In the first part of this discussion about how Big Data can help save the world, I cited a number of experts who explained why they see great potential in Big Data analytics for tackling big global problems. The areas in which they saw the most potential included: agriculture, finance and poverty reduction, healthcare, and disaster response. Let's take a brief look at how Big Data can help in each of those areas beginning with Agriculture.
"Farmers today produce three times as much food as they did 50 years ago using just 12 percent more land, thanks to new technologies and better farming practices," reports Prachi Patel. "But the global playing field isn't level. In Africa, farmers produce a fraction of what they could, according to the Forum for Agricultural Research in Africa, and most barely get by, struggling against infertile soil, drought, and diseases." ["Feeding the World With Big Data," IEEE Spectrum, 14 May 2013] It's clear from the title of his article that Patel believes Big Data has a role to play in solving the growing challenge of feeding the world's burgeoning population. This is important, he explains, because "helping farmers—in Africa and elsewhere — produce more will be key to lifting millions out of poverty and sustainably feeding a world population of 9 billion in 2050." He continues:
"Food-policy experts believe that a crucial step toward that goal is to give farmers, scientists, and entrepreneurs unhindered access to agricultural data which is generated at research centers worldwide. ... If these data sets are made freely available, the possibilities for their use are endless, says Piers Bocock of the CGIAR Consortium of International Agricultural Research Centers, in Montpellier, France. At [a] conference [held earlier this year], experts from universities and research institutions presented apps they've developed using data that's already publicly available. These included MyFarm, an Android-based country-specific multilingual app that helps train farmers to give agricultural advice to other small farmers, and Aqueduct, an interactive tool that provides high-resolution maps of water-related risks. In Africa, where even the poorest farmer carries a cellphone, open-data evangelists envision an incredible — and not completely improbable — scenario. 'Imagine this,' Bocock says. 'A woman standing in a field in Malawi has just borrowed money to start her own farm. What if an app on her mobile phone geo-locates her and then, from this ever-growing data ecosystem of knowledge, is able to identify the soil type and needs of that specific field, and then tell her where, locally, she can buy the seeds she needs and when to plant, harvest, et cetera?' Making such 'what if' scenarios a reality will require increasing amounts of free, accessible agricultural research data that's easy to use — not just by humans but also by machines."
Finance and Poverty Reduction
Gillian Tett discusses how Big Data can be used to make societies more resilient. For example, it can be used to "spot economic trends and predict looming problems in a beneficial way." ["Big data is watching you," Financial Times, 10 August 2012] She explains:
"Aid groups are not just tracking ... physical phones; they are also starting to watch levels of mobile phone usage and patterns of bill payment, too. If this suddenly changes, it can indicate rising levels of economic distress, far more accurately than, say, GDP data. ... [If any organisation] spots a sudden increase in certain keywords, this can also provide an early warning of distress."
Tett also sees a darker side to gathering financial information. She notes that "companies can use the data ... to develop credit scores for the poor." She believes such scores would lead to less access to credit than the poor already have. Mark van Rijmenam reports, "Cignifi, a Brazilian startup, for example developed a technology to recognize patterns in the usages of mobile devices. The system recognizes phone-calls, text messages and data usage and based on this information it can recognize someone's lifestyle and his/her corresponding credit risk profile." ["How Big Data Can Help the Developing World Beat Poverty," SmartData Collective, 2 August 2013] How that information is used will determine whether the poor will be helped or hindered in their efforts to escape poverty.
Several references in the first post in this two-part series touched on how healthcare can be improved through the use of Big Data. For example, van Rijmenam discussed how using Call Detail Records could be used to map changes in the slum population and, as a result, "direct latrine and water pipe building efforts for the benefit of the slums residents." As I've pointed out in previous posts, providing the poor with better sanitation facilities can dramatically improve their health. Tett provided an example how Big Data was used during the Haitian earthquake a few years back to prevent epidemics. She wrote:
"The population scattered when the tremors hit, leaving aid agencies scrambling to work out where to send help. Traditionally, they could only have done this by flying over the affected areas, or travelling on the ground. But some researchers at Columbia University and the Karolinska Institute took a different tack: they started tracking the Sim cards inside mobile phones owned by Haitians, to work out where their owners were located or moving. That helped them to 'accurately analyse the destination of more than 600,000 people who were displaced from Port au Prince”, as a UN report says. Then, when a cholera epidemic hit Haiti later, the same researchers tracked the Sim cards again, to put medicine in the correct locations – and prevent the disease from spreading. ... Medical researchers have learnt in the past couple of years that social media references to infection area are powerful early warning signal of epidemics – and more timely than official alerts from government doctors."
Mike Wheatley agrees, "'Spying' on people's public data can actually help medical professionals to save lives." ["Big Data’s Still On Track To Save The World," SiliconANGLE, 9 July 2013] He explains:
"Researchers at Johns Hopkins University have been ... downloading tweets at random and sifting through this data to flag any and all mentions of flu or cold-like symptoms. Because the tweets are geo-tagged, the researchers can then figure out where the sickness reports are coming from, cross-referencing this with flu data from the Center for Disease Control to build up a picture of how the virus spreads, and more importantly predict where it might spread to next. Of course, there are many countries where Twitter isn't all that popular and so researchers are forced to use more creative methods to track disease. One way of doing so, pioneered in Kenya, is through using the so-called metadata from cell phone calls to try and predict the spread of malaria. Now you might think that's an impossible task if you're not actually listening into people's calls, but you'd be wrong. Caroline Buckee, a professor at Harvard University's Center for Communicable Disease, hit upon the idea of assigning specific cell phone users to an 'area' based on the location their calls and SMS messages originated from. These areas where then rated according to the level of malaria risk, which was calculated according to reported cases of the disease. Using a mathematical model, the researchers were able to accurately predict someone's probability of becoming infected in each 'area'."
As the old medical adage goes, prevention is always better than cure – and a lot cheaper as well.
The final area I want to discuss is disaster response. Just like in healthcare, prevention of disasters is the best course of action. Unfortunately, preventing natural disasters isn't possible. For that reason, mitigation is often best the course to follow. Early warning can sometimes help prevent natural disasters from taking an even larger toll in life and destruction. Wheatley explains:
"Saving lives is a worthy cause, but the motivation to save money can be just as powerful (if not more so), and once again Big Data is helping us to do so. ... We can't stop the forces of nature, but by preparing for the coming onslaught we can certainly minimize the damage it'll cause."
The onslaught of disasters can only be detected and mitigated if data is collected and analyzed quickly enough to make a difference. Tett notes that applications like Twitter and Facebook can often provide the necessary early warning. "References to food or ethnic strife," she writes, "may indicate the onset of famine or civil unrest." Earlier she noted that Big Data can be used to track displaced populations so that response organizations know both the extent of the problem and where they need to be to assist. Tett continues:
"Robert Kirkpatrick, a former IT expert who now runs the UN's Global Pulse unit, ... dreams of using these data to create the social media equivalent of 'metereological stations', which can test the winds of public debate, spot economic trends and predict looming problems in a beneficial way. Even if this idea sounds far-fetched, economists can already use this information to track how economies are developing in poor regions of the world with much more precision and timeliness than ever before. That mobile phone in my pocket, in other words, does not just connect me to my friends. It is now part of a shared human experience – and database – that spans the globe, and which is growing in depth and power each day. And that has implications most of us have barely begun to understand. It is both a sobering and exciting thought, whether you are now sitting on a holiday beach, in a humdrum office – or anywhere else in the world."
Big Data may not be able to save the world on its own; but it certainly is a good tool to have in the kit of organizations and governments that can help solve global challenges.