The devastating floods of April 2022 in KwaZulu-Natal, which claimed the lives of at least 435 people, demonstrated an opportunity to leverage the power of data in both prevention and relief efforts in the future. SAS experts share further insights.
Data is generated everywhere – from the movements of mobile devices or wearables tracked by GPS to social media and retail activity. While there may seem to be no direct connection between data and coordinated flood response, the fourth industrial revolution (4IR) has created the opportunity for crowdsourcing and citizen data scientists to help solve humanitarian problems.
I-Sah Hsieh, Principal Program Manager, Corporate Social Innovation and Brand, SAS Institute, explains: “Data by itself is meaningless. This is where innovative technology like artificial intelligence (AI) can awaken data’s true potential. Using sophisticated algorithms, it is possible to transform the raw data into intelligence much more efficiently than in the past. AI can help address some of Africa’s and the world’s most pressing issues – from healthcare to education, sustainability, energy and social development.”
Using data for good means harnessing digital disruption to meet the United Nations’ Sustainable Development Goals, which serve as a useful proxy for solving the world’s social problems. Data is proliferating at a rate faster than it can be applied for social good, but there is enormous potential in harnessing it for decision-makers to draw upon.
Murray de Villiers, EMEA Emerging, Education Head, SAS Institute sheds light on a real-life example that puts this into context. “SAS in South Africa has partnered with the Department of Higher Education to determine the optimal placement for new schools in rural KwaZulu-Natal. Through using crowdsourced data we were able to measure the daily commutes of high school learners from their communities. Some learners were travelling up to 30km per day, mostly by foot. Though from an analytics point of view using satellite data to pinpoint new school locations was a relatively simple exercise, it has the potential to deliver tangible life benefits for students who may potentially miss school days regularly due to excessive distances.”
Data can be used to create insight, and insight underpins policy. Relief efforts, repair and reconstruction by civil society and the public and private sectors can be guided by these insights if enough data is collected and aggregated in a useful format.
“The data for good movement is generating a lot of energy and goodwill among our employees. With some analytics and creative data science we can use the power of data to improve and save lives,” states Hsieh.
“We need to teach the world to embrace data, through literacy programmes, so that we create a cohort of citizen data scientists. This is a virtuous circle since the use of data can support the delivery of education. Promoting data literacy, regardless of role or job description, will lead to data and analytics being employed as critical tools in decision-making,” says de Villiers.
These topics may seem complex and unfamiliar, but it is when real-world examples of successful applications demonstrate benefits for society that people begin to engage with data science and analytics. The conduit is a passion for social impact. Data for good is an easy way to start engaging the next generation of citizen data scientists about the importance of data literacy. Residents of KwaZulu-Natal reporting impact data around them do not need to be mathematicians or statisticians to appreciate the power of data in preventing and responding to future natural disasters.
In a similar way, projects around the world are achieving social good. One example is protecting the Amazon from deforestation by asking citizen data scientists to click on satellite imagery where they see signs of human impact. This activity is training artificially intelligent models to recognise signs of human impact that will ultimately accelerate research and make it more accurate. Citizen data scientists are both protecting Amazon and learning about AI simultaneously.
In another example, organisations that dig wells to provide access to fresh water in some of the world’s poorest countries are being assisted in determining well location by citizen data scientists. The same skills that are being employed commercially can be extended to deliver social good in line with Sustainable Development Goals.
“What is required for the impact of data to be maximised is an increase in participation. There are a few hurdles to overcome: most notably cooperation among competitors, who may resist sharing data related to their business model, as well as privacy concerns,” indicates Hsieh.
The solutions to the first problem lie in demonstrating the collective benefits of data sharing in both overcoming industry challenges and addressing global social issues, as well as legislators incentivizing data sharing by organisations. In general, insights and trends beyond one’s own organization lead to better responses to challenges that affect all market participants.
As far as the second problem is concerned, there is a trade-off to be made ineffectiveness of harnessing data when privacy is paramount. Anonymising data as much as possible and preventing traceability to individual level can reduce resistance to donating data toward a good cause. Data for good needn’t compromise privacy.
“The world is on the cusp of bringing together machine learning, AI, predictive analytics and optimisation to create new solutions to social problems. All that is required is some creativity and imagination to unlock the power of data for good,” concludes de Villiers.
