Understanding Well-being Data

Chapter 1 Introducing well-being data

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?’1, 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 feedback 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,((See Airoldi (2021) for the most recent example of research on recommendations and YouTube.)) 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 Noble2. 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 State3.

There has been a lot written about ‘trust in numbers’4, 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 transparently5. 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’6 and the UK is told that it has ‘had enough of experts’7.

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. 2015 []
  2. 2018 []
  3. DCMS 2020 []
  4. Porter 1996 []
  5. Steedman et al. 2020; Kennedy et al. 2020 []
  6. Batchelor 2017 []
  7. Gove 2016 []