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How can data analytics make society better?

5 ways data science is being used to drive real-world positive change

RMIT Online
RMIT Online

We often think of data analytics in terms of what it can do for business. The conversation is usually driven by design efficiency and profit margins and making better decisions. But this is really just the tip of the bar graph. The principles of data science are being used by researchers to drive real-world positive change. Change that has nothing to do with a company’s bottom line.

We can see examples everywhere. Organisations like Data Science for Social Good host yearly events where data scientists use their skills to tackle “problems that really matter”. Past projects have included helping rough sleepers in the UK, improving basic legal access in Africa and reducing corruption in Paraguay’s public procurement procedures. Companies like Kaggle are even offering cash prizes for data scientists who can crack particularly thorny problems, like how to make chest x-rays more accurate. This is the beauty of data science. Any field can benefit from better insights and creative solutions.

Here are just a few examples of how data analytics are driving better decision-making in society.




Agriculture is a business, sure, but it’s also the future of the planet. The global population is expected to hit nine billion by 2050, and the Food and Agriculture Organisation predicts we’ll need a 70 per cent growth in output to feed those nine billion mouths. We need to figure out how to farm more food, more efficiently, with less waste and a smaller footprint. And data analytics is already leading the way. Farmers are using soil sensors, weather tracking apps and GPS-equipped tractors to increase yields and boost productivity. Farmers now know when to plant, and what to plant, right down at the granular level. And the payoff can be huge: a study in Nature found that eliminating nutrient overuse could increase the production of maize, wheat and rice by 30 per cent. The more we know, the better we can grow.


Urban Planning


Looked at a certain way, cities are essentially giant, incredibly complicated data sets. Six billion moving parts that interact with one another in surprising, and often unpredictable, ways. This has given rise to a new field of data science known as ‘Urban Analytics’, which encourages city planners to design better, more efficient, data-driven cities. Take air pollution, for example. In Dublin, automatic air quality monitors collect data that informs planners about known pollutants and areas of particular concentration. Google even has its own air pollution mapping tool, which lets planners measure air quality on a street-by-street basis. Predictive analytics has also been used in Florence to improve public transport and reduce traffic congestion. There are potential benefits everywhere you look.


Public Health


Doctors have always understood that public health relies on good data. You can’t treat a medical problem without knowing the underlying cause, and data analytics are particularly good at finding trends, patterns and causes. It’s one reason public health officials have started to embraced data science: for the first time, we can take society’s pulse and measure its temperature. We can see what’s happening beneath the surface. Projects like Open Case Studies at John Hopkins Bloomberg School of Public Health show how this might work in practice: researchers have put together ten case studies that allow health professionals to tackle the biggest problems facing American healthcare, including addiction, adolescent health, obesity and violence. In India, Microsoft has shared its cloud tech and machine learning with the L V Prasad Eye Institute, which uses AI modelling to identify early signs of visual impairment and blindness. There are plenty of other examples. The future of healthcare is driven by data.   


Global Warming


Analysing data sets is basically how we know, for sure, that climate change is real. We know that our planet lost 40 football fields worth of trees per minute in 2017. We know that global temperatures have risen 1.18 degrees Celsius since the late 19th century. We know that Greenland lost approximately 279 billion tonnes of ice per year between 1993 and 2019. But data analytics could also be the way forward, and there are organisations around the world that are leveraging big data to make a real difference. Rainforest Connection, for example, has created an acoustic monitoring system, driven by machine learning, that analyses forest sounds and discovers illegal logging in real time. In China, IBM’s Green Horizon’s project is being used to monitor pollution and limit key contributors, like power plants. Trase is measuring the flow of commodities like beef, palm oil and soy (which, together, account for two thirds of tropical deforestation) to make global supply chains more transparent. Basically, we can’t improve something until we measure it. Data analytics not only shines a light on climate change, it offers the only proven solution. 





Government agencies everywhere are faced with the exact same problem: they have a finite amount of resources, and they need to work out the best, most efficient way to spend those resources, in order to drive the maximum amount of public good. This is an almost impossible challenge. But now that data science has finally jumped from the private to the public sector, there are plenty of opportunities out there. Agencies can use analytics to find cheaper (and better) vendors; identify at-risk children and improve child welfare; spot fraud or corruption in high places; or even flag police officers who are likely to respond with violence. “Data analytics can answer four key questions: what happened, why it happened, what will happen, and how to make it happen,” says Teradata, “government agencies need sound data analytics to shape their response to [various] problems.” This is obviously crucial when it comes to wide-reaching public health crises, like COVID-19. Without robust data, and the professionals to analyse it, governments wouldn’t be able to identify hotspots, allocate public health resources, contain outbreaks or distribute efficient financial relief.


Interested in learning more about Data Analytics? Check out our range of online Data Analytics courses here and read related blogs below.