Centre for Internet and Society

The Mother and Child Tracking System - understanding data trail in the Indian healthcare systems

ambika — 2019-12-30T17:18:05Z

This article was first published by Privacy International, on October 17, 2019

Case study of MCTS: Read

On October 17th 2019, the UN Special Rapporteur (UNSR) on Extreme Poverty and Human Rights, Philip Alston, released his thematic report on digital technology, social protection and human rights. Understanding the impact of technology on the provision of social protection – and, by extent, its impact on people in vulnerable situations – has been part of the work the Centre for Internet and Society (CIS) and Privacy International (PI) have been doing.

Earlier this year, PI responded to the UNSR's consultation on this topic. We highlighted what we perceived as some of the most pressing issues we had observed around the world when it comes to the use of technology for the delivery of social protection and its impact on the right to privacy and dignity of benefit claimants.

Among them, automation and the increasing reliance on AI is a topic of particular concern - countries including Australia, India, the UK and the US have already started to adopt these technologies in digital welfare programmes. This adoption raises significant concerns about a quickly approaching future, in which computers decide whether or not we get access to the services that allow us to survive. There's an even more pressing problem. More than a few stories have emerged revealing the extent of the bias in many AI systems, biases that create serious issues for people in vulnerable situations, who are already exposed to discrimination, and made worse by increasing reliance on automation.

Beyond the issue of AI, we think it is important to look at welfare and automation with a wider lens. In order for an AI to function it needs to be trained on a dataset, so that it can understand what it is looking for. That requires the collection large quantities of data. That data would then be used to train and AI to recognise what fraudulent use of public benefits would look like. That means we need to think about every data point being collected as one that, in the long run, will likely be used for automation purposes.

These systems incentivise the mass collection of people's data, across a huge range of government services, from welfare to health - where women and gender-diverse people are uniquely impacted. CIS have been looking specifically at reproductive health programmes in India, work which offers a unique insight into the ways in which mass data collection in systems like these can enable abuse.

Reproductive health programmes in India have been digitising extensive data about pregnant women for over a decade, as part of multiple health information systems. These can be seen as precursors to current conceptions of big data systems within health informatics. India’s health programme instituted such an information system in 2009, the Mother and Child Tracking System (MCTS), which is aimed at collecting data on maternal and child health. The Centre for Internet and Society, India, undertook a case study of the MCTS as an example of public data-driven initiatives in reproductive health. The case study was supported by the Big Data for Development network supported by the International Development Research Centre, Canada. The objective of the case study was to focus on the data flows and architecture of the system, and identify areas of concern as newer systems of health informatics are introduced on top of existing ones. The case study is also relevant from the perspective of Sustainable Development Goals, which aim to rectify the tendency of global development initiatives to ignore national HIS and create purpose-specific monitoring systems.

After being launched in 2011, 120 million (12 crore) pregnant women and 111 million (11 crore) children have been registered on the MCTS as of 2018. The central database collects data on each visit of the woman from conception to 42 days postpartum, including details of direct benefit transfer of maternity benefit schemes. While data-driven monitoring is a critical exercise to improve health care provision, publicly available documents on the MCTS reflect the complete absence of robust data protection measures. The risk associated with data leaks are amplified due to the stigma associated with abortion, especially for unmarried women or survivors of rape.

The historical landscape of reproductive healthcare provision and family planning in India has been dominated by a target-based approach. Geared at population control, this approach sought to maximise family planning targets without protecting decisional autonomy and bodily privacy for women. At the policy level, this approach was shifted in favour of a rights-based approach to family planning in 1994. However, targets continue to be set for women’s sterilisation on the ground. Surveillance practices in reproductive healthcare are then used to monitor under-performing regions and meet sterilisation targets for women, this continues to be the primary mode of contraception offered by public family planning initiatives.

More recently, this database - among others collecting data about reproductive health - is adding biometric information through linkage with the Aadhaar infrastructure. This data adds to the sensitive information being collected and stored without adhering to any publicly available data protection practices. Biometric linkage is aimed to fulfill multiple functions - primarily authentication of welfare beneficiaries of the national maternal benefits scheme. Making Aadhaar details mandatory could directly contribute to the denial of service to legitimate patients and beneficiaries - as has already been seen in some cases.

The added layer of biometric surveillance also has the potential to enable other forms of abuse of privacy for pregnant women. In 2016, the union minister for Women and Child Development under the previous government suggested the use of strict biometric-based monitoring to discourage gender-biased sex selection. Activists critiqued the policy for its paternalistic approach to reduce the rampant practice of gender-biased sex selection, rather than addressing the root causes of gender inequality in the country.

There is an urgent need to rethink the objectives and practices of data collection in public reproductive health provision in India. Rather than continued focus on meeting high-level targets, monitoring systems should enable local usage and protect the decisional autonomy of patients. In addition, the data protection legislation in India - expected to be tabled in the next session in parliament - should place free and informed consent, and informational privacy at the centre of data-driven practices in reproductive health provision.

This is why the systematic mass collection of data in health services is all the more worrying. When the collection of our data becomes a condition for accessing health services, it is not only a threat to our right to health that should not be conditional on data sharing but also it raises questions as to how this data will be used in the age of automation.

This is why understanding what data is collected and how it is collected in the context of health and social protection programmes is so important.

For more details visit https://cis-india.org/internet-governance/blog/privacy-international-ambika-tandon-october-17-2019-mother-and-child-tracking-system-understanding-data-trail-indian-healthcare

The Last Chance for a Welfare State Doesn’t Rest in the Aadhaar System

sumandro — 2016-04-19T13:18:42Z

Boosting welfare is the message, which is how Aadhaar is being presented in India. The Aadhaar system as a medium, however, is one that enables tracking, surveillance, and data monetisation. This piece by Sumandro Chattapadhyay was published in The Wire on April 19, 2016.

Originally published in and cross-posted from The Wire.

Once upon a time, a king desired that his parrot should be taught all the ancient knowledge of the kingdom. The priests started feeding the pages of the great books to the parrot with much enthusiasm. One day, the king asked the priests if the parrot’s education has completed. The priests poked the belly of the parrot but it made no sound. Only the rustle of undigested pages inside the belly could be heard. The priests declared that the parrot is indeed a learned one now.

The fate of the welfare system in our country is quite similar to this parrot from Tagore’s parable. It has been forcefully fed identification cards and other official documents (often four copies of the same) for years, and always with the same justification of making it more effective and fixing the leaks. These identification regimes are in effect killing off the welfare system. And some may say that that has been the actual plan in any case.

The Aadhaar number has been recently offered as the ‘last chance’ for the ailing welfare system – a last identification regime that it needs to gulp down to survive. This argument wilfully overlooks the acute problems with the Aadhaar project.

Firstly, the ‘last chance’ for a welfare state in India is not provided by implementing a new and improved identification regime (Aadhaar numbers or otherwise), but by enabling citizens to effectively track, monitor, and ensure delivery of welfare, services, and benefits. This ‘opening up’ of the welfare bureaucracy has been most effectively initiated by the Right to Information Act. Instead of a centralised biometrics-linked identity verification platform, which gives the privilege of tracking and monitoring welfare flows only to a few expert groups, an effective welfare state requires the devolution of such privilege and responsibility.

We should harness the tracking capabilities of electronic financial systems to disclose how money belonging to the Consolidated Fund of India travel around state agencies and departmental levels. Instead, the Aadhaar system effectively stacks up a range of entry barriers to accessing welfare – from malfunctioning biometric scanners, to connectivity problems, to the burden of keeping one’s fingerprint digitally legible under all labouring and algorithmic circumstances.

Secondly, authentication of welfare recipients by Aadhaar number neither make the welfare delivery process free of techno-bureaucratic hurdles, nor does it exorcise away corruption. Anumeha Yadav has recently documented the emerging ‘unrest at the ration shop’ across Rajasthan, as authentication processes face technical and connectivity delays, people get ‘locked out’ of public services for not having or having Aadhaar number with incorrect demographic details, and no mechanisms exist to provide rapid and definitive recourse.

RTI activists at the Satark Nagrik Sangathan have highlighted that the Delhi ration shops, using Aadhaar-based authentication, maintain only two columns of data to describe people who have come to the shop – those who received their ration, and those who did not (without any indication of the reason). This leads to erasure-by-design of evidence of the number of welfare-seekers who are excluded from welfare services when the Aadhaar-based authentication process fails (for valid reasons, or otherwise).

Reetika Khera has made it very clear that using Aadhaar Payments Bridge to directly transfer cash to a beneficiary’s account, in the best case scenario, may only take care of one form of corruption: deception (a different person claiming to be the beneficiary). But it does not address the other two common forms of public corruption: collusion (government officials approving undue benefits and creating false beneficiaries) and extortion (forceful rent seeking after the cash has been transferred to the beneficiary’s account). Evidently, going after only deception does not make much sense in an environment where collusion and extortion are commonplace.

Thirdly, the ‘relevant privacy question’ for Aadhaar is not limited to how UIDAI protects the data collected by it, but expands to usage of Aadhaar numbers across the public and private sectors. The privacy problem created by the Aadhaar numbers does begin but surely not end with internal data management procedures and responsibilities of the UIDAI.

On one hand, the Aadhaar Bill 2016 has reduced the personal data sharing restrictions of the NIAI Bill 2010, and has allowed for sharing of all data except core biometrics (fingerprints and iris scan) with all agencies involved in authentication of a person through her/his Aadhaar number. These agencies have been asked to seek consent from the person who is being authenticated, and to inform her/him of the ways in which the provided data (by the person, and by UIDAI) will be used by the agency. In careful wording, the Bill only asks the agencies to inform the person about “alternatives to submission of identity information to the requesting entity” (Section 8.3) but not to provide any such alternatives. This facilitates and legalises a much wider collection of personal demographic data for offering of services by public agencies “or any body corporate or person” (Section 57), which is way beyond the scope of data management practices of UIDAI.

On the other hand, the Aadhaar number is being seeded to all government databases – from lists of HIV patients, of rural citizens being offered 100 days of work, of students getting scholarships meant for specific social groups, of people with a bank account. Now in some sectors, such as banking, inter-agency sharing of data about clients is strictly regulated. But we increasingly have non-financial agencies playing crucial roles in the financial sector – from mobile wallets to peer-to-peer transaction to innovative credit ratings. Seeding of Aadhaar into all government and private databases would allow for easy and direct joining up of these databases by anyone who has access to them, and not at all by security agencies only.

When it becomes publicly acceptable that the money bill route was a ‘remedial’ instrument to put the Rajya Sabha ‘back on track’, one cannot not wonder about what was being remedied by avoiding a public debate about the draft bill before it was presented in Lok Sabha. The answer is simple: welfare is the message, surveillance is the medium.

Acceptance and adoption of all medium requires a message, a content. The users are interested in the message. The message, however, is not the business. Think of Free Basics. Facebook wants people with none or limited access to internet to enjoy parts of the internet at zero data cost. Facebook does not provide the content that the users consume on such internet. The content is created by the users themselves, and also provided by other companies. Facebook own and control the medium, and makes money out of all content, including interactions, passing through it.

The UIDAI has set up a biometric data bank and related infrastructure to offer authentication-as-a-service. As the Bill clarifies, almost all agencies (public or private, national or global) can use this service to verify the identity of Indian residents. Unlike Facebook, the content of these services do not flow through the Aadhaar system. Nonetheless, Aadhaar keeps track of all ‘authentication records’, that is records of whose identity was authenticated by whom, when, and where. This database is gold (data) mine for security agencies in India, and elsewhere. Further, as more agencies use authentication based on Aadhaar numbers, it becomes easier for them to combine and compare databases with other agencies doing the same, by linking each line of transaction across databases using Aadhaar numbers.

Welfare is the message that the Aadhaar system is riding on. The message is only useful for the medium as far as it ensures that the majority of the user population are subscribing to it. Once the users are enrolled, or on-boarded, the medium enables flow of all kinds of messages, and tracking and monetisation (perhaps not so much in the case of UIDAI) of all those flows. It does not matter if the Aadhaar system is being introduced to remedy the broken parliamentary process, or the broken welfare distribution system. What matters is that the UIDAI is establishing the infrastructure for a universal surveillance system in India, and without a formal acknowledgement and legal framework for the same.

For more details visit https://cis-india.org/internet-governance/blog/the-last-chance-for-a-welfare-state-doesnt-rest-in-the-aadhaar-system

Mathematisation of the Urban and not Urbanisation of Mathematics: Smart Cities and the Primitive Accumulation of Data - Accepted Abstract

sumandro — 2015-11-13T05:47:13Z

"Many accounts of smart cities recognise the historical coincidence of cybernetic control and neoliberal capital. Even where it is machines which process the vast amounts of data produced by the city so much so that the ruling and managerial classes disappear from view, it is usually the logic of capital that steers the flows of data, people and things. Yet what other futures of the city may be possible within the smart city, what collective intelligence may it bring forth?" The Fibreculture Journal has accepted an abstract of mine for its upcoming issue on 'Computing the City.'

Speaking to Geert Lovink, Wolfgang Ernst explains that '[t]he coupling of machine and mathematics that enables computers occurs as a mathematization of machine, not as machinization of mathematics' [1]. In this paper, I propose that the idea of smart cities be understood not as 'urbanisation of mathematics' – as often described by industry documents, design fictions, and academic analyses – but as 'mathematisation of the urban.' By the notion of 'urbanisation of mathematics,' I indicate at those reports that conceptualise smart cities as data analytics, or civic mathematics, at an urban scale. I explain how this notion is shared by design visions of actors from the networking industry, such as IBM and Cisco, emerging academic practices in urban science and informatics, and calls for urbanising the technologies of regulation and governance, in the sense of making these technologies directly and bi-directionally interact with the urban citizens [2]. Conversely, the 'mathematisation of the urban' perspective foregrounds a specific transformation at hand in the production of urban space itself, which I argue is what is captured in the idea of smart cities. This transformation is not a new thing, and has been heralded by the coming of coded infrastructures and the transduction of urban space through them [3]. The process of 'mathematisation of the urban' refers to a fundamental reorganisation of the urban itself so as to make aspects of it available to mathematical manipulation, most often undertaken by software systems. This mathematisation takes place through the rebuilding of urban infrastructures so as to facilitate sensing and recording of parts of urban lives and processes as mathematical data, and the embedding of coded assemblages that can communicate and act upon the analysis of such data, and also through re-building the relations of property around this newly-obtained and continuously-generated resource of data about the urban.

I propose in this paper that production, circulation, and ownership of data must be considered as a central problematique in the discussions of smart cities. As writings on smart cities have often focused on the dyadic relationships between code and space on one hand, and co-evolution (and splintering) of networked infrastructures and the urban form, the figure of data has remained implicit yet subdued as as an entry point to study the idea of smart cities. Even for commentators who do focus on the implications of data, the category is often treated as a feature or a capacity of new technological assemblages. Instead, I argue in this paper that it is the concerns of production, circulation, and ownership of data that drive the conceptualisation and actual material forms of the visions of smart cities. These technological assemblages, materialisation of which constitute such visions, are implementations of exclusive data collection operations targeting various portions of urban lives and processes. The imagination of 'city 2.0' takes a particularly insightful colour when thought of as an analogy to the 'web 2.0' model of capture and monetisation of user behaviour data. Further, I employ the Marxian theory of 'primitive accumulation' to describe how the material infrastructures of networked sensors and embedded data capture systems create enclosed spaces for conversion of collectively-held-information into data-as-exchangable-and-interoperable-value, through which disparate and distributed knowledge and experiences of the urban is transformed into urban data, which can be centralised and queried, and hence value can be extracted from it.

Footnotes

[1] Lovink, Geert. 2013. Interview with German Media Archeologist Wolfgang Ernst. Nettime-l. February 26. Accessed on April 20, 2015, from http://www.nettime.org/Lists-Archives/nettime-l-0302/msg00132.html.

[2] Sassen, Saskia. 2012. Urbanising Technology. LSE Cities. December. Accessed on April 20, 2015, from http://lsecities.net/media/objects/articles/urbanising-technology/en-gb/.

[3] Dodge, Martin, and Rob Kitchin. 2005. Code and the Transduction of Space. Annals of the Association of American Geographers. 95: 01. Pp. 162-180.

For more details visit https://cis-india.org/raw/smart-cities-and-the-primitive-accumulation-of-data-abstract

Studying the Emerging Database State in India: Notes for Critical Data Studies (Accepted Abstract)

sumandro — 2015-11-13T05:54:53Z

"Critical Data Studies (CDS) is a growing field of research that focuses on the unique theoretical, ethical, and epistemological challenges posed by 'Big Data.' Rather than treat Big Data as a scientifically empirical, and therefore largely neutral phenomena, CDS advocates the view that data should be seen as always-already constituted within wider data assemblages." The Big Data and Society journal has provisionally accepted a paper abstract of mine for its upcoming special issue on Critical Data Studies.

Introduction

Through the last decade, the Government of India has given shape to an digital identification infrastructure, developed and operated by the Unique Identification Authority of India (UIDAI). The infrastructure combines the task of assigning unique identification numbers, called Aadhaar numbers, to individuals submitting their biometric and demographic details, and the task of authenticating their identity when provided with an Aadhaar number and associated data (biometric data, One Time Pin sent to the pre-declared mobile number, etc.). The aim of UIDAI is to provide universal authentication-as-a-service for all residents of India who approach any public or private agencies for any kind of service or transaction. Simultaneously, the Aadhaar numbers will function as unique identifiers for joining up databases of different government agencies, and hence allow the Indian government to undertake big data analytics at a governmental scale, and not only at a departmental one.

In this paper, I am primarily motivated by the challenge of finding points and objects to enter into a critical study of such an in-progress data infrastructure. As I proceed with an understanding that data is produced within its specific social and material context, the question then is to read through the data to reflect on its possible social and material context. This is complicated when approaching a big data infrastructure that is meant to produce data for explicitly intra-governmental consumption and circulation. The problem then is not one of reading through available big data, but one of reading through the assemblage and imaginaries of big data to reflect on the kind of data it will give rise to, and thus on the politics of the data assemblage and the database state it enables.

Logic of the Database State

Application of data to inform governmental acts have taken place at least since government has been understood as responsible for the welfare of the population and the territory. The measurement of the population and the territory – the number of people, their demographic features, amounts and locations of natural resources, and so on – have always been integral to the functioning of the modern nation-state. Database state is used in this paper to identify a particular mode of mobilisation of data within governmental acts, which is fundamentally shaped by the possibilities of big data extraction, appropriation, and analytics pioneered by a range of companies since late 1990s. The reason for not using big data state but database dtate is that big data refers to a body of technologies emerging in response to a set of data management and analysis challenges situated in a certain moment of development of information technologies, whereas database refers to a symbolic form (Manovich 1999): a form in which not only the population is made visible to the government (as a collection of visual, textual, numeric, and other forms of records), but also how the acts of government are made visible to the population (as a collection of performance indicators, budget allocation and utilisation tables, and other data visualised through dashboards, analog and digital).

The data production and management logic of this database state is specifically inspired by the notion of platform introduced by the so-called Web 2.0 companies: providing a common service layer upon which various other applications may also run, but under specific arrangements (including distribution of generated user data) with the original common layer provider. Data assemblages of the database state are expected to enable the government to function as a platform, as an intensely data-driven layer that widely gathers data about population individuals and feeds it back selectively to various providers of public and private services. This transforms the data assemblage from one vertical of governmental activities to a horizontal critical infrastructure for modularisation of governmental activities.

Studying the Emerging Database State in India

Government of India is presently debating the legal and technical validity of the digital identity infrastructure programme in the Supreme Court, while simultaneously carrying out the enrollment drive for the same, linking up assignment of unique identity numbers with a national drive for population registration, and rolling out citizen-facing services and applications that implement the Aadhaar number as a necessary key to access them. With the enrollment process going on and the integration with various governmental processes (termed seeding by Aadhaar policy literature) just beginning, I enter this study through two key sets of objects reflecting the imaginaries and the technical specifications of the emerging database state in India. The first entry point is through the various official documents of vision, intentions, plans, and reconsiderations, and the second entry point is through the Application Programming Interface (API) documentations published by UIDAI to specify how its identity authentication platform will collaborate with various public and private services.

The first section of the paper provides a brief survey of pre-UIDAI attempts by the Government of India to deploy unique identification numbers and Smart Cards for specific population groups, so as to understand the initial conceptualisation of this data assemblage of a digital identification platform. The second section foregrounds how this platform undertakes a transformation of the components and relations of the pre-existing data assemblage of the Government of India, as articulated in various official documents of promised utility and proposed collaborations. The third section studies the API documentations to track how such imaginaries are materially interpreted and operationalised through the design of protocols of data interactions with various public and private agencies offering services utilising the identity authentication platform.

Notes for Critical Data Studies

Expanding the early agenda note on Critical Data Studies by Craig Dalton and Jim Thatcher (2014), Rob Kitchin and Tracey P. Lauriault have taken steps towards emphasising the responsibility of this nebulous research strategy to chart and unpack the data assemblages (2014). This is exactly what I propose to do in this paper. While Kitchin and Lauriault provide a detailed list of the components of the apparatus of a data assemblage (2014: 7), I find the concepts of infrastructural components and infrastructural relations very useful in thinking through the emerging infrastructure of authentication. Thus, my approach to these tasks of charting and unpacking is focused on the infrastructural relations that the digital identity infrastructure re-configures, instead of the infrastructural components it mobilises (Bowker et al 2010). This tactical choice of focusing on the infrastructural relations is also necessitated by the practical difficulty in having comprehensive access to the individual components of the data assemblage concerned. Addressing questions of causality and quality becomes difficult when studying the assemblage sans the produced data, and rigorously analysing concerns of security and uncertainty pre-requires an actually existing data assemblage, with a public interface to investigating its leakages, breakages, and internal functioning. In the absence of such points of entry into the data assemblage, which I fear may not be an exceptional case, I attempt an inverted reading. Turning the data infrastructure inside out, in this paper I describe how the digital identity platform is critically reshaping the basis of governmental acts in India, through a specific model of production, extraction and application of big data.

Bibliography

Bowker, Geoffrey C., Karen Baker, Florence Millerand, & David Ribes. 2010. Toward Information Infrastructure Studies: Ways of Knowing in a Networked Environment. Jeremy Hunsinger, Lisbeth Klastrup, & Matthew Allen (Eds.) International Handbook of Internet Research. Springer Dordrecht Heidelberg London New York. Pp. 97-117.

Dalton, Craig, & Jim Thatcher. 2014. What does a Critical Data Studies Look Like, and Why do We Care? Seven Points for a Critical Approach to ‘Big Data.’ Society and Space. May 19. Accessed on July 08, 2015, from http://societyandspace.com/material/commentaries/craig-dalton-and-jim-thatcher-what-does-a-critical-data-studies-look-like-and-why-do-we-care-seven-points-for-a-critical-approach-to-big-data/.

Kitchin, Rob, & Tracey P. Lauriault. 2014. Towards Critical Data Studies: Charting and Unpacking Data Assemblages and their Work. The Programmable City Working Paper 2. July 29. National University of Ireland Maynooth, Ireland. Accessed on July 08, 2015 from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2474112.

Manovich, Lev. 1999. Database as Symbolic Form. Convergence. Volume 5, Number 2. Pp. 80-99.

Note: Call for Papers for the special issue can found here: http://bigdatasoc.blogspot.in/2015/06/call-for-proposals-special-theme-on.html.

For more details visit https://cis-india.org/raw/studying-the-emerging-database-state-in-india-accepted-abstract

Data for Governance, Governance of Data, and Data Anxieties

sumandro — 2016-07-03T05:59:48Z

The Center for International Media Assistance (CIMA) organised a panel discussion on 'The Data Explosion – How the Internet of Things will Affect Media Freedom and Communication Systems?' at Deutsche Welle's Global Media Forum 2016, held in Bonn, Germany during June 13-15, 2016. Sumandro Chattapadhyay was invited as one of the panelists.

Introduction to the Panel

The emerging Internet of Things (IoT) will result in a vast network of Internet-connected devices that generate enormous volumes of data about human behavior and interactions. This data explosion will potentially reshape how media organizations both collect and report news, while at the same time fundamentally shifting how communications networks are organized worldwide. Yet currently most of the discussion about the IoT has focused on its spread in developed countries via the popularization of Internet-connected consumer devices.

In this panel we will discuss how the IoT may develop differently in the Global South and how it could present either a threat to open access to data and information, or an opportunity to improve media systems worldwide. We will also examine the impact of the data explosion in developing countries and what mechanisms need to be created in order to ensure the huge new mountain of data is used and governed responsibly.

The discussants were Carlos Affonso Souza (Director, Institute for Technology and Society of Rio de Janeiro, Brazil), Lorena Jaume-Palasi (Director for Communications, European Dialogue on Internet Governance, or EuroDIG, Switzerland), and Sumandro Chattapadhyay (Research Director, the Centre for Internet and Society, India); and the conversation was led by Mark Nelson (Senior Director, Center for International Media Assistance, or CIMA, USA).

Source: Deutsche Welle.

Audio Recording

Things/Writings I have Mentioned

For more details visit https://cis-india.org/raw/data-for-governance-governance-of-data-and-data-anxieties

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

Centre for Internet and Society

The Mother and Child Tracking System - understanding data trail in the Indian healthcare systems

This article was first published by Privacy International, on October 17, 2019

Case study of MCTS: Read

The Last Chance for a Welfare State Doesn’t Rest in the Aadhaar System

Mathematisation of the Urban and not Urbanisation of Mathematics: Smart Cities and the Primitive Accumulation of Data - Accepted Abstract

Footnotes

Studying the Emerging Database State in India: Notes for Critical Data Studies (Accepted Abstract)

Introduction

Logic of the Database State

Studying the Emerging Database State in India

Notes for Critical Data Studies

Bibliography

Data for Governance, Governance of Data, and Data Anxieties

Introduction to the Panel

Audio Recording

Things/Writings I have Mentioned

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

1. What are the Sustainable Development Goals?

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

2. The Need for a Data Revolution

Source: UN, Sustainable Development Goals.

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

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.

5.2. The UN DATA Revolution Group

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

5.4. The Global Partnership for Sustainable Development of Data

5.5. The World Economic Forum (WEF)

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

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

5.7. Data-Pop Alliance

6. Conclusion

7. Endnotes

8. Author Profile

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.

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

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