chapter 5 Getting a sense of Big Data and well-being
Despite the conflicting evidence from different approaches to ‘Big Data’, people are keen to find new ways to harness them to answer the age-old policy and philosophy questions around people’s well-being. The increase in well-being research coincides with an increase in research with and on Big Data. Both have possibilities and challenges, but could they be exacerbated by combining well-being research with these data practices? Do Big Data have a capacity for good when making decisions about young people’s exam grades or whether someone is eligible for social housing? We reflected on some important examples of where this went awry in this chapter.
New methods and metrics using Big Data, and indeed the research going into developing new tools to harness them, are not necessarily being checked for rigour before the approach is used elsewhere, as was the case with the Twitter community study, and its use of the sentiment metrics. Generalising people’s happiness based on mobile phone data has its limitations. We cannot necessarily be entirely sure of whether it is the aesthetic grandeur of an old Victorian bandstand in the park, whether there is a classical concert inside, if you had enough sleep, whether you are picnicking with your favourite friends, with your kids, or having time away from your kids; indeed, whether you are stuck on a delayed tube underneath the park, or are walking in a hailstorm, that truly adds to (or detracts from) your momentary happiness.
The ethics of studying Big Data more broadly should be considered, and the behaviours of those who are outside the sample of users of wearable tech or smartphones, especially as these people may be older or poorer, for example, which we know intersects with well-being in very significant ways. Despite this, claims are still made that findings from these studies could be used to inform policy and investment. While they can offer some insights, we must be mindful of their limits—and crucially of their implications, especially in different contexts.
All in all, Big Data and new technologies, whilst not always revolutionary in kind, can offer insights into well-being that are useful for policy-makers on a national scale, in international pandemics and for people who simply want to see what people think. But they are not without their limits, nor are they a magic bullet to the issues we have with existing data. If anything, they are also shown to have the potential to exacerbate existing problems as much as investigate solutions.
The capacity for Big Data to embrace complexity, and at greater speed, means they present new opportunities to analyse health data—and crucially how health intersects with social concerns. Reflecting back from today on how crude the Google Flu Trends analysis in 2013 now seems, it is clear that Big Data technologies and techniques are improving at pace. The COVID-19 example, BlueDot, shows that the value of Big Data analyses is in their capacity to now cope with more of Big Data’s qualities at the same time, and in fact, to harness them: their messiness, variability, size and the capacity to link previously unconnected data sources from farming information to flight sales. The value was in the variety of data and sources used. Yet harnessing the power of Big Data was not powerful enough to prevent a worldwide crisis, despite the grand claims.
What we think of as ‘Big Data’ offer a peculiar perspective on ‘well-being’. Consider the different things they capture, from sleep patterns to elite cycle trails to facial recognition and how many steps your walk to the post office takes. These devices exist to capture and produce data because data can be useful and commercialised. We are not even clear on whether more knowledge of the self is good for well-being or bad (yet?), let alone whether it is good at scale: that governments (and who else) know more about us. What is clear is that data are producing and changing culture and society, as much as they are capturing it.
We need to ask questions around the commercial value of these data practices alongside social justice issues. How would these data have had a greater chance of improving well-being were the contexts in which they were analysed different? Who should be included in these discussions, and who is excluded? Ultimately, how will decisions and trade-offs be made between the commercial and social justice dimensions?