If there is to be a coming digital development paradigm, on what technologies will it be based?
Mobile, broadband, and mobile broadband (hence smartphones and tablets) will be a key foundation for the digital development paradigm. They are already present or rapidly diffusing in developing countries.
As these diffuse, cloud, social media and other Web 2.0 applications necessary for digital platforms will become dominant. The highest growth rates for cloud are already in the global South. Social media is already dominated by the global South: by 2016 North America and Europe made up just 26% of global social network users, with 52% in Asia (including Oceania), 13% in Central/South America, and 9% in the Middle East and Africa.
Looking further ahead, of technologies likely to have a significant impact on development, the Internet of things is a main contender: the online connectivity of increasing numbers of objects. The main growth area – 50 billion devices predicted by 2020 – is seen to be two types of connection. First, stand-alone sensors – for example providing agricultural readings from fields, or medical readings from health centres. Second, sensors integrated into mainstream objects from cars and refrigerators to toilets and shoes.
All these applications become smart when they move from a passive ability to collect and transmit data to an active ability to take a decision and action on the basis of that data: smart irrigation systems that automatically water dry crops; smart electricity grids that automatically isolate and re-route around transmission failures. Even more than cloud, smart systems bring significant potential to increase efficiency and effectiveness of infrastructure and business, alongside significant potential to increase dependency and vulnerabilities to cybercrime and surveillance.
Digital ICTs have already moved us along the time dimension to a world of 24/7 everywhen connectivity (see Figure 1). Thanks to telecommunications advances, anywhere can now be connected, and we are slowly erasing the blank spaces on the digital map and moving towards everywhere being connected. In terms of nodes, pretty well anyone and anything could now be connected thanks to ubiquitous computing. There is still a very long way to go but within a generation almost everyone will be connected, and we will be steadily moving closer to everything being connected thus vastly multiplying the number of “points of potential control, resistance, and contestation”.
Figure 1: The Growing Domain of Digital Connectivity
We can therefore think of three generations of technological infrastructure for digital development (see Figure 2). The first, already well-rooted, is based largely around mobile devices. The second, currently emerging, is based around digital platforms and the Internet including Web 2.0 applications. The third, currently nascent, will be based around a ubiquitous computing model of sensors, embedded processing and near-universal connectivity, and widespread use of smart applications.
Figure 2: The Generations of Digital Infrastructure for Development
Digital development is the subject of a Development Informatics working paper: “Examining “Digital Development”: The Shape of Things to Come?”, and is the topic for other blog entries.Follow @CDIManchester
 UNCSTD (2013) Issues Paper on ICTs for Inclusive Social and Economic Development, UN Commission on Science Technology and Development, Geneva
 UNCSTD (ibid.)
 p24 of Deibert, R. & Rohozinski, R. (2012) Contesting cyberspace and the coming crisis of authority, in: Access Contested: Security, Identity, and Resistance in Asian Cyberspace, Deibert, R.J., Palfrey, J.G., Rohozinski, R. & Zittrain, J. (eds), MIT Press, Cambridge, MA, 21-41
Taking a longer-term view, the relationship between digital ICTs and international development can be divided into three paradigms – “pre-digital”, “ICT4D”, and “digital development” – that rise and fall over time (see Figure below).
Changing Paradigms of ICTs and Development
The pre-digital paradigm dominated from the mid-1940s to mid-1990s, and conceptualised a separation between digital ICTs and development. During this period, digital ICTs were increasingly available but they were initially ignored by the development mainstream. When, later, digital technologies began to diffuse into developing countries, they were still isolated from the development mainstream. ICTs were used to support the internal processes of large public and private organisations, or to create elite IT sector jobs in a few countries. But they did not touch the lives of the great majority of those living in the global South.
The ICT4D paradigm has emerged since the mid-1990s, and conceptualised digital ICTs as a useful tool for development. The paradigm arose because of the rough synchrony between general availability of the Internet – a tool in search of purposes, and the Millennium Development Goals – a purpose in search of tools. ICTs were initially idolised as the tool for delivery of development but later began to be integrated more into development plans and projects as a tool for delivery of development.
The isolationism of the pre-digital paradigm remains present: we still find policy content and policy structures that segregate ICTs. But integrationism is progressing, mainstreaming ICTs as a tool to achieve the various development goals. From the development side, we see this expressed in national policy portfolios, in Poverty Reduction Strategy Papers, in UN Development Assistance Frameworks. From the ICT side, we see this expressed in national ICT policies and World Summit on the Information Society action lines.
The ICT4D paradigm is currently dominant and will be for some years to come. Yet just at the moment when it is starting to be widely adopted within national and international development systems, a new form is hoving into view: a digital development paradigm which conceptualises ICT not as one tool among many that enables particular aspects of development, but as the platform that increasingly mediates development.
This is the subject of a Development Informatics working paper: “Examining “Digital Development”: The Shape of Things to Come?”, and will be the topic for future blog entries.Follow @CDIManchester
What would “data justice for development” mean? This is a topic of increasing interest. It sits at the intersection of greater use of justice in development theory, and greater use of data in development practice. Until recently, very little had been written about it but this has been addressed via a recent Centre for Development Informatics working paper: “Data Justice For Development: What Would It Mean?” and linked presentation / podcast.
Why concern ourselves with data justice in development? Primarily because there are data injustices that require a response: governments hacking data on political opponents; mobile phone records being released without consent; communities unable to access data on how development funds are being spent.
But to understand what data justice means, we have to return to foundational ideas on ethics, rights and justice. These identify three different mainstream perspectives on data justice:
- Instrumental data justice, meaning fair use of data. This argues there is no notion of justice inherent to data ownership or handling. Instead what matters is the purposes for which data is used.
- Procedural data justice, meaning fair handling of data. This argues that citizens must give consent to the way in which data about them is processed.
- Distributive data justice, meaning fair distribution of data. This could directly relate to the issue of who has what data, or could be interpreted in terms of rights-based data justice, relating to rights of data privacy, access, control, and inclusion / representation.
We can use these perspectives to understand the way data is used in development. But we also need to take account of two key criticisms of these mainstream views. First, that they pay too little attention to agency and practice including individual differences and choices and the role of individuals as data users rather than just data producers. Second, that they pay too little attention to social structure, when it is social structure that at least partly determines issues such as the maldistribution of data in the global South, and the fact that data systems in developing countries benefit some and not others.
To properly understand what data justice for development means, then, we need a theory of data justice that goes beyond the mainstream views to more clearly include both structure and agency.
The working paper proposes three possible approaches, each of which provides a pathway for future research on data-intensive development; albeit the current ideas are stronger on the “data justice” than the “for development” component:
- Cosmopolitan ideas such as Iris Marion Young’s social connection model of justice could link data justice to the social position of individuals within networks of relations.
- Critical data studies is a formative field that could readily be developed through structural models of the political economy of data (e.g. “data assemblages”) combined with a critical modernist sensitivity that incorporates a network view of power-in-practice.
- Capability theory that might be able to encompass all views on data justice within a single overarching framework.
Alongside this conceptual agenda could be an action agenda; perhaps a Data-Justice-for-Development Manifesto that would:
- Demand just and legal uses of development data.
- Demand data consent of citizens that is truly informed.
- Build upstream and downstream data-related capabilities among those who lack them in developing countries.
- Promote rights of data access, data privacy, data ownership and data representation.
- Support “small data” uses by individuals and communities in developing countries.
- Advocate sustainable use of data and data systems.
- Create a social movement for the “data subalterns” of the global South.
- Stimulate an alternative discourse around data-intensive development that places issues of justice at its heart.
- Develop new organisational forms such as data-intensive development cooperatives.
- Lobby for new data justice-based laws and policies in developing countries (including action on data monopolies).
- Open up, challenge and provide alternatives to the data-related technical structures (code, algorithms, standards, etc) that increasingly control international development.
Critical modernism forms a very small, rather dated trickle of ideas within development studies. How could it be updated to serve as a lens for current research?
Critical modernism can be understood as a wide sweep of ideas, particularly encompassing thinkers such as Habermas and Gramsci. But it has only a small explicit footprint within development studies largely triggered by a chapter in Peet & Hartwick’s book Theories of Development, published in 1999. Itself developed from earlier work, this was particularly a response to “post-development” ideas that arose in the 1980s.
Despite subsequent editions of Theories of Development, the core text on critical modernism by Peet & Hartwick remains unchanged, and the specific notion has gained little overt traction in development literature:
– A few works by Giles Mohan and collaborators in the mid-2000s.
– A recent paper taking a critical modernist perspective on rights-based development.
– Mark Thompson’s paper which included the question of how Development 2.0 would inform the “debate” on critical modernism within development studies.
Unfortunately there wasn’t really a debate, but we can revisit the question, to ask if critical modernism is worth rescuing from its development studies obscurity.
As a start, what is critical modernism?
As the name suggests, it is critical; meaning that – drawing from Marxist political economy – it focuses on the structures of power that shape the processes and outcomes of development. Practically, it seeks to alter distributions of power in order to improve development outcomes. Methodologically, it listens to subaltern voices: the voices of those who are excluded and marginalised; whose basic needs have yet to be met. But it differs from structuralist critical theory through two additions. First, an incorporation of post-structuralism that acknowledges – alongside the power of resources, institutions and structural relations – the power of discourse and ideas: the power of control over systems of knowledge (see diagram below). Second, an incorporation of analytical lenses other than just class; for example a feminist lens that recognises patriarchal structures of power.
As the name also suggests, it is modernist: meaning that it accepts (albeit “critically”) and is optimistic about Enlightenment values. Teleologically, this means critical modernism accepts the idea of development, with a purpose of progress and alleviation of material want. Methodologically, this means an adherence to scientific method, to evidence-based conclusions, and to theorisation. But it differs from simple modernism in two ways. First, because it critiques modernism; not the substance of modernism but its current form as reflected in late-stage capitalism. Second, because it recognise multiple modernities, as modernism interacts with multiple different localities and their contexts around the world.
From here, I suggest four developments of critical modernism, perhaps increasingly contentious:
a) Ontological development: it is an easy step to aver that critical modernism is commensurate with the research philosophy of critical realism. Hence that epistemological and methodological implications of critical realism apply when researching from a critical modernist perspective.
b) Conceptual development: listening to subaltern voices and incorporating the voluntarism of populist critiques of development means critical modernism recognises the agency of the marginalised – the ability of social movements to effect change, and the ability of the marginalised to use the tools (ideas, technologies, discourse) of the powerful to empower themselves. Hence a denial of structural determinism; instead arguing that structures of power shape but do not determine development outcomes. This requires a re-conceptualisation of power that incorporates both structural power (e.g. power over) and agentic power (e.g. power to); and which identifies power as deriving not from a monolithic structure but from multiple sources, both global and local. Network theories of power may be especially relevant here; for example incorporating the connective power and agency that comes from membership of multiple and multi-scalar networks. In practice, this means seeking universals and commonalities to link within a wider-scale network those local networks (movements and institutions) seeking to empower those at the margins.
c) Methodological development: “Critical modernism listens to what people have to say … Critical modernism finds worth in all experiences”. If we are to take this seriously then it must include listening not just to the marginalised but also those within institutions of power. Critical researchers sometimes fail on this score; standing outside such an institution and painting a caricature that does not engage with, or listen to, its members. This listening is itself universally critical: not unquestioningly believing all that is said by either the powerful or the powerless.
d) Critical development: as noted above, a central tenet of critical modernism is a “blame the player not the game” approach – “Critical modernism focuses on a critique of capitalism as the social form taken by the modern world rather than on modernism” – arguing that the problem is not modernism per se but capitalism as a particular form within modernism.
But the same logic must also be applied to capitalism. Adding the requirements for rationality and evidence base, one can argue three things. First, that capitalism – as well as being the driver for inequality and environmental unsustainability – has been the driver for many of the material gains experienced in the global South in the past two decades. Second, that capitalism is not a form but forms. And that the problems lie not with the substance of capitalism, but with particular forms that it has taken; notably the lightly-regulated forms of neoliberal capitalism and emergent digital capitalism. Capitalism is not “a corrupt form of modernism” but a corruptible form of modernism. Third, that while socialism – even communism – may be highly effective in enabling the transformation from a largely agrarian society into early-stage industrialisation, alternatives to capitalism have largely failed to deliver sustainable later-stage development gains.
Here, we teeter to the very edge of what it means to be critical; well beyond what Peet & Hartwick – with their old-school calls for collective ownership of all means of production and all social institutions – would recognise. The key dividing line lies between those who think capitalism is the problem, and those who think it will be – in some form – part of the solution.
(Likewise politically. Critical modernism eschews kneejerk direct democracy in favour of reasoned, deliberative democracy. But belief in evidence would accept this form of participative democracy only where – in practice – it proves more effective than representative democracy at delivering development.)
To summarise a (revised) critical modernist approach to development studies:
– Critical through central attention to the distributions of power that underlie distributions of development outcomes; and seeking to alter those distributions in favour of the less-powerful.
– A network conceptualisation of power that includes both structure and agency; both power over and power to.
– Critical acceptance of values of modernity including reasoning and democracy, development and progress, science and technology.
– An ontology and epistemology of critical realism
– Methodology based on scientific method and evidence that listens to both the powerless and powerful.
– Perhaps, a focus more on alternative forms of capitalism than alternatives to capitalism.Follow @CDIManchester
 Mumby, D.K. (1997) Modernism, postmodernism, and communication studies: a rereading of an ongoing debate, Communication Theory, 7(1), 1-28
 Latest edition: Peet, R. & Hartwick, R. (2015) Theories of Development, 3rd edn, Guilford Press, New York, NY
 E.g. Hickey, S. & Mohan, G. (2004) Relocating participation within a radical politics of development: critical modernism and citizenship, in: Participation – From Tyranny to Transformation?, S. Hickey & G. Mohan (eds), Zed Books, London, 59-74
 Langford, M. (2015) Rights, development and critical modernity, Development and Change, 46(4), 777-802
 Thompson, M. (2008) ICT and development studies: towards development 2.0, Journal of International Development, 20, 821-835
 Reflecting the views of many social movements that want not a rejection of development, but progress, material gains, and which often believe strongly in the power of science and technology (Hickey & Mohan (ibid)).
 Bennett, W.L. & Segerberg, A. (2012) The logic of connective action, Information, Communication & Society, 15(5), 739-768
 Peet & Hartwick (ibid:313).
 Hulme, D. (2016) Should Rich Nations Help the Poor?, Polity Press, Cambridge, UK
 Henderson, J. (1996) Globalisation and forms of capitalism, Competition & Change, 1(4), 403-410
 Peet & Hartwick (ibid:314).
 O’Neil, P.H. (2015) Essentials of Comparative Politics, WW Norton & Company, New York, NY; Kornai, J. (2000) What the change of system from socialism to capitalism does and does not mean, The Journal of Economic Perspectives, 14(1), 27-42
How can we measure the barriers to big data for development? A research paper from Manchester’s Centre for Development Informatics suggests use of the design-reality gap model.
Big data holds much promise for development: to improve the speed, quality and consistency of a wide variety of development decisions. At present, this is more potential than actuality because big data initiatives in developing countries face many barriers.
But so far there has been little sense of how these barriers can be systematically measured: work to date tends to be rather broad-brush or haphazard. Seeking to improve this, we investigated use of an ICT4D framework already known for measurement of barriers: the design-reality gap model.
In its basic form the model is straightforward:
- It records the gap between the design requirements or assumptions of big data vs. the current reality on the ground.
- The gap is typically recorded on a scale from 0 (no gap: everything needed for big data is present) to 10 (radical gap: none of the requirements for big data is present).
- The gap can be estimated via analysis of researchers, or derived directly from interviewees, or recorded from group discussions.
- It is typically measured along seven “ITPOSMO” dimensions (see below).
As proof-of-concept, the model was applied to measure barriers to big data in the Colombian public sector; gathered from a mix of participant-observation in two IT summits, interviews, and secondary data analysis.
As summarised in the figure above, the model showed serious barriers on all seven dimensions:
- Information: some variety of data but limited volume, velocity and visibility (gap size 7).
- Technology: good mobile, moderate internet and poor sensor availability with a strong digital divide (gap size 6).
- Processes: few “information value chain” processes at work to put big data into action (gap size 7).
- Objectives and values: basic data policies in place but lack of big data culture and drivers (gap size 7).
- Skills and knowledge: foundational but not specialised big data capabilities (gap size 7).
- Management systems and structures: general IT systems and structures in place but little specific to big data (gap size 7).
- Other resources: some budgets earmarked for big data projects (gap size 5).
A simple summary would be that Colombia’s public sector has a number of the foundations or precursors for big data in place, but very few of the specific components that make up a big data ecosystem. One can turn around each of the gaps to propose actions to overcome barriers: greater use of existing datasets; investments in data-capture technologies; prioritisation of value-generation rather than data-generation processes; etc.
As the working paper notes:
“Beyond the specifics of the particular case, this research provides a proof-of-concept for use of the design-reality gap model in assessing barriers to big data for development. Rephrasing the focus for the exercise, the model could equally be used to measure readiness for big data; BD4D critical success and failure factors; and risks for specific big data initiatives. …
We hope other researchers and consultants will make use of the design-reality gap model for future assessments of big-data-for-development readiness, barriers and risks.”
For those interested in taking forward research and practice in this area, please sign up with the LinkedIn group on “Data-Intensive Development”.Follow @CDIManchester
 Hilbert, M. (2016) Big data for development, Development Policy Review, 34(1), 135-174
 Spratt, S. & Baker, J. (2015) Big Data and International Development: Impacts, Scenarios and Policy Options, Evidence Report no. 163, IDS, University of Sussex, Falmer, UK
At a recent CDI brown-bag discussion on data-intensive development, we hypothesised a mirror-image power dynamic between big data and open data.
Open data has an inherent tendency to redistribute power from the few (who originally hold the data) to the many (who can now access the data). It supports sousveillance. Big data has an inherent tendency in the opposite direction. It gathers data about the many but only the few have the power to capture, store, process, interpret and use that big data. It supports surveillance.
The extent to which these are inherent affordances of these data systems vs. the extent to which these tendencies are inscribed into those data systems is a matter for further debate. But what it does suggest is that big data per se is more reproductive than transformative of power inequalities within society. Think of the way in which major users of big data – social media platforms, e-business multinationals, telecommunication companies – operate. Their uses of big data reinforce inequality much more than they challenge it.
One way to address this is to reverse the power dynamic flow shown above: big data must become open data. This could happen in various ways:
- Big data as open data: big datasets are made openly available online in accessible format (as in all cases, with due consideration for data privacy and security).
- Big data as shared data: big datasets are made available to particular organisations (e.g. those of civil society).
- Big data as small data: sub-sets of big datasets are shared with the sources of that data for their use (e.g. the particular communities or groups from which the big data derived).
But what will make a reversal happen? To understand this, we need to study open data motivations: what causes organisations to open their datasets? Reviewing our knowledge of open data, we could not find examples of intrinsic motivations driving adoption of open data. Instead, drivers to opening of big datasets seem likely to be extrinsic:
- For public sector owners of big data, domestic political economy (e.g. local campaigns for access to data; economic benefits from creation of a local data economy) and external political economy (e.g. encouraging foreign investment through a reputation for openness).
- For private sector owners of big data, government regulation to force opening of datasets, or shareholder/consumer pressure.
Without such extrinsic pressures and the openness that ensues, big data may not deliver its developmental potential.Follow @CDIManchester
In practice, there is a growing role for data within international development: what we can call “data-intensive development”. But what should be the research agenda for this emerging phenomenon?
On 12th July 2016, a group of 40 researchers and practitioners gathered in Manchester at the workshop on “Big and Open Data for Development”, organised by the Centre for Development Informatics. Identifying a research agenda was a main purpose for the workshop; particularly looking for commonalities that avoid fractionating our field by data type: big data vs. open data vs. real-time data vs. geo-located data, etc; each in its own little silo.
A key challenge for data-intensive development research is locating the “window of relevance”. Focus too far back on the curve of technical change – largely determined in the Western private sector – and you may fail to gain attention and interest in your research. Focus too far forward and you may find there no actual examples in developing countries that you can research.
In 2014 and 2015, we had two failed attempts to organise conference tracks on data-and-development; each generating just a couple of papers. By contrast, the 2016 workshop received two dozen submissions; too many to accommodate but suggesting a critical mass of research is finally starting to appear.
It is still early days – the reports from practice still give a strong sense of data struggling to find development purposes; development purposes struggling to find data. But the workshop provided enough foundational ideas, emergent issues, and reports-back from pilot initiatives to show we are putting the basic building blocks of a research domain in place.
But where next? Through a mix of day-long placing of Post-It notes on walls, presentation responses, and a set of group then plenary discussions, we identified a set of future research priorities, as shown below and also here as PDF.
The agenda divided into four sub-domains:
- Describing/Defining: working out the basic boundaries, contours and contents of the data-intensive development domain.
- Practising: measuring and learning from the practice of data-intensive development.
- Analysing: evaluating the impact of data-intensive development through various analytical lenses.
- Resisting: guiding practical actions to challenge potential state and corporate data hegemony in developing countries.
Given the size and eclectic mix of the group, many different research interests were expressed. But two came up much more than others.
First, power, politics and data-intensive development: analysing the power structures that shape DID initiatives, and that are inscribed into data systems; analysing the way in which DID produces and reproduces power; analysing what resistance to data hegemony would mean.
Second, justice, ethics, rights and data-intensive development: determining what a social justice perspective on DID would mean; analysing what DID can contribute to rights-based development; understanding how ethical principles would guide civil society interventions for better DID.
We hope, as a research community, to take these and other agenda items forward. If you would like to join us, please sign up with the LinkedIn group on “Data-Intensive Development”.Follow @CDIManchester
 My thanks to Jaco Renken for collating these.