Small Data Matters for the Development Agenda!

While there is a lot of buzz about big data also in the world of international development and humanitarian responses, this post would like to remind that non-big data still matters for us as development practitioners.

This is not merely because of a “digital divide” with existing inequalities in how people use technology or access the web, but because big data is inherently based on the passive research object. And as we all know, long gone are the days where beneficiaries and targets groups are passive recipients of help from well-meaning expert outsiders. Development interventions should place intended beneficiaries at the heart of the design, implementation and evaluation of projects.

What is big data?

Let’s focus on all the big data that is produced as a by-product of mobile technology usage, internet connectivity, and/or social media – what some scholars refer to as “data fumes” (Taylor 2017). Practical examples include Facebook mining the digital trails of its users to learn their preferences, or when Google used data from millions of searches to predict the spread of the H1N1 virus in the US (Mayer and Cukier 2013: 8, 2). Or why not when the same search engine collects anonymised location information from phones and uses it to analyse how busy a particular restaurant is, on average, during particular days and times. From the humanitarian field, a case that has gotten a lot of attention is that of the 2010 earthquake in Haiti in 2010. Here ICT technology was used to map how people had moved geographically, which would then help inform the crisis response (Read, et al 2016: 6). Experts utilised the position data from SIM cards from the largest mobile phone company in Haiti (Digicel) to estimate population movements following the earthquake as well as an ensuing cholera outbreak (Bengtsson, et al, 2011).

The big data revolution is attributed to the ubiquity of personal computing devices (Spratt and Baker 2015: 6), with for instance Facebook having access to billions of users’ data, from people from all sorts of geographic locations and socio-economic backgrounds. Add to this powerfully enhanced processing capacity and speed, with technological advancements allowing us to better analyse, store, and share the large amounts of data produced (ibid: 6-8).

Impressively, these advancements in mobile technology and social media manages to capture information not only on physical movement, but on human behaviour and preferences on a wide range of things.

Big data and the passive beneficiary

Attempting to understand research objects by solely studying data that is generated as a by-product of their online behaviour, gives us limited understanding of them. Moreover, we deny the right of those objects to answer for themselves, and provide us a better understanding of society which undeniably is complex. Without using feedback data, we condemn beneficiaries to passivity. We signal, patronisingly, that we do not value their active agency.
One aspect to this is that when it comes to situational data, big data tells us a lot about what and less about why. Another aspect is that even where we as development practitioners are confident that we actually can retrieve enough information about a given situation using the trails people leave through ICT usage, we ought to value emancipation, ownership, and agency, out of principle.

Regarding the first point Mayer and Cukier (2013:14) point out that in a big data world, we are not striving to understand causality:

(…) instead we can discover patterns and correlations in the data that offer us novel and invaluable insights. The correlations may not tell us precisely why something is happening, but they alert us that it is happening.

I agree that we should take advantage of the immense benefits and opportunities that big data brings, as it allows us to unlock the latent value of information, and use it for predictive analysis (ibid: 15). However, this datafication of life and society does not render other research methods unnecessary. We should still strive to understand people’s concerns more deeply, instead of merely accepting public preferences as revealed by big data analytics; Schroeder (2018: 143) quite poignantly problematises this by stating that “it may be problematic if activist organizations (or campaigns) are led by discrete data rather than a more holistic picture of what voters or supporters want.”

Immense amounts of data is and will be available from our increasing use of ICT equipment, but this fact will not render unnecessary the need for theoretically grounded knowledge of the contexts we operate in, knowledge that is holistic and derived from more bases than our datafied world.

Using big data to understand and predict human behaviour – and not coupled with other forms of data – and using this to inform the design, implementation, and evaluation of international development programmes, is not conducive to empowerment. “Small data” is not only relevant where internet penetration is low, but in all contexts where we value the objects (when these are people and human systems) we are striving to understand more about. This includes surveys, focus group discussions, interviews, and “volunteering of administrative data, where the citizen is aware her data is being gathered” (Taylor 2017: 3). Note that what is implied by small data here does not have to do with digital vs analogue, as surveys can be deployed using ICT technology. It has to do with valuing people’s own perceptions and assessments, allowing them to self-identify, and more.

As if asking us to brace ourselves for the big data dominated world, Mayer and Cukier (2013: 7) states that “society will need to shed some of its obsession for causality in exchange for simple correlations: not knowing why but only what.” A development practitioner however (and public administration that genuinely cares about being responsive to those to whom it caters), cannot settle for this. It is our obligation to integrate a rights-based approach into our work, and promote ownership and empowerment among intended beneficiaries. Traditional forms of data gathering not only gives us a nuanced understanding of social issues but can also encourage target groups to self-reflect and give them voice to articulate the challenges they face in their own words. At length, this is the prerequisite for people to also be able to engage meaningfully in designing solutions to those same challenges. While big data is revolutionary, feedback data is emancipatory; neither should be discarded, but combined for the benefit of development outcomes.

Bengtsson L, Lu X, Thorson A, Garfield R, and von Schreeb J (2011): Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLoS Med 8(8): e1001083.

Heeks, R. (2017): Information and Communication Technology for Development (ICT4D). Abingdon: Routledge.
Mayer-Schönberger, and V., Cukier, K. (2013): Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray Publishers.

Read, R., Taithe, B., and Mac Ginty, R. (2016): Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming.

Schroeder, R. (2018): Social Theory After the Internet: Media. Technology & Globalization. London: UCL Press.
Spratt, S,. and Baker, J (2016): Big Data and International Development: Impacts, Scenarios and Policy Options. Brighton: IDS.

Taylor, L. (2017): What is data justice? The case for connecting digital rights and freedoms on the global level, draft paper.

Taylor, L., and Schroeder R. (2015): Is bigger better? The emergence of big data as tool for international development policy. GeoJournal 80: 503-528.

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