What role for the Big Data and Human Development research network? Reflections from the UN Expert Group Meeting on Big Data and the 2030 Agenda for Sustainable Development, 14-15 December, Bangkok

UN EGM on Big Data and the 2030 Sustainable Development Agenda

Participants at the UN Expert Group Meeting on “Big Data and the 2030 Agenda for Sustainable Development”

(Photo credit: Eri Nomikou, https://twitter.com/ENomikou/status/676274507384938496)

 

With a critical view on “Big Data” and “human development” across low-, middle-, and high-income countries, we position ourselves as a unique research network in the expanding Big Data and development landscape. Having returned from a recent United Nations Expert Group Meeting on “Big Data and the 2030 Agenda for Sustainable Development,” I believe that a key contribution of our network is to foster a North-South dialogue and to offer multidisciplinary perspectives and competencies that are largely under-represented among the existing Big Data actors.

Make no mistake: The debate at this high-level forum was nuanced and critical rather than focused on utopian ideas about the limitless potential of big data. Actors from the public, private, and academic sectors agreed that we first need to focus on the development problems and then solve the respective data needs, not the other way round. Problems of data solution “patchwork,” data privacy and consent, viable private sector business models for public-private partnerships in big data, or closed versus open data environments were debated openly and honestly.

But it is also clear that many issues surrounding big data remain unresolved and require critical reflection and research. For example, because the skills and capacity to produce, handle, and analyse Big Data are likely to rest outside of government boundaries, increasing the use of Big Data in policy can be problematic. While often framed under broadening participation (e.g. Twitter data), “partnerships” for a wider data ecosystem, and private sector “data philanthropy” (i.e. companies donating data), private partners might also have vested interests to shape and select the data and indicators that correspond to desired policy changes from their perspective. This could mean a transfer of knowledge and power from the public to the private sector. How could we then ensure that transparency and accountability mechanisms apply to the same extent to private actors as to public ones? Even where such situations do not occur, continuity and reliability in such partnerships are not guaranteed. For example, voluntary donation of data by a company may be useful, but once economic constraints tighten, how would that company be prevented from stopping this practice in order to keep costs in check? How would private data sharing agreements alter if the company is bought by a competitor? What if national development policies are based on data provided by a company that is about to go bankrupt (the same applies to NGOs with erratic income streams)?

My point here is not that Big Data uses for development policy should be discouraged, but that we need to have a better grasp of such fundamental questions before we advocate alterations of fundamental policy processes in favour of non-conventional data (even existing “conventional” data appears widely underutilised). Among the numerous problem areas surfacing at the Expert Group Meeting was the need for overarching frameworks (e.g. ethical, evaluation, data governance, and knowledge management frameworks) and for a better problem-specific and micro-level understanding of Big Data’s opportunities and intrinsic limitations (e.g. the social context of data generation, informational value of Big Data vs. conventional data to monitor specific Sustainable Development Goal indicators). The illustration below summarises some of the many themes that arose in the meeting.

 

BigDataIssues

Summary of Big Data problem areas

In my view, the high-level conversations at the Expert Group Meeting serve a useful purpose for putting “Big Data” on the policy agenda. What is needed next (and we are not quite there yet) is to build a knowledge base to understand how and how not to use various kinds of data in development policy and processes. Countries may articulate data and knowledge demands for specific development problems, and work with other “stakeholders” in pilot projects to develop institutional frameworks and mechanisms to make the best use of Big Data in light of its limitations. One plausible scenario might be that Big Data solutions help governments to save money when monitoring variations in development indicators for a majority group in urban areas (e.g. mobile phone data on economic performance and incomes), but the costs saved for data collection in one area should then be reinvested in order to understand the problems of the groups omitted by these data sources (e.g. remote and disconnected population groups). (Pilot projects, despite their seemingly poor reputation at the meeting, can help to build such knowledge, provided that evaluation frameworks are developed to articulate the learning outcomes up-front.)

The participation in a debate among experts has reinforced my conviction that research networks like ours are an indispensable ingredient to a more comprehensive understanding of Big Data. It is especially the North-South dialogue and the interdisciplinary expertise in our network that can open new perspectives and add much-needed depth to a Big Data debate that is still taking place at a high level.

Big data and development mailing list

We have started a mailing list for anyone wanting to stay in touch about the ‘big data and development’ initiative, and engage in conversation with the network. Just send a message to [bigdatadevelopment-subscribe@maillist.ox.ac.uk] to sign up.

15 Oxford-27

Kickoff!

Meeting of the minds, featuring Iginio Galiardone via Skype.

The new Big Data and Human Development research network

Data are now almost ubiquitous. Sensors and software are digitising and storing all manner of social, economic, political and environmental patterns and processes. As the size of these datasets has increased exponentially, many have begun to focus on how ‘big data’ harvested from online sources can allow potentially unprecedented insights into our world that may facilitate efforts to enhance human development.

Yet relatively little is known about how best to harness ‘big data’ in ways that could effectively inform development processes, particularly for the most disadvantaged, and whether those at the margins who produce the least amount of data risk becoming even more invisible. While new expertise is emerging, it remains unclear whether, and how, ordinary citizens will be able to seize these opportunities, individually and collectively and use them to their advantage.

Our initiative:

The ‘big data and human development’ research network aims to investigate the potential uses of ‘big data’ for advancing human development and addressing equity gaps. We are establishing a cross-disciplinary and global network to map what data sources and techniques exist for harnessing new digital data and address persistent concerns regarding human development, inequity, exclusion, and participation.

Our Goals:

The goals of this network will be to stimulate policy-oriented research that seeks to understand: (1) what presences and absences of data tell us about issues of participation and exclusion; (2) what data tell us about gaps in human development: facilitating better decision-making and accountability in previously data-sparse environments; (3) what tools have emerged globally that can maximise citizen ownership of big data, by making data meaningful within the cultures of participation that characterise different localities.

Our Approach:

We plan to achieve these goals with three core activities.

1) First, we plan build a digital observatory that seeks to assess the potentials of different data sources for informing human development. This observatory will be a web-platform that can link to relevant data and metadata.

2) Through the use of detailed case studies, we aim to empirically illustrate some of the promises and perils of using big data to inform human development. These case studies will be carried out in collaboration with end-users is some of the countries that most need to access big data and harness them for human development, but lack the expertise to make use of them as they are currently offered.

3) Third, we plan to bring together research and policy from both Global North and South to ensure that methodological knowledge about big data is appropriately mapped on to the interests of stakeholders to achieve key development outcomes. We hope to bring together key figures in order to avoid what Schroeder 2014 refers to as the tail of big data wagging the dog.

Our Planned Activities:

  • An initial workshop will be held in Oxford in the first year of the initative bringing together a limited number of key stakeholders to discern the potential use of big data for human development. This workshop will help inform development of the data observatory and selection of case studies.
  • A core activity for our project is the development and maintenance of a data observatory of big data and its potential for informing human development
  • A final workshop will be held in the second year to disseminate our preliminary findings to a broader range of stakeholders and further engage with the types of empirical research being pursued.
  • We also plan to host a seminar series focusing on ‘big data and human development.’ This will be an open event for anyone in Oxford, and will be freely webcast for a broader audience.

Our hope is that this project will bring together a range of disciplinary strengths at Oxford and beyond. At this early stage, we welcome inputs, suggestions, and collaborations, to steer and guide this initiative. Bringing together work in this area will not only result in ground-breaking, cross-disciplinary research, but will also ultimately serve to inform more evidence based human development programmes around the world.