts on the Advisory Group suggested that perhaps understanding was such a ‘complicated’Footnote 3 term that maybe we might want to ask people what they understand by understanding. At this point, we all took a moment to laugh (kindly at ourselves, I like to think) and concluded that while this is important, ‘ordinary’ cultural understandings of the word understanding were a simpler experience. What I meant by this in the meeting, and still do now, was that most people move through life not really thinking about what the word ‘understanding’ means but are familiar with its meaning.

Understanding is a process by which we come to know something, the amount of or the depth of knowledge we have about something. At the same time, being understanding involves empathy, and putting yourself in someone else’s position. Shared understanding, on the other hand, requires the sharing of knowledge with someone in a way that you know they will understand it.

Hence, understanding is both knowing and feeling—crucially it is as much about ‘understanding between people’ (as cited at the beginning of the section) as it is to grasp knowledge about something. As this book has explained, data and the way science and social science knowledge are constructed are also about having a shared understanding of how things are done: how to collect and analyse data in the ‘right way’ is a matter of discipline and tradition, which are not universal. This can lead to differences in interpretation of both well-being and how to use data across disciplines. How do those who work with data share their understandings with those who don’t? Often this is done quite badly, or without thought, care and empathy.

More care is given to sharing understanding in other areas of life. When you ask a child ‘do you understand?’ after you have told them off for doing something and explained why: you are asking, do you understand why I had to tell you off? Have you learnt why what you were doing was dangerous or wrong? You are asking them to appreciate things on an emotional level and on a cognitive level—whether this is successful or not, is another matter. Understanding is both an emotional and cognitive exercise for all of us: we gain knowledge through understanding, and we become more understanding of others through experience.

You may remember that this idea of developing understanding is one of the age-old arguments for the benefits of aesthetic and cultural experiences in Chap. 6. Watching a film or reading a book can help us understand other people’s lives, and culture’s contribution to well-being is often argued because of its capacity to increase empathy. Philosophers have long seen the moment where we come to understand something as a pleasurable moment, as well as one that brings purpose and meaning to our lives. This is an idea of how understanding improves personal well-being; while of course, knowledge and understanding are seen as contributing to the development of good societies, thus improving well-being at population level. If this is indeed the case, then there is a strong case that more care and attention should be paid to understanding as good for well-being.

Using data about well-being should fulfil all of the functions of understanding: caring for and appreciating the conditions of others, building knowledge of what to do to improve it—and sharing these understandings. As an aside, it should also involve learning from mistakes. Yet, as we have discovered in this book, the limitations of ‘following the data’ are not always admitted to, but instead, often dodged around. First of all, I want to return to the importance of understanding in data. As with the rest of this book, we are going to use a case study to look under the bonnet of the data. While not strictly well-being data, this case study does show how simple processes of everyday data collection can feel ‘hostile’, and unsympathetic.

9.2.1 The Case for Understanding in Data

In 2018, I began a large-scale qualitative research project to understand data and diversity in the cultural sector. More specifically, Arts Council England (ACE)Footnote 4 wanted to introduce additional questions to its existing equality monitoring processes.Footnote 5 The research was undertaken in partnership with ACE to advise on how to improve data in the sector and introduce the potential new data to measure inequality better.

Inequality and inequality data are contentious issues across the UK cultural sector.Footnote 6 Commitment to social inclusion is integral to the sector’s identity and values, as this book has argued. However, qualitative and quantitative data reveal, first, the failure to achieve diversity goals in terms of who gets to participate in, and work in the arts (Brook et al. 2020) and, second, the amount of missing data from administrative processes (DC Research 2017; Oman 2019c). What does ‘missing data’ mean? In this instance, it means a gap where there should be a value. For example, all those households who did not complete the census in March 2021 become missing data, and so people were hired to knock on your door to remind you to complete the census. Missing data reduce the accuracy of understanding that is possible from data, which can affect government decision-making, including how resources are allocated.

An example of missing data in the cultural sector equality monitoring story can be found in organisations that refused to ask people about their sexuality. One organisation I spoke with heartily believed that this question was irrelevant to their workplace, especially as they had such good LGBTQI representation in their senior workforce. They therefore did not collect these data, or report them to ACE for a sector-wide picture. Linked to this are longstanding discussions between people who don’t like feeling audited by existing data collection processes that aim to understand inequality issues. It feels like this organisation took a pretty understanding position, then. However, an organisation may think it is being sensitive to people’s privacy in not asking them the question and may not think it has issues of discrimination, but how could it know? When asked about their sexuality in a subsequent sub-study at this organisation, one person wrote that they were relieved this issue was finally being looked at, as they had experienced discrimination. Understanding what is best for knowledge and understanding is therefore far from easy.

We can see a disconnect emerging: between collecting data for good, but it feeling bad while it is happening. This tension has exacerbated issues related to data practices and diversity practices in the sector that required attention—and at the same time. How can the sector know how to change, when it doesn’t know what changes to make and where? Data and research can help answer these questions in different ways, but research on data needed to be done first.

The thrust of the empirical research I was doing was to understand how inequality data currently worked in organisations funded by ACE and, crucially, how this might be improved (in terms of data quality and process). In essence, this was very much a project to understand the complexities of the existing context before we might know what to do to improve it. To do this, I collected and analysed many different types of dataFootnote 7 to help me understand the main problems across various areas and layers of the sector, and in different ways. You may remember that in Chap. 3 we covered how different kinds of data help us understand things from different standpoints. I describe the value of understanding a complex issue like this ‘in the round’ (Oman 2021, forthcoming). Here, I needed to capture the complex ecosystem of data collection and analysis that informs inequality policy in the publicly funded cultural sector.

As well as various desk-based policy research, 15 organisations that were funded by ACE, called National Portfolio Organisations (NPOs ), were sampled. Each NPO was chosen for a balanced distribution of geography, size of organisation, size of grant from ACE, discipline area (i.e. dance or visual arts) and social mission (i.e. reaching local working-class communities or working with disabled performers). In each NPO, I undertook participant observation, interviews with experts in data or diversity and focus groups with staff who held no management responsibilities in these areas.

One crucial aspect of this as a project was to improve understanding of how people feel about questions that are used to gather data about class and social mobility alongside other inequalitiesFootnote 8 that are protected by the Equality Act (2010). So, I am going to concentrate on my focus groups here—as these were about how people understood data in their everyday lives. People were grouped together in teams within their workplace and asked to fill in ‘fake’ equality monitoring forms. When I say fake, I mean that they were fabricated through bringing questions used elsewhere onto one form for people to answer, and then reflect on them. It was hoped that this would help me understand the data differently, through looking at the questions that generate them through other people’s eyes.

The context and set-up were important, because, as I keep saying, context is central to people’s understandings of data and how they work. It is also vital to researchers’ understanding. Context is—again—another one of those ‘contested concepts’ (Gallie 1956). It is often discussed as a problem for the researcher: qualitative researchers need to be sensitive to the contexts they are researching. The same is true of evaluative research, irrespective of your approach, a researcher should understand as much about the contexts they are evaluating as possible. It is an important concern in data studies, with the concept of ‘contextual integrity’ proposed as a framework for good practice when it comes to using personal data and protecting privacy (Nissenbaum 2009). So what is context? In this book, it is all of the whos, wheres, whats, whens, hows and whys, as well as the how much? and the so whats? and what nexts? Context is, therefore, vital in how we understand how people feel about data more generally—and how data get used, more specifically. It is also vital to sharing understandings of data, which we will return to in a bit.

Keeping context at the forefront of the research design and analyses enabled interesting insights into how the data work. People’s reflections on the questions used to gather these data offered new understanding on their utility and their accuracy. After asking everyone to complete these ‘fake’Footnote 9 equal opportunities forms, we spent time discussing how people felt about the questions: how they were formatted, what they were asking—and any other reactions. People indicated that they felt a combination of the types of understanding defined by the dictionary (mentioned earlier), of data and data processes, in which they could see benefits and harms that I discuss below.Footnote 10

I categorised four main issues, which touch on the differing aspects of understanding we have encountered above. I grouped people’s responses into political, personal, practical, proxy (Oman 2019b see below; Oman forthcoming-a). When I say political issues, I refer to those who raised objections to collecting these data in this way as an issue of public concern. These sorts of responses are characterised by people asserting it is not right to collect these data like this, from a position of sympathy and shared understanding. I used the term ‘personal issues’ to explain people’s responses which described how the process was, or could be, hurtful for, or to, themselves and others. These data were seen as too private, and the processes could disproportionately affect some more than others. There were a number of practical issues raised, including people not knowing the answer to the questions, or not being able to answer using the categories provided. This probably feels very familiar to many of you who have tried to fill in a questionnaire and not been able to make your answer fit the form. There was a lack of shared understanding between the person asking the question and the lives of the people trying to complete it. Despite the importance of all the responses across categories, I want to focus on the final category, ‘proxy’, below.

You may remember, a ‘proxy’ is an indirect measure of something. The example I gave in Chap. 2 is that someone’s income does not necessarily tell you about their quality of life directly, but because the relationship has been long-studied, assumptions are made about well-being using what we know about how income relates to well-being. Or so the theory goes. Another example from Chap. 5 is that 5% of teachers were sacked in Washington, D.C., as a result of a determined mayor wanting to turnaround the city’s underperforming schools. However, the teachers were judged and then let go off the back of a complex and flawed algorithm, called a value-added model which ‘define[d] its own reality and use[d] it to justify their results’ (O’Neil 2016, 7). The idea was that ‘the numbers would speak more clearly and be more fair’, but those who interacted with these models, numbers and judgements said, ‘I don’t think anyone understood them’ (O’Neil 2016, 5). The example of the use of proxies in managing schools is more complex than the class metric in the arts question I outline above, but the premise is the same: these proxies categorise people, telling someone else something about performance, identity and background, and are not often presented in a way that is easy to understand.

In the case of equalities data, personal characteristics are used to understand class and social mobility, but it is not as simple as measuring something like age. Class tends to be categorised in bands, but the meaning and dividing lines between these bands (e.g. working class and middle class) are not universally understood by people. People are notoriously bad at self-defining their class (O’Brien 2018). This means that a direct measure of class using self-definition is unlikely to be accurate. Instead, asking people questions about their lives can indirectly establish aspects of privilege and disadvantage as a result of their socio-economic status, or their class. Some obvious questions might be to do with the house people live in, their salary—or another one that is popular is what newspaper you read. You probably have a different picture in your head for a person reading the Sun (a UK right-wing tabloid) than you do, say, the Guardian (a UK left-wing broadsheet). These questions get at different indicators of class: salary, wealth and cultural consumption, for example, and have all been shown to have different pros and cons.Footnote 11

Although the class proxy questions that were trialled in these group discussions were new to many answering these equality monitoring forms, they have long-established methods with their own institutional histories. Many of the questions have been used for decades in sociological measures of social mobility (Goldthorpe and Hope 1972). One question asks for the occupation of the main wage earner in your household when you were 14. It is considered a more accurate measure of class than income or self-identification or any of the other proxy options (O’Brien 2018; Brook et al. 2020). This question is part of a schema that informed the National Statistics Socio-Economic Classification (NS-SEC) system used for half a century (ONS 2010). The schema identifies someone’s class origins by way of the school they attended, whether their parents attended higher education, and parental occupation at 14. While policy and data experts consider these questions most able to produce the most robust metric, the latter question in particular was queried in every one of my focus groups, because of these issues of understanding as political, practical, personal or proxy.

Returning to the findings on the proxy question, what were the issues with it? People by and large understood that this was a proxy question—even if they did not understand what is meant by the term ‘proxy’. Let me explain: one person said, ‘I know that you are trying to get at something, but I don’t know what it is, exactly’. The participant grasped that what their mum or dad did for a job years ago was not really the important thing for the researchers who would be looking at this data to understand class and social mobility. But they could not work out what the connection was between what they were being asked and inequality. What did it mean in the context of equality monitoring in their workplace at that moment, many years later. They found themselves in a process of trying to understand what the proxy question was doing, but it did not quite make sense to them.

Wanting to understand the rationale behind the question was not an isolated incident. There was a palpable moment in most of these group discussions where someone, or numerous people, identified that these are not neutral processes. There was more going on than met the eye and they wanted to understand. I was asked numerous questions by participants in almost every group, such as ‘What are you trying to get at?’, ‘Why has this question been worded like this?’, ‘Why my parents? What have they got to do with my job now?’, ‘Why the employment of only one?’ ‘Why employment at all?’ and, most frequently, ‘Why 14?’ and ‘What about the information about my life that this question does not capture?’ It is clear that this proxy question that aims to produce robust, objective data provokes many more questions when it comes into play with ordinary understandings. The key thing to learn from this was that many people did not feel comfortable answering the question for various reasons, but largely this was because they did not understand what it was doing, or how the data would be useful. They couldn’t imagine what would happen next or how it would be valuable.

As a researcher doing research for a policy organisation, I was asked to make recommendations on what to do next. So, my key recommendation was to improve communications about what was happening when people gave their data (Oman 2019d). Essentially, context is not only important to understanding how data work in context for the researcher, but communicating these contexts is vital to move towards a shared understanding of how data work and why they are important.

It seemed clear that people needed to know why a question is being asked and what that question does, and why. They also craved to understand why these personal, intimate data are important to share. The question was not a question about questions, in so much as a question about data. Given the nature of the proxy was so far removed from everyday understandings of what the aim of using these data was, this is understandable. People in the focus groups were (or at least claimed to be) committed to helping address issues of inequality, which is typical of people working in this sector (Brook et al. 2020). In other words, the people I spoke to by and large had the empathy part of understanding down, but equality monitoring processes were not designed for shared understanding.

Remember that well-being data or inequality data are data about us. Yet, it is not common practice to help people understand what their data can do and how their data can improve anything. Cultivating communications about the whats, whys, whos, hows and so whats and what nexts is important to increase public understandings and trust (Oman 2019c, d). We are seeing increasing attention to public engagement with data (Kennedy et al. 2020). Yet, to date,Footnote 12 this work is not necessarily concerned with how people come to understand data, and is still too focussed on how the tech/media company or the government wants people to engage with what they are doing.

The recommendations I made as a result of the inequality research aimed to not only improve understanding of why measuring class was important, but to be more understanding when collecting data (Oman 2019d). As a director of a major museum said to me while I was setting the research up:

This [understanding inequality] is a project of care. It’s about trying to make the sector a better place for everyone, but somehow, the way it is done is the opposite. Its unfriendly, and I think, can feel hostile. (Oman forthcoming-b)

Interestingly, this sentiment that people collecting data don’t care about people was quite common in the UK’s Measuring National Well-being debate (2010–2011). The quote below was one I chose to illustrate that you got the feeling when reading the comments people wrote in the free text fields, that people who completed the debate survey felt that the survey authors were talking a different language from them. They were almost from two different cultures.

Your [sic] talking to people about their lives, not selling them a product. Empathy and understanding with how you word your surveys will make people actually give a damn and ‘want’ to take part as they believe (rightly or wrongly) that they will be listened too [sic] and their opinion might just count for something. (Oman 2015, p. 82)

Being more understanding when collecting data reduces these ‘hostile’ conditions of data collection in a project of social justice and well-being (Oman 2019c, 2015). Those who want data, especially to improve things, need to be mindful of the well-being of those whose data they need. They need to be more understanding of those whose data they ask for, and they need to take account of the personal nature of these kinds of questions and the experience of being asked questions about your identity and your background (Oman 2019a). They also need to move towards an idea of shared understanding of data and inequality.

Context should not only be a concern for researchers to improve their understanding on their terms, but needs to account for sharing understanding more broadly. We encountered this in Chap. 8, where research to understand the culture–well-being relationship is designed to prove this relationship and presented in a way that speaks to decision-makers. When in fact work should be done in social, cultural and charity sectors so that research is designed to work with and speak to the sector that wants to better understand the value of the work it does. Again, this means moving towards more shared understandings of data and their processes.

Subsequent to my research with ACE (Oman 2019c) and policy recommendations (Oman 2019d), this advice now features in the Social Mobility Commission’s new guidelines on collecting data (SMC 2021). The focus on the questions rather than the data is more people-centred:

Asking someone what their socio-economic background is can seem like a personal question to ask, and some people may not be used to being asked it.

In order to build trust, help employees understand why the question is being asked—to help get a better picture of the socio-economic diversity in the business. People need to hear a purpose.

This movement towards being understanding when collecting data to understand society is an important one, and one that has been little acknowledged up to this point in much large-scale data collection: whether that data are about well-being or inequality. Crucially, those marginalised by inequalities are most at risk of suffering from ill-being as a result of data (Data Justice Lab n.d.; Kennedy et al. 2020). While the government statistical service (GSS) has a pledge for statistics for ‘public good’ (GSS n.d.), this still does not formallyFootnote 13 account for being understanding of the public in data’s collection, analysis and use.

9.3 Data Uses as Barriers to Understanding

Beyond the arguments I have just made about how a lack of understanding can lead to bad data practices that are bad for well-being, I also argue that they lead to bad data. If people cannot answer the questions in a survey for practical, personal or political reasons, or because they feel uncomfortable that they do not know enough about why the data are important and what is happening with them (as is the case with the proxy questions), you jeopardise possibilities for good data, instead ending up with missing or incorrect data.

What we have also encountered in this book is how data uses lead to a lack of understanding more broadly. As in the case with Google Flu Trends we covered in Chap. 5, if you do not consider the variety of contexts in which people will type the symptoms of a pandemic illness, you will not appreciate the limits to your method. This is a barrier to understanding. Similarly, if those modelling the data on COVID-19 ‘in the community’ are not aware of the fact that it is more difficult to collect tests from high-rise flats in poorer communities, whose data are missing? How might that hinder understanding of inequalities and the pandemic, if the data are to be analysed to answer those questions? Context is important to understanding. If you don’t think about who is missing from your missing data, how can you know how important the missing pieces are? How can you know how limited your understanding is?

The gift of search engines offers us access to so much more information—daily—as we go about our business. We can playfully search to prove a family member wrong at Christmas—‘no that’s not the same so-and-so that was in that thing. You’re thinking of this one…’—or cheat at the local pub quiz. However, the lists of information it presents us with are not always a simple single answer to a closed question. Searches of course enable you to put a proxy term in and see what the search comes up with. But there often are millions of results.

Search engines have been designed to learn what we might be looking for, based on information they have on our previous searches (and everyone else’s). This means that a search engine wants to understand what we might want to know. Yet, as we discovered in Chap. 1, the search engine does not only gather data on us and show us results back in some sort of neutral process. Instead, it makes decisions on what it will recommend we look at as a result of our search terms. As Noble explained, if you typed in the phrase ‘black girls’ as recently as 2011, you were shown indecent images. This is not a question and answer process, but rather one of selection and assumption.

Instead, search engines try to understand what we might want to find by making associations that may be very different from our own way of understanding things, or indeed what we are imagining we might find. Returning to an important point from Chap. 1, it is possible that being shown an association subconsciously changes an aspect of our understanding of what people do, or what they look like. Data and data practices can change culture. This is potentially dehumanising and can lead to the opposite of greater understanding—or, indeed the good society. We must design data practice, along with the ways in which we engage with data, more responsibly to ensure that well-being is improved through this engagement.

9.4 Following the Data: How We Have Come to Understand Well-being Data in This Book

We have covered a number of different understandings of well-being and data in this book, as well as considered their impact on, and relevance to, culture and society. We have identified how ideas of well-being differ and transcend time, place, culture and religion. We have encountered how people feel about well-being in their everyday life, and projects to try and understand this phenomenon, as well as the understandings of those responsible for people’s well-being, such as those in government. We have also considered how people interact, even live with data in their everyday lives, but are not always sure they understand them.

We have followed the data into ‘disciplines’, as groups of academics and professionals who look at the world in a particular way, and tend to agree on certain methods to understand it. We have considered how experts understand well-being across research disciplines (including economics, social and cultural policy, social statistics and philosophy), and how they work together in sub-disciplines, and in practice. For example, many economists look for trends in what people value over time and what that means for well-being. This book has presented documents as data to analyse what well-being economists (and other disciplines) value and how that changes over time. We found indications that happiness psychology as a new discipline suited the ends of those eager for ‘a new science of happiness’, but that when it came to deciding on data processes, some psychologists felt their expertise was overlooked. We found that economics has traditionally held much sway with policy-making institutions, but not necessarily made their ideas and principles accessible to all. Of course, these issues are not specific to economics, but most disciplines using data to understand well-being can lose sight of shared understanding, or being understanding.

In Chap. 5, some of the pros and cons for writers on different Big Data approaches were synthesised. Notably, Tables 5.1, 5.2 and 5.3 indicate that these concerns tended to reflect on the utility of data for the data scientists, or whoever else might want to use them. They did not account for whose data they were and how ethical these approaches might be. Given that Big Data are often collected in ways that are not obvious to people, what could be done better to ensure shared understanding?

There are moves towards greater fairness, accountability and transparency in data uses. Yet, following a data controversy and watching how these principles work in practice demonstrate that much effort remains to establish what a shared understanding of these values look like in practice. We briefly considered the case of the algorithm that decided on students’ A level grades, in lieu of an exam under COVID-19 restrictions in the UK. The outcome was contentious, but the regulator (Ofsted) insisted this was the fairest way to approach the problem. Yet as the headline premises behind the decision-making method emerged in the press, the process became a national scandal; notably, because of the impact on young people’s futures and current well-being. There were then calls for transparency and accountability, but when the algorithm’s methodology was published, the 319-page document was not legible to many and was only even manageable to a very select few.

Transparency could involve showing everyone everything, but how does this compromise understanding? What does that mean when the data and the actions surrounding their use are complex, highly detailed and outside of everyday understandings? Chapter 8 reviewed one research project using valuation with well-being data, step by step. It followed the data backwards, to understand the contexts from which they and the study originated. It also followed the findings forwards to understand how the research was interpreted in other contexts. The report explained that these methods were accepted by experts in government. However, the chapter found that when the methods were reproduced, using the same data, the findings differed, so what does that mean for shared understanding of experts. The chapter also showed that when the findings were reproduced in the media, they were misinterpreted to say that museums boost happiness, which was not how the research was presented in the report. What does that mean for shared understanding with non-experts in data?

Shared understandings are difficult, when within the same field. In Chap. 7, we encountered two research projects which were ostensibly looking at ‘subjective well-being’ in a similar population: people with an artistic practice and people with a creative occupation. We found that while the term ‘artistic practice’ indicates a level of professionalisation, this was not what the research was necessarily looking at. Similarly, that creative occupants didn’t need to be creative at all—as we might understand the word—according to the UK government’s Department for Culture, Media and Sport. We also found very different data were used to understand the concept of subjective well-being in these studies. What does that mean for how we join-up and share understanding of the well-being of different groups?

We have discovered that the meaning of well-being changes as the nature of data changes, and desire for data evolves and demands for data analytics increase. We have looked at well-being as it is understood as various measurements, and the benefits of understanding well-being at scale and over time, and have witnessed how knowledge and information can be gained, but also how some meanings can be lost by these exercises. Context that ties the data to the people it is about is removed, to enable patterns to become visible at scale, and yet context is rarely accounted for in narratives of the benefits of these data and their uses.

We have seen how well-being data are data about us—they are our data. They require our interactions, often our time, and are used to make decisions that are ostensibly on our behalf, but we may disagree with. We have seen how they change the workplace, how people were managed in COVID-19 and even the TV programmes we end up watching or the music we listen to. We have seen the growth of apps to track our well-being and tell us how to live better or walk more steps, and the market value of these apps increased considerably in this last decade. We have also witnessed how lucrative well-being data can be when their analysis has value to a policy sector, government or, in the case of a pandemic, the whole world.

We have also found indications that despite the fact we are ‘living with data’ (Living With Data), we don’t all necessarily grasp what is happening with our data and what they do for and against us in our day-to-day lives. Unpacking various types and forms of well-being data (data about well-being) and touching on the possible impacts that data and their uses have on our own well-being, and society more generally, is crucial to grasping some of the contexts of data that get obscured. So, understanding well-being data can help us understand data better. But more than that, contextualising well-being data—discovering the whos, whats, wheres, whens, hows and whys, as well as the so whats and the what nexts—offers insight into politics and policy. It also helps us understand how research and knowledge may claim to know things, but that these claims may have limits.

There are limits to the promise of data: what they can achieve for society is not always good. Technical progress in data and their handling are not always a development for good. The fetishisation of data and proof of value (as with the case studies of social and cultural policy) prove that attachments to data in society are flawed, opening up a market for data practices that shifts the relationship between researcher and data. Our attachment to ideas of novelty and innovation, as with the case of ‘the new sciences’ and Big Data also blindside us to their limits. These are a few of the growing concerns in critical data studies, but need to be a bigger concern in all studies of well-being, across social policy, social statistics, sociology, economics, psychology and so on. There is an opportunity to take what we are learning in critical data studies and well-being studies to help the social sciences consider how it might adopt a more understanding position.

We need to return to how we understand how data are understood and how they can make us a more understanding society. Context matters: where data come from, who they are for and about, where they go and for what purpose. But context matters for more than researchers and more effort should be placed on how it can improve shared understanding, and being a more understanding society. Without acknowledging the limits in capacity, or indeed possibilities for understanding, the What Next? or How can we do it better? questions will not be answered properly for well-being.