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Monitoring Sustainable Development Goals in India: Availability and Openness of Data (Part I)
http://editors.cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-01
<b>The Sustainable Development Goals (SDGs) are an internationally agreed upon set of developmental targets to be achieved by 2030. There are 17 SDGs with 169 targets, and each target is mapped to one or more indicators as a measure of evaluation. In this and the next blog post, Kiran AB is documenting the availability and openness of data sets in India that are relevant for monitoring the targets under the SDGs. This post offers the findings for the first 7 Goals, while the next post will cover the last 10.</b>
<p> </p>
<p><em>The second part of the post can be accessed <a href="http://cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-02/">here</a>.</em></p>
<hr />
<h3>Monitoring Sustainable Development Goals</h3>
<p>The Sustainable Development Goals (SDGs) are an internationally agreed upon set of developmental targets to be achieved by 2030. These are universal goals and targets which involve the entire world, developed and developing countries alike. They aim at integrating and balancing the three dimensions of the sustainable development – economic development, social inclusion, and environmental sustainability. There are <a href="http://sustainabledevelopment.un.org/">17 SDGs with 169 targets</a>, and each target is mapped to one or more indicators as a measure of evaluation, covering a broad range of sustainable development issues <strong>[1]</strong>.</p>
<p>To initiate the visioning process for the SDGs, the United Nations established a High Level Panel in the year 2012, comprising of 27 members. The notion of "data revolution for sustainable development" has been one of the most remarkable categories of imagination and operational requirement to emerge from the final report of this High Level Panel. It identified a significant need for massive restructuring of infrastructures for generating global,
reliable, comparable, and timely data. The Independent Expert Advisory Group (IEAG) on "data revolution for sustainable development" has also raised the need for opening up development data. It proposes that open data must be considered as an instrument of ensuring transparency and accountability of the government <strong>[2]</strong>. Further, in a recent post from the World Economic Forum meeting, Stephen Walker and Jose Alonso have noted that "Not only will governments that embrace open data improve their public accountability and efficiency, they will also reap the social and economic benefits of opening up data for citizens" <strong>[3]</strong>. Opening up of government data is expected to transform the relationship between the government and the various stakeholders.</p>
<p>Currently the data is used by the governmental institutions for self-monitoring and making only a limited data available for public access and usage. But SDGs are not only for the government to monitor and realise, the
responsibility lies with various other actors as well.</p>
<p>Open data has a major role to play in transforming the vision of the SDGs into reality, by enabling the informed participation of multiple actors – private companies, non-government organisations, academic and research institutes, civic activists, etc. To plan, monitor, and actualise the path being traversed by a country, open data becomes essential. Also to facilitate public participation in the governance.</p>
<p>In this and the next blog post, I am documenting the availability and openness of data sets in India, which are relevant for the indicators identified for monitoring of targets under the 17 SDGs. This post offers the findings for the first 7 Goals, while the next post will cover the last 10. Along with questions of availability and openness, I have also documented the technical format of the available data, the level of granularity, and also the frequency of its collection, when applicable. The chart below describe the overall situation of availability and openness of data for monitoring SDGs in India.</p>
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<iframe src="https://cis-india.github.io/charts/2016.02.21_monitoring-SDGs-India_01/index.html" frameborder="0" height="580" width="600"></iframe>
<p> </p>
<h3>Goal #01: <em>End poverty in all its forms everywhere</em></h3>
<p>The data is available for most of the indicators either directly or need to be derived, however, data doesn't exist for one of the indicators.</p>
<p>The data exists at the national level and at the state level or both, but data availability at the district/city level would give a better picture. Though NSSO sample survey data includes representative data at the state/UT level, such data is often not made freely accessible. Not all data which have been collected, i.e., from agencies like NSSO, National Family Health Survey, etc., are open in the public domain.</p>
<p>Also, the frequency of data collected for most of the indicators are either decennial or quinquennial, rather an annual survey would facilitate better/close monitoring. Health is an important measure associated with poverty, but the data is decennially collected. There is a need for regular data updation, while considering those data which are supposed to be collected annually.</p>
<p>In this context, to derive certain indicators, say Indicator 1.3.1., there is a cross agency dependency on data, and lacks disaggregation of data. The disaggregation is a key to measure inequality, especially incidences like poverty. So to monitor poverty we need to identify the different strata of poverty and policy can be formulated accordingly.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 1.3.1. Percentage of population covered by social protection floors /systems disaggregated by sex, and distinguishing children, unemployed, old age, people with disabilities, pregnant women/new-borns, work injury victims, poor and vulnerable</li></ul>
<p> </p>
<h3>Goal #02: <em>End hunger, achieve food security and improved nutrition and promote sustainable agriculture</em></h3>
<p>Indicators and the data corresponding to them reflects two things, what has been done and what has to be done. The data for fifteen indicators mapped to the targets in goal 2 are available for thirteen of the indicators. The data which are available are likely to match the indicator directly or the data has to be derived for most of the indicators. And for the remaining two indicators the data is not available.</p>
<p>For most of the indicators that have to be derived, there is a strong dependency on the dataset from NSSO sample survey for arriving at the requirement. This dependency comes at a cost, as NSSO sample data are not freely available in the public domain, thus making the overall monitoring dependent on closed data. There is a cross agency reliance on data, for arriving at the indicator, and the data on public platform are not up to date.</p>
<p>Also, the data for majority of the indicators are measured at the national as well as state level, but a goal like ending hunger – providing food security, would definitely require data in the order of district/village level. Though data is available for the Indicator 2.2.1: Prevalence of stunting (height for age <-2 SD from the median of the WHO Child Growth Standards) among children under five years of age, but, the data is from eight states only and the national data is derived from it, too small sample size to extrapolate as the nation's data.</p>
<p>On the frequency of data collection, Indicator 2.c.1: Indicator of (food) Price Anomalies (IPA), are collected monthly and some of the data are quinquennial or decennial. However, most of them are annually collected, enabling better accountability and close monitoring of the goals and to frame actionable policy steps.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 2.5.1: Ex Situ Crop Collections Enrichment index</li>
<li>b. Indicator 2.5.2: Percentage of local crops and breeds and their wild relatives, classified as being at risk, not-at-risk or unknown level of risk of extinction</li></ul>
<p> </p>
<h3>Goal #03: <em>Ensure healthy lives and promote well-being for all at all ages</em></h3>
<p>Data is available for all the twenty-five indicators corresponding to the thirteen targets set to measure goal 3 on health and well-being. Some of the data are direct to the indicator, while some have to be derived from various data set to arrive at the indicator.</p>
<p>Data is open and accessible freely in the public domain for all the indicators, most of the data are from World Health Organisation (WHO) database. However, for finer tunings and up to date data there is dependency on National Family Health Survey (NFHS) which is collected decennially.</p>
<p>The WHO data lacks updation and ones which are available are pertaining to an year, thus making the analysis of the annual trend difficult. While the frequency of data collected for most of the data are annual.</p>
<p>The dataset available are at the national and state level, and two of the data set is measured in the order of cities. Most of the WHO dataset provides data at the national level, whereas NFHS, District Family Health Surveys and other agencies provide data at the lowest order, but such dataset are not freely accessible on the public domain. The updated data on health are not made available freely accessible in the public domain which are derived through health surveys.</p>
<p> </p>
<h3>Goal #04: <em>Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</em></h3>
<p>Education in India is a fundamental right of every citizen, therefore achieving inclusive, equitable and quality education for all becomes necessary. Said this, to monitor goal 4, data is available for nine indicators out of eleven indicators, and for the remaining two indicators, the data is not accessible or in public domain for free access, and for the sub-part of the indicator on proficiency level. Though data exists for all the indicators, however, for most of the indicators we need to derive from multiple sources. Data does not exist for subparts like psychosocial wellbeing, in the Indicator 4.2.1 and proficiency in functional literacy and numeracy skills as in the Indicator 4.6.1.</p>
<p>The data are collected annually for seven indicators and for the two indicators Indicator 4.3.1 and Indicator 4.6.1, which relies on NFHS and Census data respectively, the data is collected decennially. Also, for some of the indicators the data availability is restricted to particular years or are not up to date.</p>
<p>The data which exists are collected at the national and state level for some of them and for some data set the data exists at the national level only, whereas for the Indicator 4.6.1, the data set is of the order of city. And the disaggregation issue prevails here as well, so to sort data based on the given parameter one has to consult NSSO sample survey or derive from the existing data.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 4.7.1: Percentage of 15-year old students enrolled in secondary school demonstrating at least a fixed level of knowledge across a selection of topics in environmental science and geo science. The exact choice/range of topics will depend on the survey or assessment in which the indicator is collected. Disaggregation: sex and location</li>
<li>Indicator 4.a.1: Percentage of schools with access to (i) electricity; (ii) Internet for pedagogical purposes; (iii) computers for pedagogical purposes; (iv) adapted infrastructure and materials for students with disabilities; (v) single-sex basic sanitation facilities; (vi) basic hand washing facilities</li></ul>
<p> </p>
<h3>Goal #05: <em>Achieve gender equality and empower all women and girls</em></h3>
<p>Gender as a social construct has been deprived of equality and equity, therefore, achieving equality and empowering women and girls lays down the path for an inclusive development. In this direction, to monitor the goal 5, data is available for eleven indicators and do not exist for three indicators out of fourteen indicators. However, the Indicator 5.3.2, is not relevant as India does not acknowledge FGM/C. Also, for most of the indicators, the data need to be derived from the given dataset.</p>
<p>For most of the data, the data is collected at the National or state level. Whereas for the Indicator 5.a.1, the data is available at the district/tehasil level and it is based on Agricultural census of India, carried out once in five years.</p>
<p>The collection of data is annual in most cases, decennial in the cases of NFHS data, quinquennial with regard to data on land ownership and rights based on gender. Also, in cases of proportion of women in parliament or number of legal framework – domestic/international, the frequency cannot be determined as its subject to change.</p>
<p>Regarding openness, though data exists, the data is not available to access freely. These data are either from NSSO sample survey and NFHS. For most of the indicators the data exists in general without disaggregation, but, as the goal demands sex based disaggregation, we need to derive from the existing data.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 5.3.2: Percentage of girls and women aged 15-49 who have undergone female genital mutilation/cutting (FGM/C), by age group (for relevant countries only)</li>
<li>Indicator 5.6.2. Number of countries with laws and regulations that guarantee women aged 15-49 access to sexual and reproductive health care, information and education</li>
<li>Indicator 5.c.1: Percentage of countries with systems to track and make public allocations for gender equality and women’s empowerment</li></ul>
<p> </p>
<h3>Goal #06: <em>Ensure availability and sustainable management of water and sanitation for all</em></h3>
<p>Water is a life giving source, but ensuring water and sanitation in a sustainable way is a challenge indeed. Data is available for all the ten indicators to monitor the goal 6. While for most of the indicators the data has to be derived from the given data set or from other data set. The data set available are in absolute numbers, need to modify as per the indicators.</p>
<p>The data is collected annually for most of the indicators, however, for the indicators, Indicator 6.3.2: Percentage of water bodies with good ambient water quality; Indicator 6.4.1: Percentage change in water use efficiency over time, the data pertains to the specific year, without a time series.</p>
<p>Three of the data are measured at the state level, one at the district level – Indicator 6.2.1, and another at the level of cities – Indicator 6.3.1. For most of the indicators, the data are from international agencies like WHO, UNEP, FAO, etc.</p>
<p>The data for four of the indicators are not freely accessible on the public domain, though data exists. Also, for the Indicator 6.a.1, the available data is not specific to it, but gives an overview. Overall, for the close monitoring of the goal 6, the granularity of the data should be at the district/block level, and must be freely accessible.</p>
<p> </p>
<h3>Goal #07: <em>Ensure access to affordable, reliable, sustainable and modern energy for all</em></h3>
<p>Energy is considered one of the basic needs of human life, therefore, providing energy which is reliable and affordable has to ensure sustainability and the kind of energy being produced. The data exists for five of the indicators out of six indicators, however, the data does not exist for one of the indicators. The data for two of the indicators – Indicator 7.2.1, Indicator 7.3.1, have to be derived from the given data set.</p>
<p>For most of the data, the data is collected annually and the data is collected at the national level. However, as to the data availability for the Indicator 7.2.1, the data is available at the state level.</p>
<p>To arrive at the required indicator, there is a dependency over other dataset. Though most of the data are available, for three of the indicators – Indicator 7.2.1: Renewable energy share in the total final energy consumption (%); Indicator 7.3.1. Energy intensity (%) measured in terms of primary energy and GDP; Indicator 7.a.1: Mobilized amount of USD per year starting in 2020 accountable towards the US 100 billion commitment, the data is not freely accessible.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 7.b.1. Ratio of value added to net domestic energy use, by industry</li></ul>
<p> </p>
<h3>References</h3>
<p><strong>[1]</strong> "Indicators and a Monitoring Framework for the Sustainable Development Goals." Sustainable Development Solutions Network. March 20, 2015. Accessed February 16, 2016. <a href="http://unsdsn.org/wp-content/uploads/2015/03/150320-SDSN-Indicator-Report.pdf">http://unsdsn.org/wp-content/uploads/2015/03/150320-SDSN-Indicator-Report.pdf</a>.</p>
<p><strong>[2]</strong> "A World That Counts - Mobilising the Data Revolution for Sustainable Development." Report. Independent Expert Advisory Group Secretariat, 2014. Accessed February 19, 2016.
<a href="http://www.undatarevolution.org/wp-content/uploads/2014/11/A-World-That-Counts.pdf">http://www.undatarevolution.org/wp-content/uploads/2014/11/A-World-That-Counts.pdf</a>.</p>
<p><strong>[3]</strong> Walker, Stephen, and Jose M. Alonso. "Data Will Only Get Us so Far. We Need It to Be Open." World Economic Forum. January 29, 2016. Accessed February 16, 2016. <a href="http://www.weforum.org/agenda/2016/01/data-will-only-get-us-so-far-we-need-it-to-be-open">http://www.weforum.org/agenda/2016/01/data-will-only-get-us-so-far-we-need-it-to-be-open</a>.</p>
<p> </p>
<h3>Author</h3>
<p>Kiran A B, is a student of Master of Public Policy (MPP) at the National Law School of India University, Bengaluru. Kiran has an undergraduate degree in electronics and communications engineering, and he has three years full-time work experience as a software engineer, working in different technological platforms. His research interest includes interdisciplinary linkages between policy, law and technology.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-01'>http://editors.cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-01</a>
</p>
No publisherKiran ABOpen DataOpen Government DataData RevolutionOpennessSustainable Development Goals2017-01-02T14:12:58ZBlog EntryMonitoring Sustainable Development Goals in India: Availability and Openness of Data (Part II)
http://editors.cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-02
<b>The Sustainable Development Goals (SDGs) are an internationally agreed upon set of developmental targets to be achieved by 2030. There are 17 SDGs with 169 targets, and each target is mapped to one or more indicators as a measure of evaluation. In this and the next blog post, Kiran AB is documenting the availability and openness of data sets in India that are relevant for monitoring the targets under the SDGs. This post offers the findings for the last 10 Goals. The first 7 has already been discussed in the earlier post.</b>
<p> </p>
<p><em>The first part of the post can be accessed <a href="http://cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-01/">here</a>.</em></p>
<hr />
<h3>Goal #08: <em>Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</em></h3>
<p>There are fourteen indicators to monitor the goal 8 and the data is available for all the indicators mapped to their respective targets. For most of the indicators, the data availability is not what the indicator demands, but has to be derived from the available dataset.</p>
<p>The data can be accessed freely in the public domain for all the indicators. However, for the subparts in some of the indicators, the data is not accessible freely. There is a cross agency dependency over the data, to arrive at the required indicator.</p>
<p>Data is collected annually for most of the indicators, while the indicators, viz., Indicator 8.3.1.: Share of informal employment in non-agriculture employment by sex; Indicator 8.5.2: Unemployment rate by sex, age-group and persons with disabilities, which are measured by the Census or the planning commission the frequency of data collection becomes decennial or quinquennial. And the Indicator 8.8.2 : Number of ILO conventions ratified by type of convention, which lists the number of conventions the frequency cannot be determined as it's just a list updated whenever there is a ratification of any ILO conventions. Some of the available data are restricted to particular years and most of them are not till date.</p>
<p>Two indicators, i.e., Indicator 8.5.2 and Indicator 8.10.1: Number of commercial bank branches and ATMs per 100,000 adults, which are measured at the level of districts, whereas Indicator 8.7.1: Percentage and number of children aged 5-17 years engaged in child labour, per sex and age group; Indicator 8.8.1: Frequency rates of fatal and non-fatal occupational injuries by sex and migrant status, are measured at the state level. The remaining are measured only at the national level.</p>
<p>Most of the data are collected from the international organisations like ILO, UNEP, UNWTO, etc., from whose source the data are not updated regularly. There is also a need to disaggregate according to the indicator.</p>
<p> </p>
<h3>Goal #09: <em>Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</em></h3>
<p>When development is through industrialization, sustainable and inclusiveness should be the necessary conditions to attain it. Having said this, the data is available for all the indicators, i.e., twelve indicators, corresponding to the targets as defined for the goal 9. For most of the indicators, the data have to be derived for the required measure to monitor the goal.</p>
<p>From among these indicators, the data is collected annually for most of the indicators, while for the two indicators, Indicator 9.3.1: Percentage share of small scale industries in total industry value added; Indicator 9.3.2: Percentage of small scale industries with a loan or line of credit, the frequency of data collection is once in five years.</p>
<p> </p>
<p>Excluding two indicators, i.e., Indicator 9.2.2: Manufacturing employment as a percentage of total employment; Indicator 9.1.1: Share of the rural population who live within 2km of an all season road, for which the data is available at the state level and district level respectively, for the remaining indicators the data is available only at the national level.</p>
<p>The data pertaining to eleven indicators are freely accessible in the public domain, however, for the Indicator 9.b.1: Percentage share of medium and high-tech (MHT) industry value added in total value added, the data is not freely accessible. Most of the freely available data are obtained from the international organisations, along with the official data from the government in India.</p>
<p> </p>
<h3>Goal #10: <em>Reduce inequality within and among countries</em></h3>
<p>Bridging the gap between the global north-south divide through co-operation – social, economical, political, etc., would promote equality. There are twelve indicators for measuring this goal, of which the data is not available for one of the indicators and are available for the remaining indicators.</p>
<p>From the data available, for six of the indicators the data is accessible freely in the public domain, whereas for the five of the indicators – Indicator 10.2.1; Indicator 10.3.1; Indicator 10.4.1; Indicator 10.7.3; Indicator 10.a.1, the data is closed.</p>
<p>Most of the data available are of the national level and for the Indicator 10.7.3: Number of detected and non-detected victims of human trafficking per 100,000, the data includes from the states as well. However, since the goal refers to inequalities within the country as well, the granularity of the data should have been from the state/district level as well.</p>
<p>And, the frequency of data collected are annually for some of the indicators and for some the details cannot be determined or not valid. For most of the indicators the data has to be derived from the available dataset and disaggregated as needed. Also, for some indicators the data is partially available.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 10.7.1: Recruitment cost borne by employee as percentage of yearly income earned in country of destination</li></ul>
<p> </p>
<h3>Goal #11: <em>Make cities and human settlements inclusive, safe, resilient and sustainable</em></h3>
<p>Housing and the type of settlements determines the human development and the progress of development of a nation. Therefore for monitoring the goal 11 is implicit to human development. There are thirteen indicators to monitor this goal and out of which the data is available for ten indicators and for the three indicators the data is not available.</p>
<p>For three of the indicators the available data is not freely accessible, while for the remaining ones the data is accessible. And for most of the indicators the data has to be derived as needed.</p>
<p>The data is collected annually for most of the indicators and quinquennially for the Indicator 11.5.1, and for some data the data pertains to particular year and there lacks a sequence of data availability.</p>
<p>For four of the indicators – Indicator 11.2.1; Indicator 11.3.1; Indicator 11.6.1; Indicator 11.a.1, the data is available at the state/city level along with national level. And for the remaining indicators the data is available at the national level alone. Also, some of the data are not up-to-date and refers to data more than 3 or years old.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 11.3.2: Percentage of cities with direct participation structure of civil society in urban planning and management, which operate regularly and democratically</li>
<li>Indicator 11.7.1: The average share of the built-up areas of cities that is open space in public use for all, disaggregated by age, sex, and persons with disabilities</li>
<li>Indicator 11.b.1: Percentage of cities implementing risk reduction and resilience strategies aligned with accepted international frameworks (such as the successor to the Hyogo Framework for Action on Disaster Risk Reduction) that include vulnerable and marginalised groups in their design, implementation and monitoring</li></ul>
<p> </p>
<h3>Goal #12: <em>Ensure sustainable consumption and production patterns</em></h3>
<p>Production and consumption should go hand in hand, but over consumption or over production would only lead to destruction of the environment. Therefore goal 12 seeks to ensure a sustainability in both. The data is available for ten indicators out of twelve indicators, and for the two indicators the data is not available, so as to monitor the respective goals. Some of the data are partially available and using the available data the indicators can be derived.</p>
<p>Moreover, the data for six of the indicators which are available are freely accessible in the public domain whereas for the remaining four indicators – Indicator 12.4.1; Indicator 12.4.2; Indicator 12.5.1; Indicator 12.b.1, the data is not open.</p>
<p>While for most of the indicators say, Indicator 12.2.1; Indicator 12.3.1; Indicator 12.5.1; Indicator 12.a.1; Indicator 12.c.1, the data is collected annually, whereas for the others, the data which are available are for particular years or cannot be determined. Except for the Indicator 12.5.1, for which the data is available at the city level, the data for the remaining are of the national order. The data is collected from both the national institutions, ministries and also from the international organisations.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 12.1.1: Number of countries with SCP National Actions Plans or SCP mainstreamed as a priority or target into national policies.</li>
<li>Indicator 12.8.1: Percentage of educational institutions with formal and informal education curricula on sustainable development and lifestyle topics</li></ul>
<p> </p>
<h3>Goal #13: <em>Take urgent action to combat climate change and its impacts</em></h3>
<p>The impact of climate change is severe, therefore taking an urgent action ensures could reduce the impact. The data is available for four of the indicators out of five, and for one of indicators the data is not available.</p>
<p>The data for three indicators are freely accessible in the public domain, whereas for the Indicator 13.3.1: Number of countries that have integrated mitigation, adaptation, impact reduction and early warning into primary, secondary and tertiary curricula, the data is not open and also not specific to the indicator. The data for some of the indicators are partially available and have to be derived.</p>
<p>The frequency of the data is not uniform and cannot be determined, by the virtue of the indicator itself. For example, the occurrence of a disaster event is random. However, for some of the indicators the reporting is either annual or quadrennial.</p>
<p>The data availability is at the national level and in case of the Indicator 13.3.1., the data is available for two states – Orissa and Tamil Nadu. Data for almost all the indicators are obtained from international organizations and very less data availability from the national databases.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 13.2.1.: Number of countries that have formally communicated the establishment of integrated low-carbon, climate-resilient, disaster risk reduction development strategies</li></ul>
<p> </p>
<h3>Goal #14: <em>Conserve and sustainably use the oceans, seas and marine resources for sustainable development</em></h3>
<p> </p>
<p>Oceans are the torchbearers for all the countries. Therefore everything related to oceans, seas and marine resources have an impact on the human life. There are ten indicators corresponding to the targets, of which the data is available for nine indicators and for one indicator the data is not available. The data for some of the indicators are not direct, but need to be derived, while for some indicators the data is partially available. To derive some indicators we need to rely on cross agency data.</p>
<p>For the Indicator 14.a.1: Budget allocation to research in the field of marine technology as a percentage of total budget to research, the data on budgetary allocation doesn't specify to marine technology.</p>
<p>The frequency of data collected for most of the indicators are not available or cannot be determined or not applicable, whereas for some the data is collected annually. And for most of the indicators the data is available at the national level and for the Indicator 14.5.1: Coverage of protected areas in relation to marine areas, the data is available for the states also.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 14.6.1: Dollar value of negative fishery subsidies against 2015 baseline</li></ul>
<p> </p>
<h3>Goal #15: <em>Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</em></h3>
<p> </p>
<p>This goal on restoring, promoting ecosystem and stopping biodiversity loss, etc., has fifteen indicators mapped to twelve corresponding targets. Of which, the data is available for fourteen of the indicators and the data is not available for the one of the indicators. Data for some of the indicators exist partially and for some the data has to be derived to match the indicators. To arrive at the indicators, the data has to be derived from different datasets available.</p>
<p>Most of the data which are available are closed and only five are accessible in the public platform – Indicator 15.1.1 : Forest area as a percentage of total land area; Indicator 15.4.2: Mountain Green Cover Index; Indicator 15.8.1: Adoption of national legislation relevant to the prevention or control of invasive alien species; Indicator 15.9.1: Number of national development plans and processes integrating biodiversity and ecosystem services values; Indicator 15.a.1: Official development assistance and public expenditure on conservation and sustainable use of biodiversity and ecosystems.</p>
<p>The frequency of data collected is not available or cannot be determined for majority of the indicators, while the data is annually collected for the ones which can be determined. Furthermore, the data is available at the national level for all the indicators, except the Indicator 15.b.1: Forestry official development assistance and forestry FDI, for which the data is available at the level of states as well.</p>
<p>The data available are collected by international organisations like OECD, FAO, Convention on Biological Diversity, etc., as well as by the national institutions and ministries like Planning Commission, Ministry of Environment, Forest and Climate Change, etc.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 15.2.2: Net permanent forest loss</li></ul>
<p> </p>
<h3>Goal #16: <em>Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</em></h3>
<p> </p>
<p>A society which is inclusive, peaceful, provides justice and accountable in all its forms would ensure sustainable development, therefore to promote the aforementioned parameters one has to monitor them through an established measure. There are twenty-one indicators for this goal mapped to the respective targets and out of which the data is not available for five indicators to monitor the goal. From the available dataset, the values need to be derived for some of the indicators and for some indicators the data is directly/partially available.</p>
<p>From among the data which are available, for nine indicators the data is not freely accessible in the public platform, while the remaining six data set are open to access. They are available both from national and international agencies and most of the data are not up to the date.</p>
<p>The data which are available are collected/reported annually. And, excluding four indicators. i.e.; Indicator 16.1.3, Indicator 16.3.1, Indicator 16.4.2, Indicator 16.b.1, the data is available at the state level, while for the remaining indicators the data is available only at the national level. Most of the indicators require data from past 12 months, but the available dataset does not cater the needs, as they are not updated regularly. Finally, the indicators seeks disaggregated data for monitoring the goal.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 16.1.4: Proportion of people that feel safe walking alone around the area they live</li>
<li>Indicator 16.2.3. Percentage of young women and men aged 18-24 years who experienced sexual violence by age 18</li>
<li>Indicator 16.6.2: Percentage of population satisfied with their last experience of public services</li>
<li>Indicator 16.7.2: Proportion of countries that address young people's multisectoral needs with their national development plans and poverty reduction strategies</li>
<li>Indicator 16.a.1: Percentage of victims who report physical and/or sexual crime to law enforcement agencies during past 12 months disaggregated by age, sex, region and population group</li></ul>
<p> </p>
<h3>Goal #17: <em>Strengthen the means of implementation and revitalize the global partnership for sustainable development</em></h3>
<p> </p>
<p>Moving towards achieving SDGs in the global scenario requires support – financial, technological, etc. This support can be strengthened the relationship between the developing and the developed countries. There are twenty-four indicators to monitor the goal 17, out of which the data is available for twenty-three of the indicators and for one of the indicators the data does not exist.</p>
<p>The data which are available are direct as per the indicators, whereas for most of the indicators the data need to be derived. Data is partially available for the Indicator 17.16.1: Indicator 7 from Global Partnership Monitoring Exercise: Mutual accountability among development co-operation actors is strengthened through inclusive reviews.</p>
<p>From the data available for twenty-three indicators, fourteen of the data set are freely accessible and the nine are not open. Also, some of the data which are open are not up to date or the latest data is not open.</p>
<p>The data is collected annually for most of the indicators and for some the data is available for particular year. Also for some of the indicators like Indicator 17.5.1: Number of national & investment policy reforms adopted that incorporate sustainable development objectives or safeguards x country; Indicator 17.6.1: Access to patent information and use of the international intellectual property (IP) system; Indicator 17.18.2: Number of countries that have national statistical legislation that complies with the Fundamental Principles of Official statistics, the frequency cannot be determined or not valid.</p>
<p>Since this indicator speaks at the national level, the granularity of the data pertains to the nation. Most of the data are obtained from the international organisations say UN, World Bank, IMF, OECD, etc., and some are from the national institutions/ministries like Planning Commission, Finance Ministry, etc.</p>
<p><strong>Data Not Available:</strong></p>
<ul><li>Indicator 17.17.1: Amount of US$ committed to public-private partnerships and civil society partnerships</li></ul>
<p> </p>
<h3>Conclusion</h3>
<p>Decision making depends on data, a data should be representative, with high quality and has to be timely collected, which ensures precise assessment of the decision being made. From the analysis it was found that, most of the data which are available are either not freely accessible, outdated and not precise to the need. Most of the SDG indicators are based on disaggregation. The disaggregation is a key to measure to the precision, especially incidences like poverty, food security, health, etc. Therefore, to monitor different parameters we need to identify the different levels prevailing in the parameter to ensure inclusivity.</p>
<p>Said above, the frequency of data collection is either annual, quinquennial and decennial. To enable real time evaluation, the data should be up-to-date. Moreover, for most of the indicators the data availability is at the national level or at the state level and sometimes at the district level. The granularity of data ensures geographic inclusiveness.</p>
<p>In a country like India for close monitoring of progress/development of any sort the data availability should be;</p>
<ul><li>at a granular level of district/block,</li>
<li>collected and updated regularly,</li>
<li>disaggregated by age, sex, and also by social group, and</li>
<li>the data should be open to be able to access in the public domain freely.</li></ul>
<p>Open data will be a crucial tool for governments to meet the transparency and efficiency challenges. For this reason, government data should be open – freely accessible, presented in a format that is comparable and reusable and, ideally, released in a timely manner.</p>
<p> </p>
<h3>Author</h3>
<p>Kiran A B, is a student of Master of Public Policy (MPP) at the National Law School of India University, Bengaluru. Kiran has an undergraduate degree in electronics and communications engineering, and he has three years full-time work experience as a software engineer, working in different technological platforms. His research interest includes interdisciplinary linkages between policy, law and technology.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-02'>http://editors.cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-02</a>
</p>
No publishersumandroDevelopmentOpen DataOpen Government DataData RevolutionOpennessSustainable Development Goals2016-04-12T04:14:27ZBlog EntryBig Data Governance Frameworks for 'Data Revolution for Sustainable Development'
http://editors.cis-india.org/internet-governance/blog/big-data-governance-frameworks-for-data-revolution-for-sustainable-development
<b>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.</b>
<p> </p>
<p><strong>1.</strong> <a href="#1">What are the Sustainable Development Goals?</a></p>
<p><strong>2.</strong> <a href="#2">The Need for a Data Revolution</a></p>
<p><strong>3.</strong> <a href="#3">Big Data: Characteristics and Use for Development</a></p>
<p><strong>3.1.</strong> <a href="#3-1">Characteristics of Big Data</a></p>
<p><strong>3.2.</strong> <a href="#3-2">Using Big Data for Development</a></p>
<p><strong>4.</strong> <a href="#4">Sustainable Development and Data Rights</a></p>
<p><strong>5.</strong> <a href="#5">Governance Frameworks Proposed</a></p>
<p><strong>5.1.</strong> <a href="#5-1">UN Sustainable Development Solutions Network</a></p>
<p><strong>5.2.</strong> <a href="#5-2">The UN DATA Revolution Group</a></p>
<p><strong>5.3.</strong> <a href="#5-3">Organization for Economic Co-Operation and Development</a></p>
<p><strong>5.4.</strong> <a href="#5-4">The Global Partnership for Sustainable Development of Data</a></p>
<p><strong>5.5.</strong> <a href="#5-5">The World Economic Forum (WEF)</a></p>
<p><strong>5.6.</strong> <a href="#5-6">Dr. Julia Lane - A Quadruple Data Helix</a></p>
<p><strong>5.7.</strong> <a href="#5-7">Data Pop Alliance</a></p>
<p><strong>6.</strong> <a href="#6">Conclusion</a></p>
<p><strong>7.</strong> <a href="#7">Endnotes</a></p>
<p><strong>8.</strong> <a href="#8">Author Profile</a></p>
<hr />
<p>Speaking on Big Data, Dan Ariely commented that, "<em>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</em>" <strong>[1]</strong>. 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.</p>
<p>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.</p>
<p> </p>
<h2 id="1">1. What are the Sustainable Development Goals?</h2>
<p>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 <strong>[2]</strong>.</p>
<p> </p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_un-sdg.png" alt="Sustainable Development Goals" />
<h6>Source: UN Data Revolution Group, <em><a href="http://www.undatarevolution.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf">A World that Counts</a></em>, 2014, p.12.<br /></h6>
<p> </p>
<h2 id="2">2. The Need for a Data Revolution</h2>
<p>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 <strong>[3]</strong>. 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.</p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_undrg_mdg-data.png" alt="UN Stats - Percentage of MDG data currently available for developing countries by nature of source." />
<h6>Source: UN, <a href="http://i0.wp.com/www.un.org/sustainabledevelopment/wp-content/uploads/2015/12/english_SDG_17goals_poster_all_languages_with_UN_emblem_1.png">Sustainable Development Goals</a>.<br /></h6>
<p>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" <strong>[4]</strong>.</p>
<p>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, "<em>There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days</em> <strong>[5]</strong>.</p>
<p>In its report <em>A World that Counts</em>, 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 <strong>[6]</strong>.</p>
<p>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:</p>
<blockquote>[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 <strong>[7]</strong>.</blockquote>
<p>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.</p>
<p> </p>
<h2 id="3">3. Big Data: Characteristics and Use for Development</h2>
<h3 id="3-1">3.1. Characteristics of Big Data</h3>
<p>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 <strong>[8]</strong>.</p>
<p>The characteristics which broadly distinguish Big Data are sometimes called the “3 V’s”: more volume, more variety and higher rates of velocity <strong>[9]</strong>. Big data sources generally share some or all of these features <strong>[10]</strong>:</p>
<ul><li>Digitally generated,</li>
<li>Passively produced,</li>
<li>Automatically collected,</li>
<li>Geographically or temporally trackable, and</li>
<li>Continuously analysed.</li></ul>
<p>Increasingly, Big Data is recognised as creating "new possibilities for international development" <strong>[11]</strong>. It could provide faster, cheaper, more granular data and help meet growing and changing demands. It was claimed, for example, that "<em>Google knows or is in a position to know more about France than INSEE</em>" <strong>[12]</strong>, 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 <strong>[13]</strong>. They have the options of:</p>
<ul><li>Getting part of their food at a nearby World Food Programme distribution centre,</li>
<li>Reducing mobile usage,</li>
<li>Temporarily taking their children out of school,</li>
<li>Calling a health hotline when children show signs of malnutrition related diseases, and</li>
<li>Venting about their frustration on social media.</li></ul>
<p>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:</p>
<ul><li>WFP might record that it serves twice as many meals a day,</li>
<li>The local mobile operator may see reduced usage,</li>
<li>UNICEF data may indicate that school attendance has dropped,</li>
<li>Health hotlines might see increased volumes of calls reporting malnutrition, and</li>
<li>Tweets mentioning the difficulty to “afford food” might begin to rise.</li></ul>
<p>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.</p>
<p>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.</p>
<h3 id="3-2">3.2. Using Big Data for Development</h3>
<p>There are several international organizations attempting to use such data.</p>
<p>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 <strong>[14]</strong>.</p>
<p>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 <strong>[15]</strong>.</p>
<p>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 <strong>[16]</strong>.</p>
<p>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 <strong>[17]</strong>.</p>
<p>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 <strong>[18]</strong>.</p>
<p>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 <strong>[19]</strong>. The overall consensus is that Big Data to track SDGs must complement traditional data sources <strong>[20]</strong>. 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.</p>
<p>In a survey by the Global Working Group on Big Data for Official Statistics <strong>[21]</strong>, 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.</p>
<p>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.</p>
<p> </p>
<h2 id="4">4. Sustainable Development and Data Rights</h2>
<p>Any discussion on governance frameworks would be incomplete without defining the kind of data rights they must seek to protect.</p>
<p>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.</p>
<p>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) <strong>[22]</strong>:</p>
<ul><li>Right to be counted,</li>
<li>Right to an identity,</li>
<li>Right to privacy and to ownership of personal data,</li>
<li>Right to due process (for example when data is used as evidence in proceedings, or in administrative decisions),</li>
<li>Freedom of expression,</li>
<li>Right to participation,</li>
<li>Right to non-discrimination and equality, and</li>
<li>Principles of consent.</li></ul>
<p>Personal data is broadly defined as "<em>any information relating to an identified or identifiable individual</em>" <strong>[23]</strong>. Often primary data producers (users of services and devices generating data) are unaware of individual privacy infringements <strong>[24]</strong>.</p>
<p>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 <strong>[25]</strong>.</p>
<p>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 "<em>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</em>" <strong>[26]</strong>. 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 <strong>[27]</strong>. 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 <strong>[28]</strong>.</p>
<p>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 - "<em>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</em>" <strong>[29]</strong>.</p>
<p>Thus, there is a push for the arguement that the historical approaches to protecting privacy and confidentiality — namely, <em>informed consent</em> and <em>anonymity</em> — no longer hold <strong>[30]</strong>. 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 <strong>[31]</strong>.</p>
<p>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 <strong>[32]</strong>, without any clear agreement on what these rights may be.</p>
<p> </p>
<h2 id="5">5. Governance Frameworks Proposed</h2>
<p>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.</p>
<h3 id="5-1">5.1. UN Sustainable Development Solutions Network</h3>
<p>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) <strong>[33]</strong>. It has proposed the following.</p>
<p><strong>Collection</strong></p>
<p>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.</p>
<p><strong>Analysis</strong></p>
<p>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 <strong>[34]</strong>.</p>
<p>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 <strong>[35]</strong>.</p>
<p>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 <strong>[36]</strong>.</p>
<p><strong>Management</strong></p>
<p>Four levels of monitoring, national, regional, global, and thematic, should be "<em>organized in an integrated architecture</em>" <strong>[37]</strong>.</p>
<p>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.</p>
<p>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 <strong>[38]</strong>.</p>
<p>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 <strong>[39]</strong>.</p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_unsdsn_monitoring.png" alt="UN Sustainable Development Solutions Network - Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring." />
<h6>Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring.<br />Source: UN Sustainable Development Solutions Network, <em><a href="http://unsdsn.org/wp-content/uploads/2015/05/150612-FINAL-SDSN-Indicator-Report1.pdf">Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a Data Revolution for the SDGs</a></em>, 2015, p.3.<br /></h6>
<p><strong>Role of NSOs</strong></p>
<p>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) <strong>[40]</strong>.</p>
<p><strong>Coordination</strong></p>
<p>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.</p>
<p>A high-level, powerful group of businesses and states must convene the various data and transparency sustainable development initiatives under one umbrella.</p>
<p>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.</p>
<p>The following table indicates the suggested Lead Agencies for individual SDGs <strong>[41]</strong>.</p>
<table>
<tbody>
<tr>
<td><strong>Number</strong></td>
<td><strong>Sustainable Development Goal</strong></td>
<td><strong>Lead Agencies</strong></td>
</tr>
<tr>
<td>1.</td>
<td>No Poverty</td>
<td>World Bank, UNDP, UNSD, UNICEF, ILO, FAO, UN-Habitat, UNISDR, WHO, CRED, UNFPA, and UN Population Division</td>
</tr>
<tr>
<td>2.</td>
<td>No Hunger</td>
<td>FAO, WHO, UNICEF, and Internal Fertilizer Industry Associaton (IFA)</td>
</tr>
<tr>
<td>3.</td>
<td>Good Health</td>
<td>WHO, UN Population Division, UNICEF, World Bank, GAVI, UN AIDS, and UN-Habitat</td>
</tr>
<tr>
<td>4.</td>
<td>Quality Education</td>
<td>UNESCO, UNICEF, and World Bank</td>
</tr>
<tr>
<td>5.</td>
<td>Gender Equality</td>
<td>UNICEF, UN Women, WHO, UNSD, ILO, UN Population Division, and UNFPA</td>
</tr>
<tr>
<td>6.</td>
<td>Clean Water and Sanitation</td>
<td>WHO/UNICEF Joint Monitoring Programme (JMP), FAO, UN Water, and UNEP</td>
</tr>
<tr>
<td>7.</td>
<td>Renewable Energy</td>
<td>Sustainable Energy for All, IEA, WHO, World Bank, and UNFCC</td>
</tr>
<tr>
<td>8.</td>
<td>Good Jobs and Economic Growth</td>
<td>IMF, World Bank, UNSD, and ILO</td>
</tr>
<tr>
<td>9.</td>
<td>Innovation and Infrastructure</td>
<td>World Bank, OECD, UNIDO, UNFCC, UNESCO, and ITU</td>
</tr>
<tr>
<td>10.</td>
<td>Reduced Inequalities</td>
<td>UNSD, World Bank, and OECD</td>
</tr>
<tr>
<td>11.</td>
<td>Sustainable Cities and Communities</td>
<td>UN-Habitat, Global City Indicators Facility, WHO, CRED, UNISDR, FAO, and UNEP</td>
</tr>
<tr>
<td>12.</td>
<td>Responsible Consumption</td>
<td>EITI, UNCTAD, UN Global Compact, FAO, UNEP Ozone Secretariat, WBCSD, GRI, IIRC, and Global Compact</td>
</tr>
<tr>
<td>13.</td>
<td>Climate Action</td>
<td>OECD DAC, UNFCCC, and IEA</td>
</tr>
<tr>
<td>14.</td>
<td>Life below Water</td>
<td>UNEP-WCMC, IUCN, and FMC</td>
</tr>
<tr>
<td>15.</td>
<td>Life on Land</td>
<td>FAO, UNEP, IUCN, and UNEP- WCMC</td>
</tr>
<tr>
<td>16.</td>
<td>Peace and Justice</td>
<td>UNODC, WHO, UNOCHA, UNCHR, IOM, OCHA, OECD, UN Global Compact, EITI, UNCTAD, UNICEF, UNESCO, and Transparency International</td>
</tr>
<tr>
<td>17.</td>
<td>Partnership for the Goals</td>
<td>BIS, IASB, IFRS, IMF, WIPO, WTO, UNSD, OECD, World Bank, OECD DAC, and SDSN</td>
</tr>
</tbody>
</table>
<h3 id="5-2">5.2. The UN DATA Revolution Group</h3>
<p>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' <strong>[42]</strong>. In its report, <em>A World that Counts</em>, it makes the following recommendations <strong>[43]</strong>.</p>
<p><strong>Collection</strong></p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p><strong>Analysis</strong></p>
<p>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.</p>
<p>A dashboard on ”the state of the world” will engage the UN, think-tanks, academics and NGOs in analysing, and auditing data.</p>
<p>Academics and scientists are to analyse data to provide long-term perspectives, knowledge and data resources at all levels.</p>
<p>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.</p>
<p>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.</p>
<p><strong>Storage</strong></p>
<p>A “world statistics cloud” will store data and metadata produced by different institutions but according to common standards, rules and specifications.</p>
<p><strong>Role of NSOs</strong></p>
<p>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.</p>
<p>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.</p>
<p><strong>Coordination</strong></p>
<p>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.</p>
<p>A UN-led “Global Partnership for Sustainable Development Data” is proposed, to coordinate and broker key global public-private partnerships for data sharing <strong>[44]</strong>.</p>
<p>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.</p>
<h3 id="5-3">5.3. Organization for Economic Co-Operation and Development (OECD)</h3>
<p>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 <strong>[45]</strong>.</p>
<p><strong>Collection</strong></p>
<p>Data is to be collected from National statistical agencies, national and international researchers and international organisations.</p>
<p><strong>Role of NSOs</strong></p>
<p>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 <strong>[46]</strong>.</p>
<p><strong>Coordination</strong></p>
<p>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.</p>
<p>Social science research communities must contribute to national plans of action after a needs assessment <strong>[47]</strong>. Research funding agencies must collaborate at the international level for a common system for referencing datasets in research publications <strong>[48]</strong>.</p>
<h3 id="5-4">5.4. The Global Partnership for Sustainable Development of Data</h3>
<p>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 <strong>[49]</strong>.</p>
<p><strong>Analysis</strong></p>
<p>There must be a common framework for information processing. At minimum, a simple lexicon must tag each datum specifying:</p>
<ul><li><strong>What:</strong> i.e. the type of information contained in the data,</li>
<li><strong>Who:</strong> the observer or reporter,</li>
<li><strong>How:</strong> the channel through which the data was acquired,</li>
<li><strong>How much:</strong> whether the data is quantitative or qualitative, and</li>
<li><strong>Where and when:</strong> the spatio-temporal granularity of the data.</li></ul>
<p>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.</p>
<p>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.</p>
<p><strong>Role of NSOs</strong></p>
<p>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, "<em>Do not trust any statistics that you did not fake yourself</em>."</p>
<p>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 <strong>[50]</strong>.</p>
<p>However Big Data if understood correctly, can inform where further targeted investigation is necessary and give immediate responses to favourably change outcomes.</p>
<h3 id="5-5">5.5. The World Economic Forum (WEF)</h3>
<p>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 <strong>[51]</strong>. In the report titled <em>Big Data, Big Impact: New Possibilities for International Development</em>, it makes the following recommendations <strong>[52]</strong>.</p>
<p><strong>Collection</strong></p>
<p>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.</p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_wef_01.png" alt="" />
<h6>World Economic Forum - Diagram on Data Commons.<br />
Source: World Economic Forum, <em><a href="http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf">Big Data, Big Impact: New Possibilities for International Development</a></em>, 2012, p.4.<br /></h6>
<p>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.</p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_wef_02.png" alt="" />
<h6>World Economic Forum - Diagram on Global Coordination.<br />
Source: World Economic Forum, <em><a href="http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf">Big Data, Big Impact: New Possibilities for International Development</a></em>, 2012, p.7.<br /></h6>
<h3 id="5-6">5.6. Dr. Julia Lane - A Quadruple Data Helix</h3>
<p>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 <strong>[53]</strong>. 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 <strong>[54]</strong>.</p>
<p><strong>Collection</strong></p>
<p>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</p>
<p>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.</p>
<p><strong>Analysis</strong></p>
<p>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</p>
<p>Universities can act as a trusted independent third party to process, store, analyze, and disseminate data. ii</p>
<p><strong>Management</strong></p>
<p>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.</p>
<p><strong>Coordination</strong></p>
<p>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.</p>
<h3 id="5-7">5.7. Data-Pop Alliance</h3>
<p>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 <strong>[55]</strong>. It makes the following suggestions.</p>
<p><strong>Collection</strong></p>
<p>The idea of <em>shared responsibility</em> 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.</p>
<p>The media too, could avoid publishing statistical data collected by unexplained methodologies by employing "statistical editors" and disseminate verified information.</p>
<p><strong>Role of NSOs</strong></p>
<p>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.</p>
<p>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 <strong>[56]</strong>.</p>
<p> </p>
<h2 id="6">6. Conclusion</h2>
<p>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.</p>
<p><strong>Collection</strong></p>
<p>There appears consensus across models that collection must be effected through public private partnerships while providing incentives.</p>
<p><strong>Analysis</strong></p>
<p>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.</p>
<p><strong>Role of NSOs</strong></p>
<p>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.</p>
<p><strong>Coordination</strong></p>
<p>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.</p>
<p>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.</p>
<p> </p>
<h2 id="7">7. Endnotes</h2>
<p><strong>[1]</strong> Dan Ariely, Facebook, January 06, 2013, <a href="https://www.facebook.com/dan.ariely/posts/904383595868">https://www.facebook.com/dan.ariely/posts/904383595868</a>.</p>
<p><strong>[2]</strong> United Nations Organizations, 'Sustainable Development Goals' (United Nations Sustainable Development, 26 September 2015), <a href="http://www.un.org/sustainabledevelopment/sustainable-development-goals/">http://www.un.org/sustainabledevelopment/sustainable-development-goals/</a>, accessed 6 June 2016.</p>
<p><strong>[3]</strong> Data Revolution Group, 'A World that Counts: Mobilising the Data Revolution for Sustainable Development' (November 2014), <a href="http://www.undatarevolution.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf">http://www.undatarevolution.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf</a>, accessed 8 June 2016.</p>
<p><strong>[4]</strong> 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), <a href="http://www.post2015hlp.org/">http://www.post2015hlp.org/</a>, accessed 8 June 2016.</p>
<p><strong>[5]</strong> Gary King, 'Ensuring the Data-Rich Future of the Social Sciences' [2011] 3(2) Science, <a href="http://gking.harvard.edu/files/datarich.pdf">http://gking.harvard.edu/files/datarich.pdf</a>, accessed 8 June 2016.</p>
<p><strong>[6]</strong> See <strong>[3]</strong>.</p>
<p><strong>[7]</strong> Ibid.</p>
<p><strong>[8]</strong> Michael Horrigan, 'Big Data: A Perspective from the BLS' (Amstatorg, 1 January 2013) <a href="http://magazine.amstat.org/blog/2013/01/01/sci-policy-jan2013/">http://magazine.amstat.org/blog/2013/01/01/sci-policy-jan2013/</a>, accessed 4 June 2016.</p>
<p><strong>[9]</strong> UN Global Pulse, 'Big Data for Development: Challenges & Opportunities' (6 May 2012) <a href="http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf">http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf</a>, accessed 5 June 2016.</p>
<p><strong>[10]</strong> 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, <a href="https://www.odi.org/sites/odi.org.uk/files/odi-assets/events-documents/5161.pdf">https://www.odi.org/sites/odi.org.uk/files/odi-assets/events-documents/5161.pdf</a>, accessed 4 June 2016.</p>
<p><strong>[11]</strong> See <strong>[9]</strong>.</p>
<p><strong>[12]</strong> See <strong>[10]</strong>.</p>
<p><strong>[13]</strong> See <strong>[9]</strong>.</p>
<p><strong>[14]</strong> UN Global Pulse, 'About: United Nations Global Pulse' (2016) <a href="http://www.unglobalpulse.org/about-new">http://www.unglobalpulse.org/about-new</a>, accessed 7 June 2016.</p>
<p><strong>[15]</strong> UN Stats, 'Global Working Group' (2014) <a href="http://unstats.un.org/unsd/bigdata/">http://unstats.un.org/unsd/bigdata/</a>, accessed 8 June 2016.</p>
<p><strong>[16]</strong> 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), <a href="http://www1.nyc.gov/office-of-the-mayor/news/291-12/mayor-bloomberg-police-commissioner-kelly-microsoft-new-state-of-the-art-law">http://www1.nyc.gov/office-of-the-mayor/news/291-12/mayor-bloomberg-police-commissioner-kelly-microsoft-new-state-of-the-art-law</a>, accessed 2 July 2016.</p>
<p><strong>[17]</strong> Francesco Mancini, 'New Technology and the Prevention of Violence and Conflict' (Reliefwebint, April 2013), <a href="http://reliefweb.int/sites/reliefweb.int/files/resources/ipi-e-pub-nw-technology-conflict-prevention-advance.pdf">http://reliefweb.int/sites/reliefweb.int/files/resources/ipi-e-pub-nw-technology-conflict-prevention-advance.pdf</a>, accessed 2 July 2016.</p>
<p><strong>[18]</strong> 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) <a href="https://www.omidyar.com/sites/default/files/file_archive/insights/Big%20Data,%20Small%20Credit%20Report%202015/BDSC_Digital%20Final_RV.pdf">https://www.omidyar.com/sites/default/files/file_archive/insights/Big%20Data,%20Small%20Credit%20Report%202015/BDSC_Digital%20Final_RV.pdf</a>, accessed 2 July 2016.</p>
<p><strong>[19]</strong> United Nations Economic and Social Council, 'Report of the Global Working Group on Big Data for Official Statistics' (UN Stats, 3 March 2015), <a href="http://unstats.un.org/unsd/statcom/doc15/2015-4-BigData-E.pdf">http://unstats.un.org/unsd/statcom/doc15/2015-4-BigData-E.pdf</a>, accessed 8 June 2016.</p>
<p><strong>[20]</strong> Ibid.</p>
<p><strong>[21]</strong> Ibid.</p>
<p><strong>[22]</strong> See <strong>[3]</strong>.</p>
<p><strong>[23]</strong> OECD, 'OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data' (23 September 1980), <a href="http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm">http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm</a>, accessed 29 May 2016.</p>
<p><strong>[24]</strong> Amir Efrati, ''Like' Button Follows Web Users' (WSJ, 18 May 2011) <a href="http://www.wsj.com/articles/SB10001424052748704281504576329441432995616">http://www.wsj.com/articles/SB10001424052748704281504576329441432995616</a>, accessed 23 May 2016.</p>
<p><strong>[25]</strong> See <strong>[15]</strong>.</p>
<p><strong>[26]</strong> Robert Kirkpatrick, 'Data Philanthropy: Public and Private Sector Data Sharing for Global Resilience' (UN Global Pulse, 16 September 2011), <a href="http://www.unglobalpulse.org/blog/data-philanthropy-public-private-sector-data-sharing-global-resilience">http://www.unglobalpulse.org/blog/data-philanthropy-public-private-sector-data-sharing-global-resilience</a>, accessed 4 June 2016.</p>
<p><strong>[27]</strong> Ibid.</p>
<p><strong>[28]</strong> Arvind Narayanan, 'No silver bullet: De-identification still doesn't work' (1 April 2016), <a href="http://randomwalker.info/publications/no-silver-bullet-de-identification.pdf">http://randomwalker.info/publications/no-silver-bullet-de-identification.pdf</a>, accessed 3 July 2016.</p>
<p><strong>[29]</strong> OECD Global Science Forum, 'New Data for Understanding the Human Condition: International Perspectives,' (February 2013) <a href="http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf">http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf</a>, accessed 2 June 2016.</p>
<p><strong>[30]</strong> S. Barocas, 'The Limits of Anonymity and Consent in the Big Data Age,' in <em>Privacy, Big Data, and the public good: Frameworks for Engagement</em> (Cambridge University Press, 2014).</p>
<p><strong>[31]</strong> A. Pentland, 'Institutional Controls: The New Deal on Data,' in <em>Privacy, Big Data, and the public good: Frameworks for Engagement</em> (Cambridge University Press, 2014).</p>
<p><strong>[32]</strong> See <strong>[3]</strong>.</p>
<p><strong>[33]</strong> UN Sustainable Development Solutions Network, 'About Us: Vision and Organization' (2012) <a href="http://unsdsn.org/about-us/vision-and-organization/">http://unsdsn.org/about-us/vision-and-organization/</a>, accessed 2 June 2016.</p>
<p><strong>[34]</strong> 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) <a href="http://unsdsn.org/wp-content/uploads/2015/05/150612-FINAL-SDSN-Indicator-Report1.pdf">http://unsdsn.org/wp-content/uploads/2015/05/150612-FINAL-SDSN-Indicator-Report1.pdf</a>, accessed 4 June 2016.</p>
<p><strong>[35]</strong> UNICEF, 'CME Info - Child Mortality Estimates' (2014) <a href="http://www.childmortality.org/">http://www.childmortality.org/</a>, accessed 1 June 2016.</p>
<p><strong>[36]</strong> See <strong>[10]</strong>.</p>
<p><strong>[37]</strong> 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) <a href="http://www.uis.unesco.org/ScienceTechnology/Documents/unsc-post-2015-draft-indicators.pdf">http://www.uis.unesco.org/ScienceTechnology/Documents/unsc-post-2015-draft-indicators.pdf</a>, accessed 3 June 2016.</p>
<p><strong>[38]</strong> UN, 'The Road to Dignity by 2030: Ending Poverty, Transforming All Lives and Protecting the Planet ' (4 December 2014) <a href="http://www.un.org/disabilities/documents/reports/SG_Synthesis_Report_Road_to_Dignity_by_2030.pdf">http://www.un.org/disabilities/documents/reports/SG_Synthesis_Report_Road_to_Dignity_by_2030.pdf</a>, accessed 7 June 2016.</p>
<p><strong>[39]</strong> Ibid.</p>
<p><strong>[40]</strong> UN Sustainable Development Solutions Network, 'Data for Development: An Action Plan to Finance the Data Revolution for Sustainable Development' (10 July 2015) <a href="http://unsdsn.org/wp-content/uploads/2015/04/Data-For-Development-An-Action-Plan-July-2015.pdf">http://unsdsn.org/wp-content/uploads/2015/04/Data-For-Development-An-Action-Plan-July-2015.pdf</a>, accessed 3 June 2016.</p>
<p><strong>[41]</strong> See <strong>[34]</strong>.</p>
<p><strong>[42]</strong> UN Data Revolution Group, 'About the Independent Expert Advisory Group' (6 November 2014) <a href="http://www.undatarevolution.org/about-ieag/">http://www.undatarevolution.org/about-ieag/</a>, accessed 4 June 2016.</p>
<p><strong>[43]</strong> See <strong>[3]</strong>.</p>
<p><strong>[44]</strong> The Partnership has already been established, and it is developing a further framework.</p>
<p><strong>[45]</strong> Organisation for Economic Co-Operation and Development), 'The Organisation for Economic Co-operation and Development (OECD): About' (2016) <a href="http://www.oecd.org/about/">http://www.oecd.org/about/</a>, accessed 2 June 2016.</p>
<p><strong>[46]</strong> Organisation for Economic Co-Operation and Development, 'Strengthening National Statistical Systems to Monitor Global Goals' (2015) <a href="http://www.oecd.org/dac/POST-2015%20P21.pdf">http://www.oecd.org/dac/POST-2015%20P21.pdf</a>, accessed 1 June 2016.</p>
<p><strong>[47]</strong> Ibid.</p>
<p><strong>[48]</strong> OECD Global Science Forum, 'New Data for Understanding the Human Condition: International Perspectives' (February 2013) <a href="http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf">http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf</a>, accessed 2 June 2016.</p>
<p><strong>[49]</strong> The Global Partnership On Sustainable Development Data, 'Who We Are: The Data Ecosystem and the Global Partnership' (2016) <a href="http://www.data4sdgs.org/who-we-are/">http://www.data4sdgs.org/who-we-are/</a>, accessed 5 June 2016.</p>
<p><strong>[50]</strong> World Economic Forum, 'Big Data, Big Impact: New Possibilities for International Development' (22 January 2012) <a href="http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf">http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf</a>, accessed 8 June 2016.</p>
<p><strong>[51]</strong> World Economic Forum, 'Our Mission: The World Economic Forum' (12 January 2016) <a href="https://www.weforum.org/about/world-economic-forum/">https://www.weforum.org/about/world-economic-forum/</a>, accessed 7 June 2016.</p>
<p><strong>[52]</strong> See <strong>[50]</strong>.</p>
<p><strong>[53]</strong> Julia Lane, Homepage, <a href="http://www.julialane.org/">http://www.julialane.org/</a>.</p>
<p><strong>[54]</strong> Julia Lane, 'Big Data for Public Policy: The Quadruple Helix' (2016) 8(1) <em>Journal of Policy Analysis and Management</em>, <a href="http://onlinelibrary.wiley.com/doi/10.1002/pam.21921/abstract">DOI:10.1002/pam.21921</a>, accessed 1 June 2016.</p>
<p><strong>[55]</strong> Data-Pop Alliance, 'Data-Pop Alliance: Our Mission' (May 2014) <a href="http://datapopalliance.org/">http://datapopalliance.org/</a>, accessed 1 June 2016.</p>
<p><strong>[56]</strong> See <strong>[10]</strong>.</p>
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<h2 id="8">8. Author Profile</h2>
<p>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.</p>
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For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/big-data-governance-frameworks-for-data-revolution-for-sustainable-development'>http://editors.cis-india.org/internet-governance/blog/big-data-governance-frameworks-for-data-revolution-for-sustainable-development</a>
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No publisherMeera ManojDevelopmentBig DataData SystemsInternet GovernanceBig Data for DevelopmentSustainable Development Goals2016-07-05T13:13:32ZBlog Entry