Centre for Internet and Society

Bios and Photos of Speakers for Big Data in the Global South International Workshop

praskrishna — 2015-11-06T02:01:15Z

For more details visit https://cis-india.org/internet-governance/blog/big-data-global-south-international-workshop-bios-and-photos.pdf

Biometry Is Watching

praskrishna — 2011-04-02T12:08:26Z

In its first steps, the UID drive encounters practical problems, raises ethical questions, reports Sugata Srinivasaraju in Outlook.

Three women are fighting to take one chair in a classroom of a government school in Chelur village, in Gubbi taluka of Tumkur district. One sits on the lap of another and the third tries to push them both off the chair. What all three want is to be the first to be profiled under the Centre’s ambitious Aadhaar or unique identity number (UID) project. Their squabbling holds up the documentation by nearly 20 minutes, and the crowd outside, standing in line in the afternoon sun, grows restless. To calm them, the village revenue secretary orders the distribution of another round of buttermilk.

Chelur is one of four villages in the district picked for field trials before the 12-digit UIDs are assigned to people later in the year. Besides Tumkur, the pilot project is simultaneously running in seven villages of Mysore district. Each village has been given a target of 2,400-2,500 profiles to be completed in 20 days. This involves photographing the face, imaging the iris and scanning all ten digits of each person profiled and assigned a UID.

Villagers are enthusiastic about this rigorous profiling process even though there’s little awareness about the true purpose of the exercise. This is because of some falsehoods that have somehow spread in these areas. Nagamma, an elderly woman coming out after being profiled, thinks her eyes had been tested and found to be in perfect condition. Another middle-aged woman thought the exercise would bring her a new ration card—one that would entitle her family to an extra four kilos of rice. Some others were in a tizzy that if they didn’t undergo this “photography” their BPL cards would be taken away. Most, however, had queued up because they didn’t want to be left out of a sarkari exercise their neighbours were submitting to. Of the dozen people Outlook spoke to, only Muniswamy could tell us that this process would ensure that no one had more than one voter ID card or ration card—the way it should be, unlike some in his village who had illegally acquired two of each.

The village authorities have been doing little to counter the misinformation because their attention is focused on other compelling matters, like meeting the assigned target. This is an important issue because gram panchayat elections have been declared in Karnataka and lots of youngsters set off for campaigning early in the morning and would be difficult to locate for profiling.

According to official figures, Chelur has a population of 5,000, with 3,640 people above the age of 18. A random selection of 2,400 has been made from this to meet the UID target. The number seems small, but handling it at the village level can be demanding for the local authorities—there’s no police for crowd control, refreshments have to be distributed and the computerised work has to be done despite the power outages. Using generators has become inevitable, for the villages get hardly four or five hours’ power supply.

Another problem lies in obtaining the fingerprints of rural folk: most of them are engaged in manual labour or farm work and arrive with dirty palms that defeat the biometric reading machines. Pails of water, detergent and towels are provided for cleaning up. Much time is lost in such rescanning and it goes against the official estimate of five minutes for the young, nine for the elderly.

Prasanna Kumar, the village secretary, admits to the problems. “We did not make an open announcement for the UID pilot because we didn’t want to attract large crowds. We quietly prepared a random list of 2,400 people from the ration card database and went door-to-door to invite them,” he says. “We have left out people above the age of 80 and under 18. We told people this identity number will help them access various government schemes. Fingerprinting is the toughest problem. Initially people were reluctant, but suddenly they have become curious.”

H. Gnanesh, the tehsildar, says the ongoing step is only “concept testing”; the next will be rechecking, in which those already profiled will verify their identity details against what has been stored. Only after that will the UID be issued. In Chelur village, the concept testing ended on May 4; rechecking began the very next day.

Some NGOs observing the process have noted the lack of awareness in villagers. “They are clueless about what they are taking part in,” say Mahadev Prasad and Murthy, of the Basava Seva Trust. Murthy says fingerprinting is a big problem. He speaks of Hommaragalli, in Mysore district, where some foreign experts had to be called in to take a look at the scanning machines.

Then there are larger issues. Away from the surging crowds in Chelur, civil society organisations like the Centre for Internet & Society, the Alternative Law Forum and PUCL have been demanding greater dialogue. In fact, they have even suggested a review of the scheme. They argue there is no clarity on how the government proposes to store and secure personal and biometric data, given the fact that various agencies such as banks, telecom companies and government departments would potentially access it. Security is a huge concern also because the software, hardware and expertise of foreign companies is being used. These NGOs say the UID data, the National Population Registry and the NATGRID, when connected, could prove a grave threat to civil liberties.

“First, a centralised database, like the UID will create, has never been safe,” says Sunil Abraham of CIS. “It needs to be decentralised like our various mail servers. Second, we feel the collection of biometric data is happening at the wrong end of the pyramid. Instead of putting the poor through the process first, why not start with those with financial dealings of Rs 1 crore and above. Third, one study says 48 per cent of our people cannot remember a 12-digit ID. Fourth, we need privacy laws in place before the UID regime sets in.”

Those problems apart, even raising the level of the current exercise—from small samples of a few thousand each to profiling the billion-plus population of India—could place severe demands on our shaky administration. There’s a mountain to be moved.

Read the original article in the Outlook

For more details visit https://cis-india.org/news/biometry-is-watching

BigDog is Watching You! The Sci-fi Future of Animal and Insect Drones

maria — 2013-07-12T15:38:33Z

Do you think robotic aeroplanes monitoring us are scary enough? Wait until you read about DARPA´s new innovative and subtle way to keep us all under the microscope! This blog post presents a new reality of drones which is depicted in none other than animal and insect-like robots, equipped with cameras and other surveillance technologies.

This research was undertaken as part of the 'SAFEGUARDS' project that CIS is undertaking with Privacy International and IDRC.

Just when we thought we had seen it all, the US Defence Advanced Research Projects Agency (DARPA) funded another controversial surveillance project which makes even the most bizarre sci-fi movie seem like a pleasant fairy-tale in comparison to what we are facing: animal and insect drones.

Up until recently, unmanned aerial vehicles (UAVs), otherwise called drones, depicted the scary reality of surveillance, as robotic pilot-less planes have been swarming the skies, while monitoring large amounts of data without people´s knowledge or consent. Today, DARPA has come up with more subtle forms of surveillance: animal and insect drones. Clearly animal and insect-like drones have a much better camouflage than aeroplanes, especially since they are able to go to places and obtain data that mainstream UAVs can not.

India´s ´DARPA´, the Defence Research and Development Organisation (DRDO), has been creating UAVs over the last ten years, while the Indian Army first acquired UAVs from Israel in the late 1990s. Yet the use of all UAVs in India is still poorly regulated! Drones in the U.S. are regulated by the Federal Aviation Administration (FAA), whilst the European Aviation Safety Agency (EASA) regulates drones in the European Union. In India, the Ministry of Civil Aviation regulates drones, whilst the government is moving ahead with plans to replace the Directorate General of Civil Aviation (DGCA) with a Civil Aviation Authority. However, current Indian aviation laws are vague in regards to data acquired, shared and retained, thus not only posing a threat to individual´s right to privacy and other human rights, but also enabling the creation of a secret surveillance state.

The DRDO appears to be following DARPA´s footsteps in terms of surveillance technologies and the questions which arise are: will animal and insect drones be employed in India in the future? If so, how will they be regulated?

BigDog/LS3

Apparently having UAVs flying above us and monitoring territories and populations without our knowledge or consent was not enough. DARPA is currently funding the BigDog project, which is none other than a drone dog, a four-legged robot equipped with a camera and capable of surveillance in disguise. DARPA and Boston Dynamics are working on the latest version of BigDog, called the Legged Squad Support System (LS3), which can carry 400 pounds of gear for more than 20 miles without refuelling. Not only can the LS3 walk and run on all types of surfaces, including ice and snow, but it also has ´vision sensors´ which enable it to autonomously maneuver around obstacles and follow soldiers in the battle field. The LS3 is expected to respond to soldiers' voice commands, such as 'come', 'stop' and 'sit', as well as serve as a battery charger for electronic devices.

BigDog/LS3 is undoubtedly an impressive technological advancement in terms of aiding squads with surveillance, strategic management and a mobile auxiliary power source, as well as by carrying gear. Over the last century most technological developments have manifested through the military and have later been integrated in societies. Many questions arise around the BigDog/LS3 and its potential future use by governments for non-military purposes. Although UAVs were initially used for strictly military purposes, they are currently also being used by governments on an international level for civil purposes, such as to monitor climate change and extinct animals, as well as to surveille populations. Is it a matter of time before BigDog is used by governments for ´civil purposes´ too? Will robotic dogs swarm cities in the future to provide ´security´?

Like any other surveillance technology, the LS3 should be legally regulated and current lack of regulation could create a potential for abuse. Is authorisation required to use a LS3? If so, who has the legal right to authorise its use? Under what conditions can authorisation be granted and for how long? What kind of data can legally be obtained and under what conditions? Who has the legal authority to access such data? Can data be retained and if so, for how long and under what conditions? Do individuals have the right to be informed about the data withheld about them? Just because it´s a ´dog´ should not imply its non-regulation. This four-legged robot has extremely intrusive surveillance capabilities which may breach the right to privacy and other human rights when left unregulated.

Humming Bird Drone


Source: HighTech Edge

TIME magazine recognised DARPA for its Hummingbird nano air vehicle (NAV) and named the drone bird one of the 50 best inventions of 2011. True, it is rather impressive to create a robot which looks like a bird, behaves like a bird, but serves as a secret spy.

During the presentation of the humming bird drone, Regina Dugan, former Director of DARPA, stated:

"Since we took to the sky, we have wanted to fly faster and farther. And to do so, we've had to believe in impossible things and we've had to refuse to fear failure."

Although believing in 'impossible things' is usually a prerequisite to innovation, the potential implications on human rights of every innovation and their probability of occurring should be examined. Given the fact that drones already exist and that they are used for both military and non-military purposes, the probability is that the hummingbird drone will be used for civil purposes in the future. The value of data in contemporary information societies, as well as government's obsession with surveillance for ´national security´ purposes back up the probability that drone birds will not be restricted to battlefields.

So should innovation be encouraged for innovation’s sake, regardless of potential infringement of human rights? This question could open up a never-ending debate with supporters arguing that it´s not technology itself which is harmful, but its use or misuse. However the current reality of drones is this: UAVs and NAVs are poorly regulated (if regulated at all in many countries) and their potential for abuse is enormous, given that ´what happens to our data happens to ourselves....who controls our data controls our lives.´ If UAVs are used to surveille populations, why would drone birds not be used for the same purpose? In fact, they have an awesome camouflage and are potentially capable of acquiring much more data than any UAV! Given the surveillance benefits, governments would appear irrational not to use them.

MeshWorms and Remote-Controlled Insects


Source: NY Daily News

Think insects are creepy? Now we can have a real reason to be afraid of them. Clearly robotic planes, dogs and birds are not enough.

DARPA´s MeshWorm project entails the creation of earthworm-like robots that crawl along surfaces by contracting segments of their bodies. The MeshWorm can squeeze through tight spaces and mold its shape to rough terrain, as well as absorb heavy blows. This robotic worm will be used for military purposes, while future use for ´civil purposes´ remains a probability.

Robots, however, are not only the case. Actual insects are being wirelessly controlled, such as beetles with implanted electrodes and a radio receiver on their back. The giant flower beetle´s size enables it to carry a small camera and a heat sensor, which constitutes it as a reliable mean for surveillance.

Other drone insects look and fly like ladybugs and dragonflies. Researchers at the Wright State University in Dayton, Ohio, have been working on a butterfly drone since 2008. Former software engineer Alan Lovejoy has argued that the US is developing mosquito drones. Such a device could potentially be equipped with a camera and a microphone, it could use its needle to abstract a DNA sample with the pain of a mosquito bite and it could also inject a micro RFID tracking device under peoples´ skin. All such micro-drones could potentially be used for both military and civil purposes and could violate individuals´ right to privacy and other civil liberties.

Security vs. Privacy: The wrong debate

09/11 was not only a pioneering date for the U.S., but also for India and most countries in the world. The War on Terror unleashed a global wave of surveillance to supposedly enable the detection and prevention of crime and terrorism. Governments on an international level have been arguing over the last decade that the use of surveillance technologies is a prerequisite to safety. However, security expert, Bruce Schneier, argues that the trade-off of privacy for security is a false dichotomy.

Everyone can potentially be a suspect within a surveillance state. Analyses of Big Data can not only profile individuals and populations, but also identify ‘branches of communication’ around every individual. In short, if you know someone who may be considered a suspect by intelligence agencies, you may also be a suspect. The mainstream argument “I have nothing to hide, I am not a terrorist’ is none other than a psychological coping mechanism when dealing with surveillance. The reality of security indicates that when an individual’s data is being intercepted, the probability is that those who control that data can also control that individual’s life. Schneier has argued that privacy and security are not on the opposite side of a seesaw, but on the contrary, the one is a prerequisite of the other. Governments should not expect us to give up our privacy in exchange for security, as loss of privacy indicates loss of individuality and essentially, loss of freedom. We can not be safe when we trade-off our personal data, because privacy is what protects us from abuse from those in power. Thus the entire War on Terror appears to waged through a type of phishing, as the promise of ´security´ may be bait to acquire our personal data.

Since the 2008 Mumbai terrorist attacks, India has had more reasons to produce, buy and use surveillance technologies, including drones. Last New Year´s Eve, the Mumbai police used UAVs to monitor hotspots, supposedly to help track down revellers who sexually harass women. The Chennai police recently procured three UAVs from Anna University to assist them in keeping an eye on the city´s vehicle flow. Raj Thackeray´s rally marked the biggest surveillance exercise ever launched for a single event, which included UAVs. The Chandigarh police are the first Indian police force to use the ´Golden Hawk´ - a UAV which will keep a ´bird´s eye on criminal activities´. This new type of drone was manufactured by the Aeronautical Development Establishment (one of DRDO's premier laboratories based in Bangalore) and as of 2011 is being used by Indian law enforcement agencies.

Although there is no evidence that India currently has any animal or insect drones, it could be a probability in the forthcoming years. Since India is currently using many UAVs either way, why would animal and/or insect drones be excluded? What would prevent India from potentially using such drones in the future for ´civil purposes´? More importantly, how are ´civil purposes´ defined? Who defines ´civil purposes´and under what criteria? Would the term change and if so, under what circumstances? The term ´civil purposes´ varies from country to country and is defined by many political, social, economic and cultural factors, thus potentially enabling extensive surveillance and abuse of human rights.

Drones can potentially be as intrusive as other communications surveillance technologies, depending on the type of technology they´re equipped with, their location and the purpose of their use. As they can potentially violate individuals´ right to privacy, freedom of expression, freedom of movement and many other human rights, they should be strictly regulated. In Europe UAVs are regulated based upon their weight, as unmanned aircraft with an operating mass of less than 150kg are exempt by the EASA Regulation and its Implementation Rules. This should not be the case in India, as drones lighter than 150kg can potentially be more intrusive than other heavier drones, especially in the case of bird and insect drones.

Laws which explicitly regulate the use of all types of drones (UAVs, NAVs and micro-drones) and which legally define the term ´civil purposes´ in regards to human rights should be enacted in India. Some thoughts on the authorisation of drones include the following: A Special Committee on the Use of All Drones (SCUAD) could be established, which would be comprised of members of the jury, as well as by other legal and security experts of India. Such a committee would be the sole legal entity responsible for issuing authorisation for the use of drones, and every authorisation would have to comply with the constitutional and statutory provisions of human rights. Another committee, the Supervisory Committee on the Authorisation of the Use of Drones (lets call this ´SCAUD´), could also be established, which would also be comprised by (other) members of the jury, as well as by (other) legal and security experts of India. This second committee would supervise the first and it would ensure that SCUAD provides authorisations in compliance with the laws, once the necessity and utility of the use of drones has been adequately proven.

It´s not about ´privacy vs. security´. Nor is it about ´privacy or security´. In every democratic state, it should be about ´privacy and security´, since the one cannot exist without the other. Although the creation of animal and insect drones is undoubtedly technologically impressive, do we really want to live in a world where even animal-like robots can be used to spy on us? Should we be spied on at all? How much privacy do we give up and how much security do we gain in return through drones? If drones provided the ´promised security´, then India and all other countries equipped with these technologies should be extremely safe and crime-free; however, that is not the case.

In order to ensure that the use of drones does not infringe upon the right to privacy and other human rights, strict regulations are a minimal prerequisite. As long as people do not require that the use of these spying technologies are strictly regulated, very little can be done to prevent a scary sci-fi future. That´s why this blog has been written.

For more details visit https://cis-india.org/internet-governance/blog/big-dog-is-watching-you

Big Data, People's Lives, and the Importance of Openness

nishant — 2013-07-03T04:23:11Z

Openness has become the buzzword for everything in India right now. From the new kids on the block riding the wave of Digital Humanities investing in infrastructure of open knowledge initiatives to the rhetoric of people-centered open government data projects that are architected to create 'empowered citizens', there is an inherent belief that Opening up things will make everything good.

This blog post was published in DML Central on June 24, 2013.

I am not an Open-data party pooper. In fact, I firmly believe that opening up data – through hardware, through software, through intellectual property regimes on content – and enabling access to information and data is one of the most basic needs of the information age. I also advocate for strong policies that curb corporate and government control and monopolies over data and information. Along with my colleagues at the Centre for Internet and Society, and the many networks we work with, I have thought of myself as an open data advocate and have worked towards examining openness not only at the level of content, but also openness in infrastructure and conditions of access, distribution and storage. More than ever, it is necessary to build systems of Open Data that not only have distributed, collective and ethical ownership but also ensure the fair use and integration of information in our everyday life – especially given the sinister age of relentless remembering, as lives get incessantly archived through ubiquitous and pervasive technologies of portable computing.

Having said that, there is a strange thing happening around Openness right now. Openness seems to have been separated from the fact that it is a response to things being gated and closed. Openness, as it is being deployed right now, in e-government initiatives or rapid digitization processes in university libraries, seems to suggest that Openness is merely about making things in the physical format available in the digital medium. Hence, for example, the National Mission for Education through Information and Communication Technologies, India’s largest flagship government initiative to build learning conditions of the future, is investing almost all of its budget on digitizing historical and local language material in digitally intelligible and legible records that can be easily distributed. While the effort at building the infrastructure and preserving this material is absolutely worth supporting, making it the be-all and end-all of Open data initiatives is symptomatic of what I call the ‘politics of the benign’. We need to realize that Openness is not merely about making already available content in physical formats in the digital domain. Openness is about battles with Intellectual Property Regimes, which charge an extraordinary amount of money for high-value knowledge to anybody who wants to access it. In other words, openness is not about digitizing our grandparents’ pictures; it is about claiming access to knowledge and information hidden behind paywalls and gateways that is often produced using public resources.

As you can imagine, the perpetuation of this politics of benign fits many agendas; there are numerous stakeholders and actors who seek to neuter the radical nature of demands made by the Openness movements while retaining the vocabulary of political change. And that is why, if you look at the ways in which openness debates have changed, they get immediately deflected to questions around infrastructure, access, last-mile, etc. – which are all presented to us as the infrastructure of being political and being open. In the last few years, especially with Digital Humanities emerging as the playground where politics is not allowed, I find too many instances where the Humanities and Social Sciences questions get morphed into similar sounding questions that pretend to be the same but dislocate the political content and intention from the engagement.

One of the ways this works really well is by a separation of data from the lived reality of people. Data is seen as something that is out there – something that is about the real rather than the real. It is seen as an abstraction, which, when it further enters the circuits of pretty visualizations and graphic representations, becomes so entrenched in questions of reading and coding that it often becomes a surrogate for the larger realities that it is supposed to intervene in. So, for example, in India, the concerns around agriculture infrastructure and conditions of the farmers have easily been replaced by agriculture informatics – leading to a strange paradox where the states with the highest community informatics infrastructure also have the highest incidences of farmer suicides. I am not suggesting there is a cause-and-effect relationship here. However, it is a telling story that the community informatics infrastructure which was supposed to change the bleak realities of agriculture and farming in India has definitely not changed the nature of the reality it set out to solve.

Or in a similar vein, the ways in which the landscape of education is changing in the country, because of the emergence of the digital as the new organizing principle and in some instances, the raison de etre for building education infrastructure also needs to be examined. So, for instance, India has seen a rapid improvement of the Gross Enrollment Ratio in education that measures the annual intake and successful completion of education programs by students in the country. The GER shows that with remote education processes, the attempts at building distributed learning environments and the building of digital infrastructure has led to more students in different parts of the country getting enrolled in formal education systems. There is a celebration that more children are entering schools and colleges and are also in a state of socio-economic mobility. There is a clear causal relationship established in producing digital infrastructure and greater access to education and learning resources for an emerging information society like India.

However, this particular mode of looking at education, through the lens of access and inclusion, is no longer able to reflect on the core concerns that education institutions in the country were historically supposed to address. If the primary function of education was to address the questions of inequity, uneven modernity, disparate wealth distribution, and widespread socio-cultural conservatism, these are no longer questions that are featured in the Data-Technology driven education programs. These problems, which have been at the center of education debates in the country – leading to widespread affirmative action and violent resistance to it – have now been reformulated around quantifiable parameters of intake, credits, employability, affordability, accessibility, merit, etc. So there is silence about the nature of the students who enter education. There is an implicit push for the disinvestment of the state from education resources in favor of privatization. We remain enamored by the numbers joining the system, without worrying about the categories of discrimination – caste, gender, sexuality, language, location – that have affected the quality, intention and function of education. These issues have become moot points, to be replaced by visualizations and data sets that remain opaque in looking at the negotiations of identity politics and the need to embed education processes in lived realities of the students who enter formal education.

These problems are not new. And the intention of this articulation is not to deny the power of digital technologies or the opportunities they produce. Instead, it is a call to say that we need to stop thinking of data – an abstraction, an artifact, a manual product – as a natural state of being. We need to remind ourselves that engagement with data is not a sterile engagement, rendered beautiful through visualizations and infographics that can make reality intelligible. It is perhaps time to realize that Data has replaced People as the central concern of being human, social and political. Time to start re-introducing People back into debates around Data, and acknowledging that Data Informatics is People Informatics and data wars have a direct effect on the ways in which people live. And Die.

Banner image credit: sugree http://www.flickr.com/photos/73462957

For more details visit https://cis-india.org/openness/blog-old/dml-central-june-24-2013-nishant-shah-big-data-peoples-lives-and-importance-of-openness

Big Data in the Global South - An Analysis

tanvi — 2016-01-24T02:54:45Z

I. Introduction

"The period that we have embarked upon is unprecedented in history in terms of our ability to learn about human behavior." [1]

The world we live in today is facing a slow but deliberate metamorphosis of decisive information; from the erstwhile monopoly of world leaders and the captains of industry obtained through regulated means, it has transformed into a relatively undervalued currency of knowledge collected from individual digital expressions over a vast network of interconnected electrical impulses.[2] This seemingly random deluge of binary numbers, when interpreted represents an intricately woven tapestry of the choices that define everyday life, made over virtual platforms. The machines we once employed for menial tasks have become sensorial observers of our desires, wants and needs, so much so that they might now predict the course of our future choices and decisions.[3] The patterns of human behaviour that are reflected within this data inform policy makers, in both a public and private context. The collective data obtained from our digital shadows thus forms a rapidly expanding storehouse of memory, from which interested parties can draw upon to resolve problems and enable a more efficient functioning of foundational institutions, such as the markets, the regulators and the government.[4]

The term used to describe a large volume of collected data, in a structured as well as unstructured form is called Big Data. This data requires niche technology, outside of traditional software databases, to process; simply because of its exponential increment in a relatively short period of time. Big Data is usually identified using a "three V" characterization - larger volume, greater variety and distinguishably high rates of velocity. [5] This is exemplified in the diverse sources from which this data is obtained; mobile phone records, climate sensors, social media content, GPS satellite identifications and patterns of employment, to name a few. Big data analytics refers to the tools and methodologies that aim to transform large quantities of raw data into "interpretable data", in order to study and discern the same so that causal relationships between events can be conclusively established.[6] Such analysis could allow for the encouragement of the positive effects of such data and a concentrated mitigation of negative outcomes.

This paper seeks to map out the practices of different governments, civil society, and the private sector with respect to the collection, interpretation and analysis of big data in the global south, illustrated across a background of significant events surrounding the use of big data in relevant contexts. This will be combined with an articulation of potential opportunities to use big data analytics within both the public and private spheres and an identification of the contextual challenges that may obstruct the efficient use of this data. The objective of this study is to deliberate upon how significant obstructions to the achievement of developmental goals within the global south can be overcome through an accurate recognition, interpretation and analysis of big data collected from diverse sources.

II. Uses of Big Data in the Global Development

Big Data for development is the process though which raw, unstructured and imperfect data is analyzed, interpreted and transformed into information that can be acted upon by governments and policy makers in various capacities. The amount of digital data available in the world today has grown from 150 exabytes in 2005 to 1200 exabytes in 2010.[7] It is predicted that this figure would increase by 40% annually in the next few years[8], which is close to 40 times growth of the world's population. [9] The implication of this is essentially that the share of available data in the world today that is less than a minute old is increasing at an exponential rate. Moreover, an increasing percentage of this data is produced and created real-time.

The data revolution that is incumbent upon us is characterized by a rapidly accumulating and continuously evolving stock of data prevalent` in both industrialized as well as developing countries. This data is extracted from technological services that act as sensors and reflect the behaviour of individuals in relation to their socio-economic circumstances.

For many global south countries, this data is generated through mobile phone technology. This trend is evident in Sub Saharan Africa, where mobile phone technology has been used as an effective substitute for often weak and unstructured State mechanisms such as faulty infrastructure, underdeveloped systems of banking and inferior telecommunication networks.[10]

For example, a recent study presented at the Data for Development session at the NetMob Conference at MIT used mobile phone data to analyze the impact of opening a new toll highway in Dakar, Senegal on human mobility, particularly how people commute to work in the metropolitan area. ^{^[11]} A huge investment, the improved infrastructure is expected to result in a significant increase of people in and out of Dakar, along with the transport of essential goods. This would initiate rural development in the areas outside of Dakar and boost the value of land within the region.^{^[12]} The impact of the newly constructed highway can however only be analyzed effectively and accurately through the collection of this mobile phone data from actual commuters, on a real time basis.

Mobile phones technology is no longer used just for personal communication but has been transformed into an effective tool to secure employment opportunities, transfer money, determine stock options and assess the prices of various commodities.[13] This generates vast amounts of data about individuals and their interactions with the government and private sector companies. Internet Traffic is predicted to grow between 25 to 30 % in the next few years in North America, Western Europe and Japan but in Latin America, The Middle East and Africa this figure has been expected to touch close to 50%.[14] The bulk of this internet traffic can be traced back to mobile devices.

The potential applicability of Big Data for development at the most general level is the ability to provide an overview of the well being of a given population at a particular period of time.[15] This overcomes the relatively longer time lag that is prevalent with most other traditional forms of data collection. The analysis of this data has helped, to a large extent, uncover "digital smoke signals" - or inherent changes in the usage patterns of technological services, by individuals within communities.[16] This may act as an indicator of the changes in the underlying well-being of the community as a whole. This information about the well-being of a community derived from their usage of technology provides significantly relevant feedback to policy makers on the success or failure of particular schemes and can pin point changes that need to be made to status quo. [17]The hope is that this feedback delivered in real-time, would in turn lead to a more flexible and accessible system of international development, thus securing more measurable and sustained outcomes. [18]

The analysis of big data involves the use of advanced computational technology that can aid in the determination of trends, patterns and correlations within unstructured data so as to transform it into actionable information. It is hoped that this in addition to the human perspective and experience afforded to the process could enable decision makers to rely upon information that is both reliable and up to date to formulate durable and self-sustaining development policies.

The availability of raw data has to be adequately complemented with intent and a capacity to use it effectively. To this effect, there is an emerging volume of literature that seeks to characterize the primary sources of this Big Data as sharing certain easily distinguishable features. Firstly, it is digitally generated and can be stored in a binary format, thus making it susceptible to requisite manipulation by computers attempting to engage in its interpretation. It is passively produced as a by-product of digital interaction and can be automatically extracted for the purpose of continuous analysis. It is also geographically traceable within a predetermined time period. It is however important to note that "real time" does not necessarily refer to information occurring instantly but is reflective of the relatively short time in which the information is produced and made available thus making it relevant within the requisite timeframe. This allows efficient responsive action to be taken in a short span of time thus creating a feedback loop. [19]

In most cases the granularity of the data is preferably sought to be expanded over a larger spatial context such as a village or a community as opposed to an individual simply because this affords an adequate recognition of privacy concerns and the lack of definitive consent of the individuals in the extraction of this data. In order to ease the process of determination of this data, the UN Global Pulse has developed taxonomy of sorts to assess the types of data sources that are relevant to utilizing this information for development purposes.[20] These include the following sources;

Data Exhaust or the digital footprint left behind by individuals' use of technology for service oriented tasks such as web purchases, mobile phone transactions and real time information collected by UN agencies to monitor their projects such as levels of food grains in storage units, attendance in schools etc.

Online Information which includes user generated content on the internet such as news, blog entries and social media interactions which may be used to identify trends in human desires, perceptions and needs.

Physical sensors such as satellite or infrared imagery of infrastructural development, traffic patterns, light emissions and topographical changes, thus enabling the remote sensing of changes in human activity over a period of time.

Citizen reporting or crowd sourced data , which includes information produced on hotlines, mobile based surveys, customer generated maps etc. Although a passive source of data collection, this is a key instrument in assessing the efficacy of action oriented plans taken by decision makers.

The capacity to analyze this big data is hinged upon the reliance placed on technologically advanced processes such as powerful algorithms which can synthesize the abundance of raw data and break down the information enabling the identification of patterns and correlations. This process would rely on advanced visualization techniques such "sense-making tools"[21]

The identification of patterns within this data is carried out through a process of instituting a common framework for the analysis of this data. This requires the creation of a specific lexicon that would help tag and sort the collected data. This lexicon would specify what type of information is collected and who it is interpreted and collected by, the observer or the reporter. It would also aid in the determination of how the data is acquired and the qualitative and quantitative nature of the data. Finally, the spatial context of the data and the time frame within which it was collected constituting the aspects of where and when would be taken into consideration. The data would then be analyzed through a process of Filtering, Summarizing and Categorizing the data by transforming it into an appropriate collection of relevant indicators of a particular population demographic. [22]

The intensive mining of predominantly socioeconomic data is known as "reality mining" [23] and this can shed light on the processes and interactions that are reflected within the data. This is carried out via a tested three fold process. Firstly, the " Continuous Analysis over the streaming of the data", which involves the monitoring and analyzing high frequency data streams to extract often uncertain raw data. For example, the systematic gathering of the prices of products sold online over a period of time. Secondly, "The Online digestion of semi structured data and unstructured data", which includes news articles, reviews of services and products and opinion polls on social media that aid in the determination of public perception, trends and contemporary events that are generating interest across the globe. Thirdly, a 'Real-time Correlation of streaming data with slowly accessible historical data repositories,' which refers to the "mechanisms used for correlating and integrating data in real-time with historical records."[24] The purpose of this stage is to derive a contextualized perception of personalized information that seeks to add value to the data by providing a historical context to it. Big Data for development purposes would make use of a combination of these depending on the context and need.

(i) Policy Formulation

The world today has become increasingly volatile in terms of how the decisions of certain countries are beginning to have an impact on vulnerable communities within entirely different nations. Our global economy has become infinitely more susceptible to fluctuating conditions primarily because of its interconnectivity hinged upon transnational interdependence. The primordial instigators of most of these changes, including the nature of harvests, prices of essential commodities, employment structures and capital flows, have been financial and environmental disruptions. [25] According to the OECD, " Disruptive shocks to the global economy are likely to become more frequent and cause greater economic and social hardship. The economic spillover effects of events like the financial crisis or a potential pandemic will grow due to the increasing interconnectivity of the global economy and the speed with which people, goods and data travel."[26]

The local impacts of these fluctuations may not be easily visible or even traceable but could very well be severe and long lasting. A vibrant literature on the vulnerability of communities has highlighted the impacts of these shocks on communities often causing children to drop out of school, families to sell their productive assets, and communities to place a greater reliance on state rations.[27] These vulnerabilities cannot be definitively discerned through traditional systems of monitoring and information collection. The evidence of the effects of these shocks often take too long to reach decision makers; who are unable to formulate effective policies without ascertaining the nature and extent of the hardships suffered by these in a given context. The existing early warning systems in place do help raise flags and draw attention to the problem but their reach is limited and veracity compromised due to the time it takes to extract and collate this information through traditional means. These traditional systems of information collection are difficult to implement within rural impoverished areas and the data collected is not always reliable due to the significant time gap in its collection and subsequent interpretation. Data collected from surveys does provide an insight into the state of affairs of communities across demographics but this requires time to be collected, processed, verified and eventually published. Further, the expenses incurred in this process often prove to be difficult to offset.

The digital revolution therefore provides a significant opportunity to gain a richer and deeper insight into the very nature and evolution of the human experience itself thus affording a more legitimate platform upon which policy deliberations can be articulated. This data driven decision making, once the monopoly of private institutions such as The World Economic Forum and The McKinsey Institute [28] has now emerged at the forefront of the public policy discourse. Civil society has also expressed an eagerness to be more actively involved in the collection of real-time data after having perceived its benefits. This is evidenced by the emergence of 'crowd sourcing'[29] and other 'participatory sensing' [30] efforts that are founded upon the commonalities shared by like minded communities of individuals. This is being done on easily accessible platforms such as mobile phone interfaces, hand-held radio devices and geospatial technologies. [31]

The predictive nature of patterns identifiable from big data is extremely relevant for the purpose of developing socio-economic policies that seek to bridge problem-solution gaps and create a conducive environment for growth and development. Mobile phone technology has been able to quantify human behavior on an unprecedented scale.[32] This includes being able to detect changes in standard commuting patterns of individuals based on their employment status[33] and estimating a country's GDP in real-time by measuring the nature and extent of light emissions through remote sensing. [34]

A recent research study has concluded that "due to the relative frequency of certain queries being highly correlated with the percentage of physician visits in which individuals present influenza symptoms, it has been possible to accurately estimate the levels of influenza activity in each region of the United States, with a reporting lag of just a day." Online data has thus been used as a part of syndromic surveillance efforts also known as infodemiology. [35] The US Centre for Disease Control has concluded that mining vast quantities of data through online health related queries can help detect disease outbreaks " before they have been confirmed through a diagnosis or a laboratory confirmation." [36] Google trends works in a similar way.

Another public health monitoring system known as the Healthmap project compiles seemingly fragmented data from news articles, social media, eye-witness reports and expert discussions based on validated studies to "achieve a unified and comprehensive view of the current global state of infectious diseases" that may be visualized on a map. [37]

Big Data used for development purpose can reduce the reliance on human inputs thus narrowing the room for error and ensuring the accuracy of information collected upon which policy makers can base their decisions.

(ii) Advocacy and Social Change

Due to the ability of Big Data to provide an unprecedented depth of detail on particular issues, it has often been used as a vehicle of advocacy to highlight various issues in great detail. This makes it possible to ensure that citizens are provided with a far more participative experience, capturing their attention and hence better communicating these problems. Numerous websites have been able to use this method of crowd sourcing to broadcast socially relevant issues[38]. Moreover, the massive increase in access to the internet has dramatically improved the scope for activism through the use of volunteered data due to which advocates can now collect data from volunteers more effectively and present these issues in various forums. Websites like Ushahidi[39] and the Black Monday Movement [40] being prime examples of the same. These platforms have championed various causes, consistently exposing significant social crises' that would otherwise go unnoticed.

The Ushahidi application used crowd sourcing mechanisms in the aftermath of the Haiti earthquake to set up a centralized messaging system that allowed mobile phone users to provide information on injured and trapped people.[41] An analysis of the data showed that the concentration of text messages was correlated with the areas where there was an increased concentration of damaged buildings. [42] Patrick Meier of Ushahidi noted "These results were evidence of the system's ability to predict, with surprising accuracy and statistical significance, the location and extent of structural damage post the earthquake." [43]

Another problem that data advocacy hopes to tackle, however, is that of too much exposure, with advocates providing information to various parties to help ensure that there exists no unwarranted digital surveillance and that sensitive advocacy tools and information are not used inappropriately. An interesting illustration of the same is The Tactical Technology Collective[44] that hopes to improve the use of technology by activists and various other political actors. The organization, through various mediums such as films, events etc. hopes to train activists regarding data protection and privacy awareness and skills among human rights activists. Additionally, Tactical Technology also assists in ensuring that information is used in an appealing and relevant manner by human rights activists and in the field of capacity building for the purposes of data advocacy.

Observed data such as mobile phone records generated through network operators as well as through the use of social media are beginning to embody an omnipotent role in the development of academia through detailed research. This is due to the ability of this data to provide microcosms of information within both contexts of finer granularity and over larger public spaces. In the wake of natural disasters, this can be extremely useful, as reflected by the work of Flowminder after the 2010 Haiti earthquake.[45] A similar string of interpretive analysis can be carried out in instances of conflict and crises over varying spans of time. Flowminder used the geospatial locations of 1.9 million subscriber identity modules in Haiti, beginning 42 days before the earthquake and 158 days after it. This information allowed researches to empirically determine the migration patterns of population post the earthquake and enabled a subsequent UNFPA household survey.[46] In a similar capacity, the UN Global Pulse is seeking to assist in the process of consultation and deliberation on the specific targets of the millennium development goals through a framework of visual analytics that represent the big data procured on each of the topics proposed for the post- 2015 agenda online.[47]

A recent announcement of collaboration between RTI International, a non-profit research organization and IBM research lab looks promising in its initiative to utilize big data analytics in schools within Mombasa County, Kenya.[48] The partnership seeks to develop testing systems that would capture data that would assist governments, non-profit organizations and private enterprises in making more informed decisions regarding the development of education and human resources within the region. Äs observed by Dr. Kamal Bhattacharya, The Vice President of IBM Research, "A significant lack of data on Africa in the past has led to misunderstandings regarding the history, economic performance and potential of the government." The project seeks to improve transparency and accountability within the schooling system in more than 100 institutions across the county. The teachers would be equipped with tablet devices to collate the data about students, classrooms and resources. This would allow an analysis of the correlation between the three aspects thus enabling better policy formulation and a more focused approach to bettering the school system. [49] This is a part of the United States Agency for International Development's Education Data for Decision Making (EdData II) project. According to Dr Kommy Weldemariam, Research Scientist , IBM Research, "… there has been a significant struggle in making informed decisions as to how to invest in and improve the quality and content of education within Sub-Saharan Africa. The Project would create a school census hub which would enable the collection of accurate data regarding performance, attendance and resources at schools. This would provide valuable insight into the building of childhood development programs that would significantly impact the development of an efficient human capital pool in the near future."[50]

A similar initiative has been undertaken by Apple and IBM in the development of the "Student Achievement App" which seeks to use this data for "content analysis of student learning". The Application as a teaching tool that analyses the data provided to develop actionable intelligence on a per-student basis." [51] This would give educators a deeper understanding of the outcome of teaching methodologies and subsequently enable better leaning. The impact of this would be a significant restructuring of how education is delivered. At a recent IBM sponsored workshop on education held in India last year , Katharine Frase, IBM CTO of Public Sector predicted that "classrooms will look significantly different within a decade than they have looked over the last 200 years."[52]

(iii) Access and the exchange of information

Big data used for development serves as an important information intermediary that allows for the creation of a unified space within which unstructured heterogeneous data can be efficiently organized to create a collaborative system of information. New interactive platforms enable the process of information exchange though an internal vetting and curation that ensures accessibility to reliable and accurate information. This encourages active citizen participation in the articulation of demands from the government, thus enabling the actualization of the role of the electorate in determining specific policy decisions.

The Grameen Foundation's AppLab in Kampala aids in the development of tools that can use the information from micro financing transactions of clients to identify financial plans and instruments that would be be more suitable to their needs.[53] Thus, through working within a community, this technology connects its clients in a web of information sharing that they both contribute to and access after the source of the information has been made anonymous. This allows the individual members of the community to benefit from this common pool of knowledge. The AppLab was able to identify the emergence of a new crop pest from an increase in online searches for an unusual string of search terms within a particular region. Using this as an early warning signal, the Grameen bank sent extension officers to the location to check the crops and the pest contamination was dealt with effectively before it could spread any further.[54]

(iv) Accountability and Transparency

Big data enables participatory contributions from the electorate in existing functions such as budgeting and communication thus enabling connections between the citizens, the power brokers and elites. The extraction of information and increasing transparency around data networks is also integral to building a self-sustaining system of data collection and analysis. However it is important to note that this information collected must be duly analyzed in a responsible manner. Checking the veracity of the information collected and facilitating individual accountability would encourage more enthusiastic responses from the general populous thus creating a conducive environment to elicit the requisite information. The effectiveness of the policies formulated by relying on this information would rest on the accuracy of such information.

An example of this is Chequeado, a non-profit Argentinean media outlet that specializes in fact-checking. It works on a model of crowd sourcing information on the basis of which it has fact checked everything from the live presidential speech to congressional debates that have been made open to the public. [55] It established a user friendly public database, DatoCHQ, in 2014 which allowed its followers to participate in live fact-checks by sending in data, which included references, facts, articles and questions, through twitter. [56] This allowed citizens to corroborate the promises made by their leaders and instilled a sense of trust in the government.

III. Big Data and Smart Cities in the Global South

Smart cities have become a buzzword in South Asia, especially after the Indian government led by Prime Minister Narendra Modi made a commitment to build 100 smart cities in India[57]. A smart city is essentially designed as a hub where the information and communication technologies (ICT) are used to create feedback loops with an almost minimum time gap. In traditional contexts, surveys carried out through a state sponsored census were the only source of systematic data collection. However these surveys are long drawn out processes that often result in a drain on State resources. Additionally, the information obtained is not always accurate and policy makers are often hesitant to base their decisions on this information. The collection of data can however be extremely useful in improving the functionality of the city in terms of both the 'hard' or physical aspects of the infrastructural environment as well as the 'soft' services it provides to citizens. One model of enabling this data collection, to this effect, is a centrally structured framework of sensors that may be able to determine movements and behaviors in real-time, from which the data obtained can be subsequently analyzed. For example, sensors placed under parking spaces at intersections can relay such information in short spans of time. South Korea has managed to implement a similar structure within its smart city, Songdo.[58]

Another approach to this smart city model is using crowd sourced information through apps, either developed by volunteers or private conglomerates. These allow for the resolving of specific problems by organizing raw data into sets of information that are attuned to the needs of the public in a cohesive manner. However, this system would require a highly structured format of data sets, without which significantly transformational result would be difficult to achieve.[59]

There does however exist a middle ground, which allows the beneficiaries of this network, the citizens, to take on the role of primary sensors of information. This method is both cost effective and allows for an experimentation process within which an appropriate measure of the success or failure of the model would be discernible in a timely manner. It is especially relevant in fast growing cities that suffer congestion and breakdown of infrastructure due to the unprecedented population growth. This population is now afforded with the opportunity to become a part of the solution.

The principle challenge associated with extracting this Big Data is its restricted access. Most organizations that are able to collect this big data efficiently are private conglomerates and business enterprises, who use this data to give themselves a competitive edge in the market, by being able to efficiently identify the needs and wants of their clientele. These organizations are reluctant to release information and statistics because they fear it would result in them losing their competitive edge and they would consequently lose the opportunity to benefit monetarily from the data collected. Data leaks would also result in the company getting a bad name and its reputation could be significantly hampered. Despite the individual anonymity, the transaction costs incurred in ensuring the data of their individual customers is protected is often an expensive process. In addition to this there is a definite human capital gap resulting from the significant lack of scientists and analysts to interpret raw data transmitted across various channels.

(i) Big Data in Urban Planning

Urban planning would require data that is reflective of the land use patterns of communities, combined with their travel descriptions and housing preferences. The mobility of individuals is dependent on their economic conditions and can be determined through an analysis of their purchases, either via online transactions or from the data accumulated by prominent stores. The primary source of this data is however mobile phones, which seemed to have transcend economic barriers. Secondary sources include cards used on public transport such as the Oyster card in London and the similar Octopus card used in Hong Kong. However, in most developing countries these cards are not available for public transport systems and therefore mobile network data forms the backbone of data analytics. An excessive reliance on the data collected through Smart phones could however be detrimental, especially in developing countries, simply because the usage itself would most likely be concentrated amongst more economically stable demographics and the findings from this data could potentially marginalize the poor.[60]

Mobile network big data (MNBD) is generated by all phones and includes CDRs, which are obtained from calls or texts that are sent or received, internet usage, topping up a prepaid value and VLR or Visitor Location Registry data which is generated whenever the phone is question has power. It essentially communicates to the Base Transceiver Stations (BSTs) that the phone is in the coverage area. The CDR includes records of calls made, duration of the call and information about the device. It is therefore stored for a longer period of time. The VLR data is however larger in volume and can be written over. Both VLR and CDR data can provide invaluable information that can be used for urban planning strategies. [61] LIRNEasia, a regional policy and regulation think-tank has carried out an extensive study demonstrating the value of MNBD in SriLanka.[62] This has been used to understand and sometimes even monitor land use patterns, travel patterns during peak and off seasons and the congregation of communities across regions. This study was however only undertaken after the data had been suitably pseudonymised.[63] The study revealed that MNBD was incredibly valuable in generating important information that could be used by policy formulators and decision makers, because of two primary characteristics. Firstly, it comes close to a comprehensive coverage of the demographic within developing countries, thus using mobile phones as sensors to generate useful data. Secondly, people using mobile phones across vast geographic areas reflect important information regarding patterns of their travel and movement. [64]

MNBD allows for the tracking and mapping of changes in population densities on a daily basis, thus identifying 'home' and 'work' locations, informing policy makers of population congestion so that thy may be able to formulate policies with respect to easing this congestion. According to Rohan Samarajiva, founding chair of LIRNEasia, "This allows for real-time insights on the geo-spatial distribution of population, which may be used by urban planners to create more efficient traffic management systems."^{^[65]} This can also be used for the developmental economic policies. For example, the northern region of Colombo, a region inhabited by the low income families shows a lower population density on weekdays. This is reflective of the large numbers travelling to southern Colombo for employment. ^{^[66]}Similarly, patterns of land use can be ascertained by analyzing the various loading patterns of base stations. Building on the success of the Mobile Data analysis project in SriLanka LIRNEasia plans to collaborate with partners in India and Bangladesh to assimilate real time information about the behavioral tendencies of citizens, using which policy makers may be able to make informed decisions. When this data is combined with user friendly virtual platforms such as smartphone Apps or web portals, it can also help citizens make informed choices about their day to day activities and potentially beneficial long term decisions. ^{^[67]}

Challenges of using Mobile Network Data

Mobile networks invest significant sums of money in obtaining information regarding usage patterns of their services. Consequently, they may use this data to develop location based advertizing. In this context, there is a greater reluctance to share data for public purposes. Allowing access to one operator's big data by another could result in significant implications on the other with respect to the competitive advantage shared by the operator. A plausible solution to this conundrum is the accumulation of data from multiple sources without separating or organizing it according to the source it originates from. There is thus a lesser chance of sensitive information of one company being used by another. However, even operators do have concerns about how the data would be handled before this "mashing up" occurs and whether it might be leaked by the research organization itself. LIRNEasia used comprehensive non-disclosure agreements to ensure that the researchers who worked with the data were aware of the substantial financial penalties that may be imposed on them for data breaches. The access to the data was also restricted. [68]

Another line of argumentation advocates for the open sharing of data. A recent article in the Economist has articulated this in the context of the Ebola outbreak in West Africa. " Releasing the data, though, is not just a matter for firms since people's privacy is involved. It requires governmental action as well. Regulators in each affected country would have to order operators to make their records accessible to selected researchers, who through legal agreements would only be allowed to use the data in a specific manner. For example, Orange, a major mobile phone network operator has made millions of CDRs from Senegal and The Ivory Coast available for researchers for their use under its Data Development Initiative. However the Political will amongst regulators and Network operators to do this seems to be lacking."[69]

It would therefore be beneficial for companies to collaborate with the customers who create the data and the researchers who want to use it to extract important insights. This however would require the creation of and subsequent adherence to self regulatory codes of conduct. [70] In addition to this cooperation between network operators will assist in facilitating the transference of the data of their customers to research organizations. Sri Lanka is an outstanding example of this model of cooperation which has enabled various operators across spectrums to participate in the mobile-money enterprise.[71]

(ii) Big Data and Government Delivery of Services and Functions

The analysis of Data procured in real time has proven to be integral to the formulation of policies, plans and executive decisions. Especially in an Asian context, Big data can be instrumental in urban development, planning and the allocation of resources in a manner that allows the government to keep up with the rapidly growing demands of an empowered population whose numbers are on an exponential rise. Researchers have been able to use data from mobile networks to engage in effective planning and management of infrastructure, services and resources. If, for example, a particular road or highway has been blocked for a particular period of time an alternative route is established before traffic can begin to build up creating a congestion, simply through an analysis of information collected from traffic lights, mobile networks and GPS systems.[72]

There is also an emerging trend of using big data for state controlled services such as the military. The South Korean Defense Minister Han Min Koo, in his recent briefing to President Park Geun-hye reflected on the importance of innovative technologies such as Big Data solutions. [73]

The Chinese government has expressed concerns regarding data breaches and information leakages that would be extremely dangerous given the exceeding reliance of governments on big data. A security report undertaken by Qihoo 360, China's largest software security provider established that 2,424 of the 17,875 Web security loopholes were on government websites. Considering the blurring line between government websites and external networks, it has become all the more essential for authorities to boost their cyber security protections.[74]

The Japanese government has considered investing resources in training more data scientists who may be able to analyze the raw data obtained from various sources and utilize requisite techniques to develop an accurate analysis. The Internal Affairs and Communication Ministry planned to launch a free online course on big data, the target of which would be corporate workers as well as government officials.[75]

Data analytics is emerging as an efficient technique of monitoring the public transport management systems within Singapore. A recent collaboration between IBM, StarHub, The Land Transport Authority and SMRT initiated a research study to observe the movement of commuters across regions. ^{^[76]} This has been instrumental in revamping the data collection systems already in place and has allowed for the procurement of additional systems of monitoring.^{^[77]} The idea is essentially to institute a "black box" of information for every operational unit that allows for the relaying of real-time information from sources as varied as power switches, tunnel sensors and the wheels, through assessing patterns of noise and vibration. ^{^[78]}

In addition to this there are numerous projects in place that seek to utilize Big Data to improve city life. According to Carlo Ritti, Director of the MIT Senseable City Lab, "We are now able to analyze the pulse of a city from moment to moment. Over the past decade, digital technologies have begun to blanket our cities, forming the backbone of a large, intelligent infrastructure." ^{^[79]} The professor of Information Architecture and Founding Director of the Singapore ETH Centre, Gerhart Schmitt has observed that "the local weather has a major impact on the behavior of a population." In this respect the centre is engaged in developing a range of visual platforms to inform citizens on factors such as air quality which would enable individuals to make everyday choices such as what route to take when planning a walk or predict a traffic jam. ^{^[80]} Schmitt's team has also been able to arrive at a pattern that connects the demand for taxis with the city's climate. The amalgamation of taxi location with rainfall data has been able to help locals hail taxis during a storm. This form of data can be used in multiple ways allowing the visualization of temperature hotspots based on a "heat island" effect where buildings, cars and cooling units cause a rise in temperature. ^{^[81]}

Microsoft has recently entered into a partnership with the Federal University of Minas Gerais, one of the largest universities in Brazil to undertake a research project that could potentially predict traffic jams up to an hour in advance. ^{^[82]} The project attempts to analyze information from transport departments, road traffic cameras and drivers social network profiles to identify patterns that they could use to help predict traffic jams approximately 15 to 60 minutes before they actually happen.^{^[83]}

In anticipation of the increasing demand for professionals with requisite training in data sciences, the Malaysian Government has planned to increase the number of local data scientists from the present 80 to 1500 by 2020, through the support of the universities within the country.

IV. Big Data and the Private Sector in the Global South

Essential considerations in the operations of Big Data in the Private sector in the Asia Pacific region have been extracted by a comprehensive survey carried out by the Economist Intelligence Unit.[84] Over 500 executives across the Asia Pacific region were surveyed, from across industries representing a diverse range of functions. 69% of these companies had an annual turnover of over US $500m. The respondents were senior managers responsible for taking key decisions with regard to investment strategies and the utilization of big data for the same.

The results of the Survey conclusively determined that firms in the Asia Pacific region have had limited success with implementing Big Data Practices. A third of the respondents claimed to have an advanced knowledge of the utilization of big data while more than half claim to have made limited progress in this regard. Only 9% of the Firms surveyed cited internal barriers to implementing big data practices. This included a significant difficulty in enabling the sharing of information across boundaries. Approximately 40% of the respondents surveyed claimed they were unaware of big data strategies, even if they had in fact been in place simply because these had been poorly communicated to them. Almost half of the firms however believed that big data plays an important role in the success of the firm and that it can contribute to increasing revenue by 25% or more.

Numerous obstacles in the adoption of big data were cited by the respondents. These include the lack of suitable software to interpret the data and the lack of in-house skills to analyze the data appropriately. In addition to this, the lack of willingness on the part of various departments to share their data for the fear of a breach or leak was thought to be a major hindrance. This combined with a lack of communication between the various departments and exceedingly complicated reports that cannot be analyzed given the limited resources and lack of human capital qualified enough to carry out such an analysis, has resulted in an indefinite postponement of any policy propounding the adoption of big data practices.

Over 59% of the firms surveyed agreed that collaboration is integral to innovation and that information silos are a huge hindrance within a knowledge based economy. There is also a direct correlation between the size of the company and its progress in adopting big data, with larger firms adopting comprehensive strategies more frequently than smaller ones. A major reason for this is that large firms with substantially greater resources are able to actualize the benefits of big data analytics more efficiently than firms with smaller revenues. These businesses which have advanced policies in place outlining their strategies with respect to their reliance on big data are also more likely to communicate these strategies to their employees to ensure greater clarity in the process.

The use of big data was recently voted as the "best management practice" of the past year according to a cumulative ranking published by Chief Executive China Magazine, a Trade journal published by Global Sources on 13th January, 2015 in Beijing. The major benefit cited was the real-time information sourced from customers, which allows for direct feedback from clients when making decisions regarding changes in products or services. [85]

A significant contributor to the lack of adequate usage of data analytics is the belief that a PhD is a prerequisite for entering the field of data science. This misconception was pointed out by Richard Jones, vice president of Cloudera in the Australia, New Zealand and the Asean region. Cloudera provides businesses with the requisite professional services that they may need to effectively utilize Big Data. This includes a combination of the necessary manpower, technology and consultancy services.^{^[86]} Deepak Ramanathan, the chief technology officer, SAS Asia Pacific believes that this skill gap can be addressed by forming data science teams within both governments and private enterprises. These teams could comprise of members with statistical, coding and business skills and allow them to work in a collaborative manner to address the problem at hand.^{^[87]} SAS is an Enterprise Software Giant that creates tools tailored to suit business users to help them interpret big data. Eddie Toh, the planning and marketing manager of Intel's data center platform believes that businesses do not necessarily need data scientists to be able to use big data analytics to their benefit and can in fact outsource the technical aspects of the interpretation of this data as and when required.^{^[88]}

The analytical team at Dell has forged a partnership with Brazilian Public Universities to facilitate the development of a local talent pool in the field of data analytics. The Instituto of Data Science (IDS) will provide training methodologies for in person or web based classes. ^{^[89]} The project is being undertaken by StatSoft, a subsidiary of Dell that was acquired by the technology giant last year. ^{^[90]}

V. Conclusion

There have emerged numerous challenges in the analysis and interpretation of Big Data. While it presents an extremely engaging opportunity, which has the potential to transform the lives of millions of individuals, inform the private sector and influence government, the actualization of this potential requires the creation of a sustainable foundational framework ; one that is able to mitigate the various challenges that present themselves in this context.

A colossal increase in the rate of digitization has resulted in an unprecedented increment in the amount of Big Data available, especially through the rapid diffusion cellular technology. The importance of mobile phones as a significant source of data, especially in low income demographics cannot be overstated. This can be used to understand the needs and behaviors of large populations, providing an in depth insight into the relevant context within which valuable assessments as to the competencies, suitability and feasibilities of various policy mechanisms and legal instruments can be made. However, this explosion of data does have a lasting impact on how individuals and organizations interact with each other, which might not always be reflected in the interpretation of raw data without a contextual understanding of the demographic. It is therefore vital to employ the appropriate expertise in assessing and interpreting this data. The significant lack of a human resource to capital to analyze this information in an accurate manner poses a definite challenge to its effective utilization in the Global South.

The legal and technological implications of using Big Data are best conceptualized within the deliberations on protecting the privacy of the contributors to this data. The primary producers of this information, from across platforms, are often unaware that they are in fact consenting to the subsequent use of the data for purposes other than what was intended. For example people routinely accept terms and conditions of popular applications without understanding where or how the data that they inadvertently provide will be used.[91] This is especially true of media generated on social networks that are increasingly being made available on more accessible platforms such as mobile phones and tablets. Privacy has and always will remain an integral pillar of democracy. It is therefore essential that policy makers and legislators respond effectively to possible compromises of privacy in the collection and interpretation of this data through the institution of adequate safeguards in this respect.

Another challenge that has emerged is the access and sharing of this data. Private corporations have been reluctant to share this data due to concerns about potential competitors being able to access and utilize the same. In addition to this, legal considerations also prevent the sharing of data collected from their customers or users of their services. The various technical challenges in storing and interpreting this data adequately also prove to be significant impediments in the collection of data. It is therefore important that adequate legal agreements be formulated in order to facilitate a reliable access to streams of data as well as access to data storage facilities to accommodate for retrospective analysis and interpretation.

In order for the use of Big Data to gain traction, it is important that these challenges are addressed in an efficient manner with durable and self-sustaining mechanisms of resolving significant obstructions. The debates and deliberations shaping the articulation of privacy concerns and access to such data must be supported with adequate tools and mechanisms to ensure a system of "privacy-preserving analysis." The UN Global Pulse has put forth the concept of data philanthropy to attempt to resolve these issues, wherein " corporations [would] take the initiative to anonymize (strip out all personal information) their data sets and provide this data to social innovators to mine the data for insights, patterns and trends in realtime or near realtime."[92]

The concept of data philanthropy highlights particular challenges and avenues that may be considered for future deliberations that may result in specific refinements to the process.

One of the primary uses of Big Data, especially in developing countries is to address important developmental issues such as the availability of clean water, food security, human health and the conservation of natural resources. Effective Disaster management has also emerged as one of the key functions of Big Data. It therefore becomes all the more important for organizations to assess the information supply chains pertaining to specific data sources in order to identify and prioritize the issues of data management. [93] Data emerging from different contexts, across different sources may appear in varied compositions and would differ significantly across economic demographics. The Big Data generated from certain contexts would be inefficient due to the unavailability of data within certain regions and the resulting studies affecting policy decisions should take into account this discrepancy. This data unavailability has resulted in a digital divide which is especially prevalent in the global south. [94]

Appropriate analysis of the Big Data generated would provide a valuable insight into the key areas and inform policy makers with respect to important decisions. However, it is necessary to ensure that the quality of this data meets a specific standard and appropriate methodological processes have been undertaken to interpret and analyze this data. The government is a key actor that can shape the ecosystem surrounding the generation, analysis and interpretation of big data. It is therefore essential that governments of countries across the global south recognize the need to collaborate with civic organizations as well technical experts in order to create appropriate legal frameworks for the effective utilization of this data.

[1] Onella, Jukka- Pekka. "Social Networks and Collective Human Behavior." UN Global Pulse. 10 Nov.2011. <http://www.unglobalpulse.org/node/14539>

[2] http://www.business2community.com/big-data/evaluating-big-data-predictive-analytics-01277835

[3] Ibid

[4] http://unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf

[5] Ibid, p.13, pp.5

[6] Kirkpatrick, Robert. "Digital Smoke Signals." UN Global Pulse. 21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals>

[7] Helbing, Dirk , and Stefano Balietti. "From Social Data Mining to Forecasting Socio-Economic Crises." Arxiv (2011) 1-66. 26 Jul 2011 http://arxiv.org/pdf/1012.0178v5.pdf.

[8] Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh andAngela H. Byers. "Big data: The next frontier for innovation, competition, and productivity." McKinsey

Global Institute (2011): 1-137. May 2011.

[9] "World Population Prospects, the 2010 Revision." United Nations Development Programme. <http://esa.un.org/unpd/wpp/unpp/panel_population.htm>

[10] Mobile phone penetration, measured by Google, from the number of mobile phones per 100 habitants, was 96% in Botswana, 63% in Ghana, 66% in Mauritania, 49% in Kenya, 47% in Nigeria, 44% in Angola, 40% in Tanzania (Source: Google Fusion Tables)

[11] http://www.brookings.edu/blogs/africa-in-focus/posts/2015/04/23-big-data-mobile-phone-highway-sy

[12] Ibid

[13] <http://www.google.com/fusiontables/Home/>

[14] "Global Internet Usage by 2015 [Infographic]." Alltop. <http://holykaw.alltop.com/global-internetusage-by-2015-infographic?tu3=1>

[15] Kirkpatrick, Robert. "Digital Smoke Signals." UN Global Pulse. 21 Apr. 2011 <http://www.unglobalpulse.org/blog/digital-smoke-signals>

[16] Ibid

[17] Ibid

[18] Ibid

[19] Goetz, Thomas. "Harnessing the Power of Feedback Loops." Wired.com. Conde Nast Digital, 19 June 2011. <http://www.wired.com/magazine/2011/06/ff_feedbackloop/all/1>.

[20] Kirkpatrick, Robert. "Digital Smoke Signals." UN Global Pulse. 21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals>

[21] Bollier, David. The Promise and Peril of Big Data. The Aspen Institute, 2010. <http://www.aspeninstitute.org/publications/promise-peril-big-data>

[22] Ibid

[23] Eagle, Nathan and Alex (Sandy) Pentland. "Reality Mining: Sensing Complex Social Systems",Personal and Ubiquitous Computing, 10.4 (2006): 255-268.

[24] Kirkpatrick, Robert. "Digital Smoke Signals." UN Global Pulse. 21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals>

[25] OECD, Future Global Shocks, Improving Risk Governance, 2011

[26] "Economy: Global Shocks to Become More Frequent, Says OECD." Organisation for Economic Cooperationand Development. 27 June. 2011.

[27] Friedman, Jed, and Norbert Schady. How Many More Infants Are Likely to Die in Africa as a Result of the Global Financial Crisis? Rep. The World Bank <http://siteresources.worldbank.org/INTAFRICA/Resources/AfricaIMR_FriedmanSchady_060209.pdf>

[28] Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute,June 2011<http://www.mckinsey.com/mgi/publications/big_data/pdfs/MGI_big_data_full_report.pdf>

[29] The word "crowdsourcing" refers to the use of non-official actors ("the crowd") as (free) sources of information, knowledge and services, in reference and opposition to the commercial practice of

outsourcing. "

[30] Burke, J., D. Estrin, M. Hansen, A. Parker, N. Ramanthan, S. Reddy and M.B. Srivastava. ParticipatorySensing. Rep. Escholarship, University of California, 2006. <http://escholarship.org/uc/item/19h777qd>.

[31] "Crisis Mappers Net-The international Network of Crisis Mappers." <http://crisismappers.net>, http://haiti.ushahidi.com and Goldman et al., 2009

[32] Alex Pentland cited in "When There's No Such Thing As Too Much Information". The New York Times.23 Apr. 2011<http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=1&src=tptw>.

[33] Nathan Eagle also cited in "When There's No Such Thing As Too Much Information". The New YorkTimes. 23 Apr. 2011. <http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=1&src=tptw>.

[34] Helbing and Balietti. "From Social Data Mining to Forecasting Socio-Economic Crisis."

[35] Eysenbach G. Infodemiology: tracking flu-related searches on the Web for syndromic surveillance.AMIA (2006)<http://yi.com/home/EysenbachGunther/publications/2006/eysenbach2006cinfodemiologyamia proc.pdf>

[36] Syndromic Surveillance (SS)." Centers for Disease Control and Prevention. 06 Mar. 2012.<http://www.cdc.gov/ehrmeaningfuluse/Syndromic.html>.

[37] Health Map <http://healthmap.org/en/>

[38] see www.detective.io

[39] www.ushahidi.com

[40] www.facebook.com/BlackMondayMovement

[41] Ushahidi is a nonprofit tech company that was developed to map reports of violence in Kenya followingthe 2007 post-election fallout. Ushahidi specializes in developing "free and open source software for

information collection, visualization and interactive mapping." <http://ushahidi.com>

[42] Conducted by the European Commission's Joint Research Center against data on damaged buildingscollected by the World Bank and the UN from satellite images through spatial statistical techniques.

[43] www.ushahidi.com

[44] See https://tacticaltech.org/

[45] see www. flowminder.org

[46] Ibid

[47] http://post2015.unglobalpulse.net/

[48] http://allafrica.com/stories/201507151726.html

[49] Ibid

[50] Ibid

[51] http://www.computerworld.com/article/2948226/big-data/opinion-apple-and-ibm-have-big-data-plans-for-education.html

[52] Ibid

[53] http://www.grameenfoundation.org/where-we-work/sub-saharan-africa/uganda

[54] Ibid

[55] http://chequeado.com/

[56] http://datochq.chequeado.com/

[57] Times of India (2015): "Chandigarh May Become India's First Smart City," 12 January, http://timesofi ndia.indiatimes.com/india/Chandigarh- may-become-Indias-fi rst-smart-city/articleshow/ 45857738.cms

[58] http://www.cisco.com/web/strategy/docs/scc/ioe_citizen_svcs_white_paper_idc_2013.pdf

[59] Townsend, Anthony M (2013): Smart Cities: Big Data, Civic Hackers and the Quest for a New Utopia, New York: WW Norton.

[60] See "Street Bump: Help Improve Your Streets" on Boston's mobile app to collect data on roadconditions, http://www.cityofboston.gov/DoIT/ apps/streetbump.asp

[61] Mayer-Schonberger, V and K Cukier (2013): Big Data: A Revolution That Will Transform How We Live, Work, and Think, London: John Murray.

[62] http://www.epw.in/review-urban-affairs/big-data-improve-urban-planning.html

[63] Ibid

[64] Newman, M E J and M Girvan (2004): "Finding and Evaluating Community Structure in Networks,"Physical Review E, American Physical Society, Vol 69, No 2.

[65] http://www.sundaytimes.lk/150412/sunday-times-2/big-data-can-make-south-asian-cities-smarter-144237.html

[66] Ibid

[67] Ibid

[68] http://www.epw.in/review-urban-affairs/big-data-improve-urban-planning.html

[69] GSMA (2014): "GSMA Guidelines on Use of Mobile Data for Responding to Ebola," October, http:// www.gsma.com/mobilefordevelopment/wpcontent/ uploads/2014/11/GSMA-Guidelineson-

protecting-privacy-in-the-use-of-mobilephone- data-for-responding-to-the-Ebola-outbreak-_ October-2014.pdf

[70] An example of the early-stage development of a self-regulatory code may be found at http:// lirneasia.net/2014/08/what-does-big-data-sayabout- sri-lanka/

[71] See "Sri Lanka's Mobile Money Collaboration Recognized at MWC 2015," http://lirneasia. net/2015/03/sri-lankas-mobile-money-colloboration- recognized-at-mwc-2015/

[72] http://www.thedailystar.net/big-data-for-urban-planning-57593

[73] http://koreaherald.com , 19/01/2015

[74] http://www.news.cn/, 25/11/2014

[75] http://the-japan-news.com , 20/01/2015

[76] http://www.todayonline.com/singapore/can-big-data-help-tackle-mrt-woes

[77] Ibid

[78] Ibid

[79] http://edition.cnn.com/2015/06/24/tech/big-data-urban-life-singapore/

[80] Ibid

[81] Ibid

[82] http://venturebeat.com/2015/04/03/how-microsofts-using-big-data-to-predict-traffic-jams-up-to-an-hour-in-advance/

[83] Ibid

[84] https://www.hds.com/assets/pdf/the-hype-and-the-hope-summary.pdf

[85] http://www.news.cn , 14/01/2015

[86] http://www.techgoondu.com/2015/06/29/plugging-the-big-data-skills-gap/

[87] Ibid

[88] Ibid

[89] http://www.zdnet.com/article/dell-to-create-big-data-skills-in-brazil/

[90] Ibid

[91] Efrati, Amir. "'Like' Button Follows Web Users." The Wall Street Journal. 18 May 2011.

<http://online.wsj.com/article/SB10001424052748704281504576329441432995616.html>

[92] Krikpatrick, Robert. "Data Philanthropy: Public and Private Sector Data Sharing for Global Resilience."

UN Global Pulse. 16 Sept. 2011. <http://www.unglobalpulse.org/blog/data-philanthropy-public-privatesector-data-sharing-global-resilience>

[93] Laney D (2001) 3D data management: Controlling data volume, velocity and variety. Available at: http://blogs. gartner.com/doug-laney/files/2012/01/ad949-3D-DataManagement-Controlling-Data-Volume-Velocity-andVariety.pdf

[94] Boyd D and Crawford K (2012) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication, & Society 15(5): 662-679.

For more details visit https://cis-india.org/internet-governance/blog/big-data-in-the-global-south-an-analysis

Big Data in India: Benefits, Harms, and Human Rights - Workshop Report

Vidushi Marda, Akash Deep Singh and Geethanjali Jujjavarapu — 2016-11-18T12:58:19Z

The Centre for Internet and Society held a one-day workshop on “Big Data in India: Benefits, Harms and Human Rights” at India Habitat Centre, New Delhi on the 1st of October, 2016. This report is a compilation of the the issues discussed, ideas exchanged and challenges recognized during the workshop. The objective of the workshop was to discuss aspects of big data technologies in terms of harms, opportunities and human rights. The discussion was designed around an extensive study of current and potential future uses of big data for governance in India, that CIS has undertaken over the last year with support from the MacArthur Foundation.

Contents

Big Data: Definitions and Global South Perspectives

Aadhaar as Big Data

Seeding

Aadhaar and Data Security

Aadhaar’s Relational Arrangement with Big Data Scheme

The Myths surrounding Aadhaar

IndiaStack and FinTech Apps

Problems with UID

Big Data: Definitions and Global South Perspectives

“Big Data” has been defined by multiple scholars till date. The first consideration at the workshop was to discuss various definitions of big data, and also to understand what could be considered Big Data in terms of governance, especially in the absence of academic consensus. One of the most basic ways to define it, as given by the National Institute of Standards and Technology, USA, is to take it to be the data that is beyond the computational capacity of current systems. This definition has been accepted by the UIDAI of India. Another participant pointed out that Big Data is not only indicative of size, but rather the nature of data which is unstructured, and continuously flowing. The Gartner definition of Big Data relies on the three Vs i.e. Volume (size), Velocity (infinite number of ways in which data is being continuously collected) and Variety (the number of ways in which data can be collected in rows and columns).

The presentation also looked at ways in which Big Data is different from traditional data. It was pointed out that it can accommodate diverse unstructured datasets, and it is ‘relational’ i.e. it needs the presence of common field(s) across datasets which allows these fields to be conjoined. For e.g., the UID in India is being linked to many different datasets, and they don’t constitute Big Data separately, but do so together. An increasingly popular definition is to define data as “Big Data” based on what can be achieved through it. It has been described by authors as the ability to harness new kinds of insight which can inform decision making. It was pointed out that CIS does not subscribe to any particular definition, and is still in the process of coming up with a comprehensive definition of Big Data.

Further, discussion touched upon the approach to Big Data in the Global South. It was pointed out that most discussions about Big Data in the Global South are about the kind of value that it can have, the ways in which it can change our society. The Global North, on the other hand, has moved on to discussing the ethics and privacy issues associated with Big Data.

After this, the presentation focussed on case studies surrounding key Central Government initiatives and projects like Aadhaar, Predictive Policing, and Financial Technology (FinTech).

Aadhaar as Big Data

In presenting CIS’ case study on Aadhaar, it was pointed out that initially, Aadhaar, with its enrollment dataset was by itself being seen as Big Data. However, upon careful consideration in light of definitions discussed above, it can be seen as something that enables Big Data. The different e-governance projects within Digital India, along with Aadhaar, constitute Big Data. The case study discussed the Big Data implications of Aadhaar, and in particular looked at a ‘cradle to grave’ identity mapping through various e-government projects and the datafication of various transaction generated data.

Seeding

Any digital identity like Aadhaar typically has three features: 1. Identification i.e. a number or card used to identify yourself; 2. Authentication, which is based on your number or card and any other digital attributes that you might have; 3. Authorisation: As bearers of the digital identity, we can authorise the service providers to take some steps on our behalf. The case study discussed ‘seeding’ which enables the Big Data aspects of Digital India. In the process of seeding, different government databases can be seeded with the UID number using a platform called Ginger. Due to this, other databases can be connected to UIDAI, and through it, data from other databases can be queried by using your Aadhaar identity itself. This is an example of relationality, where fractured data is being brought together. At the moment, it is not clear whether this access by UIDAI means that an actual physical copy of such data from various sources will be transferred to UIDAI’s servers or if they will just access it through internet, but the data remains on the host government agency’s server. An example of even private parties becoming a part of this infrastructure was raised by a participant when it was pointed out that Reliance Jio is now asking for fingerprints. This can then be connected to the relational infrastructure being created by UIDAI. The discussion then focused on how such a structure will function, where it was mentioned that as of now, it cannot be said with certainty that UIDAI will be the agency managing this relational infrastructure in the long run, even though it is the one building it.

Aadhaar and Data Security

This case study also dealt with the sheer lack of data protection legislation in India except for S.43A of the IT Act. The section does not provide adequate protection as the constitutionality of the rules and regulations under S.43A is ambivalent. More importantly, it only refers to private bodies. Hence, any seeding which is being done by the government is outside the scope of data protection legislation. Thus, at the moment, no legal framework covers the processes and the structures being used for datasets. Due to the inapplicability of S.43A to public bodies, questions were raised as to the existence of a comprehensive data protection policy for government institutions. Participants answered the question in the negative. They pointed out that if any government department starts collecting data, they develop their own privacy policy. There are no set guidelines for such policies and they do not address concerns related to consent, data minimisation and purpose limitation at all. Questions were also raised about the access and control over Big Data with government institutions. A tentative answer from a participant was that such data will remain under the control of the domain specific government ministry or department, for e.g. MNREGA data with the Ministry of Rural Development, because the focus is not on data centralisation but rather on data linking. As long as such fractured data is linked and there is an agency that is responsible to link them, this data can be brought together. Such data is primarily for government agencies. But the government is opening up certain aspects of the data present with it for public consumption for research and entrepreneurial purposes.The UIDAI provides you access to your own data after paying a minimal fee. The procedure for such access is still developing.

Aadhaar’s Relational Arrangement with Big Data Scheme

The various Digital India schemes brought in by the government were elucidated during the workshop. It was pointed out that these schemes extend to myriad aspects of a citizen’s daily life and cover all the essential public services like health, education etc. This makes Aadhaar imperative even though the Supreme Court has observed that it is not mandatory for every citizen to have a unique identity number. The benefits of such identity mapping and the ecosystem being generated by it was also enumerated during the discourse. But the complete absence of any data ethics or data confidentiality principles make us unaware of the costs at which these benefits are being conferred on us. Apart from surveillance concerns, the knowledge gap being created between the citizens and the government was also flagged. Three main benefits touted to be provided by Aadhaar were then analysed. The first is the efficient delivery of services. This appears to be an overblown claim as the Aadhaar specific digitisation and automation does not affect the way in which employment will be provided to citizens through MNREGA or how wage payment delays will be overcome. These are administrative problems that Aadhaar and associated technologies cannot solve. The second is convenience to the citizens. The fallacies in this assertion were also brought out and identified. Before the Aadhaar scheme was rolled in, ration cards were issued based on certain exclusion and inclusion criteria.. The exclusion and inclusion criteria remain the same while another hurdle in the form of Aadhaar has been created. As India is still lacking in supporting infrastructure such as electricity, server connectivity among other things, Aadhaar is acting as a barrier rather than making it convenient for citizens to enroll in such schemes.The third benefit is fraud management. Here, a participant pointed out that this benefit was due to digitisation in the form of GPS chips in food delivery trucks and electronic payment and not the relational nature of Aadhaar. Aadhaar is only concerned with the linking up or relational part. About deduplication, it was pointed out how various government agencies have tackled it quite successfully by using technology different from biometrics which is unreliable at the best of times.

The Myths surrounding Aadhaar

The discussion also reflected on the fact that Aadhaar is often considered to be a panacea that subsumes all kinds of technologies to tackle leakages. However, this does not take into account the fact that leakages happen in many ways. A system should have been built to tackle those specific kinds of leakages, but the focus is solely on Aadhaar as the cure for all. Notably, participants who have been a part of the government pointed out how this myth is misleading and should instead be seen as the first step towards a more digitally enhanced country which is combining different technologies through one medium.

IndiaStack and FinTech Apps

What is India Stack?

The focus then shifted to another extremely important Big Data project, India Stack, being conceptualised and developed by a team of private developers called iStack, for the NPCI. It builds on the UID project, Jan Dhan Yojana and mobile services trinity to propagate and develop a cashless, presence-less, paperless and granular consent layer based on UID infrastructure to digitise India.

A participant pointed out that the idea of India Stack is to use UID as a platform and keep stacking things on it, such that more and more applications are developed. This in turn will help us to move from being a ‘data poor’ country to a ‘data rich’ one. The economic benefits of this data though as evidenced from the TAGUP report - a report about the creation of National Information Utilities to manage the data that is present with the government - is for the corporations and not the common man. The TAGUP report openly talks about privatisation of data.

Problems with India Stack

The granular consent layer of India Stack hasn’t been developed yet but they have proposed to base it on MIT Media Lab’s OpenPDS system. The idea being that, on the basis of the choices made by the concerned person, access to a person’s personal information may be granted to an agency like a bank. What is more revolutionary is that India Stack might even revoke this access if the concerned person expresses a wish to do so or the surrounding circumstances signal to India Stack that it will be prudent to do so. It should be pointed out that the the technology required for OpenPDS is extremely complex and is not available in India. Moreover, it’s not clear how this system would work. Apart from this, even the paperless layer has its faults and has been criticised by many since its inception, because an actual government signed and stamped paper has been the basis of a claim.. In the paperless system, you are provided a Digilocker in which all your papers are stored electronically, on the basis of your UID number. However, it was brought to light that this doesn’t take into account those who either do not want a Digilocker or UID number or cases where they do not have access to their digital records. How in such cases will people make claims?

A Digital Post-Dated Cheque: It’s Ramifications

A key change that FinTech apps and the surrounding ecosystem want to make is to create a digital post-dated cheque so as to allow individuals to get loans from their mobiles especially in remote areas. This will potentially cut out the need to construct new banks, thus reducing the capital expenditure , while at the same time allowing the credit services to grow. The direct transfer of money between UID numbers without the involvement of banks is a step to further help this ecosystem grow. Once an individual consents to such a system, however, automatic transfer of money from one’s bank accounts will be affected, regardless of the reason for payment. This is different from auto debt deductions done by banks presently, as in the present system banks have other forms of collateral as well. The automatic deduction now is only affected if these other forms are defaulted upon. There is no knowledge as to whether this consent will be reversible or irreversible. As Jan Dhan Yojana accounts are zero balance accounts, the account holder will be bled dry. The implication of schemes such as “Loan in under 8 minutes” were also discussed. The advantage of such schemes is that transaction costs are reduced.The financial institution can thus grant loans for the minimum amount without any additional enquiries. It was pointed out that this new system is based on living on future income much like the US housing bubble crash. Interestingly, in Public Distribution Systems, biometrics are insisted upon even though it disrupts the system. This can be seen as a part of the larger infrastructure to ensure that digital post-dated cheques become a success.

The Role of FinTech Apps

FinTech ‘apps’ are being presented with the aim of propagating financial inclusion. The Technology Advisory Group for Unique Projects report stated that as managing such information sources is a big task, just like electricity utilities, a National Information Utilities (NIU) should be set up for data sources. These NIUs as per the report will follow a fee based model where they will be charging for their services for government schemes. The report identified two key NIUs namely the National Payments Corporation of India (NPCI) and the Goods and Services Tax Network (GSTN). The key usage that FinTech applications will serve is credit scoring. The traditional credit scoring data sources only comprised a thin file of records for an individual, but the data that FinTech apps collect - a person’s UID number, mobile number. and bank account number all linked up, allow for a far more comprehensive credit rating. Government departments are willing to share this data with FinTech apps as they are getting analysis in return. Thus, by using UID and the varied data sources that have been linked together by UID, a ‘thick file’ is now being created by FinTech apps. Banking apps have not yet gone down the route of FinTech apps to utilise Big Data for credit scoring purposes.

The two main problems with such apps is that there is no uniform way of credit scoring. This distorts the rate at which a person has to pay interest. The consent layer adds another layer of complication as refusal to share mobile data with a FinTech app may lead to the app declaring one to be a risky investment thus, subjecting that individual to a higher rate of interest .

Regulation of FinTech Apps and the UID Infrastructure

India Stack and the applications that are being built on it, generate a lot of transaction metadata that is very intimate in nature. The privacy aspects of the UID legislation doesn't cover such data. The granular consent layer which has been touted to cover this still has to come into existence. Also, Big Data is based on sharing and linking of data. Here, privacy concerns and Big Data objectives clash. Big Data by its very nature challenges privacy principles like data minimisation and purpose limitation.The need for regulation to cover the various new apps and infrastructure which are being developed was pointed out.

Problems with UID

It has been observed that any problem present with Aadhaar is usually labelled as a teething problem, it’s claimed that it will be solved in the next 10 years. But, this begs the question - why is the system online right now?

Aadhaar is essentially a new data condition and a new exclusion or inclusion criteria. Data exclusion modalities as observed in Rajasthan after the introduction of biometric Point of Service (POS) machines at ration shops was found to be 45% of the population availing PDS services. This number also includes those who were excluded from the database by being included in the wrong dataset. There is no information present to tell us how many actual duplicates and how many genuine ration card holders were weeded out/excluded by POS.

It was also mentioned that any attempt to question Aadhaar is considered to be an attempt to go back to the manual system and this binary thinking needs to change. Big Data has the potential to benefit people, as has been evidenced by the scholarship and pension portals. However, Big Data’s problems arise in systems like PDS, where there is centralised exclusion at the level of the cloud. Moreover, the quantity problem present in the PDS and MNREGA systems persists. There is still the possibility of getting lesser grains and salary even with analysis of biometrics, hence proving that there are better technologies to tackle these problems. Presently, the accountability mechanisms are being weakened as the poor don’t know where to go to for redressal. Moreover, the mechanisms to check whether the people excluded are duplicates or not is not there. At the time of UID enrollment, out of 90 crores, 9 crore were rejected. There was no feedback or follow-up mechanism to figure out why are people being rejected. It was just assumed that they might have been duplicates.

Another problem is the rolling out of software without checking for inefficiencies or problems at a beta testing phase. The control of developers over this software, is so massive that it can be changed so easily without any accountability.. The decision making components of the software are all proprietary like in the the de-duplication algorithm being used by the UIDAI. Thus, this leads to a loss of accountability because the system itself is in flux, none of it is present in public domain and there are no means to analyse it in a transparent fashion..

These schemes are also being pushed through due to database politics. On a field study of NPR of citizens, another Big Data scheme, it was found that you are assumed to be an alien if you did not have the documents to prove that you are a citizen. Hence, unless you fulfill certain conditions of a database, you are excluded and are not eligible for the benefits that being on the database afford you.

Why is the private sector pushing for UIDAI and the surrounding ecosystem?

Financial institutions stand to gain from encouraging the UID as it encourages the credit culture and reduces transaction costs.. Another advantage for the private sector is perhaps the more obvious one, that is allows for efficient marketing of products and services..

The above mentioned fears and challenges were actually observed on the ground and the same was shown through the medium of a case study in West Bengal on the smart meters being installed there by the state electricity utility. While the data coming in from these smart meters is being used to ensure that a more efficient system is developed,it is also being used as a surrogate for income mapping on the basis of electricity bills being paid. This helps companies profile neighbourhoods. The technical officer who first receives that data has complete control over it and he can easily misuse the data. This case study again shows that instruments like Aadhaar and India Stack are limited in their application and aren’t the panacea that they are portrayed to be.

A participant pointed out that in the light of the above discussions, the aim appears to be to get all kinds of data, through any source, and once you have gotten the UID, you link all of this data to the UID number, and then use it in all the corporate schemes that are being started. Most of the problems associated with Big Data are being described as teething problems. The India Stack and FinTech scheme is coming in when we already know about the problems being faced by UID. The same problems will be faced by India Stack as well.

Can you opt out of the Aadhaar system and the surrounding ecosystem?

The discussion then turned towards whether there can be voluntary opting out from Aadhaar. It was pointed out that the government has stated that you cannot opt out of Aadhaar. Further, the privacy principles in the UIDAI bill are ambiguously worded where individuals only have recourse for basic things like correction of your personal information. The enforcement mechanism present in the UIDAI Act is also severely deficient. There is no notification procedure if a data breach occurs. . The appellate body ‘Cyber Appellate Tribunal’ has not been set up in three years.

CCTNS: Big Data and its Predictive Uses

What is Predictive Policing?

The next big Big Data case study was on the Crime and Criminal Tracking Network & Systems (CCTNS). Originally it was supposed to be a digitisation and interconnection scheme where police records would be digitised and police stations across the length and breadth of the country would be interconnected. But, in the last few years some police departments of states like Chandigarh, Delhi and Jharkhand have mooted the idea of moving on to predictive policing techniques. It envisages the use of existing statistical and actuarial techniques along with many other tropes of data to do so. It works in four ways: 1. By predicting the place and time where crimes might occur; 2. To predict potential future offenders; 3. To create profiles of past crimes in order to predict future crimes; 4. Predicting groups of individuals who are likely to be victims of future crimes.

How is Predictive Policing done?

To achieve this, the following process is followed: 1. Data collection from various sources which includes structured data like FIRs and unstructured data like call detail records, neighbourhood data, crime seasonal patterns etc. 2. Analysis by using theories like the near repeat theory, regression models on the basis of risk factors etc. 3. Intervention

Flaws in Predictive Policing and questions of bias

An obvious weak point in the system is that if the initial data going into the system is wrong or biased, the analysis will also be wrong. Efforts are being made to detect such biases. An important way to do so will be by building data collection practices into the system that protect its accuracy. The historical data being entered into the system is carrying on the prejudices inherited from the British Raj and biases based on religion, caste, socio-economic background etc.

One participant brought about the issue of data digitization in police stations, and the impact of this haphazard, unreliable data on a Big Data system. This coupled with paucity of data is bound to lead to arbitrary results. An effective example was that of black neighbourhoods in the USA. These are considered problematic and thus they are policed more, leading to a higher crime rate as they are arrested for doing things that white people in an affluent neighbourhood get away with. This in turn further perpetuates the crime rate and it becomes a self-fulfilling prophecy. In India, such a phenomenon might easily develop in the case of migrants, de-notified tribes, Muslims etc. A counter-view on bias and discrimination was offered here. One participant pointed out that problems with haphazard or poor quality of data is not a colossal issue as private companies are willing to fill this void and are actually doing so in exchange for access to this raw data. It was also pointed out how bias by itself is being used as an all encompassing term. There are multiplicities of biases and while analysing the data, care should be taken to keep it in mind that one person’s bias and analysis might and usually does differ from another. Even after a computer has analysed the data, the data still falls into human hands for implementation.

The issue of such databases being used to target particular communities on the basis of religion, race, caste, ethnicity among other parameters was raised. Questions about control and analysis of data were also discussed, i.e. whether it will be top-down with data analysis being done in state capitals or will this analysis be done at village and thana levels as well too. It was discussed as topointed out how this could play a major role in the success and possible persecutory treatment of citizens, as the policemen at both these levels will have different perceptions of what the data is saying. . It was further pointed out, that at the moment, there’s no clarity on the mode of implementation of Big Data policing systems. Police in the USA have been seen to rely on Big Data so much that they have been seen to become ‘data myopic’. For those who are on the bad side of Big Data, in the Indian context, laws like preventive detention can be heavily misused.There’s a very high chance that predictive policing due to the inherent biases in the system and the prejudices and inefficiency of the legal system will further suppress the already targeted sections of the society. A counterpoint was raised and it was suggested that contrary to our fears, CCTNS might lead to changes in our understanding and help us to overcome longstanding biases.

Open Knowledge Architecture as a solution to Big Data biases?

The conference then mulled over the use of ‘Open Knowledge’ architecture to see whether it can provide the solution to rid Big Data of its biases and inaccuracies if enough eyes are there. It was pointed out that Open Knowledge itself can’t provide foolproof protection against these biases as the people who make up the eyes themselves are predominantly male belonging to the affluent sections of the society and they themselves suffer from these biases.

Who exactly is Big Data supposed to serve?

The discussion also looked at questions such as who is this data for? Janata Information System (JIS), is a concept developed by MKSS where the data collected and generated by the government is taken to be for the common citizens. For e.g. MNREGA data should be used to serve the purposes of the labourers. The raw data as is available at the moment, usually cannot be used by the common man as it is so vast and full of information that is not useful for them at all. It was pointed out that while using Big Data for policy planning purposes, the actual string of information that turned out to be needed was very little but the task of unravelling this data for civil society purposes is humongous. By presenting the data in the right manner, the individual can be empowered. The importance of data presentation was also flagged. It was agreed upon that the content of the data should be for the labourer and not a MNC, as the MNC has the capability to utilise the raw data on it’s own regardless.

Concerns about Big Data usage

Participants pointed out that privacy concerns are usually brushed under the table due to a belief that the law is sufficient or that the privacy battle has already been lost.
In the absence of knowledge of domain and context, Big Data analysis is quite limited. Big Data’s accuracy and potential to solve problems needs to be factually backed.
The narrative of Big Data often rests on the assumption that descriptive statistics take over inferential statistics, thus eliminating the need for domain specific knowledge. It is claimed that the data is so big that it will describe everything that we need to know.
Big Data is creating a shift from a deductive model of scientific rigour to an inductive one. In response to this, a participant offered the idea that troves of good data allow us to make informed questions on the basis of which the deductive model will be formed. A hybrid approach combining both deductive and inductive might serve us best.
The need to collect the right data in the correct format, in the right place was also expressed.

Potential Research Questions & Participants’ Areas of Research

Following this discussion, participants brainstormed to come up with potential areas of research and research questions. They have been captured below:

Big Data, Aadhaar and India Stack:

Has Aadhaar been able to tackle illegal ways of claiming services or are local negotiations and other methods still prevalent?
Is the consent layer of India Stack being developed in a way that provides an opportunity to the UID user to give informed consent? The OpenPDS and its counterpart in the EU i.e. the My Data Structure were designed for countries with strong privacy laws. Importantly, they were meant for information shared on social media and not for an individual’s health or credit history. India is using it in a completely different sphere without strong data protection laws. What were the granular consent layer structures present in the West designed for and what were they supposed to protect?
The question of ownership of data needs to be studied especially in context of a globalised world where MNCs are collecting copious amounts of data of Indian citizens. What is the interaction of private parties in this regard?

Big Data and Predictive Policing:

How are inequalities being created through the Big Data systems? Lessons should be taken from the Western experience with the advent of predictive policing and other big data techniques - they tend to lead to perpetuation of the current biases which are already ingrained in the system.
It was also pointed out how while studying these topics and anything related to technology generally, we become aware of a divide that is present between the computational sciences and social sciences. This divide needs to be erased if Big Data or any kind of data is to be used efficiently. There should be a cross-pollination between different groups of academics. An example of this can be seen to be the ‘computational social sciences departments’ that have been coming up in the last 3-4 years.
Why are so many interim promises made by Big Data failing? A study of this phenomenon needs to be done from a social science perspective. This will allow one to look at it from a different angle.

Studying Big Data:

What is the historical context of the terms of reference being used for Big Data? The current Big Data debate in India is based on parameters set by the West. For better understanding of Big Data, it was suggested that P.C. Mahalanobis’ experience while conducting the Indian census, (which was the Big Data of that time) can be looked at to get a historical perspective on Big Data. This comparison might allow us to discover questions that are important in the Indian context. It was also suggested that rather than using ‘Big Data’ as a catchphrase to describe these new technological innovations, we need to be more discerning.
What are the ideological aspects that must be considered while studying Big Data? What does the dialectical promise of technology mean? It was contended that every time there is a shift in technology, the zeitgeist of that period is extremely excited and there are claims that it will solve everything. There’s a need to study this dialectical promise and the social promise surrounding it.
Apart from the legitimate fears that Big Data might lead to exclusion, what are the possibilities in which it improve inclusion too?
The diminishing barrier between the public and private self, which is a tangent to the larger public-private debate was mentioned.
How does one distinguish between technology failure and process failure while studying Big Data?

Big Data: A Friend?

In the concluding session, the fact that the Big Data moment cannot be wished away was acknowledged. The use of analytics and predictive modelling by the private sector is now commonplace and India has made a move towards a database state through UID and Digital India. The need for a nuanced debate, that does away with the false equivalence of being either a Big Data enthusiast or a luddite is crucial.

A participant offered two approaches to solving a Big Data problem. The first was the Big Data due process framework which states that if a decision has been taken that impacts the rights of a citizen, it needs to be cross examined. The efficacy and practicality of such an approach is still not clear. The second, slightly paternalistic in nature, was the approach where Big Data problems would be solved at the data science level itself. This is much like the affirmative algorithmic approach which says that if in a particular dataset, the data for the minority community is not available then it should be artificially introduced in the dataset. It was also suggested that carefully calibrated free market competition can be used to regulate Big Data. For e.g. a private personal wallet company that charges higher, but does not share your data at all can be an example of such competition.

Another important observation was the need to understand Big Data in a Global South context and account for unique challenges that arise. While the convenience of Big Data is promising, its actual manifestation depends on externalities like connectivity, accurate and adequate data etc that must be studied in the Global South.

While the promises of Big Data are encouraging, it is also important to examine its impacts and its interaction with people's rights. Regulatory solutions to mitigate the harms of big data while also reaping its benefits need to evolve.

For more details visit https://cis-india.org/internet-governance/big-data-in-india-benefits-harms-and-human-rights-a-report

Big Data Governance Frameworks for 'Data Revolution for Sustainable Development'

Meera Manoj — 2016-07-05T13:13:32Z

A key component of the process to achieve the Sustainable Development Goals is the call for a global 'data revolution' to better understand, monitor, and implement development interventions. Recently there has been several international proposals to use big data, along with reconfigured national statistical systems, to operationalise this 'data revolution for sustainable development.' This analysis by Meera Manoj highlights the different models of collection, management, sharing, and governance of global development data that are being discussed.

1. What are the Sustainable Development Goals?

2. The Need for a Data Revolution

3. Big Data: Characteristics and Use for Development

3.1. Characteristics of Big Data

3.2. Using Big Data for Development

4. Sustainable Development and Data Rights

5. Governance Frameworks Proposed

5.1. UN Sustainable Development Solutions Network

5.2. The UN DATA Revolution Group

5.3. Organization for Economic Co-Operation and Development

5.4. The Global Partnership for Sustainable Development of Data

5.5. The World Economic Forum (WEF)

5.6. Dr. Julia Lane - A Quadruple Data Helix

5.7. Data Pop Alliance

6. Conclusion

7. Endnotes

8. Author Profile

Speaking on Big Data, Dan Ariely commented that, "Everyone talks about it, nobody really knows how to do it, and everyone thinks everyone else is doing it, so everyone claims they are doing it" [1]. This offers a useful insight into the lack of adequate discourse on the kind of governance and accountability frameworks that are needed to facilitate the developmental, sustainable, and responsible uses of big data.

In light of the recent international proposals to use big data to track the Sustainable Development Goals, this paper highlights the different models of management, sharing, and governance of data that are being discussed, and concurrently, how they conceptualise the various rights around big data and how are they to be protected.

1. What are the Sustainable Development Goals?

The Sustainable Development Goals, otherwise known as the Global Goals, build on the Millennium Development Goals (MDGs). Adopted on 1 January 2016, these universally applicable 17 goals of the 2030 Agenda for Sustainable Development, seek to end all forms of poverty, fight inequalities, tackle climate change and address a range of social needs like education, health, social protection and job opportunities over the next 15 years [2].

Source: UN Data Revolution Group, A World that Counts, 2014, p.12.

2. The Need for a Data Revolution

An overwhelming cause of concern regarding the precursor to the SDGs, the MDGs, is the data unavailability to monitor their progress. For instance, the figure below indicates that there is no five-year period when the availability of MDG related data is more than 70% of what is required. Entire groups of people and key issues remain invisible [3]. Lack of data is not only a problem for global statisticians, but also for people whose needs and demands remain invisible due to lack of quantitative representation of the same. For instance, the incidences of gender related crimes when not recorded could lead to a misconception on the achievement of the MDG of gender equality.

Source: UN, Sustainable Development Goals.

As the new goals (SDGs) cover a wider range of issues it is clear that a far higher level of detail is required. To this effect the High-Level Panel of Eminent Persons on the post-2015 agenda has called for a "data revolution for sustainable development" [4].

The world is experiencing a Data Revolution and a "data deluge." One estimate has it that 90% of the data in the world has been created in the last 2 years. As Eric Schmidt of Google in 2010 famously said, "There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days [5].

In its report A World that Counts, the UN Data Revolution Group defines the data revolution as an explosion in the volume of data, the speed with which data are produced, the number of producers of data, the dissemination of data, and the range of things on which there is data, coming from new technologies such as mobile phones and the “internet of things”, and from other sources, such as qualitative data, citizen-generated data and perceptions data [6].

This data revolution in the context of sustainable development has been defined by the UN Secretary General’s Independent Expert Advisory Group (IEAG) as follows:

[T]he integration of data coming from new technologies with traditional data in order to produce relevant high‐quality information with more details and at higher frequencies to foster and monitor sustainable development. This revolution also entails the increase in accessibility to data through much more openness and transparency, and ultimately more empowered people for better policies, better decisions and greater participation and accountability, leading to better outcomes for the people and the planet [7].

The majority of such “data coming from new technologies” is what can be called big data. It is data being generated in real-time, in high velocity and volume, in a variety of forms and formats, and on an increasing range of phenomenon that are being mediated by digital technologies – from governance to human communication. Further, a good part of such big data is not about the content of the phenomenon concerned but about its process – for example, Call Detail Records are generated for each mobile phone call a person makes and it contains data about the process of the call (time, location, duration, recipient, etc.) but not about the content of the call. Big data about various governmental and human processes are becoming a crucial instrument for documenting and monitoring of the same.

3. Big Data: Characteristics and Use for Development

3.1. Characteristics of Big Data

The simplest definition of big data is that it is a dataset of more than 1 petabyte. The US Bureau of Labour Statistics terms it to be non-sampled data, characterized by the creation of databases from electronic sources whose primary purpose is something other than statistical inference [8].

The characteristics which broadly distinguish Big Data are sometimes called the “3 V’s”: more volume, more variety and higher rates of velocity [9]. Big data sources generally share some or all of these features [10]:

Digitally generated,
Passively produced,
Automatically collected,
Geographically or temporally trackable, and
Continuously analysed.

Increasingly, Big Data is recognised as creating "new possibilities for international development" [11]. It could provide faster, cheaper, more granular data and help meet growing and changing demands. It was claimed, for example, that "Google knows or is in a position to know more about France than INSEE" [12], its highly resourceful national statistical agency. To illustrate, Global Pulse gives the example of a hypothetical small household facing soaring commodity prices, particularly food and fuel [13]. They have the options of:

Getting part of their food at a nearby World Food Programme distribution centre,
Reducing mobile usage,
Temporarily taking their children out of school,
Calling a health hotline when children show signs of malnutrition related diseases, and
Venting about their frustration on social media.

Such a systemic shock of food insecurity will prompt thousands of households to react in roughly similar ways. These collective behavioural changes may show up in different digital data sources:

WFP might record that it serves twice as many meals a day,
The local mobile operator may see reduced usage,
UNICEF data may indicate that school attendance has dropped,
Health hotlines might see increased volumes of calls reporting malnutrition, and
Tweets mentioning the difficulty to “afford food” might begin to rise.

Thus the power of real-time, digital data to predict paths for development is immense. Amassing such a large volume of data which tracks practically every aspect of social behavious can revolutionize the field of official statistics and policy making.

Two points to be noted are: 1) all these data sources are not available for comparison in the real-time by default, so one task before using big data in developmental work is to make data from different sources available across agencies and make them comparable, and 2) finding repeating patterns within large data sets, sourced from varied origins, can not only allow for monitoring but also (statistically) predicting future possibilities and implications for development action.

3.2. Using Big Data for Development

There are several international organizations attempting to use such data.

Global Pulse, a United Nations initiative, launched by the Secretary-General in 2009, seeks to leverage innovations in digital data, rapid data collection and analysis to help decision-makers gain a real-time understanding of how crises impact vulnerable populations. To this end, Global Pulse is establishing an integrated, global network of Pulse Labs, anchored in Pulse Lab New York, to pilot the approach at country level [14].

The Global Working Group on Big Data for Official Statistics, created in May 2014, pursuant to Statistical Commission, makes an inventory of ongoing activities and examples regarding the use of big data, addresses concerns related to methodology, human resources, quality and confidentiality, and develops guidelines on classifying various types of big data sources [15].

There have been applications even on a national and individual level. For instance, in 2013, various sources reported that the CIA had admitted to the “full monitoring of Facebook, Twitter, and other social networks” to identify links between events and sequences or paths leading to national security threats, ultimately leading to forecasting future activities and events [16].

In the field of conflict prevention is the emerging applications to map and analyse unstructured data generated by politically active Internet use by academics, activists, civil society organizations, and even general citizens. In reference to Iran’s post-election crisis beginning in 2009, it is possible to detect web-based usage of terms that reflect a general shift from awareness towards mobilization, and eventually action within the population [17].

The "Big Data, Small Credit" report proposes that financial inclusion can be promoted by allowing consumers with mobile phones to access credit formally as customers [18].

At a national level, the biggest challenge for most big data projects is the limited or restricted access the government agencies have to potential big data sets owned by the private sector [19]. The overall consensus is that Big Data to track SDGs must complement traditional data sources [20]. This is because big data may not always be available for the entire population, or include a diverse enough sample of the population. Moreover most big data projects measure development indicators through a correlation which may not always be correct unlike official data. For instance big data might help in predicting lowered household income through reducing mobile bills while traditional data directly collects income statistics.

In a survey by the Global Working Group on Big Data for Official Statistics [21], it was found that only a few countries have developed a long-term vision for the use of big data, while many are formulating a big data strategy. Most countries have not yet defined business processes for integrating big data sources and results into their work and do not have a defined structure for managing big data projects.

Thus there exists a need to identify a governance framework for big data for sustainable development, not only at national level, but also at the international level.

4. Sustainable Development and Data Rights

Any discussion on governance frameworks would be incomplete without defining the kind of data rights they must seek to protect.

In the famous parable of the six blind men and the elephant they conclude that the elephant is like a wall, snake, spear, tree, fan or rope, depending upon where they touch. Similarly Internet experiences of individual users (what they touch) often contrast drastically with different views (what they conclude) on what would constitute data rights.

The IEAG in its report has identified the following set of data related rights, but has not defined any actual framework or process for ensuring them (yet) [22]:

Right to be counted,
Right to an identity,
Right to privacy and to ownership of personal data,
Right to due process (for example when data is used as evidence in proceedings, or in administrative decisions),
Freedom of expression,
Right to participation,
Right to non-discrimination and equality, and
Principles of consent.

Personal data is broadly defined as "any information relating to an identified or identifiable individual" [23]. Often primary data producers (users of services and devices generating data) are unaware of individual privacy infringements [24].

A survey by the Global Working Group on Big Data for Official Statistics found that only a few countries have a specific privacy framework for big data, while most apply the privacy framework for traditional statistics to big data as well [25].

Conventionally, safeguards against the re-use of big data to protect data rights have involved the “anonymization” or “de-identification” of data, to conceal individual identities. Global Pulse, for instance, is putting forth the concept of Data Philanthropy, whereby "corporations take the initiative to anonymize (strip out all personal information) their data sets and provide this data to social innovators to mine the data for insights, patterns and trends in real-time or near real-time" [26]. There however exists a debate on whether data can actually be anonymized effectively. Several state that data can never be effectively de-anonymized due to technological challenges [27]. For instance, when the New York City government released de-anonymised data sets of New York cab drivers were made re-identifiable by approaching a separate method. Within less than 2 hours work, researchers knew which driver drove every single trip in this entire dataset. It would be even be easy to calculate drivers’ gross income, or infer where they live [28].

Even the OECD opines that the current model of limiting identifiability of individuals is unsustainable. It recommends moving towards one where the focus is on transparency around how data is being used, rather than preventing specific types of use, stating that - "research funding agencies and data protection authorities should collaborate to develop an internationally recognized framework code of conduct covering the use of new forms of personal data, particularly those generated via network communication. This framework, built on best practice procedures for consent from data subjects, data sharing and re-use, anonymization methods, etc., could be adapted as necessary for specific national circumstances" [29].

Thus, there is a push for the arguement that the historical approaches to protecting privacy and confidentiality — namely, informed consent and anonymity — no longer hold [30]. Some have even suggested using big data itself to keep track of user permissions for each piece of data to act as a legal contract [31].

There is an overall consensus that any legal or regulatory mechanisms set up to mobilise the 'data revolution for sustainable development' should protect the data rights of the people [32], without any clear agreement on what these rights may be.

5. Governance Frameworks Proposed

A largely unanswered question that is posed in light of the emerging consensus on the use of Big Data for monitoring SDGs is within what sort of governance frameworks these data collection and analysis methods will operate. Methods of collection and the key actors involved in data analysis, management, storage and coordination. The role of NGOs and CSOs, if any, within these systems must be delineated. Certain key global organizations and eminent researchers have suggested the following models.

5.1. UN Sustainable Development Solutions Network

In 2012, the UN Secretary-General launched the UN Sustainable Development Solutions Network (SDSN) to mobilize global scientific and technological expertise to promote practical problem solving for sustainable development, including the design and implementation of the Sustainable Development Goals (SDGs) [33]. It has proposed the following.

Collection

The Inter-Agency and Expert Group on Sustainable Development Goal Indicators (IAEGSDG) and the United Nations Statistical Commission are to establish roadmaps for strengthening specific data collection tools that enable the monitoring of SDG indicators.

Analysis

Based on discussions with a large number of statistical offices, including Eurostat, BPS Indonesia, the OECD, the Philippines, the UK, and many others, 100 is recommended to be the maximum number of global indicators to analyse data for which NSOs can report and communicate effectively in a harmonized manner. This conclusion was strongly endorsed during the 46th UN Statistical Commission and the Expert Group Meeting on SDG indicators [34].

Specialist indicators developed by thematic communities must be used for data analysis as they include input and process metrics that are helpful complements to official indicators, which tend to be more outcome-focused. For example, the UN Inter-Agency Group on Child Mortality Estimation has developed a specialist hub responsible for analysing, checking, and improving mortality estimation. This is a leading source for child morality information for both governments and non-governmental actors [35].

Research arms of private companies such as Microsoft Research, IBM research, SAS, and R&D arms of telecom companies could directly partner with official statistical systems to share sophisticated analysing techniques [36].

Management

Four levels of monitoring, national, regional, global, and thematic, should be "organized in an integrated architecture" [37].

Countries must decide individually whether official data must be complemented with non-official indicators from big data which can add richness to the monitoring of the SDGs.

Where possible, regional monitoring should build on existing regional mechanisms, such as the Regional Economic Commissions, the Africa Peer Review Mechanism, or the Asia-Pacific Forum on Sustainable Development [38].

To coordinate thematic monitoring under the SDGs, each thematic initiative may have one or more lead specialist agencies or “custodians” as per the IAEG-MDG monitoring processes. Lead agencies would be responsible for convening multi-stakeholder groups, compiling detailed thematic reports, and encouraging ongoing dialogues on innovation. These thematic groups can become testing grounds in launching a data revolution for the SDGs, trialling new measurements and metrics that in time can feed into the global monitoring process with annual reports [39].

Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring.
Source: UN Sustainable Development Solutions Network, Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a Data Revolution for the SDGs, 2015, p.3.

Role of NSOs

Monitoring the SDG agenda will require substantive improvements in national statistical capacity. Assessments of existing capacity to fulfil SDG monitoring expectations must be undertaken and needs be integrated into National Strategies for the Development of Statistics (NSDSs) [40].

Coordination

A Global Partnership for Sustainable Development Data must be established and a World Forum on Sustainable Development Data be convened in 2016 to create mechanisms for ongoing collaboration and innovation.

A high-level, powerful group of businesses and states must convene the various data and transparency sustainable development initiatives under one umbrella.

To ensure comparability, Global Monitoring Indicators must be harmonized across countries by one lead technical or specialist agency which will additionally coordinate data standards and collection and provide technical support.

The following table indicates the suggested Lead Agencies for individual SDGs [41].

Number	Sustainable Development Goal	Lead Agencies
1.	No Poverty	World Bank, UNDP, UNSD, UNICEF, ILO, FAO, UN-Habitat, UNISDR, WHO, CRED, UNFPA, and UN Population Division
2.	No Hunger	FAO, WHO, UNICEF, and Internal Fertilizer Industry Associaton (IFA)
3.	Good Health	WHO, UN Population Division, UNICEF, World Bank, GAVI, UN AIDS, and UN-Habitat
4.	Quality Education	UNESCO, UNICEF, and World Bank
5.	Gender Equality	UNICEF, UN Women, WHO, UNSD, ILO, UN Population Division, and UNFPA
6.	Clean Water and Sanitation	WHO/UNICEF Joint Monitoring Programme (JMP), FAO, UN Water, and UNEP
7.	Renewable Energy	Sustainable Energy for All, IEA, WHO, World Bank, and UNFCC
8.	Good Jobs and Economic Growth	IMF, World Bank, UNSD, and ILO
9.	Innovation and Infrastructure	World Bank, OECD, UNIDO, UNFCC, UNESCO, and ITU
10.	Reduced Inequalities	UNSD, World Bank, and OECD
11.	Sustainable Cities and Communities	UN-Habitat, Global City Indicators Facility, WHO, CRED, UNISDR, FAO, and UNEP
12.	Responsible Consumption	EITI, UNCTAD, UN Global Compact, FAO, UNEP Ozone Secretariat, WBCSD, GRI, IIRC, and Global Compact
13.	Climate Action	OECD DAC, UNFCCC, and IEA
14.	Life below Water	UNEP-WCMC, IUCN, and FMC
15.	Life on Land	FAO, UNEP, IUCN, and UNEP- WCMC
16.	Peace and Justice	UNODC, WHO, UNOCHA, UNCHR, IOM, OCHA, OECD, UN Global Compact, EITI, UNCTAD, UNICEF, UNESCO, and Transparency International
17.	Partnership for the Goals	BIS, IASB, IFRS, IMF, WIPO, WTO, UNSD, OECD, World Bank, OECD DAC, and SDSN

5.2. The UN DATA Revolution Group

The group constituted by the UN Secretary-General Ban Ki-moon in August 2014, is an Independent Expert Advisory Group with the aim of making concrete recommendations on bringing about a 'data revolution for sustainable development' [42]. In its report, A World that Counts, it makes the following recommendations [43].

Collection

Clear standards on data collection methods must be developed based on the UN Fundamental Principles of Official Statistics. Periodic audits must be conducted by professional and independent third parties to ensure data quality.

Governments, civil society, academia and the philanthropic sector must work together strengthening statistical literacy so that all people have capacity to input into and evaluate the quality of data.

Social entrepreneurs, private sector, academia, media, civil society and other individuals and institutions must be engaged globally with incentives (prizes, data challenges) to encourage data sharing.

Analysis

A SDGs Analysis and Visualisation Platform is to be set up for fostering private-public partnerships and community-led peer-production efforts for data analysis.

A dashboard on ”the state of the world” will engage the UN, think-tanks, academics and NGOs in analysing, and auditing data.

Academics and scientists are to analyse data to provide long-term perspectives, knowledge and data resources at all levels.

The “Global Forum of SDG-Data Users” will ensure feedback loops between data producers, processors and users to improve the usefulness of data and information produced.

A “SDGs data lab” to support the development of a first wave of SDG indicators is to be established mobilizing key public, private and civil society data providers, academics and stakeholders working with the Sustainable Development Solutions Network.

Storage

A “world statistics cloud” will store data and metadata produced by different institutions but according to common standards, rules and specifications.

Role of NSOs

Civil society organisations must share data and processing methods with private and public counterparts on the basis of agreements. They must hold governments and companies accountable using evidence on the impact of their actions, provide feedback to data producers, develop data literacy and help communities and individuals generate and use data.

NSOs are the central players of the Data Revolution. Their autonomy must be strengthened to maintain data quality. They must abandon expensive and cumbersome production processes, incorporate new data sources like big data that is human and machine-readable, compatible with geospatial information systems and available quickly enough to ensure that the data cycle matches the decision cycle. Collaborations with the private sector can boost technical and financial investments.

Coordination

Key stakeholders must create a “Global Consensus on Data”, to adopt principles concerning legal, technical, privacy, geospatial and statistical standards. Best practices related to public data such as the Open Government Partnership (OGP) and the G8 Open Data Charter are recommended foundations for such principles.

A UN-led “Global Partnership for Sustainable Development Data” is proposed, to coordinate and broker key global public-private partnerships for data sharing [44].

A “World Forum on Sustainable Development Data” and “Network of Data Innovation Networks” will be a converging point for the data ecosystem to share ideas and experiences for improvements, innovation and technology transfer.

5.3. Organization for Economic Co-Operation and Development (OECD)

The Organisation for Economic Co-operation and Development (OECD) is an inter-governmental organization that seeks to promote policies that will improve the economic and social well-being of people globally. It has made the following proposals [45].

Collection

Data is to be collected from National statistical agencies, national and international researchers and international organisations.

Role of NSOs

By leveraging the expertise of telecommunications companies and software developers, for instance, national statistical systems could potentially reduce costs and improve the availability of data to monitor development goals [46].

Coordination

National Data Forums for Social Science Data must be created for the development of social science data for improved coordination between social scientists, data producers (national statistical agencies, government departments, large private sector businesses and sources undertaking academic direction), and data curators.

Social science research communities must contribute to national plans of action after a needs assessment [47]. Research funding agencies must collaborate at the international level for a common system for referencing datasets in research publications [48].

5.4. The Global Partnership for Sustainable Development of Data

The partnership is a global network of governments, NGOs, and businesses working to strengthen the inclusivity, trust, and innovation in the way that data is used to address the world’s sustainable development efforts [49].

Analysis

There must be a common framework for information processing. At minimum, a simple lexicon must tag each datum specifying:

What: i.e. the type of information contained in the data,
Who: the observer or reporter,
How: the channel through which the data was acquired,
How much: whether the data is quantitative or qualitative, and
Where and when: the spatio-temporal granularity of the data.

Analysis of data involves filtering relevant information, summarising keywords and categorising into indicators. This intensive mining of socioeconomic data, known as “reality mining,” can be done by: (1) Continuous analysis of real time streaming data, (2) Digestion of semi-structured and unstructured data to determine perceptions, needs and wants. (3) Real-time correlation of streaming data with slowly accessible historical data repositories.

Use of big data for developmental goals can draw upon all three techniques to various degrees depending on availability of data and the specific needs.

Role of NSOs

NSOs have a pivotal part to play in the data revolution. Countries and organizations believe that big data cannot replace traditional official statistical data as it is based more on perception than facts. To quote Winston Churchill, "Do not trust any statistics that you did not fake yourself."

For instance, a study found that Google Flu Trends, to detect influenza epidemics, predicted nonspecific flu-like respiratory illnesses well but not actual flu. The mismatch was due to popular misconceptions on influenza symptoms. This has important policy implications. Doctors using Google Flu Trends may overstock on flu vaccines or be overly inclined to diagnose normal respiratory illnesses as influenza [50].

However Big Data if understood correctly, can inform where further targeted investigation is necessary and give immediate responses to favourably change outcomes.

5.5. The World Economic Forum (WEF)

The WEF is an International Organization for Public-Private Cooperation. It engages the foremost political, business and other leaders of society to shape global, regional and industry agendas [51]. In the report titled Big Data, Big Impact: New Possibilities for International Development, it makes the following recommendations [52].

Collection

Data production and development actors include individuals, public sector and the private sector. Each produce different kinds of data that have unique requirements. The private sector maintains vast troves of transactional data, much of which is "data exhaust," or data created as a by-product of other transactions. The public sector maintains enormous datasets in the form of census data, health indicators, and tax and expenditure information. The following figure highlights the different kinds of data that each sector collects and what incentives they have to share the data along with requirements to maintain such data.

World Economic Forum - Diagram on Data Commons.
Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.4.

Business models must be created to provide the appropriate incentives for private-sector actors to share data. Such models already exist in the Internet environment. For instance companies in search and social networking profit from products they offer at no charge to end users because the usage data these products generate is valuable to other ecosystem actors. Similar models could be created in garnering Big Data for SDGs. The following flowchart illustrates how different sectors must work together to incentivise data collection and sharing.

World Economic Forum - Diagram on Global Coordination.
Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.7.

5.6. Dr. Julia Lane - A Quadruple Data Helix

Dr. Julia Lane is a Professor in the Wagner School of Public Policy at New York University; and also a Provostial Fellow in Innovation Analytics and a Professor in the Center for Urban Science and Policy [53]. She has done extensive research on the uses of big data. In her paper titled "Big Data for Public Policy: A Quadruple Data Helix," she makes the following suggestions [54].

Collection

In the future there will exist a model of a quadruple data helix for data collection which will have four strands — state and city agencies, universities, private data providers, and federal agencies.i

A new set of institution, city/university data facilities, must be established. These institutions should form the backbone of the quadruple helix, with direct connections to the private sector and to the federal statistical agencies.

Analysis

There is a need for graduate training for non-traditional students, who need to understand how to use data science tools as part of their regular employment. They must identify and capture the appropriate data, understand how data science models and tools can be applied, and determine how associated errors and limitations can be identified from a social science perspective.i

Universities can act as a trusted independent third party to process, store, analyze, and disseminate data. ii

Management

The new infrastructure must ensure that data from disparate sources are collected managed and used in a manner that is informed by end users. There are many technical challenges: disparate data sets must be ingested, their provenance determined, and metadata documented. Researchers must be able to query data sets to know what data are available and how they can be used. And if data sets are to be joined, they must be joined in a scientific manner, which means that workflows need to be traced and managed in such a way that the research can be replicated.

Coordination

The role of State and City agencies is to address immediate policy issues, rather than to build long-term data infrastructures as their mandate is to work with city data than the full spectrum of available data.

5.7. Data-Pop Alliance

Data-Pop Alliance is a global coalition on Big Data and development created by the Harvard Humanitarian Initiative, MIT Media Lab, and Overseas Development Institute that brings together researchers, experts, practitioners, and activists to promote a people-centred big data revolution through collaborative research, capacity building, and community engagement [55]. It makes the following suggestions.

Collection

The idea of shared responsibility between the public and private sector is a proposed operational principles to create a deliberative space. Mechanisms and legal frameworks must be devised for private companies to share their big data under formalized and stable arrangements instead of being compelled by ad hoc requests from researchers and policymakers.

The media too, could avoid publishing statistical data collected by unexplained methodologies by employing "statistical editors" and disseminate verified information.

Role of NSOs

For official statistics, engaging with Big Data is not a technical consideration but a political obligation. In a two tier system of official and non-official statistics, the public and investors tend to distrust official figures. For instance, the results of the 2010 census in the UK are being disputed on the basis of sewage data.

It is imperative for NSOs to retain, or regain, their primary role as the legitimate custodian of knowledge and creator of a deliberative public space to democratically drive human development [56].

6. Conclusion

The Big data frameworks provide some useful insights on monitoring mechanisms though some questions remain unanswered in each model. Key actors that have been proposed include city and state agencies like NSOs, private companies, social scientists, private individuals and international research agencies. Data analysis can be through public-private collaborations, data philanthropy, and using indicators by thematic communities.

Collection

There appears consensus across models that collection must be effected through public private partnerships while providing incentives.

Analysis

While several methods of analysis have been proposed by the Global Partnership it is unclear on who will be conducting the analysis. The UNSDSN has suggested that it be conducted by academics and scientists with Julia Lane stating it must be through public private partnerships which appear more feasible and transparent.

Role of NSOs

All frameworks agree on the pivotal role of NSOs and acknowledge them as the key players and coordinators at the national level. They must be strengthened financially, technologically and politically. Most frameworks seek to empower national agencies which will coordinate collaborations with the private sector through incentives while protecting personal data.

Coordination

Several international fora have been proposed to enable coordination while there is consensus that the NSOs. A Global Partnership for Sustainable Development Data, a Global Consensus on Data and a World Forum on Sustainable Development Data have been suggested. UN organizations appear to be suggesting more responsibility for those in the UN framework with UNSDSN giving an extensive list of lead agencies (UNDP, UN Women, Who etc) while the WEF emphasises on the private sector, Data Pop Alliance on NSOs, and Prof. Lane on State and City agencies.

On an international level countries can opt to join international organization that are being setup for the purpose. It remains to be seen whether all countries globally can achieve such a feat in a coordinated manner without infringing on data rights when unanswerable to any set international organization. The burden appears to fall on civil society and market forces within the private sector to regulate this process. For instance when a private sector company starts providing large un-anonymized data sets for government use, the privacy concerns of civil society that result in them opting for the company’s competitor’s more privacy friendly products will result in a regulation through market forces. However these forces may have disparate strengths in different contexts and countries depending on market practices and information asymmetry resulting in the lack of a uniform accountability mechanism.

7. Endnotes

[1] Dan Ariely, Facebook, January 06, 2013, https://www.facebook.com/dan.ariely/posts/904383595868.

[2] United Nations Organizations, 'Sustainable Development Goals' (United Nations Sustainable Development, 26 September 2015), http://www.un.org/sustainabledevelopment/sustainable-development-goals/, accessed 6 June 2016.

[3] Data Revolution Group, 'A World that Counts: Mobilising the Data Revolution for Sustainable Development' (November 2014), http://www.undatarevolution.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf, accessed 8 June 2016.

[4] High level panel on the post-2015 development agenda , 'A New Global Partnership: Eradicate Poverty and Transform Economies through Sustainable Development'(Post2015hlp,0rg, July 2012), http://www.post2015hlp.org/, accessed 8 June 2016.

[5] Gary King, 'Ensuring the Data-Rich Future of the Social Sciences' [2011] 3(2) Science, http://gking.harvard.edu/files/datarich.pdf, accessed 8 June 2016.

[6] See [3].

[7] Ibid.

[8] Michael Horrigan, 'Big Data: A Perspective from the BLS' (Amstatorg, 1 January 2013) http://magazine.amstat.org/blog/2013/01/01/sci-policy-jan2013/, accessed 4 June 2016.

[9] UN Global Pulse, 'Big Data for Development: Challenges & Opportunities' (6 May 2012) http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf, accessed 5 June 2016.

[10] Emmanuel Letouzé and Johannes Jütting, 'Official Statistics, Big Data and Human Development: Towards a New Conceptual and Operational Approach' (2014) 12(3), Data-Pop Alliance White papers Series, https://www.odi.org/sites/odi.org.uk/files/odi-assets/events-documents/5161.pdf, accessed 4 June 2016.

[11] See [9].

[12] See [10].

[13] See [9].

[14] UN Global Pulse, 'About: United Nations Global Pulse' (2016) http://www.unglobalpulse.org/about-new, accessed 7 June 2016.

[15] UN Stats, 'Global Working Group' (2014) http://unstats.un.org/unsd/bigdata/, accessed 8 June 2016.

[16] New York City Press Release, ‘Mayor Bloomberg, Police Commissioner Kelly and Microsoft Unveil New, State-of-the-Art Law Enforcement Technology that Aggregates and Analyzes Existing Public Safety Data in Real Time to Provide a Comprehensive View of Potential Threats and Criminal Activity’ (New York City, 8 August 2012), http://www1.nyc.gov/office-of-the-mayor/news/291-12/mayor-bloomberg-police-commissioner-kelly-microsoft-new-state-of-the-art-law, accessed 2 July 2016.

[17] Francesco Mancini, 'New Technology and the Prevention of Violence and Conflict' (Reliefwebint, April 2013), http://reliefweb.int/sites/reliefweb.int/files/resources/ipi-e-pub-nw-technology-conflict-prevention-advance.pdf, accessed 2 July 2016.

[18] Arjuna Costa, Anamitra Deb, and Michael Kubzansky, 'Big Data, Small Credit: The Digital Revolution and Its Impact on Emerging Market Consumers,' (Omidyar, 3 March 2013) https://www.omidyar.com/sites/default/files/file_archive/insights/Big%20Data,%20Small%20Credit%20Report%202015/BDSC_Digital%20Final_RV.pdf, accessed 2 July 2016.

[19] United Nations Economic and Social Council, 'Report of the Global Working Group on Big Data for Official Statistics' (UN Stats, 3 March 2015), http://unstats.un.org/unsd/statcom/doc15/2015-4-BigData-E.pdf, accessed 8 June 2016.

[20] Ibid.

[21] Ibid.

[22] See [3].

[23] OECD, 'OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data' (23 September 1980), http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm, accessed 29 May 2016.

[24] Amir Efrati, ''Like' Button Follows Web Users' (WSJ, 18 May 2011) http://www.wsj.com/articles/SB10001424052748704281504576329441432995616, accessed 23 May 2016.

[25] See [15].

[26] Robert Kirkpatrick, 'Data Philanthropy: Public and Private Sector Data Sharing for Global Resilience' (UN Global Pulse, 16 September 2011), http://www.unglobalpulse.org/blog/data-philanthropy-public-private-sector-data-sharing-global-resilience, accessed 4 June 2016.

[27] Ibid.

[28] Arvind Narayanan, 'No silver bullet: De-identification still doesn't work' (1 April 2016), http://randomwalker.info/publications/no-silver-bullet-de-identification.pdf, accessed 3 July 2016.

[29] OECD Global Science Forum, 'New Data for Understanding the Human Condition: International Perspectives,' (February 2013) http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf, accessed 2 June 2016.

[30] S. Barocas, 'The Limits of Anonymity and Consent in the Big Data Age,' in Privacy, Big Data, and the public good: Frameworks for Engagement (Cambridge University Press, 2014).

[31] A. Pentland, 'Institutional Controls: The New Deal on Data,' in Privacy, Big Data, and the public good: Frameworks for Engagement (Cambridge University Press, 2014).

[32] See [3].

[33] UN Sustainable Development Solutions Network, 'About Us: Vision and Organization' (2012) http://unsdsn.org/about-us/vision-and-organization/, accessed 2 June 2016.

[34] UN Sustainable Development Solutions Network, 'Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a data revolution for the SDGs' (12 June 2015) http://unsdsn.org/wp-content/uploads/2015/05/150612-FINAL-SDSN-Indicator-Report1.pdf, accessed 4 June 2016.

[35] UNICEF, 'CME Info - Child Mortality Estimates' (2014) http://www.childmortality.org/, accessed 1 June 2016.

[36] See [10].

[37] UNESCO, 'Technical report by the Bureau of the United Nations Statistical Commission (UNSC) on the process of the development of an indicator framework for the goals and targets of the post-2015 development agenda' (6 March 2015) http://www.uis.unesco.org/ScienceTechnology/Documents/unsc-post-2015-draft-indicators.pdf, accessed 3 June 2016.

[38] UN, 'The Road to Dignity by 2030: Ending Poverty, Transforming All Lives and Protecting the Planet ' (4 December 2014) http://www.un.org/disabilities/documents/reports/SG_Synthesis_Report_Road_to_Dignity_by_2030.pdf, accessed 7 June 2016.

[39] Ibid.

[40] UN Sustainable Development Solutions Network, 'Data for Development: An Action Plan to Finance the Data Revolution for Sustainable Development' (10 July 2015) http://unsdsn.org/wp-content/uploads/2015/04/Data-For-Development-An-Action-Plan-July-2015.pdf, accessed 3 June 2016.

[41] See [34].

[42] UN Data Revolution Group, 'About the Independent Expert Advisory Group' (6 November 2014) http://www.undatarevolution.org/about-ieag/, accessed 4 June 2016.

[43] See [3].

[44] The Partnership has already been established, and it is developing a further framework.

[45] Organisation for Economic Co-Operation and Development), 'The Organisation for Economic Co-operation and Development (OECD): About' (2016) http://www.oecd.org/about/, accessed 2 June 2016.

[46] Organisation for Economic Co-Operation and Development, 'Strengthening National Statistical Systems to Monitor Global Goals' (2015) http://www.oecd.org/dac/POST-2015%20P21.pdf, accessed 1 June 2016.

[47] Ibid.

[48] OECD Global Science Forum, 'New Data for Understanding the Human Condition: International Perspectives' (February 2013) http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf, accessed 2 June 2016.

[49] The Global Partnership On Sustainable Development Data, 'Who We Are: The Data Ecosystem and the Global Partnership' (2016) http://www.data4sdgs.org/who-we-are/, accessed 5 June 2016.

[50] World Economic Forum, 'Big Data, Big Impact: New Possibilities for International Development' (22 January 2012) http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf, accessed 8 June 2016.

[51] World Economic Forum, 'Our Mission: The World Economic Forum' (12 January 2016) https://www.weforum.org/about/world-economic-forum/, accessed 7 June 2016.

[52] See [50].

[53] Julia Lane, Homepage, http://www.julialane.org/.

[54] Julia Lane, 'Big Data for Public Policy: The Quadruple Helix' (2016) 8(1) Journal of Policy Analysis and Management, DOI:10.1002/pam.21921, accessed 1 June 2016.

[55] Data-Pop Alliance, 'Data-Pop Alliance: Our Mission' (May 2014) http://datapopalliance.org/, accessed 1 June 2016.

[56] See [10].

8. Author Profile

Meera Manoj is a law student at the Gujarat National Law University, Gandhinagar and has completed her first year. She is passionate about civil rights, feminism, economics in law and anything involving paneer. She aspires to travel the world and build up a vast library, with unparalleled sections on International Law and Archie comics.

For more details visit https://cis-india.org/internet-governance/blog/big-data-governance-frameworks-for-data-revolution-for-sustainable-development

Big Data Compilation

praskrishna — 2017-02-26T16:18:07Z

For more details visit https://cis-india.org/internet-governance/files/big-data-compilation.pdf

Big Data and Positive Social Change in the Developing World: A White Paper for Practitioners and Researchers

nishant — 2014-10-01T03:52:35Z

I was a part of a working group writing a white paper on big data and social change, over the last six months. This white paper was produced by a group of activists, researchers and data experts who met at the Rockefeller Foundation’s Bellagio Centre to discuss the question of whether, and how, big data is becoming a resource for positive social change in low- and middle-income countries (LMICs).

Bellagio Big Data Workshop Participants. (2014). “Big data and positive social change in the developing world: A white paper for practitioners and researchers.” Oxford: Oxford Internet Institute. Available online: http://ssrn.com/abstract=2491555.

Summary

Our working definition of big data includes, but is not limited to, sources such as social media, mobile phone use, digitally mediated transactions, the online news media, and administrative records. It can be categorised as data that is provided explicitly (e.g. social media feedback); data that is observed (e.g. mobile phone call records); and data that is inferred and derived by algorithms (for example social network structure or inflation rates). We defined four main areas where big data has potential for those interested in promoting positive social change: advocating and facilitating; describing and predicting; facilitating information exchange and promoting accountability and transparency.

In terms of advocating and facilitating, we discussed ways in which volunteered data may help organisations to open up new public spaces for discussion and awareness-building; how both aggregating data and working across different databases can be tools for building awareness, and howthe digital data commons can also configure new communities and actions (sometimes serendipitously) through data science and aggregation. Finally, we also looked at the problem of overexposure and howactivists and organisations can protect themselves and hide their digital footprints. The challenges we identified in this area were how to interpret data correctly when supplementary information may be lacking; organisational capacity constraints around processing and storing data, and issues around data dissemination, i.e. the possible negative consequences of inadvertently identifying groups or individuals.

Next, we looked at the way big data can help describe and predict, functions which are particularly important in the academic, development and humanitarian areas of work where researchers can combine data into new dynamic, high-resolution datasets to detect new correlations and surface new questions. With data such as mobile phone data and Twitter analytics, understanding the data’s comprehensiveness, meaning and bias are the main challenges, accompanied by the problem of developing new and more comprehensive ethical systems to protect data subjects where data is observed rather than volunteered.

The next group of activities discussed was facilitating information exchange. We looked at mobile-based information services, where it is possible for a platform created around a particular aim (e.g. agricultural knowledge-building) to incorporate multiple feedback loops which feed into both research and action. The pitfalls include the technical challenge of developing a platform which is lean yet multifaceted in terms of its uses, and particularly making it reliably available to low-income users. This kind of platform, addressed by big data analytics, also offers new insights through data discovery and allows the provider to steer service provision according to users’ revealed needs and priorities.

Our last category for big data use was accountability and transparency, where organisations are using crowdsourcing methods to aggregate and analyse information in real time to establish new spaces for critical discussion, awareness and action. Flows of digital information can be managed to prioritise participation and feedback, provide a safe space to engage with policy decisions and expose abuse. The main challenges are how to keep sensitive information (and informants) safe while also exposing data and making authorities accountable; how to make the work sustainable without selling data, and how to establish feedback loops so that users remain involved in the work beyond an initial posting. In the crowdsourcing context, new challenges are also arising in terms of how to verify and moderate real-time flows of information, and how to make this process itself transparent.

Finally, we also discussed the relationship between big and open data. Open data can be seen as a system of governance and a knowledge commons, whereas big data does not by its nature involve the idea of the commons, so we leaned toward the term ‘opening data’, i.e. processes which could apply to commercially generated as much as public-sector datasets. It is also important to understand where to prioritise opening, and where this may exclude people who are not using the ‘right’ technologies: for example, analogue methods (e.g. nailing a local authority budget to a town hall door every month) may be more open than ‘open’ digital data that’s available online.

Our discussion surfaced many questions to do with representation and meaning: must datasets be interpreted by people with local knowledge? For researchers to get access to data that is fully representative, do we need a data commons? How are data proprietors engaging with the power dynamics and inequalities in the research field, and how can civil society engage with the private sector on its own terms if data access is skewed towards elites? We also looked at issues of privacy and risk: do we need a contextual risk perspective rather than a single set of standards? What is the role of local knowledge in protecting data subjects, and what kinds of institutions and practices are necessary? We concluded that there is a case to be made for building a data commons for private/public data, and for setting up new and more appropriate ethical guidelines to deal with big data, since aggregating, linking and merging data present new kinds of privacy risk. In particular, organisations advocating for opening datasets must admit the limitations of anonymisation, which is currently being ascribed more power to protect data subjects than it merits in the era of big data.

Our analysis makes a strong case that it is time for civil society groups in particular to become part of the conversation about the power of data. These groups are the connectors between individuals and governments, corporations and governance institutions, and have the potential to promote big data analysis that is locally driven and rooted. Civil society groups are also crucially important but currently underrepresented in debates about privacy and the rights of technology users, and civil society as a whole has a responsibility for building critical awareness of the ways big data is being used to sort, categorise and intervene in LMICs by corporations, governments and other actors. Big data is shaping up to be one of the key battlefields of our era, incorporating many of the issues civil society activists worldwide have been working on for decades. We hope that this paper can inform organisations and
individuals as to where their particular interests may gain traction in the debate, and what their contribution may look like.

Click to download the full white paper here. (PDF, 1.95 Mb)

For more details visit https://cis-india.org/internet-governance/blog/big-data-and-positive-social-change-in-developing-world

Big Data and Positive Social Change in the Developing World

praskrishna — 2014-10-01T03:49:58Z

For more details visit https://cis-india.org/internet-governance/blog/big-data-and-positive-social-change.pdf

Big Brother is here: Amid snooping row, govt report says monitoring system 'practically complete'

Admin — 2018-12-26T15:22:27Z

The recently released 2017-18 annual report of the Centre for Development of Telematics (C-DOT) says that surveillance equipment is being rolled out in 21 service areas across the country.

The article by Keerthana Sankaran was published in New Indian Express on December 26, 2018.

While last week's government order on snooping caused an uproar, the Centre's plans for a far-reaching monitoring system have been in the making for almost a decade -- with the groundwork being done by the previous UPA regime. The recently released 2017-18 annual report of the Centre for Development of Telematics (C-DOT) says that India’s ‘Central Monitoring System’ (CMS) is “practically complete”, confirming that the Orwellian ‘Big Brother’ is here.

The report says that surveillance equipment is being rolled out in 21 service areas across the country and operations have commenced in 12 service areas. The system will monitor and intercept calls and messages.

The government claims the CMS is based on the Telegraph Act of 1885 which states that the central or state government may intercept messages if the government is “satisfied that it is necessary or expedient to do so in the interests of the sovereignty and integrity of India, the security of the state, friendly relations with foreign states or public order or for preventing incitement to the commission of an offence.”

Even though the surveillance system was publicly announced in 2009, C-DOT’s annual report of 2007-2008 had hinted at a testing phase for a “lawful interception, monitoring” system.

A post from the website of the Centre for Internet and Society describes how the CMS could work. Network providers are all required to give interconnected Regional Monitoring Centres access to their network servers. The article also points out that there is no law that describes the CMS.

The CMS was approved by the Cabinet Committee on Security during the UPA government in 2011, receiving flak from experts and the press for not safeguarding the citizen’s right to privacy. However, in a Lok Sabha session in May 2016, Telecom Minister Ravi Shankar Prasad said that the system is for the “process of lawful interception”, adding that regional monitoring centres in Delhi and Mumbai had been operationalised.

The latest C-DOT report also talks about a Centre of Excellence for Lawful Interception being set up, which would use high-end technologies - such as open source intelligence, image processing and search engine tools to scan Twitter and Facebook - for surveillance.

On Thursday, the Ministry of Home Affairs released a notification, authorising 10 central agencies to intercept, monitor and decrypt any "information generated, transmitted, received or stored in any computer." While the public and opposition parties expressed alarm over the new order, the C-DOT report clearly shows that state surveillance plans are already in an advanced stage.

These government moves are taking place despite the August 2017 landmark judgement by the Supreme Court, which declared the right to privacy as a fundamental right which will protect citizens from intrusive activities by the state.

For more details visit https://cis-india.org/internet-governance/news/new-indian-express-keerthana-sankaran-december-26-2018-big-brother-is-here-amid-snooping-row-govt-report-says-monitoring-system-practically-complete

Bhuvana Meenakshi elected Mozilla Rep for July 2019

Admin — 2019-09-25T16:33:55Z

Bhuvana Meenakshi was selected as a Rep of the Month (July 2019) by Mozilla for my active contributions.

Reps are community coordinators interested in:

finding and connecting new talent with Mozilla projects they are contributing to
developing communities on a functional or local level
supporting local communities and Mozilla to have an effective and decentralized environment for contribution
creating collaborations with adjacent communities to spread Mozilla’s mission and expand Mozilla’s outreach in the open source ecosystem
collecting local knowledge from Mozilla communities to inform and inspire Mozilla’s strategy, activities and priorities

How is the Rep of the Month nominated?

Every Rep can be nominated by any Reps Mentor to receive this recognition. Therefore it's possible to nominate other Reps, not just the mentor's mentees. This nomination is meant as a recognition for outstanding contributions to Mozilla through the Reps program. Mentors are encouraged to nominate any Reps that have stood out in the past month.

For more see here

For more details visit https://cis-india.org/a2k/news/bhuvana-meenakshi-elected-mozilla-rep-for-july-2019-1

Bhutan's Google Apps

praskrishna — 2014-01-30T12:17:53Z

For more details visit https://cis-india.org/internet-governance/blog/bhutan-google.pdf

BHASHA - Indian Languages Digital Festival

praskrishna — 2016-06-18T17:14:37Z

Subhashish Panigrahi gave a talk at the Bhasha- India Languages Digital Festival a conference organized by news media YourStory, at New Delhi on March 11, 2016. In the panel "The challenges of making regional language content available on the Web and on mobiles" Panigrahi spoke about some of the challenges in growing the Indian-language Wikipedia projects and the communities.

The notes for the talk are below:

Wikipedia, that exists in many Indian languages are not known to masses. I personally did not know about the Odia Wikipedia until 2011 when a friend told me about its existence. Back then the project was completely inactive. And then a couple of friends and I started contributing, more contributors joined and the project grew up to what it is today. The site now has over 300,000 visitors every month and it is the largest visited site in Odia language on the Internet. This could probably be the same case for your language.

It has been quite challenging in the past to grow the Indian-language Wikipedia projects. There are many challenges and I would talk about eight of them:

1. Language communities

The language communities of many of the Indian languages are such that many of them even do no know how to search any information online in their language typed in their script. Some even share that because Google's home page does not have their script means that their language does not exist on the Internet. There exist a large gap of ignorance.

2. Wikipedia's editor community

Wikipedia, as you all know, is written by people like you and me. And from writing to editing everything happens voluntarily. As many people do not probably know or do not try to learn that they themselves can correct the mistakes and inaccuracy that exist in many Wikipedia articles. The Wikipedia editor communities for several Indian languages are really small. When these languages are spoken by millions of people, only a handful editors contribute in editing the Wikipedia in these languages.

3. Language input

There is a vast majority of people in this country that do not know how to type in their own language.

4. Low availability of Indian-language content on the Internet

There are two stages to the lack of Open Access to information. First, lack of native language content on the Internet bars many to access knowledge. Take the example of my state. When the Kerala government's official tourism portal is available in Odia and other Indian languages, my state, the Odisha government's tourism portal has no information in Odia-language. Our languages are neglected largely in our own states.

5. Mismatch of conventional and new media

Many conventional have still been using non-standard variants of ASCII/ISCII script encoding systems instead of adopting the Unicode standard. Unicode being a global standard and having the advantage of unifying the world has been available for Indian languages about 25 years now.^[1] But many of our traditional media has failed to adopt this. Malayala Manorama, one of the most circulated dailies in Malayalam languages and one of the oldest Indian newspaper still has not started using Unicode on their website. Same is the case for many other newspapers in India.

6. Lack of Open Access

The information produced on the Internet in general and by the government, in particular, are mostly copyrighted. The paywalled garden of copyright restrictions keeps the information closed and stop people from sharing and learning more. On the contrary, Wikipedia is available under a Creative Commons Share-Alike license which allows anyone to make use of the content and even distribute commercial copies of its content. The idea of opening up information for masses in a free license could make the information reach to millions of people.

7. Mobile input

With over 1 billion people with mobile phones, the 15% internet penetration rate will soon grow meaning lot many Indians will have access to the Internet. If these people are not educated about native language input then they will be victims of the English-centric Internet rather than being able to enjoy the virtue of the same. Many Indians that have smartphones need pre-built input methods to be able to contribute in their own language Wikipedia.

8. People with disabilities

Many cannot read, speak and write. India has over 60 million people with hearing impairment.^[2] There is a need for good quality text to and speech to text engine for these languages. Also, these software products have to be free software so common people, that cannot afford to buy expensive proprietary software like JAWS, can contribute to Wikipedia in their language. Many text-to-speech engines that are available today for Indian languages sound so mechanical that it is way hard for common speakers to use them.

For more details visit https://cis-india.org/a2k/news/bhasha-indian-languages-digital-festival

Bharat Majhi Writings Now Available Under a Creative Commons License

subha — 2014-11-07T15:34:38Z

Before the advent of Odia Wikisource becoming live, Centre for Internet and Society's Access to Knowledge (CIS-A2K) got in touch with notable Odia author Bharat Majhi for bringing his writings on the Internet.

Dr. Sailen Routray, Director, Kalinga Institute of Social Sciences (KISS) (also an institutional partner of CIS-A2K) played a key role in initiating a dialog with Mr. Majhi. Five of his books are now available under Creative Commons Share Alike 4.0 (CC-by-SA 4.0) license:

Agadhu Duari
Saralarekhaa
Murtikaara
Mahanagara Padya
Highwayre Kuhudi

Bharat Majhi is a known name in Odia literary circle. His writings on societal structures in rural Odisha, the people, their life, aspiration and suffering have been critically acclaimed. These books also become the first Odia modern poetry books to be available under a CC-by-SA license and Mr. Majhi takes another leap of being the first Odia poet to release his literary work under CC-by-SA 4.0 license.

Video

In this video, Odia-language poet Bharat Majhi reciting a poem from his first book Agadhu Duari. He recently released 5 of his books under CC-by-SA 4.0 license by the efforts of Centre for Internet and Society's Access To Knowledge (CIS-A2K) and Kalinga Institute of Social Sciences (KISS).He also tells Subhashish Panigrahi about his dreams of poetry becoming a performing art coming true by it becoming more open. After other Odia author and poet Dr Jagannath Mohanty, Majhi is the first Odia poet to release poem online under CC-by-SA license and his poetry earn the title of the first Odia modern poetry under the same license.

For more details visit https://cis-india.org/openness/blog-old/bharat-majhi-writings-now-available-under-cc-license

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Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring.Source: UN Sustainable Development Solutions Network, Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a Data Revolution for the SDGs, 2015, p.3.

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Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring.
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Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.4.

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Source: World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, 2012, p.7.