Centre for Internet and Society

CPRsouth 2016 – Young Scholars Programme

praskrishna — 2016-05-30T02:01:21Z

Rohini Lakshané, Amber Sinha and Vidushi Marda have been selected to attend the two-day Young Scholars' Programme to be held in Zanzibar, Tanzania in early September this year. The programme is a part of the CPRSouth conference.

Read the original announcement published by CPRSouth here.

Following highly successful joint Afro-Asian CPR conferences in Mauritius in 2012, and India in 2013, CPRafrica and CPRsouth formally merged under the banner of CPRsouth in 2014. Since then, CPRsouth has hosted conferences in the Cradle of Humankind in South Africa (2014), and at the Innovation Center for Big Data and Digital Convergence at Yuan Ze University, Taiwan (2015).

This year’s conference is co-hosted by COSTECH and TCRA in Zanzibar, and will include sessions on cutting-edge developments on ICT policy and regulation in the South and discussion of the research-policy interface.

30 Young Scholars from Africa and the Asia-Pacific region will be selected to participate in a tutorial programme taught by recognised scholars and practitioners from Africa and Asia, and they will attend the main conference thereafter.

Tutorials are scheduled to be held on the 6^th and 7^th of September 2016, prior to the main CPRsouth conference.

Who will qualify?

Masters/PhD students in Economics, Public policy, Communications and Journalism
Officers of government/regulatory agencies undertaking ICT policy research, developing/gathering indicators (monitoring and evaluation)
Staff of private companies in the communication industries working in regulatory affairs
Officers in NGOs/INGOs working in policy and regulation
Researchers from think tanks, university research centres
Journalists covering communication public policy and regulation

Seminar

The seminar will cover a number of topics of the two days, such as:

policy analysis using supply-side or demand-side data;
ICT impact analysis;
convergence, net neutrality;
funding broadband network extension, open access networks, spectrum;
sector and competition regulation;
research to policy interventions;
Internet governance – privacy, surveillance, human rights online; and
introduction to big data, open data.

(2016 tutorial programme still to be confirmed)

Previous tutorial presentations can be accessed at http://www.cprsouth.org/

Application deadline: 22 April 2016

Application guidelines

Applications should be submitted via this link by 22 April 2016, and must contain the following:

one-page curriculum vitae; and
one-page write-up outlining why you wish to become an African or Asia-Pacific based expert capable of contributing to ICT related policy and regulatory reform in the region

Applicants’ write-ups and biographies should be in a single word document, and named: CPRsouth2016_YoungScholar_ApplicantLastName.

Kindly note: Late applications and applications that do not conform to the prescribed format above will automatically be disqualified.

Review Criteria

Applications will be reviewed according to the following criteria:

content of application;
evidence of interest in, and commitment to, policy-relevant research for Africa or the Asia-Pacific region;
quality of writing; and
gender and country representation

The selection committee may contact your supervisor or mentor before making the final selections.

Candidates selected to participate in the tutorial programme must:

provide a one-page research proposal upon acceptance onto the tutorial programme
participate in all tutorial sessions
participate in the entire CPRsouth 2016 conference

Funding

Selected young scholars who are passport holders of, and travelling from, low and middle income countries within the Asia Pacific and Africa (as classified by the World Bank http://data.worldbank.org/about/country-classifications/country-and-lending-groups#Low_income) will be provided with:

lowest-cost economy airfare to conference destination (less USD 150 registration fee);
ground transfers between the conference venue and airport; and
twin sharing accommodation on bed and breakfast basis, 5 lunches and 1 dinner for the duration of the conference and tutorials (6 – 10 September 2016). Not all meals are covered.

The registration fee for young scholars to attend the conference and tutorials is USD150, and airfares will be reimbursed less this registration fee. Participants will be required to cover:

transport to and from airports in their home countries;
visa fees (if any);
meals not provided; and
any other incidental costs

As the registration fee is so low and should be met personally even if there is no institutional support for attendance of the course and conference, please note that only under exceptional circumstances of extreme financial hardship may the organisers consider a waiver of the conference registration fee. Such waivers will be considered on a case-by-case basis and only where a scholar would otherwise be prevented from attending the YS programme and conference.

Visas

Letters of invitation will be provided for purposes of visa applications after participant selections have been made. Participants are responsible for securing their own visas to enter Tanzania, and are strongly advised to initiate visa approval procedures immediately on receipt of confirmation of their participation.

Kindly direct all enquiries to Ondine Bello: admin@researchictafrica.net orinfo@CPRsouth.org

For more details visit https://cis-india.org/internet-governance/news/cprsouth-2016-2013-young-scholars-programme

Connected Trouble

sunil — 2015-10-28T16:47:58Z

The internet of things phenomenon is based on a paradigm shift from thinking of the internet merely as a means to connect individuals, corporations and other institutions to an internet where all devices in (insulin pumps and pacemakers), on (wearable technology) and around (domestic appliances and vehicles) humans beings are connected.

The guest column was published in the Week, issue dated November 1, 2015.

Proponents of IoT are clear that the network effects, efficiency gains, and scientific and technological progress unlocked would be unprecedented, much like the internet itself.

Privacy and security are two sides of the same coin―you cannot have one without the other. The age of IoT is going to be less secure thanks to big data. Globally accepted privacy principles articulated in privacy and data protection laws across the world are in conflict with the big data ideology. As a consequence, the age of internet of things is going to be less stable, secure and resilient. Three privacy principles are violated by most IoT products and services.

Data minimisation

According to this privacy principle, the less the personal information about the data subject that is collected and stored by the data controller, the more the data subject's right to privacy is protected. But, big data by definition requires more volume, more variety and more velocity and IoT products usually collect a lot of data, thereby multiplying risk.

Purpose limitation

This privacy principle is a consequence of the data minimisation principle. If only the bare minimum of personal information is collected, then it can only be put to a limited number of uses. But, going beyond that would harm the data subject. IoT innovators and entrepreneurs are trying to rapidly increase features, efficiency gains and convenience. Therefore, they don't know what future purposes their technology will be put to tomorrow and, again by definition, resist the principle of purpose limitation.

Privacy by design

Data protection regulation required that products and services be secure and protect privacy by design and not as a superficial afterthought. IoT products are increasingly being built by startups that are disrupting markets and taking down large technology incumbents. The trouble, however, is that most of these startups do not have sufficient internal security expertise and in their tearing hurry to take products to the market, many IoT products may not be comprehensively tested or audited from a privacy perspective.

There are other cyber security principles and internet design principles that are disregarded by the IoT phenomenon, further compromising security and privacy of users.

Centralisation

Most of the network effects that IoT products contribute to require centralisation of data collected from users and their devices. For instance, if users of a wearable physical activity tracker would like to use gamification to keep each other motivated during exercise, the vendor of that device has to collect and store information about all its users. Since some users always wear them, they become highly granular stores of data that can also be used to inflict privacy harms.

Decentralisation was a key design principle when the internet was first built. The argument was that you can never take down a decentralised network by bombing any of the nodes. Unfortunately, because of the rise of internet monopolies like Google, the age of cloud computing, and the success of social media giants, the internet is increasingly becoming centralised and, therefore, is much more fragile than it used be. IoT is going to make this worse.

Complexity

The more complex a particular technology is, the more fragile and vulnerable it is. This is not necessarily true but is usually the case given that more complex technology needs more quality control, more testing and more fixes. IoT technology raises complexity exponentially because the devices that are being connected are complex themselves and were not originally engineered to be connected to the internet. The networks they constitute are nothing like the internet which till now consisted of clients, web servers, chat servers, file servers and database servers, usually quite removed from the physical world. Compromised IoT devices, on the other hand, could be used to inflict direct harm on life and property.

Death of the air gap

The things that will be connected to the internet were previously separated from the internet through the means of an air gap. This kept them secure but also less useful and usable. In other words, the very act of connecting devices that were previously unconnected will expose them to a range of attacks. Security and privacy related laws, standards, audits and enforcement measures are the best way to address these potential pitfalls. Governments, privacy commissioners and data protections authorities across the world need to act so that the privacy of people and the security of our information society are protected.

For more details visit https://cis-india.org/internet-governance/blog/the-week-november-1-2015-sunil-abraham-connected-trouble

Comments on the Report of the Committee on Digital Payments (December 2016)

Sumandro Chattapadhyay and Amber Sinha — 2017-01-12T12:32:22Z

The Committee on Digital Payments constituted by the Ministry of Finance and chaired by Ratan P. Watal, Principal Advisor, NITI Aayog, submitted its report on the "Medium Term Recommendations to Strengthen Digital Payments Ecosystem" on December 09, 2016. The report was made public on December 27, and comments were sought from the general public. Here are the comments submitted by the Centre for Internet and Society.

1. Preliminary

1.1. This submission presents comments by the Centre for Internet and Society (“CIS”) [1] in response to the report of the Committee on Digital Payments, chaired by Mr. Ratan P. Watal, Principal Advisor, NITI Aayog, and constituted by the Ministry of Finance, Government of India (“the report”) [2].

2. The Centre for Internet and Society

2.1. The Centre for Internet and Society, CIS, is a non-profit organisation that undertakes interdisciplinary research on internet and digital technologies from policy and academic perspectives. The areas of focus include digital accessibility for persons with diverse abilities, access to knowledge, intellectual property rights, openness (including open data, free and open source software, open standards, and open access), internet governance, telecommunication reform, digital privacy, and cyber-security.

2.2. CIS is not an expert organisation in the domain of banking in general and payments in particular. Our expertise is in matters of internet and communication governance, data privacy and security, and technology regulation. We deeply appreciate and are most inspired by the Ministry of Finance’s decision to invite entities from both the sectors of finance and information technology. This submission is consistent with CIS’ commitment to safeguarding general public interest, and the interests and rights of various stakeholders involved, especially the citizens and the users. CIS is thankful to the Ministry of Finance for this opportunity to provide a general response on the report.

3. Comments

3.1. CIS observes that the decision by the Government of India to withdraw the legal tender character of the old high denomination banknotes (that is, Rs. 500 Rs. 1,000 notes), declared on November 08, 2016 [3], have generated unprecedented data about the user base and transaction patterns of digital payments systems in India, when pushed to its extreme use due to the circumstances. The majority of this data is available with the National Payments Corporation of India and the Reserve Bank of India. CIS requests the authorities concerned to consider opening up this data for analysis and discussion by public at large and experts in particular, before any specific policy and regulatory decisions are taken towards advancing digital payments proliferation in India. This is a crucial opportunity for the Ministry of Finance to embrace (open) data-driven regulation and policy-making.

3.2. While the report makes a reference to the European General Data Protection Directive, it does not make a reference to any substantive provisions in the Directive which may be relevant to digital payments. Aside from the recommendation that privacy protections around the purpose limitation principle be relaxed to ensure that payment service providers be allowed to process data to improve fraud monitoring and anti-money laundering services, the report is silent on significant privacy and data protection concerns posed by digital payments services. CIS strongly warns that the existing data protection and security regulations under Information Technology (Reasonable security practices and procedures and sensitive personal data or information), Rules are woefully inadequate in their scope and application to effectively deal with potential privacy concerns posed by digital payments applications and services. Some key privacy issues that must be addressed either under a comprehensive data protection legislation or a sector specific financial regulation are listed below. The process of obtaining consent must be specific, informed and unambiguous and through a clear affirmative action by the data subject based upon a genuine choice provided along with an option to opt out at any stage. The data subjects should have clear and easily enforceable right to access and correct their data. Further, data subjects should have the right to restrict the usage of their data in circumstances such as inaccuracy of data, unlawful purpose and data no longer required in order to fulfill the original purpose.

3.3. The initial recommendation of the report is to “[m]ake regulation of payments independent from the function of central banking” (page 22). This involves a fundamental transformation of the payment and settlement system in India and its regulation. We submit that a decision regarding transformation of such scale and implications is taken after a more comprehensive policy discussion, especially involving a wider range of stakeholders. The report itself notes that “[d]igital payments also have the potential of becoming a gateway to other financial services such as credit facilities for small businesses and low-income households” (page 32). Thus, a clear functional, and hence regulatory, separation between the (digital) payments industry and the lending/borrowing industry may be either effective or desirable. Global experience tells us that digital transactions data, along with other alternative data, are fast becoming the basis of provision of financial and other services, by both banking and non-banking (payments) companies. We appeal to the Ministry of Finance to adopt a comprehensive and concerted approach to regulating, enabling competition, and upholding consumers’ rights in the banking sector at large.

3.4. The report recognises “banking as an activity is separate from payments, which is more of a technology business” (page 154). Contemporary banking and payment businesses are both are primarily technology businesses where information technology particularly is deployed intimately to extract, process, and drive asset management decisions using financial transaction data. Further, with payment businesses (such as, pre-paid instruments) offering return on deposited money via other means (such as, cashbacks), and potentially competing and/or collaborating with established banks to use financial transaction data to drive lending decisions, including but not limited to micro-loans, it appears unproductive to create a separation between banking as an activity and payments as an activity merely in terms of the respective technology intensity of these sectors. CIS firmly recommends that regulation of these financial services and activities be undertaken in a technology-agnostic manner, and similar regulatory regimes be deployed on those entities offering similar services irrespective of their technology intensity or choice.

3.5. The report highlights two major shortcomings of the current regulatory regime for payments. Firstly “the law does not impose any obligation on the regulator to promote competition and innovation in the payments market” (page 153). It appears to us that the regulator’s role should not be to promote market expansion and innovation but to ensure and oversee competition. We believe that the current regulator should focus on regulating the existing market, and the work of the expansion of the digital payments market in particular and the digital financial services market in general be carried out by another government agency, as it creates conflict of interest for the regulator otherwise. Secondly, the report mentions that Payment and Settlement Systems Act does not “focus the regulatory attention on the need for consumer protection in digital payments” and then it notes that a “provision was inserted to protect funds collected from customers” in 2015 (page 153). This indicates that the regulator already has the responsibility to ensure consumer protection in digital payments. The purview and modalities of how this function of course needs discussion and changes with the growth in digital payments.

3.6. The report identifies the high cost of cash as a key reason for the government’s policy push towards digital payments. Further, it mentions that a “sample survey conducted in 2014 across urban and rural neighbourhoods in Delhi and Meerut, shows that despite being keenly aware of the costs associated with transacting in cash, most consumers see three main benefits of cash, viz. freedom of negotiations, faster settlements, and ensuring exact payments” (page 30). It further notes that “[d]igital payments have significant dependencies upon power and telecommunications infrastructure. Therefore, the roll out of robust and user friendly digital payments solutions to unelectrified areas/areas without telecommunications network coverage, remains a challenge.” CIS much appreciates the discussion of the barriers to universal adoption and rollout of digital payments in the report, and appeals to the Ministry of Finance to undertake a more comprehensive study of the key investments required by the Government of India to ensure that digital payments become ubiquitously viable as well as satisfy the demands of a vast range of consumers that India has. The estimates about investment required to create a robust digital payment infrastructure, cited in the report, provide a great basis for undertaking studies such as these.

3.7. CIS is very encouraged to see the report highlighting that “[w]ith the rising number of users of digital payment services, it is absolutely necessary to develop consumer confidence on digital payments. Therefore, it is essential to have legislative safeguards to protect such consumers in-built into the primary law.” We second this recommendation and would like to add further that financial transaction data is governed under a common data protection and privacy regime, without making any differences between data collected by banking and non-banking entities.

3.8. We are, however, very discouraged to see the overtly incorrect use of the word “Open Access” in this report in the context of a payment system disallowing service when the client wants to transact money with a specific entity [4]. This is not an uncommon anti-competitive measure adopted by various platform players and services providers so as to disallow users from using competing products (such as, not allowing competing apps in the app store controlled by one software company). The term “Open Access” is not only the appropriate word to describe the negation of such anti-competitive behaviour, its usage in this context undermines its accepted meaning and creates confusion regarding the recommendation being proposed by the report. The closest analogy to the recommendation of the report would perhaps be with the principle of “network neutrality” that stands for the network provider not discriminating between data packets being processed by them, either in terms of price or speed.

3.9. A major recommendation by the report involves creation of “a fund from savings generated from cash-less transactions … by the Central Government,” which will use “the trinity of JAM (Jan Dhan, Adhaar, Mobile) [to] link financial inclusion with social protection, contributing to improved Social and Financial Security and Inclusion of vulnerable groups/ communities” (page 160-161). This amounts to making Aadhaar a mandatory ID for financial inclusion of citizens, especially the marginal and vulnerable ones, and is in direct contradiction to the government’s statements regarding the optional nature of the Aadhaar ID, as well as the orders by the Supreme Court on this topic.

3.10. The report recommends that “Aadhaar should be made the primary identification for KYC with the option of using other IDs for people who have not yet obtained Aadhaar” (page 163) and further that “Aadhaar eKYC and eSign should be a replacement for paper based, costly, and shared central KYC registries” (page 162). Not only these measures would imply making Aadhaar a mandatory ID for undertaking any legal activity in the country, they assume that the UIDAI has verified and audited the personal documents submitted by Aadhaar number holders during enrollment. A mandate for replacement of the paper-based central KYC agencies will only remove a much needed redundancy in the the identity verification infrastructure of the government.

3.11. The report suggests that “[t]ransactions which are permitted in cash without KYC should also be permitted on prepaid wallets without KYC” (page 164-165). This seems to negate the reality that physical verification of a person remains one of the most authoritative identity verification process for a natural person, apart from DNA testing perhaps. Thus, establishing full equivalency of procedure between a presence-less transaction and one involving a physically present person making the payment will only amount to removal of relatively greater security precautions for the former, and will lead to possibilities of fraud.

3.12. In continuation with the previous point, the report recommends promotion of “Aadhaar based KYC where PAN has not been obtained” and making of “quoting Aadhaar compulsory in income tax return for natural persons” (page 163). Both these measures imply a replacement of the PAN by Aadhaar in the long term, and a sharp reduction in growth of new PAN holders in the short term. We appeal for this recommendation to be reconsidered as integration of all functionally separate national critical information infrastructures (such as PAN and Aadhaar) into a single unified and centralised system (such as Aadhaar) engenders massive national and personal security threats.

3.13. The report suggest the establishment of “a ranking and reward framework” to recognise and encourage for the best performing state/district/agency in the proliferation of digital payments. It appears to us that creation of such a framework will only lead to making of an environment of competition among these entities concerned, which apart from its benefits may also have its costs. For example, the incentivisation of quick rollout of digital payment avenues by state government and various government agencies may lead to implementation without sufficient planning, coordination with stakeholders, and precautions regarding data security and privacy. The provision of central support for digital payments should be carried out in an environment of cooperation and not competition.

3.14. CIS welcomes the recommendation by the report to generate greater awareness about cost of cash, including by ensuring that “large merchants including government agencies should account and disclose the cost of cash collection and cash payments incurred by them periodically” (page 164). It, however, is not clear to whom such periodic disclosures should be made. We would like to add here that the awareness building must simultaneously focus on making public how different entities shoulder these costs. Further, for reasons of comparison and evidence-driven policy making, it is necessary that data for equivalent variables are also made open for digital payments - the total and disaggregate cost, and what proportion of these costs are shouldered by which entities.

3.15. The report acknowledges that “[t]oday, most merchants do not accept digital payments” and it goes on to recommend “that the Government should seize the initiative and require all government agencies and merchants where contracts are awarded by the government to provide at-least one suitable digital payment option to its consumers and vendors” (page 165). This requirement for offering digital payment option will only introduce an additional economic barrier for merchants bidding for government contracts. We appeal to the Ministry of Finance to reconsider this approach of raising the costs of non-digital payments to incentivise proliferation of digital payments, and instead lower the existing economic and other barriers to digital payments that keep the merchants away. The adoption of digital payments must not lead to increasing costs for merchants and end-users, but must decrease the same instead.

3.16. As the report was submitted on December 09, 2016, and was made public only on December 27, 2016, it would have been much appreciated if at least a month-long window was provided to study and comment on the report, instead of fifteen days. This is especially crucial as the recently implemented demonetisation and the subsequent banking and fiscal policy decisions taken by the government have rapidly transformed the state and dynamics of the payments system landscape in India in general, and digital payments in particular.

Endnotes

[1] See: http://cis-india.org/.

[2] See: http://finmin.nic.in/reports/Note-watal-report.pdf and http://finmin.nic.in/reports/watal_report271216.pdf.

[3] See: http://finmin.nic.in/cancellation_high_denomination_notes.pdf.

[4] Open Access refers to “free and unrestricted online availability” of scientific and non-scientific literature. See: http://www.budapestopenaccessinitiative.org/read.

For more details visit https://cis-india.org/internet-governance/blog/comments-on-the-report-of-the-committee-on-digital-payments-dec-2016

CISxScholars Delhi - Harsh Gupta - FAT ML for Lawyers and Lawmakers (June 29, 5:30 pm)

sumandro — 2017-06-27T09:16:48Z

We are proud to announce that Harsh Gupta will discuss "FAT ML (Fairness, Accountability, and Transparency in Machine Learning) for Lawyers and Lawmakers" at the CIS office in Delhi on Thursday, June 29, at 5:30 pm. This will be a two and half hour session: beginning with a 45 minute talk, followed by 15 minute break, another talk for 45 minutes, and then a discussion session. Please RSVP if you are joining us: .

CISxScholars are informal events organised by CIS for presentation, discussion, and exchange of academic research and policy analysis.

FAT ML (Fairness, Accountability, and Transparency in Machine Learning) for Lawyers and Lawmakers

From tagging people in photos to determining risk of loan defaults, use of data based tools is affecting more and areas of our lives. In some areas there have been very successful applications of such tools, in others areas they has been found to not only reflect the existing bias and discrimination found in today's society but also exaggerate it.

Harsh Gupta

Harsh Gupta is a recent graduate from IIT Kharagpur with B.Sc and M.Sc in Mathematics and Computing and will be joining JP Morgan and Chase as a data scientist. He completed his master's thesis in "Discrimination Aware Machine Learning". He was also an intern at The Center for Internet and Society during summer of 2016.

For more details visit https://cis-india.org/raw/cisxscholars-harsh-gupta-machine-learning-for-lawyers-and-lawmakers-20170629

CFI-ACCION - Panel Discussion on 'Big Data: Challenge or Opportunity?' (Delhi, December 06)

sumandro — 2019-03-16T04:41:52Z

The Centre for Financial Inclusion of ACCION International is organising a panel discussion on "Big Data: Challenge or Opportunity?" as an associated event of the Inclusive Finance India Summit 2016, Hotel Ashok, Delhi, December 05-06. The discussion will be held at 12:30 on Tuesday, December 06. It will be moderated by Amy Jensen Mowl, CFI Fellow at IFMR, and M.S. Sriram, Distinguished Fellow at the Institute for Development of Research in Banking Technology. Sumandro Chattapadhyay will participate as a panelist.

Inclusive Finance India Summit: http://inclusivefinanceindia.org/.

For more details visit https://cis-india.org/internet-governance/news/cfi-accion-panel-discussion-on-big-data-delhi-dec-06

Can data ever know who we really are?

sumandro — 2019-12-06T05:02:53Z

This is an excerpt from an essay by Zara Rahman, written for and published as part of the Bodies of Evidence collection of Deep Dives. The Bodies of Evidence collection, edited by Bishakha Datta and Richa Kaul Padte, is a collaboration between Point of View and the Centre for Internet and Society, undertaken as part of the Big Data for Development Network supported by International Development Research Centre, Canada.

Please read the full essay on Deep Dives: Can data ever know who we really are?

Zara Rahman: The Engine Room, Website, and Twitter

If I didn’t define myself for myself, I would be crunched into other people’s fantasies for me and eaten alive.
– Audre Lorde

The proliferation of digital data and the technologies that allow us to gather that data can be used in another way too — to allow us to define for ourselves who we are, and what we are.

Amidst a growing political climate of fear, mistrust and competition for resources, activists and advocates working in areas that are stigmatised within their societies often need data to ‘prove’ that what they are working on matters. One way of doing this is by gathering data through crowdsourcing. Crowdsourced data isn’t ‘representative’, as statisticians say, but gathering data through unofficial means can be a valuable asset for advocates. For example, data collating the experiences of women who have reported incidents of sexual violence to the police in India, can then be used to advocate for better police responses, and to inform women of their rights. Deservedly or not, quantifiable data takes precedence over personal histories and lived experience in getting the much-desired currency of attention.

And used right, quantifiable data — whether it’s crowdsourced or not — can also be a powerful tool for advocates. Now, we can use quantifiable data to prove beyond a question of a doubt that disabled people, queer people, people from lower castes, face intersecting discrimination, prejudice, and systemic injustices in their lives. It’s an unnecessary repetition in a way, because anybody from those communities could have told reams upon reams of stories about discrimination — all without any need for counting.

Regardless, to play within this increasingly digitised system, we need to repeat what we’ve been saying in a new, digitally-legible way. And to do that, we need to collect data from people who have often only ever been de-humanised as data subjects.

Artist and educator Mimi Onuoha writes about the challenges that arise while collecting such data, from acknowledging the humans behind that collection to understanding that missing data points might tell just as much of a story as the data that has been collected. She outlines how digital data means that we have to (intentionally or not) make certain choices about what we value. And the collection of this data means making human choices solid, and often (though not always) making these choices illegible to others.

We speak of black boxes when it comes to the mystery choices that algorithms make, but the same could be said of the many human decisions that are made in categorising data too, whether that be choosing to limit the gender drop-down field to just ‘male/female’ as with Fitbits, or a variety of apps incorrectly assuming that all people who menstruate also want to know about their ‘fertile window’. In large systems with many humans and machines at work, we have no way of interrogating why a category was merged or not, of understanding why certain anomalies were ignored rather than incorporated, or of questioning why certain assumptions were made.

The only thing we can do is to acknowledge these limitations, and try to use those very systems to our advantage, building our own alternatives or workarounds, collecting our own data, and using the data that is out there to tell the stories that matter to us.

For more details visit https://cis-india.org/raw/zara-rahman-can-data-ever-know-who-we-really-are

Call for Proposal: Big Data for Development – Initial Field Studies

sumandro — 2016-04-28T07:28:23Z

The Centre for Internet and Society, as part of a project with the University of Manchester and University of Sheffield, is inviting calls from researchers to undertake a brief initial study of a specific instance of use of big data for development in India. This is an exercise to build preliminary understanding of the landscape of big data for development in India, identify key research questions and priorities, and start developing connections with researchers interested in the field. The studies will be 6 weeks long - running from May to June 2016 - and the researchers are expected to produce a 3,000 words long report. We will support three field studies.

Study Process and Deliverable

The researcher is expected to propose and undertake a 6 weeks long study – starting from May 09 and ending on June 17 – of an instance of big data is being used to inform, target, operationalise, monitor, or support developmental and/or humanitarian activity in India.

During this period, the researcher is expected to interview 4-5 persons directly involved in the big data for development project concerned, and 2-3 other persons to get a wider sense of the context of the project.

By the end of the 6 weeks period, the researcher is expected to submit a 3,000 words long report. The report will be commented upon by Prof. Richard Heeks (University of Manchester), Dr. Christopher Foster (University of Sheffield), and Sumandro Chattapadhyay (CIS), and revised accordingly during the last weeks of June.

The individual reports will be published independently and as part of the larger project report, under Creative Commons Attribution 4.0 International license. The authors will be attributed appropriately.

All researchers will take part in a work-in-progress meeting (held over internet) during last week of May or first week of June.

Research Questions

The interviews will focus on the following topics:

Innovation: What is the nature of the innovation being done by the use of big data? What technical systems and/or applications are being deployed and replaced/superceded? Who are key actors in this innovation process?
Implementation: What is the grounded experience of implementing the big data technology? What are the key enablers and constraints being faced, both in the data collection stage, and the analysis and decision making stage?
Value: What is the value being created, and how is it understood? Is it organisational value, or socio-economic value? Who is gaining this value?
Ethics: What ethical concerns are emerging? Do they involve concerns about data quality, representation, privacy, or security? Is there concerns about a data divide being created among people who are represented in data and who are not, or among people who can gain value from the data and who cannot?

Application, Eligibility, and Remuneration

Please submit the following documents to apply:

Proposal: A one page note on the big data for development project that you would like to study. Please share a brief description of the project and how you will study it, including the name/designation of key people you will speak to.
Writing Sample: An article or a collection of articles, of not more than 8,000 words length in total.
CV: A short CV, two pages or less.

Please e-mail the documents to raw[at]cis-india[dot]org by Wednesday, May 04, 2016.

There is no eligibility criteria for submitting proposals. However, we will prioritise researchers living and studying big data for development projects in non X-class cities, that is in cities other than Ahmedabad, Bangalore, Chennai, Delhi, Hyderabad, Kolkata, Mumbai, and Pune.

We will select three researchers, and will offer Rs. 35,000 to each of them for this study. The amount will be paid in a single installment, after the draft field study report is submitted for comments.

For more details visit https://cis-india.org/jobs/call-for-proposal-big-data-for-development-field-studies

Big Data in the Global South International Workshop

praskrishna — 2015-11-06T02:04:49Z

Institute for Technology and Society of Rio de Janeiro welcomes you to an international workshop on Big Data at Hotel Windsor Florida, Rua Ferreira Viana, Flamengo, Rio de Janeiro, Brazil on November 16 and 17, 2015. Open Society Foundations and British Embassy Brasilia are sponsors for the event. The Centre for Internet & Society (CIS) is a research partner. Sunil Abraham, Pranesh Prakash and Vipul Kharbanda will be speaking at this event.

The event will bring together key representatives from government, civil society, the business sector and academia from Brazil, India, United Kingdom and several other countries. This is a closed multistakeholder round-table to discuss and map international examples of Big Data uses and regulation, both by private and public sectors, in order to develop practical strategies to promote adoption of harmonized rules by different actors. The event will also map existing initiatives involving the use of Big Data and present the results of a joint research initiative conducted by ITS and CIS in this field.

Resources

For more details visit https://cis-india.org/internet-governance/events/big-data-in-the-global-south-international-workshop

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 in Governance in India: Case Studies

2017-02-26T16:24:11Z

This research seeks to understand the most effective way of researching Big Data in the Global South. Towards this goal, the research planned for the development of a Global South big data Research Network that identifies the potential opportunities and harms of big data in the Global South and possible policy solutions and interventions.

This work has been made possible by a grant from the John D. and Catherine T. MacArthur Foundation. The conclusions, opinions, or points of view expressed in the report are those of the authors and do not necessarily represent the views of the John D. and Catherine T. MacArthur Foundation.

Introduction

The research was for a duration of 12 months and in form of an exploratory study which sought to understand the potential opportunity and harm of big data as well as to identify best practices and relevant policy recommendations. Each case study has been chosen based on the use of big data in the area and the opportunity that is present for policy recommendation and reform. Each case study will seek to answer a similar set of questions to allow for analysis across case studies.

What is Big Data

Big data has been ascribed a number of definitions and characteristics. Any study of big data must begin with first conceptualizing defining what big data is. Over the past few years, this term has been become a buzzword, used to refer to any number of characteristics of a dataset ranging from size to rate of accumulation to the technology in use.[1]

Many commentators have critiqued the term big data as a misnomer and misleading in its emphasis on size. We have done a survey of various definitions and understandings of big data and we document the significant ones below.

Computational Challenges

The condition of data sets being large and taxing the capacities of main memory, local disk, and remote disk have been seen as problems that big data solves. While this understanding of big data focusses only on one of its features—size, other characteristics posing a computational challenge to existing technologies have also been examined. The (US) National Institute of Science and Technology has defined big data as data which “exceed(s) the capacity or capability of current or conventional methods and systems.” [2]

These challenges are not merely a function of its size. Thomas Davenport provides a cohesive definition of big data in this context. According to him, big data is “data that is too big to fit on a single server, too unstructured to fit into a row-and-column database, or too continuously flowing to fit into a static data warehouse.” [3]

Data Characteristics

The most popular definition of big data was put forth in a report by Meta (now Gartner) in 2001, which looks at it in terms of the three 3V’s—volume[4], velocity and variety. It is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.[5]

Aside from volume, velocity and variety, other defining characteristics of big data articulated by different commentators are— exhaustiveness,[6] granularity (fine grained and uniquely indexical),[7] scalability,[8] veracity,[9] value[10] and variability.[11] It is highly unlikely that any data-sets satisfy all of the above characteristics. Therefore, it is important to determine what permutation and combination of these gamut of attributes lead us to classifying something as big data.

Qualitative Attributes

Prof. Rob Kitchin has argued that big data is qualitatively different from traditional, small data. Small data has used sampling techniques for collection of data and has been limited in scope, temporality and size, and are “inflexible in their administration and generation.”[12]

In this respect there are two qualitative attributes of big data which distinguish them from traditional data. First, the ability of big data technologies to accommodate unstructured and diverse datasets which hitherto were of no use to data processors is a defining feature. This allows the inclusion of many new forms of data from new and data heavy sources such as social media and digital footprints. The second attribute is the relationality of big data.[13]

This relies on the presence of common fields across datasets which allow for conjoining of different databases. This attribute is usually a feature of not the size but the complexity of data enabling high degree of permutations and interactions within and across data sets.

Patterns and Inferences

Instead of focussing on the ontological attributes or computational challenges of big data, Kenneth Cukier and Viktor Mayer Schöenberger define big data in terms of what it can achieve.[14]

They defined big data as the ability to harness information in novel ways to produce useful insights or goods and services of significant value. Building on this definition, Rohan Samarajiva has categorised big data into non-behavioral big data and behavioral big data. The latter leads to insights about human behavior.[15]

Samarajiva believes that transaction-generated data (commercial as well as non-commercial) in a networked infrastructure is what constitutes behavioral big data. Scope of Research The initial scope arrived at for this case-study on role of big data in governance in India focussed on the UID Project, the Digital India Programme and the Smart Cities Mission. Digital India is a programme launched by the Government of India to ensure that Government services are made available to citizens electronically by improving online infrastructure and by increasing Internet connectivity or by making the country digitally empowered in the field of technology.[16]

The Programme has nine components, two of which focus on e-governance schemes. Read More [PDF, 1948 Kb]

[1]. Thomas Davenport, Big Data at Work: Dispelling the Myths, Uncovering the opportunities, Harvard Business Review Press, Boston, 2014.

[2]. MIT Technology Review, The Big Data Conundrum: How to Define It?, available at https://www. technologyreview.com/s/519851/the-big-data-conundrum-how-to-define-it/

[3]. Supra note 1.

[4]. What constitutes as high volume remains an unresolved matter. Intel defined Big Data volumes are emerging in organizations generating a median of 300 terabytes of data a week.

[5]. http://www.gartner.com/it-glossary/big-data/

[6]. Viktor Mayer Schöenberger and Kenneth Cukier, Big Data: A Revolution that will transform how we live, work and think” John Murray, London, 2013.

[7]. Rob Kitchin, The Data Revolution: Big Data, Open Data, Data Infrastructures and their consequences, Sage, London, 2014.

[8]. Nathan Marz and James Warren, Big Data: Principles and best practices of scalable realtime data systems, Manning Publication, New York, 2015.

[9]. Bernard Marr, Big Data: the 5 Vs everyone should know, available at https://www.linkedin. com/pulse/20140306073407-64875646-big-data-the-5-vs-everyone-must-know.

[10]. Id.

[11]. Eileen McNulty, Understanding Big Data: the 7 Vs, available at http://dataconomy.com/sevenvs-big-data/.

[12]. Supra Note 7.

[13]. Danah Boyd and Kate Crawford, Critical questions for big data. Information, Communication and Society 15(5): 662–679, available at https://www.researchgate.net/publication/281748849_Critical_questions_for_big_data_Provocations_for_a_cultural_technological_and_scholarly_ phenomenon

[14]. Supra Note 6.

[15]. Rohan Samarajiva, What is Big Data, available at http://lirneasia.net/2015/11/what-is-bigdata/.

[16]. http://www.digitalindia.gov.in/content/about-programme

For more details visit https://cis-india.org/internet-governance/blog/big-data-in-governance-in-india-case-studies

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 for governance

Admin — 2017-11-08T01:42:18Z

Recent times have witnessed an explosion of data as users started leaving a huge data footprint everywhere they go. Interestingly, this period has seen a phenomenal increase in computing power couple by a drop in costs of storage.

The article by Alekhya Hanumanthu was published in Telangana Today on November 4, 2017.

India is now sitting on the data so generated and subjecting it to data analytics for uses in various sectors like insurance, education, healthcare, governance, so on and so forth.

According to Centre for Internet and Society (CIS), in 2015, the Government of Narendra Modi launched Digital India Programme to ensure availability of government services to citizens electronically by improving online infrastructure and Internet connectivity.

Amongst other things, e-Governance and e-Kranti intend to reform governance through technology and enable electronic delivery of services. Needless to say, it will involve large scale digitisation, electronic collection of data from residents and processing. The Big data so created will help policy making evolve into a data backed, action oriented initiative with accountability asserted where it is due.

Let’s take a look at some Big Data based initiatives underway according to analyticsindiamag:

Project insight: Undertaken up by Indian tax agencies, Project Insight is an advanced analytical tool that is a comprehensive platform that encourages compliance of tax while at the same time it prevents non-compliance. Significantly, it will be used to detect fraud, support investigations and provide insights for policy making. For instance, it will detect the social media activity of a person to glean their spending and check if it is commensurate with the tax they have paid during that year. Needless to say, this will also unearth sources of black money.

Economic Development Board in Andhra: CORE-CM Office Realtime Executive Dashboard is an integrated dashboard established to monitor category-wise key performance indicators of various departments/schemes in real time. Users can check key performance indicators of various departments, schemes, initiatives, programmes, etc. With a panoply of services information ranging from Women and Child Welfare to Street lights monitoring, it has become an exemplary role model of governance.

Geo-tagging of assets under Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA): Under the guidance of Narendra Modi, online monitoring of assets to check leakages Ministry of Rural development was started. To achieve this, they were tied up with ISRO and National Informatics Centre to geo tag MGNREGA assets. According to India Today, the assets created range from plantations, rural infrastructure, water harvesting structures, flood control measures such as check dams etc. To do this, a junior engineer takes a photo of an asset and uploads it on the Bhuvan web portal run by ISRO’s National Remote Sensing Centre via a mobile app. Once a photo is uploaded, time and location gets encrypted automatically. Thus, the Government hopes to hold an ironclad control of the resources thus disseminated.

CAG’s centre for Data Management and Analytics: According to Comptroller and Auditor General of India, The CAG’s Centre for Data Management and Analytics (CDMA) is going to play a catalytic role to synthesise and integrate relevant data into auditing process. According to an announcement on National Informatics Centre (NIC), it aims to build up capacity in the Indian Audit and Accounts Department in Big Data Analytics to explore the data rich environment at the Union and State levels. What’s more, this initiative of CAG of India, puts it amongst the pioneers in institutionalising data analytics in government audit in the international community.

Task Force to spruce up Employment Data: The data provided by Labour Bureau is limited and not timely enough for policymakers to assess the need for job creation. To address this gap, the Government has set up a committee tasked to fill the employment data gap and ensure the timely availability of reliable information regarding job creation. Thus the top line of Government has a direct view of where the employment gaps are so that it can facilitate creation of appropriate jobs.

What’s the big picture?

Policy making and governance by Indian government have traditionally been rife with red tape, bureaucracy and corruption. Lack of accountability on part of Government workforce not only impacted the quantity and quality of work delivered but also invited corrupt practices and leakages. So, Big data is a welcome change in direction.

For more details visit https://cis-india.org/internet-governance/news/telangana-today-november-8-2017-alekhya-hanumanthu-big-data-for-governance

Big Data and the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules 2011

elonnai — 2015-08-11T07:01:12Z

Experts and regulators across jurisdictions are examining the impact of Big Data practices on traditional data protection standards and principles. This will be a useful and pertinent exercise for India to undertake as the government and the private and public sectors begin to incorporate and rely on the use of Big Data in decision making processes and organizational operations.This blog provides an initial evaluation of how Big Data could impact India's current data protection standards.

Experts and regulators across the globe are examining the impact of Big Data practices on traditional data protection standards and principles. This will be a useful and pertinent exercise for India to undertake as the government and the private and public sectors begin to incorporate and rely on the use of Big Data in decision making processes and organizational operations.

Below is an initial evaluation of how Big Data could impact India's current data protection standards.

India currently does not have comprehensive privacy legislation - but the Reasonable Security Practices and Procedures and Sensitive Personal Data or Information Rules 2011 formed under section 43A of the Information Technology Act 2000[1] define a data protection framework for the processing of digital data by Body Corporate. Big Data practices will impact a number of the provisions found in the Rules:

Scope of Rules: Currently the Rules apply to Body Corporate and digital data. As per the IT Act, Body Corporate is defined as "Any company and includes a firm, sole proprietorship or other association of individuals engaged in commercial or professional activities."

The present scope of the Rules excludes from its purview a number of actors that do or could have access to Big Data or use Big Data practices. The Rules would not apply to government bodies or individuals collecting and using Big Data. Yet, with technologies such as IoT and the rise of Smart Cities across India – a range of government, public, and private organizations and actors could have access to Big Data.

Definition of personal and sensitive personal data: Rule 2(i) defines personal information as "information that relates to a natural person which either directly or indirectly, in combination with other information available or likely to be available with a body corporate, is capable of identifying such person."

Rule 3 defines sensitive personal information as:

Password,
Financial information,
Physical/physiological/mental health condition,
Sexual orientation,
Medical records and history,
Biometric information

The present definition of personal data hinges on the factor of identification (data that is capable of identifying a person). Yet this definition does not encompass information that is associated to an already identified individual - such as habits, location, or activity.

The definition of personal data also addresses only the identification of 'such person' and does not address data that is related to a particular person but that also reveals identifying information about another person - either directly - or when combined with other data points.

By listing specific categories of sensitive personal information, the Rules do not account for additional types of sensitive personal information that might be generated or correlated through the use of Big Data analytics.

Importantly, the definitions of sensitive personal information or personal information do not address how personal or sensitive personal information - when anonymized or aggregated – should be treated.

Consent: Rule 5(1) requires that Body Corporate must, prior to collection, obtain consent in writing through letter or fax or email from the provider of sensitive personal data regarding the use of that data.

In a context where services are delivered with little or no human interaction, data is collected through sensors, data is collected on a real time and regular basis, and data is used and re-used for multiple and differing purposes - it is not practical, and often not possible, for consent to be obtained through writing, letter, fax, or email for each instance of data collection and for each use.

Notice of Collection: Rule 5(3) requires Body Corporate to provide the individual with a notice during collection of information that details the fact that information is being collected, the purpose for which the information is being collected, the intended recipients of the information, the name and address of the agency that is collecting the information and the agency that will retain the information. Furthermore body corporate should not retain information for longer than is required to meet lawful purposes.

Though this provision acts as an important element of transparency, in the context of Big Data, communicating the purpose for which data is collected, the intended recipients of the information, the name and address of the agency that is collecting the information and the agency that will retain the information could prove to be difficult to communicate as they are likely to encompass numerous agencies and change depending upon the analysis being done.

Access and correction: Rule 5(6) provides individuals with the ability to access sensitive personal information held by the body corporate and correct any inaccurate information.

This provision would be difficult to implement effectively in the context of Big Data as vast amounts of data are being generated and collected on an ongoing and real time basis and often without the knowledge of the individual.

Purpose Limitation: Rule 5(5) requires that body corporate should use information only of the purpose which it has been collected.

In the context of Big Data this provision would overlook the re-use of data that is inherent in such practices.

Security: Rule 8 states that any Body Corporate or person on its behalf will be understood to have complied with reasonable security practices and procedures if they have implemented such practices and have in place codes that address managerial, technical, operational and physical security control measures. These codes could follow the IS/ISO/IEC 27001 standard or another government approved and audited standard.

This provision importantly requires that data controllers collecting and processing data have in place strong security practices. In the context of Big Data – the security of devices that might be generating or collecting data and algorithms processing and analysing data is critical. Once generated, it might be challenging to ensure the data is being transferred to or being analysed by organisations that comply with such security practices as listed.

Data Breach : Rule 8 requires that if a data breach occurs, Body Corporate would have to be able to demonstrate that they have implemented their documented information security codes.

Though this provision holds a company accountable for the implementation of security practices, it does not address how a company should be held accountable for a large scale data breach as in the context of Big Data the scope and impact of a data breach is on a much larger scale.

Opt in and out and ability to withdraw consent : Rule 5(7) requires Body Corporate or any person on its behalf, prior to the collection of information - including sensitive personal information - must give the individual the option of not providing information and must give the individual the option of withdrawing consent. Such withdrawal must be sent in writing to the body corporate.

The feasibility of such a provision in the context of Big Data is unclear, especially in light of the fact that Big Data practices draw upon large amounts of data, generated often in real time, and from a variety of sources.

Disclosure of Information: Rule 6 maintains that disclosure of sensitive personal data can only take place with permission from the provider of such information or as agreed to through a lawful contract.

This provision addresses disclosure and does not take into account the “sharing” of information that is enabled through networked devices, as well as the increasing practice of companies to share anonymized or aggregated data.

Privacy Policy : Rule 4 requires that body corporate have in place a privacy policy on their website that provides clear and accessible statements of its practices and policies, type of personal or sensitive personal information that is being collected, purpose of the collection, usage of the information, disclosure of the information, and the reasonable security practices and procedures that have been put in place to secure the information.

In the context of Big Data where data from a variety of sources is being collected, used, and re-used it is important for policies to 'follow data' and appear in a contextualized manner. The current requirement of having Body Corporate post a single overarching privacy policy on its website could prove to be inadequate.

Remedy : Section 43A of the Act holds that if a body corporate is negligent in implementing and maintain reasonable security practices and procedures which results in wrongful loss or wrongful gain to any person, the body corporate can be held liable to pay compensation to the affected person.

This provision will provide limited remedy for an affected individual in the context of Big Data. Though important to help prevent data breaches resulting from negligent data practices, implementation of reasonable security practices and procedures cannot be the only hinging point for determining liability of a Body Corporate for violations and many of the harms possible through Big Data are not in the form of wrongful loss or wrongful gain to another person. Indeed many harms possible through Big Data are non-economic in nature – including physical invasion of privacy, and discriminatory practices that can arise from decisions based on Big Data analytics. Nor does the provision address the potential for future damage that can result from a 'Big Data data breach'.

The safeguards noted in the above section are not the only legal provisions that speak to privacy in India. There are over fifty sectoral legislation that have provisions addressing privacy - for example provisions addressing confidentiality of health and banking information. The government of India is also in the process of drafting a privacy legislation. In 2012 the Report of the Group of Experts on Privacy provided recommendations for a privacy framework in India. The Report envisioned a framework of co-regulation - with sector level self regulatory organization developing privacy codes (that are not lower than the defined national privacy principles) and that are enforced by a privacy commissioner.[2] Perhaps this method would be optimal for the regulation of Big Data- allowing for the needed flexibility and specificity in standards and device development. Though the Report notes that individuals can seek remedy from the court and the Privacy Commissioner can issue fines for a violation, the development of privacy legislation in India has yet to clearly integrate the importance of due process and remedy. With the onset of Big Data - this will become more important than ever.

Conclusion

The use and generation of Big Data in India is growing. Plans such as free wifi zones in cities[3], city wide CCTV networks with facial recognition capabilities[4], and the implementation of an identity/authentication platform for public and private services[5], are indicators towards a move of data generation that is networked and centralized, and where the line between public and private is blurred through the vast amount of data that is collected.

In such developments and innovations what is privacy and what role does privacy play? Is it the archaic inhibitor - limiting the sharing and use of data for new and innovative purposes? Will it be defined purely by legislative norms or through device/platform design as well? Is it a notion that makes consumers think twice about using a product or service or is it a practice that enables consumer and citizen uptake and trust and allows for the growth and adoption of these services?

How privacy will be regulated and how it will be perceived is still evolving across jurisdictions, technologies, and cultures - but it is clear that privacy is not being and cannot be overlooked. Governments across the world are reforming and considering current and future privacy regulation targeted towards life in a quantified society. As the Indian government begins to roll out initiatives that create a "Digital India" indeed a "quantified India", taking privacy into consideration could facilitate the uptake, expansion, and success of these practices and services. As the Indian government pursues the opportunities possible through Big Data it will be useful to review existing privacy protections and deliberate on if, and in what form, future protections for privacy and other rights will be needed.

[1]Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information Rules 2011). Available at: http://deity.gov.in/sites/upload_files/dit/files/GSR313E_10511(1).pdf

[2]Group of Experts on Privacy. (2012). Report of the Group of Experts on Privacy. New Delhi: Planning Commission, Government of India. Retrieved May 20, 2015, from http://planningcommission.nic.in/reports/genrep/rep_privacy.pdf

[3] NDTV. “Free Public Wi-Fi Facility in Delhi to Have Daily Data Limit. NDTV, May 25^th 2015, Available at: http://gadgets.ndtv.com/internet/news/free-public-wi-fi-facility-in-delhi-to-have-daily-data-limit-695857. Accessed: July 2^nd 2015.

[4]FindBiometrics Global Identity Management. “Surat Police Get NEC Facial Recognition CCTV System”. July 21^st 2015. Available at: http://findbiometrics.com/surat-police-nec-facial-recognition-27214/

[5]UIDAI Official Website. Available at: https://uidai.gov.in/

For more details visit https://cis-india.org/internet-governance/blog/big-data-and-information-technology-rules-2011

Big Data and Reproductive Health in India: A Case Study of the Mother and Child Tracking System

ambika — 2019-12-06T04:57:55Z

In this case study undertaken as part of the Big Data for Development (BD4D) network, Ambika Tandon evaluates the Mother and Child Tracking System (MCTS) as data-driven initiative in reproductive health at the national level in India. The study also assesses the potential of MCTS to contribute towards the big data landscape on reproductive health in the country, as the Indian state’s imagination of health informatics moves towards big data.

Case study: Download (PDF)

Introduction

The reproductive health information ecosystem in India comprises of a range of different databases across state and national levels. These collect data through a combination of manual and digital tools. Two national-level databases have been launched by the Ministry of Health and Family Welfare - the Health Management Information System (HMIS) in 2008, and the MCTS in 2009. 4 The MCTS focuses on collecting data on maternal and child health. It was instituted due to reported gaps in the HMIS, which records monthly data across health programmes including reproductive health. There are several other state-level initiatives on reproductive health data that have either been subsumed into, or run in parallel with, the MCTS.

With this case study, we aim to evaluate the MCTS as data-driven initiative in reproductive health at the national level. It will also assess its potential to contribute towards the big data landscape on reproductive health in the country, as the Indian state’s imagination of health informatics moves towards big data. The methodology for the case study involved a desk-based review of existing literature on the use of health information systems globally, as well as analysis of government reports, journal articles, media coverage, policy documents, and other material on the MCTS.

The first section of this report details the theoretical framing of the case study, drawing on the feminist critique of reproductive data systems. The second section maps the current landscape of reproductive health data produced by the state in India, with a focus on data flows, and barriers to data collection and analysis at the local and national level. The case of abortion data is used to further the argument of flawed data collection systems at the national level. Section three briefly discusses the state’s imagination of reproductive health policy and the role of data systems through a discussion on the National Health Policy, 2017 and the National Health Stack, 2018. Finally, we make some policy recommendations and identify directions for future research, taking into account the ongoing shift towards big data globally to democratise reproductive healthcare.

For more details visit https://cis-india.org/raw/big-data-reproductive-health-india-mcts

Centre for Internet and Society

CPRsouth 2016 – Young Scholars Programme

Connected Trouble

Data minimisation

Purpose limitation

Privacy by design

Centralisation

Complexity

Death of the air gap

Comments on the Report of the Committee on Digital Payments (December 2016)

1. Preliminary

2. The Centre for Internet and Society

3. Comments

Endnotes

CISxScholars Delhi - Harsh Gupta - FAT ML for Lawyers and Lawmakers (June 29, 5:30 pm)

FAT ML (Fairness, Accountability, and Transparency in Machine Learning) for Lawyers and Lawmakers

Harsh Gupta

CFI-ACCION - Panel Discussion on 'Big Data: Challenge or Opportunity?' (Delhi, December 06)

Inclusive Finance India Summit: http://inclusivefinanceindia.org/.

Can data ever know who we really are?

Please read the full essay on Deep Dives: Can data ever know who we really are?

Zara Rahman: The Engine Room, Website, and Twitter

Call for Proposal: Big Data for Development – Initial Field Studies

Study Process and Deliverable

Research Questions

Application, Eligibility, and Remuneration

Big Data in the Global South International Workshop

Resources

Big Data in the Global South - An Analysis

I. Introduction

II. Uses of Big Data in the Global Development

III. Big Data and Smart Cities in the Global South

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

V. Conclusion

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

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

What is India Stack?

Problems with India Stack

A Digital Post-Dated Cheque: It’s Ramifications

The Role of FinTech Apps

Regulation of FinTech Apps and the UID Infrastructure

Problems with UID

Big Data in Governance in India: Case Studies

Introduction

What is Big Data

Computational Challenges

Data Characteristics

Qualitative Attributes

Patterns and Inferences

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

Big Data for governance

Let’s take a look at some Big Data based initiatives underway according to analyticsindiamag:

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.