In our forthcoming Springer book, 1 what we think of as 'data', and what it means for each of us to be recorded by it, to generate it and to interact with it, is shown to be a crucial part of any cross-sector debate concerning digital inclusion. This is particularly important too given the intimate relations that data-driven technologies, Artificial Intelligence (AI), the Internet of Things (IoT), algorithmic culture, facial recognition systems (Ada Lovelace Institute 2019), and wearable technologies have now assumed in our lives and learning, either with or without, our consent. Whether this concerns software code, data analytics, social media interactions, or other infrastructures involving data of some sort, these have 'become inseparable from policy processes and modes of governance' (Williamson 2019(Williamson , 2020. During the Covid-19 pandemic lockdowns of 2020/2021, much additional online activity, tracking systems and applications, have added extra complexity too. In many policies though, facts and figures concerning matters of digital inclusion may be referred to as data, but they do not illuminate the complex forms that data now takes. Nor do they address the diverse and unequal ways in which people have capacity to interact with, or understand their relations with, data.Having identified these issues, there is no quick remedy for one group of scholars, digital entrepreneurs, charitable agencies, or policy makers to address them alone. Research into the automation of digital poverty management has already demonstrated frightening, life threatening impacts for the vulnerable, from algorithmic decisions in data-driven eligibility systems and predictive models, to poor privacy and data security, or infringement of rights via surveillance (Eubanks 2018: 11). The 'digital poorhouse' described by Eubanks refers to the automation of decision making about access to services for the disadvantaged in society, where shared social decisions instead become 'systems engineering problems' (Eubanks 2018: 12). Yet even in discussing powerful analogies between public assistance programmes that have moved 'from poorhouse to database' (Eubanks 2018: 14), the emphasis is on how data gathered on people's circumstances is being managed. How each of us are managing data ourselves, to interact with it in every shape and form, remains under-explored.The relatively new field of Human-Data Interaction (HDI) was proposed in order to 'open up a dialogue amongst interested parties in the personal and big data ecosystems' (Mortier et al. 2014). Intended to offer a framework for more meaningful relationships with data, it was designed by these authors primarily to guide the practices of those who are developing data-intensive systems. Comprising of three