Posts Tagged ‘ict4d’

The Affordances and Impacts of Data-Intensive Development

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.

[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; Taylor, L. (2017) What Is Data Justice? The Case for Connecting Digital Rights and Freedoms on the Global Level, TILT, Tilburg University, Netherlands

[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).


ICT4D Course Curriculum and Teaching Materials

The draft materials below are for use in conjunction with the textbook, “Information and Communication Technology for Development (ICT4D)” under publication as part of the Routledge Perspectives on Development series.

They are designed for use in a ten-session teaching module consisting of nine teaching sessions plus one external visit / speaker / lab session.  For each teaching session, a set of PowerPoint slides is provided including notes on session content and class exercises.

ICT4D Course Introduction

Session 1: Understanding ICT4D

Session 2: Foundations of ICT4D

Session 3: Implementing ICT4D

Session 4: ICTs and Economic Growth

Session 5: ICTs, Poverty and Livelihoods

Session 6: ICTs and Social Development

Session 7: e-Governance and Development

Session 8: ICTs and Environmental Sustainability

Session 9: The Future of ICT4D

If used as presented, the material is suitable for a two/three-hour interactive session including a mix of presentation, class questions and class exercises.  Alternatively, the presentation material only can be presented in a lecture session with exercises and discussion left for a separate tutorial session.

Categories: Teaching ICT4D Tags: ,

The Demographics of Digital Development

13 April 2017 2 comments

Any emergent digital development paradigm will be shaped by three changing demographics of ICT usage: geographical, maturational and experiential.

Geographically, we have already moved from domination of the old Internet world (the US and Europe) to domination of the new Internet world (emerging nations of the global East and South), as summarised in the table below[1].  Use of digital technology in developing countries[2] now represents the majority not minority global experience.


Region % Share in 2001 % Share in 2017
Africa 1% 9%
Middle East 1% 4%
Latin America/Caribbean 5% 10%
Asia 32% 50%
North America 30% 9%
Oceania 2% 1%
Europe 29% 17%

Regional Share of Global Internet Users (2001, 2017)


Maturationally, there are growing numbers of digital natives: defined as those 15-24 year olds with five or more years of online experience[3].  While only around one-fifth of the youth cohort in developing countries are digital natives (compared to four-fifths in the global North), youth in the global South as twice as likely to be digital natives as the total population, and so they have a disproportionate role which might be worth specific encouragement.  Given they see ICTs as more important and more beneficial than others do, and given they make proportionately greater use of digital technologies and of social networks, then engagement of digital natives – for example in education or politics – may be enhanced by ensuring there are effective digital channels in these sectors.

Experientially, ICT users are experiencing changes that include[4]:

  • Time-space compression: a shortening of timespans for activities moving towards Castells’ notion of “timeless time” in which biological and clock time are replaced by compressed, desequenced notions of time; and a new geography that replaces physical distance with virtual space so that individual experience moves from a “space of places” to a “space of flows”[5].
  • Public to private: moving from shared-use to individual-use models of ICT interaction. Voice communication is moving from public payphones to shared mobile phones to individually-owned mobile phones.  Internet access is moving from public access telecentres and cybercafés to semi-public home or work computers to personal mobile devices.  The digital experience thus becomes increasingly private and personal.
  • Fixed to mobile: as mobile devices become the dominant means of access to digital infrastructure and content.
  • Text/audio to audio-visual: while it may be premature to call the emergence of a post-literate society, increasing bandwidth and technical capabilities mean digital experiences can increasingly resemble rich, natural real-life experiences rather than the artificial restrictions of just text or just audio.

One can argue that all four cases, represent an increasing presence yet decreasing visibility of the digital as its mediation merges more seamlessly into everyday life and activities.  This growth-but-disappearance of mediation thus represents a final experiential trend – that digital technologies more-and-more intercede between us and our experiences, and yet we notice them doing this less-and-less.  If the medium is the message, our conscious awareness of the message may be diminishing.

All three of these trends – geographical, maturational and experiential – form the emerging background underlying digital development, which 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] IWS (2017) Internet Usage Statistics, Internet World Stats


[3] ITU (2013) Measuring the Information Society 2013, International Telecommunication Union, Geneva

[4] Barney, D. (2004) The Network Society, Polity Press, Cambridge, UK; Boettiger, S., Toyama, K. & Abed, R. (2012) Natural obsolescence of Village Phone, in: ICTD’12, ACM, New York, NY, 221-229; Molony, T. (2012) ICT and human mobility: cases from developing countries and beyond, Information Technology for Development, 18(2), 87-90; Ridley, M. (2009) Beyond literacy, in: Pushing the Edge, D.M. Mueller (ed), American Library Association, Chicago, IL, 210-213

[5] Castells, M. (2000) Materials for an exploratory theory of the network society, British Journal of Sociology, 51(1), 5-24

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 5 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).


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

%d bloggers like this: