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Technology Foundations for Digital Development

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[1].  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[2].

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[3] – 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[4].

Digital ICTs have already moved us along the time dimension to a world of 24/7 everywhen connectivity (see Figure 1[5]).  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”[6].

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.

 

[1] UNCSTD (2013) Issues Paper on ICTs for Inclusive Social and Economic Development, UN Commission on Science Technology and Development, Geneva

[2] WAS (2016) Digital in 2016, We Are Social, Singapore

[3] Pew Research Center (2014) The Internet of Things Will Thrive by 2025, Pew Research Center, Washington, DC

[4] UNCSTD (ibid.)

[5] Adapted from ITU (2005) The Internet of Things, International Telecommunication Union, Geneva

[6] 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

An Emerging Digital Development Paradigm?

28 February 2017 3 comments

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).

ict4d-paradigms

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[1].  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[2].  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.

 

[1] Heeks, R. (2009) The ICT4D 2.0 Manifesto: Where Next for ICTs and International Development?, Development Informatics Working Paper no.42, IDPM, University of Manchester, UK

[2] ibid.

Data Justice for Development

13 October 2016 Leave a comment

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:

  1. Demand just and legal uses of development data.
  2. Demand data consent of citizens that is truly informed.
  3. Build upstream and downstream data-related capabilities among those who lack them in developing countries.
  4. Promote rights of data access, data privacy, data ownership and data representation.
  5. Support “small data” uses by individuals and communities in developing countries.
  6. Advocate sustainable use of data and data systems.
  7. Create a social movement for the “data subalterns” of the global South.
  8. Stimulate an alternative discourse around data-intensive development that places issues of justice at its heart.
  9. Develop new organisational forms such as data-intensive development cooperatives.
  10. Lobby for new data justice-based laws and policies in developing countries (including action on data monopolies).
  11. Open up, challenge and provide alternatives to the data-related technical structures (code, algorithms, standards, etc) that increasingly control international development.

Measuring Barriers to Big Data for Development

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[1].  At present, this is more potential than actuality because big data initiatives in developing countries face many barriers[2].

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.
WP62 Graphic v2

 

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”.

[1] Hilbert, M. (2016) Big data for development, Development Policy Review, 34(1), 135-174

[2] 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

The Power Dynamics of Big and Open Data

At a recent CDI brown-bag discussion on data-intensive development, we hypothesised a mirror-image power dynamic between big data and open data.

Big Open Data Few Many

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).

Reversing Big Data Inequalities

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.

 

A Research Agenda for Data-Intensive Development

18 July 2016 1 comment

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.

IMG_0828

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[1], we identified a set of future research priorities, as shown below and also here as PDF.

DID Research Agenda

 

 

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”.

 

[1] My thanks to Jaco Renken for collating these.

The Politics of Disconnection: Network Geography, Trump, Sanders, Brexit, et al

Disconnection

Due to advances in transport and digital infrastructure, we live in an increasingly-connected world.  The value of global flows rose from US$5tr in 1990 to US$30tr in 2014[1].  In the same period, international travel grew from 435m to 1.1bn per year.

But this global interconnection – and the economic crash that was its direct result – has led to a powerful counter-reaction, with challenger politics emerging from both right and left.  The figureheads in the global North are various and sometimes curious: Trump, Sanders, Farage, Iglesias, Tsipras, Le Pen, Hofer, and more.  While differing in many policies, they share common ground that boils down to the slogan, “Disconnect!”.

Examples of insurgent policies include:

  • Disconnection from human networks through anti-immigration initiatives.
  • Disconnection from governance networks such as leaving the EU or abandoning free trade agreements.
  • Disconnection from production networks through support for localised production, and disincentives to globalised production.
  • Disconnection from – or at least restrictions on – capital networks through tax and other financial controls.
  • Disconnection from geo-political networks through increasing reticence for overseas military intervention.

There are many other policy examples: British disconnection from international development networks; French disconnection from the euro; etc.

Who is this coming from?  Setting aside the catalysis and aspirations of individual leaders, there are differences but also similarities between the demographics of those disconnecting from the right and those disconnecting from the left[2].  Right-wing disconnectors tend to be older, poorer, less-well-educated; left-wing disconnectors the reverse. But they appear to have two things in common: they are more often from the ethnic majority, and they are more often men.

We can understand these people in terms of positional network geography (see earlier discussion).  Rarely excluded from key global networks, instead these are people who perceive themselves – or can be persuaded to perceive themselves – as adversely incorporated, peripheralised in those networks.  They see a network core that benefits at their expense; they see new, mobile members seeking to join their network and potentially displace them.  For those who are white men perhaps there is particularly a gap between the promise or expectation of benefitting from the growth of global networks, and a perceived reality of not doing so.

As the complexity of the networks into which we are connected grows, and as the number of our network connections grows, we become increasingly connected into contexts that are too complex to either understand or control.  Yet we demand that our politicians control these uncontrollable networks.  And this takes place in an environment of growing digital politics in which form matters more than content.

Combine these two and we encourage the confident assertion of simple solutions: on the right, disconnecting from global flows of labour; on the left, disconnecting from global flows of capital; both disconnecting from global governance networks.

This is reminiscent of the disconnections of the 1920s following the shock of the First World War.  Remind me, how did that work out?

[1] MGI (2016) Digital Globalization, McKinsey Global Institute, San Francisco, CA

[2] http://www.newstatesman.com/politics/2015/02/si-we-can-how-left-wing-podemos-party-rattling-spanish-establishment; https://www.quora.com/Whats-the-demographic-profile-of-a-Bernie-Sanders-supporter; https://yougov.co.uk/news/2016/03/24/eu-referendum-provincial-england-versus-london-and/; http://www.theatlantic.com/politics/archive/2016/03/who-are-donald-trumps-supporters-really/471714/

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