Council Post: Data science and analytics applications to support SDG’s

Data is the foundation of accountability and the lifeblood of decision-making. If used responsibly, new data sources, technology, and analytical methods—such as satellite data—can enable more swift, effective, and evidence-based decision-making, as well as more accurately and fairly measure progress towards the Sustainable Development Goals (SDGs).
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Data is the foundation of accountability and the lifeblood of decision-making. Using consumer profiling, personalised services, and predictive analysis for marketing, advertising, and management are now prevalent in the private sector. Similar methods could be used to monitor people’s wellbeing in real time and concentrate aid on the most vulnerable populations. If used responsibly, new data sources, technology, and analytical methods—such as satellite data—can enable more swift, effective, and evidence-based decision-making, as well as more accurately and fairly measure progress towards the Sustainable Development Goals (SDGs).

The Sustainable Development Goals, which serve as the basis for the new development agenda, were first adopted in 2015. An emphasis on inclusive, participatory development that leaves no one behind is necessary to achieve these goals, which call for combined action on social, environmental, and economic concerns.

There is still an absence of essential data for national, regional, and global development policymaking. Many governments still lack access to sufficient information about their whole population. This is especially true for the poorest and most marginalised people, who are also the ones that leaders must prioritise if they are to eliminate extreme poverty and all emissions by 2030 and, in the process, to “leave no one behind”.


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The term sustainable development was first coined in the Brundtland Commission report in 1987 which specifies it as, “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” There are three areas that contribute to it—economical, social and environmental. A UN tracker has been introduced by Our world in data that credit data from UN and other organisations. The tracker offers explanations of the indicators and data visualisations with the specific goal of holding governments accountable for their commitments. Data gaps prevent a thorough assessment of a number of objectives. This brings to light the issues with data accessibility that macro indicators face. To map the existing quo, data must be gathered and converted into formats that computers can comprehend.

One of the major issues with data collection for SDGs is that the statistics are collected in a span of two to ten years, which accounts for problems of data availability. In this century, where data is expanding more quickly than ever before, surveys, administrative data, and censuses cannot be the only sources of data for SDG reporting. Huge amounts of data are now readily accessible to direct improvements in storage technologies and data collection techniques, but they must be methodically sorted by government organisations in order to yield useful insights for improving governance to the benefit of the general population.

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Here are some ways by which data can be incorporated in each of the SDGs.

SDG 1: No poverty

Mobile phone service spending trends can serve as benchmarks for measures of income levels.

SDG 2: Zero Hunger

Online food price tracking or crowdsourcing can be used to check food security almost immediately.

SDG 3: Good Health and Well-Being

Predicting the spread of infectious diseases by the movement of mobile phone users.

SDG 4: Quality Education

Reasons for high student dropout rates can be found through citizen reporting.

SDG 5: Gender Equality

Financial transaction analysis can show spending habits and the various effects of economic shocks on men and women.

SDG 6: Clean Water and Sanitation

Water pump sensors can monitor who has access to clean water.

SDG 7: Affordable and Clean Energy 

Utility companies can alter the flow of energy, gas, or water with smart metres to cut waste and guarantee a sufficient supply during peak hours.

SDG 8: Work and Economic Growth

Global postal traffic patterns can provide data on trade, GDP, remittances, and other economic indicators.

SDG 9: Industry, Innovation and Infrastructure

GPS data can be utilised to improve public transportation and traffic control.

SDG 10: Reduced Inequality

Local radio content’s speech-to-text analytics can highlight discrimination issues and help with policy responses.

SDG 11:  Sustainable Cities and Communities

The invasion of public lands and spaces, such as parks and forests, can be monitored via satellite remote sensing.

SDG 12: Responsible Consumption and Production

The frequency of e-commerce or online search activity can provide insight into the rate of the transition to energy-efficient items.

SDG 13: Climate Action

Tracking deforestation by combining satellite photography, witness reports from the general public, and open data.

SDG 14: Life Below Water

Data from tracking maritime vessels can identify unreported, uncontrolled, and illegal fishing activity.

SDG 15: Life on Land

Monitoring social media in real time can provide information on the location of victims, the severity of forest fires, and the smoke cloud.

SDG 16: Peace, Justice and Strong Institutions

Social media sentiment analysis can show people’s perspective on good governance, public service delivery, and human rights.

SDG 17: Partnerships for the Goals

Partnerships that make it possible to combine statistical, mobile, and internet data can help us understand today’s hyperconnected world better and in real time.

Sustainability and data science are two terms that are often used in the context of greater socio-economic challenges. Not only IT companies use data to streamline operations, produce insightful insights, and develop new products—Everyone wants to use data for something. A future with less inequality and greater prosperity for a wider portion of the global population is what we are working for.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.

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Anirban Nandi
With close to 15 years of professional experience, Anirban specialises in Data Sciences, Business Analytics, and Data Engineering, spanning various verticals of online and offline Retail and building analytics teams from the ground up. Following his Masters from JNU in Economics, Anirban started his career at Target and spent more than eight years developing in-house products like Customer Personalisation, Recommendation Systems, and Search Engine Classifiers. Post Target, Anirban became one of the founding members at Data Labs (Landmark Group) and spent more than 4.5 years building the onshore and offshore team of ~100 members working on Assortment, Inventory, Pricing, Marketing, eCommerce and Customer analytics solutions.

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