Continuing from Part 1 which went through how Big Data supports the Sustainable Development Goals (SDGs) 1 to 6 of UN’s 2030 Agenda for Sustainable Development, Part 2 goes through the same for the SDGs 7 to 12.
Thankfully for SDG 7, Big Data Science can help the renewable energy sector through:
The first thing that came to my colleague’s mind regarding using Big Data to support this SDG is the flexibility of remote goal-driven work. Here’s what he said: “The future is how much work you put in your hours to gain results and not how much hours your put in your work. Work is more and more about learning and generating results, not just to attend somewhere.”
It’s agreeable that it’s a great idea for taking advantage of Big Data Science‘s potential but that’s not what this SDG is referring to. It’s more to do with boosting the economy and creating decent jobs with labour rights protection, as well as expanded access to banking and financial services.
Several studies have shown that economic growth is positively related to job creation. Khan (2007) found that employment elasticity of GDP growth in developing countries is 0.7. Meanwhile, Kapsos (2005) found that for every 1-percentage point of additional GDP growth, total employment grew between 0.3 and 0.38 percentage points during the three periods between 1991 and 2003.
Big Data obtained through mobile phone data can identify shocks in the work force such as large-scale lay-offs, identifying individuals affected by such shocks and predicting changes in aggregate unemployment rates.
Basic infrastructure like roads, electrical power and access to water and sanitation are still lacking in many developing countries. SDG 9 strives to improve this with access to sustainable infrastructure of high quality, reliability and resilience.
A mixture of satellite and machine learning tech can be used to identify such infrastructure and assess rural communities’ access to them. While working on this article, we learned that there are two ways to use satellite data for infrastructure development:
With income inequality on the rise, it’s more important now than ever that measures are taken to narrow that gap and Big Data can drive these measures, hence SDG 10. We found two examples of data-driven measures:
The importance of using Big Data to support SDG 11 can’t be emphasised enough. A smart city uses technology to improve aspects of a city’s quality of life through its operations and services.
Reliable sensor data, Internet of Things (IoT) devices, cloud platforms, analytics applications and Machine Learning applications are required to generate relevant real-time insights to enable the development of a smart city.
There are so many aspects of a smart city: energy efficiency, waste management, better housing, better healthcare, smoother traffic flow, air quality control, better water systems, better crime detection, etc.
One example of a smart city initiative is Italy’s major train operator, Trenitalia. Trenitalia installed sensors on the trains to get real-time status updates on each train’s mechanical condition and maintenance predictions for Trenitalia to plan ahead of a breakdown.
This Big Data-driven application of a public transport system can allow a city to prevent major train disruptions and instead provide its train-dependent residents a smooth daily ride to school or work.
In the context of sustainability, less is more. It’s becoming increasingly important that production and consumption of resources like food and energy (connection to SDGs 2 and 7) are sustainable, efficient and not wasteful.
Since SDG 12 is connected to SDGs 2 and 7, the Big Data-driven use cases of those goals in minimising food waste and energy usage can fulfill this goal as well.
SDGs 13 to 17 will be discussed in Part 3, so stay tuned again for the rest of the list. 😉
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