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‘Data is the new oil; like oil, it must be refined before it can be used.’

Summary

Of concern to us in the developing world is that the current ecosystem around big

data creates a new kind of digital divide: the big data rich (developed world) and the

big data poor (developing world). This report argues that big data poses a threat to

those it overlooks, namely a large percentage of Africa’s populace, who remain on

big data’s periphery. As most Africans use feature phones, and not smartphones

‘they do not regularly contribute data to be analysed, as they do not routinely engage

in activities that big data is designed to capture’1. Additionally, the report discusses

the political economy of big data, its implications on policymaking, warns against a

scramble for Africa’s data and outlines opportunities for the Continent to fully exploit

the advent of big data. It is argued that that there is a requirement for the active

involvement of policymakers, business and civil society to ensure that Africa

leverages the benefits, and addresses the potential pitfalls that the big data

phenomenon may create.

Background

The phenomenal adoption of mobile phones on the African continent over the last

twenty years2, in tandem with the proliferation of connected devices and fledgling

‘Internet of things’ has heralded the arrival of the big data era on the Continent.

Africans are increasingly emitting and creating digital information with their mobile

phones, Internet use and various forms of digital transactions. Globally, ‘computing

has become ubiquitous, creating countless new digital puddles and oceans of

information’3. Google states that ‘the first five exabytes of information created were

between the dawn of civilisation and 2003, whereas that much information is now

created every two days, with the pace increasing.’4 Every animate and inanimate

1 Lerman J. (2013) Big Data and its exclusions. (Stanford Law Review: Stanford) 2 ITU estimates 63.5% mobile penetration in 2013 3 Bollier D. (2010) The Promise and Peril of big Data. (The Aspen Institute: Maryland) 4 Ibid

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object on earth will soon be generating data, and Cisco forecasts that thirty-seven

billion intelligent devices will connect to the Internet by 2020.5 These devices and

sensors drive exponentially growing data traffic, which in 2012 was almost twelve

times larger than all global Internet traffic in 2000.

This wealth of new data, in turn, accelerates advances in computing – creating a

virtuous cycle of big data. Analytics is now more accessible, owing to both the

precipitous drop in the price of storage technologies and processing bandwidth6.

Cluster computing systems provide the storage capacity, computing power and high-

speed local area networks to handle these large data sets. In conjunction with ‘new

forms of computation combining statistical analysis, optimisation and artificial

intelligence’ 7 , researchers are able to construct statistical models from large

collections of data to infer how the system should respond to new data.

Notwithstanding these developments, the analytics of big data is still in its infancy

globally, and more so in emerging economies.

5 http://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf 6 Russom P. (2011) Big Data Analytics. (TDWI research: Washington) 7 Bollier D. (2010) The Promise and Peril of big Data. (The Aspen Institute: Maryland)

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Big data is, in many ways, a poor term. Traditionally, it has been understood using

three characteristics, namely: volume, variety and velocity.

Source: TDWI research

Big data is thus conceived of as a ‘massive volume of both structured and

unstructured data, generated internally by and externally to organisations, that is so

large that it's difficult to process with traditional database and software techniques’8.

Though there is little doubt that the quantities of data now available are often quite

large, this is not the defining characteristic of this new data ecosystem. ‘Big data is

less about data that is big, than it is about a capacity to search, aggregate and cross-

reference large data sets.’9 Karen Levy argues that ‘data is big not because of the

number of points that comprise a particular dataset, nor the statistical methods used 8 Lerman J. (2013) Big Data and its exclusions. (Stanford Law Review: Stanford) 9 Boyd D., and Crawford K. (2013) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly

phenomenon. (Information, Communication and Society Journal)

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to analyse them, nor the computational power on which such analysis relies. Instead,

data is big because of the depth to which it has come to pervade our personal

connections to one another’10. Big data is thus better defined as a socio-technical

phenomenon that rests on the interplay of:

• ‘Technology: maximising computation power and algorithmic accuracy

to gather, analyse, link, and compare large data sets;

• Analysis: drawing on large data sets to identify patterns in order to

make economic, social, technical, and legal claims; and

• Mythology: the widespread belief that large data sets offer a higher

form of intelligence and knowledge that can generate insights that were

previously impossible, with the aura of truth, objectivity, and

accuracy’11

Big Data and its Discontents

Since the turn of the century, and particularly since the advent of social media,

consumers have volunteered volumes of personal data. Unstructured data, which

constitutes 80% of all data, describes information formatted as natural language

rather than numerical figures. Unstructured data encompasses everything from

social media interactions, to recordings, to emails and more. As previously stated,

the proliferation of smartphones, tablets and other devices has exponentially

accelerated data creation to the extent that it is now estimated that the rate at which

data is generated and captured is doubling every 90 days.

Though the promise of big data lies within the ability to make predictions based on it,

it is imperative that a cautionary note be sounded to data evangelists who have a

utopian view of the promise of big data. Though admittedly more useful than

traditional statistics, big data is not a panacea as there are questions around the

reliability, accuracy and representativeness of its data sets. ‘Technology is neither

good nor bad; nor is it neutral. Technology’s interaction with the social ecology is

10 Levy K. (2013) Relational Big Data. (Stanford Law Review: Stanford) 11 Boyd D., and Crawford K. (2013) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly

phenomenon. (Information, Communication and Society Journal)

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such that technical developments frequently have environmental, social and human

consequences that go far beyond the immediate purposes of the technical devices

and practices themselves’12. Like other socio-technical phenomena, big data triggers

both utopian and dystopian rhetoric. ‘On one hand, big data is seen as a powerful

tool to address various societal ills, offering the potential of new insights into areas

as diverse as medical research and climate change. On the other, big data is seen

as a troubling manifestation of big brother enabling invasions of privacy, decreasing

civil liberties and increasing state control’13.

Of particular concern to us in the developing world is that the current ecosystem

around big data creates a new kind of digital divide: the big data rich (developed

world) and the big data poor (developing world). This report argues that big data

poses a threat to those it overlooks, namely a large percentage of Africa’s populace,

who remain on big data’s periphery. As most Africans use feature phones, and not

smartphones ‘they do no regularly contribute data to be analysed, as they do not

routinely engage in activities that big data is designed to capture’14. Consequently,

their preferences and needs risk being ignored when governments use big data and

advanced analytics to shape public policy. The danger is that as we increasingly rely

on big data’s numbers to speak for themselves, we risk misunderstanding the results

and in turn misallocating important public resources15. Thus, with every big data set,

we need to ask which people and data sets are excluded. Many African data sets

exhibit this ‘signal problem’ where data are assumed to accurately reflect the social

world, whereas there are significant gaps with little or no signal coming from

particular communities.

In a future where big data, and the predictions it makes possible, will fundamentally

reorder government and the marketplace, ‘the exclusion of poor and otherwise

marginalised people from datasets has troubling implications for economic

opportunity, social mobility and democratic participation’16. These technologies may

create a new kind of voicelessness, where certain groups’ preferences and

behaviours receive little or no consideration when political elites decide how to

12 Ibid 13 Ibid 14 Lerman J. (2013) Big Data and its exclusions. (Stanford Law Review: Stanford) 15 http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/ 16 Lerman J. (2013) Big Data and its exclusions. (Stanford Law Review: Stanford)

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distribute goods and services, and how to reform public and private institutions. Of

course, the poor (and most Africans by extension) are in many ways already

marginalised, but big data could reinforce and exacerbate existing problems.

Moreover, the use, abuse and misuse of data are a troubling lesson about the

limitations of information as the world hurtles toward the big data era. The underlying

data in most African countries are of poor quality, unrepresentative and can be

biased meaning it is more likely they will be misanalysed and used misleadingly.

Even more damning is that data can fail to capture what it purports to quantify. As

big data is largely in the languages of the developed world, it further isolates African

language content. However, an opportunity exists for the creation of these African

language specific data sets by Africans, whether by converting existing large

amounts of analogue African data (through crowd sourcing the digitisation process),

or uploading the extensive video content that resides in African broadcasters’

archives.

Big data holds substantial potential for the future, and large dataset analysis has

important uses. However, the promise of big data is, and will be, best fulfilled when

its limitations, biases and features are adequately understood and taken into account

when interpreting the data 17 . As is evident, big data is the source of both

tremendous promise and disquieting surveillance. In reality, like any complex social

phenomenon, big data is both of these, a set of heterogeneous resources and

practices deployed in multiple ends toward diverse ends.

The Political Economy of Big Data

As articulated above, big data has the potential to ‘solidify existing inequalities and

stratifications, and to create new ones’18. It could restructure societies so that the

only people who matter – quite literally the ones who count – are those who regularly

contribute to the right data flows. Manovich has argued that there are three classes

of people in the realm of big data, namely: ‘those who create data (both consciously

and by leaving digital footprints), those who have the means to collect it, and those

17 United Nations. (2012) Big Data for Development: Challenges and Opportunities. (United Nations Global Pulse: New York) 18 Ibid

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who have the expertise to analyse it. This last group is the smallest, and most

privileged as they are the ones that get to determine the rules about how big data will

be used and who gets to participate’ 19 . However, in the African context it is

necessary to ask questions about what all this data means, who gets to access it,

how data analysis is deployed, and to what ends. It is worth noting that there is a

scarcity of data analysts on the Continent, which then begs the question of who will

determine the African agenda, asking relevant questions and ensuring inclusivity in

the research undertaken. It is imperative that big data on the Continent do away with

the ‘politics of the missing’ to render visible the poor and marginalised in developing

countries. However, Africa’s paucity of reliable communications infrastructure poses

a significant challenge for the application of big data, as the network backbone

required for big data systems is sorely lacking. Key constraints are that current

network deployments do not have sufficient reach into the populace, are of poor

quality, overpriced and a low capacity. It is imperative that these factors be

addressed, as they are vital for a thriving big data ecosystem.

The data emanating from mobile phones holds particular promise, in part because

for many low-income people it is their only form of interactive technology. Utilising

this data created by mobile phones can improve our understanding of vulnerable

populations, and quicken governments’ response to the emergence of new trends20.

Big Data and Development

Though big data and real-time analytics are no modern panacea for age-old

development challenges, ‘the diffusion of data science into the realm of development

constitutes a genuine opportunity to bring powerful new tools to the fight against

poverty, hunger and disease’21. To this end, the United Nations launched Global

Pulse in 2009 ‘to leverage innovations in digital data, rapid data collection and

analysis to help decision makers gain a real-time understanding of how crises impact

19 Boyd D., and Crawford K. (2013) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. (Information, Communication and Society Journal) 20 World Economic Forum. (2012) Big Data, Big Impact: New Possibilities for International Development. (World Economic

Forum: Geneva) 21 Ibid

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vulnerable populations22 .’ Big data for development is about turning imperfect,

complex, often unstructured data into actionable information. This implies using

advanced computational tools, such as machine learning, which have developed in

other fields, to reveal trends and correlations within and across large datasets that

would otherwise remain undiscovered. Additionally, the GSMA has developed a

‘Mobile for Development Intelligence’ with the aim of persuading mobile operators to

share data with researchers and development organisations. Its mission statement

reads that ‘open access to high quality data will improve decision making, increase

total investment from the commercial mobile industry and development sector, and

accelerate economic, environmental and social impact from mobile solutions23.’

The data philanthropy discussed above, which entails corporations anonymising their

data and providing it to development organisations to mine for insights, patterns and

trends in (or near) real-time is still in its infancy. Data philanthropy is a laudable

advancement as it seeks to minimise Africa’s information asymmetries through the

creation of data commons, which are a critical input of big data for development. This

data can be conceived of as a public good, as it is both non-rivalrous and non-

excludable, ensuring that one’s use of the data does not restrict its availability to

others. As such, the benefits of creating and maintaining a data commons are that

the information benefits society as a whole, while protecting individual security. A

more concerted effort is required to make open data commons a reality, and

success.

22 Ibid 23 United Nations. (2012) Big Data for Development: Challenges and Opportunities. (United Nations Global Pulse: New York)

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Source: United Nations Global Pulse

However, though data may be public (or semi-public) this does not simplistically

equate with full permission being given for all uses24. Big data researchers rarely

acknowledge that there is a ‘considerable difference between being in public (eg.

sitting in a park) and being public (eg. actively courting attention)’. The ethical and

policy implications of big data will be addressed below.

24 Boyd D., and Crawford K. (2013) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly

phenomenon. (Information, Communication and Society Journal)

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Policy Implications of Big Data

The advent of big data presents significant opportunities and challenges for Africa’s

information and communications technologies (ICT) policy making. Big data is, at its

core, a social phenomenon – though the dominant narrative reduces people to mere

data points to be acted upon. ‘Big data and its attendant practices aren’t monoliths;

rather diverse and socially contingent, a fact which any policy analysis of big data

phenomena must consider’ 25 . As lines between the physical and digital world

continue to blur, and as big data and advanced analytics increasingly ‘shape

governmental decision-making about the allocation of resources, equality and

privacy principles will grow increasingly intertwined’ 26 . Moreover, exclusion or

underrepresentation in government datasets, then could mean losing out on

important government services and public goods. Policymakers thus need to be

aware of the possibility that the big data revolution may create new forms of

inequality and subordination, which raise broad democracy concerns.

As such, ensuring that the big data revolution is a joint revolution, ‘one whose

benefits are broadly and equitably shared, may also require, paradoxically, a right

not to be forgotten – a right against exclusion’27. A data antisubordination policy28

would ensure this. This antisubordination policy would, at a minimum ‘provide those

who live outside or on the margins of data flows some guarantee that their status as

persons with ‘light data footprints’ will not subject them to unequal treatment by the

state in the allocation of public goods and services’ 29 . This mooted data

antisubordination policy would also ensure that public institutions be required to

mitigate the disparate impact that their use of big data may have on persons who live

outside or on the margins of government datasets. Similarly, public servants relying

on big data for policymaking and other core democratic functions should be

compelled to take steps to ensure that big data’s marginalised groups continue to

have a voice in democratic processes.

25 Levy K. (2013) Relational Big Data. (Stanford Law Review: Stanford) 26 Lerman J. (2013) Big Data and its exclusions. (Stanford Law Review: Stanford) 27 Ibid 28 Ibid 29 Ibid

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In the field of public policy, ‘it is the predictive power of big data analytics that

understandably attracts the most attention as insights on human behaviour can be

gleaned from these data’30. The increase in the availability of data has occurred

relatively fast, and as such is not yet balanced by the emergence of privacy

legislation or ethical frameworks that can mitigate potentially damaging uses of the

data. As the big data pools are predominantly in the hands of powerful intermediary

institutions, not ordinary people, they may thus be misused and abused. If

policymakers do not insist on ‘building privacy, transparency, autonomy and other

protections into big data related activities from the outset, this will diminish big data’s

lofty ambitions’31. There is a need for a healthier balance of power between those

who generate the data, and those who make inferences and decisions based on it.

‘African countries represent a strong testing ground for data protections as the power

imbalance between the producers and users (mainly large multinational

corporations) of personal data there, is one of the largest anywhere’32. It is highly

likely that individuals, and even governments, may lack the information, resources or

access to make corporations or countries accountable when they breach data

protection guidelines.

It is evident that increasingly powerful and secretive algorithms, such as PRISM,

combined with numerous other massive datasets pose a significant risk to personal

privacy and civil liberties, especially in the African context. In the era of big data,

policy should include protections lest these worrisome orthodoxies crystalise.

30 United Nations. (2012) Big Data for Development: Challenges and Opportunities. (United Nations Global Pulse: New York) 31 http://blogs.oii.ox.ac.uk/policy/the-scramble-for-africas-data 32 Ibid

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The Scramble for Africa’s Data

After the last decade’s exponential rise in ICT use, Africa is fast becoming a source

of big data as Africans increasingly ‘emit digital information with their mobile phone

calls, internet use and various forms of digitised transactions’33. ‘The emergence of

big data in Africa has the potential to make the continent’s citizens a rich mine of

information, with the default mode being for this to happen without their consent or

involvement, and without ethical and normative frameworks to ensure data protection

or to weigh the risks against the benefits’34. It is increasingly likely that there will be a

new scramble for Africa: a digital resource grab, and African countries need to be

fully cognisant of this, and circumspect in their approach and monitoring thereof.

Opportunities for Africa

Notwithstanding the severe lack of qualified people on the Continent to exploit the

attendant benefits of big data, significant opportunities exist. ‘In light of the serious

problems with both illiteracy and information access in the developing world,

especially Africa, there is a widespread belief that speech technology can play a

significant role in improving the quality of life of developing-world citizens’35. It is oft

said that African societies rely on oral traditions to transfer knowledge and culture

inter-generationally, and the developing field of phonetic search, machine learning

and natural language processing coupled with big data portend well for Africa’s

ability to fully harness and leverage analytic power. There is a great number of

languages on the Continent, over 2000, with the development of ‘voice-search

systems being a useful tool in delivering on the original promise’ of big data

analytics. Though ‘speech technology has to date played a much smaller role in the

developing world, the rapid spread of telephone networks through the developing

world’36, leads to optimism that this situation will change significantly in years to

33 Ibid 34 Ibid 35 Barnard E., Moreno P., Schalkwyk J., and van Heerden C. (2010) Voice Search for Development. (Human Language

Technologies Research Group: Pretoria) 36 Ibid

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come. Recently, ‘a novel application of speech technology, namely the use of

speech recognition to perform searches through Web content and personal

information’37, has become increasingly popular in the developed world and it is this

paper’s contention that this could be duplicated and appropriated for the African

continent.

However, though ‘voice search lends itself to efficient and low-cost data collection

(thereby addressing resource constraints)’38, ‘digital content that is relevant to people

of the developing world is generally scarce and distributed across numerous sources

without any form of integration’39. African universities and the graduates produced

(mainly computer scientists and linguists) will thus have to redouble their efforts to

ensure that ‘voice search makes web-based content available regardless of the

original source of the data, which will go some way towards solving issues of content

availability’40.

Africa has already demonstrated its excellent science and engineering skills by

designing and starting to build the 64-dish MeerKAT telescope – as a pathfinder to

the Square Kilometre Array (SKA) – in South Africa. ‘The technology being

developed is cutting-edge and the project is creating a large group of young

scientists and engineers with world-class expertise in technologies that will be crucial

for development’41. Though many of today’s data scientists are formally trained in

computer science, maths or economics, they can emerge from any field with a strong

data and computational focus. Hal Varian argues that ‘the ability to take data -

understand it, process it, extract value from it, visualise it, and communicate it, is

going to be a hugely important skill in coming decades’42. Ben Fry takes this a step

further and argues for an entirely new field that combines the skills from often

disjointed areas of expertise in the analytics of big data.

37 Ibid 38 Ibid 39 Ibid 40 Ibid 41 Botman H. (2013) The role of universities in the development of Africa. (Paper presented to the Swiss Federal Institute of

Technology) 42 http://www.mckinsey.com/insights/innovation/hal_varian_on_how_the_web_challenges_managers

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He argues that fields such as statistics, data mining, graphic design, and information

visualisation each offer meaning to and can find patterns to data, but practitioners of

each are often unaware of, or unskilled in, the methods of the adjacent fields

required for a solution. As such, to fully exploit opportunities that stem from big data,

African universities need to reorientate themselves and their curricula to ensure that

all their graduates are ‘data literate’ (meaning competent in finding, manipulating,

managing, and interpreting data), as well as being adept at mathematical and

hypothetical deductive reasoning.

Conclusion

The increased analytics and predictive power associated with big data conjure

utopian and dystopian scenarios. This paper argues that the advancement of big

data and the Internet of things, though a significant milestone in the development of

social science and the Internet, is not an end in itself. Having highlighted the gains of

big data analytics and its ability to transform society, the paper warns against a

‘dictatorship of data’ wherein data governs us in ways that may do as much harm as

good. The cautionary note sounded cited ‘political and social equality considerations

where the vulnerable are likely to be further relegated to an inferior status’43, as well

as the policy implications of big data and a likely scramble for Africa’s data as

reasons to be circumspect of the promise of big data. ‘Technology is neither good

nor bad; nor is it neutral’44.

43 Lerman J. (2013) Big Data and its exclusions. (Stanford Law Review: Stanford) 44 Boyd D., and Crawford K. (2013) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. (Information, Communication and Society Journal)

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