What is special about “data-intensive development”: the growing presence and application of data in the processes of international development?
We can identify three levels of understanding: qualities, affordances, and development impacts.
A. Data Qualities
Overused they may be but it still helps to recall the 3Vs. Data-intensive development is based on a greater volume, velocity and variety of data than previously seen. These are the core differentiating qualities of data from which affordances and impacts flow.
B. Data Affordances
The qualities are inherent functionalities of data. From these qualities, combined with purposive use by individuals or organisations, the following affordances emerge[1]:
- Datafication: an expansion of the phenomena about which data are held. A greater breadth: holding data about more things. A greater depth: holding more data about things. And a greater granularity: holding more detailed data about things. This is accelerated by the second affordance . . .
- Digitisation: not just the conversion of analogue to digital data but the same conversion for all parts of the information value chain. Data processing and visualisation for development becomes digital; through growth of algorithms, development decision-making becomes digital; through growth of automation and smart technology, development action becomes digital. Digitisation means dematerialisation of data (its separation from physical media) and liquification of data (its consequent fluidity of movement across media and networks), which underlie the third affordance . . .
- Generativity: the use of data in ways not planned at the origination of the data. In particular, data’s reprogrammability (i.e. using data gathered for one purpose for a different purpose); and data’s recombinability (i.e. mashing up different sets of data to get additional, unplanned value from their intersection).
C. Data-Intensive Development Impacts
In turn, these affordances give rise to development impacts. There are many ways in which these could be described, with much written about the (claimed) positive impacts. Here I use a more critical eye to select four that can be connected to the concept of data (in)justice for development[2]:
i. (In)Visibility. The affordances of data create a far greater visibility for those development entities – people, organisations, processes, things, etc. – about which data is captured. They can more readily be part of development activity and decision making. And they can also suffer loss of privacy and growth in surveillance from the state and private sector[3].
Conversely, those entities not represented in digital data suffer greater invisibility, as they are thrown further into shadow and exclusion from development decision-making.
Dematerialisation and generativity also make the whole information value chain increasingly invisible. Data is gathered without leaving a physical trace. Data is processed and decisions are made by algorithms whose code is not subject to external scrutiny. The values, assumptions and biases inscribed into data, code and algorithms are unseen.
ii. Abstraction. A shift from primacy of the physical representation of development entities to their abstract representation: what Taylor & Broeders (2015) call the “data doubles” of entities, and the “shadow maps” of physical geographies. This abstraction typically represents a shift from qualitative to quantitative representation (and a shift in visibility from the physical to the abstract; from the real thing to its data imaginary).
iii. Determinism. Often thought of in terms of solutionism: the growing use of data- and technology-driven approaches to development. Alongside this growth in technological determinism of development, there is an epistemic determinism that sidelines one type of knowledge (messy, local, subjective) in favour of a different type of knowledge (remote, calculable and claiming-to-be-but-resolutely-not objective). We could also identify the algorithmic determinism that increasingly shapes development decisions.
iv. (Dis)Empowerment. As the affordances of data change the information value chain, they facilitate change in the bases of power. Those who own and control the data, information, knowledge, decisions and actions of the new data-intensive value chains – including its code, visualisations, abstractions, algorithms, terminologies, capabilities, etc – are gaining in power. Those who do not are losing power in relative terms.
D. Review
The idea of functionalities leading to affordances leading to impacts is too data-deterministic. These impacts are not written, and they will vary through the different structural inscriptions imprinted into data systems, and through the space for agency that new technologies always permit in international development. Equally, though, we should avoid social determinism. The technology of data systems is altering the landscape of international development. Just as ICT4D research and practice must embrace the affordances of its digital technologies, so data-intensive development must do likewise.
Follow @CDIManchester[1] Developed from: Lycett, M. (2013) ‘Datafication’: making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381-386; Nambisan, S. (2016) Digital entrepreneurship: toward a digital technology perspective of entrepreneurship, Entrepreneurship Theory and Practice, advance online publication
[2] Developed from: Johnson, J.A. (2014) From open data to information justice. Ethics And Information Technology, 16(4), 263-274; Taylor, L. & Broeders, D. (2015) In the name of development: power, profit and the datafication of the global South. Geoforum, 64, 229-237; Sengupta, R., Heeks, R., Chattapadhyay, S. & Foster, C. (2017) Exploring Big Data for Development: An Electricity Sector Case Study from India, GDI Development Informatics Working Paper no.66, University of Manchester, UK; Shaw, J. & Graham, M. (2017) An informational right to the city? Code, content, control, and the urbanization of information. Antipode, advance online publication http://onlinelibrary.wiley.com/doi/10.1111/anti.12312/full; Taylor, L. (2017) What Is Data Justice? The Case for Connecting Digital Rights and Freedoms on the Global Level, TILT, Tilburg University, Netherlands http://dx.doi.org/10.2139/ssrn.2918779
[3] What Taylor & Broeders (2015) not entirely convincingly argue is a change from overt and consensual “legibility” to tacit and contentious “visibility” of citizens (who now morph into data subjects).
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