Data Analytics For World Peace

We hear a lot about data use in weapons, but what about for creating peace?


Achieving world peace has been a primary goal of governments and beauty pageant contestants since cavemen first started hitting each other with clubs. As democracy spreads and nations become relatively prosperous, we are seeing an end to war in the conventional sense. Conflict is now less a release of pressure after a prolonged period of tension, and more a sudden outburst, flaring up suddenly with far less warning and opportunity to prevent it politically.

Technological advancement has, traditionally, been associated with war in a negative sense, helping to create bigger weapons to enable more killing. However, it could equally be used to bring peace. Kalev H. Leetaru, creator of the Global Database of Events, Language and Tone (GDELT) project, which describes itself as a comprehensive ‘database of human society’, has argued that big data has the potential to be a tremendous tool in the fight for peace, asking, ‘Can big data give us peace? I think the short answer is we're starting to explore that. We're at the very early stages, where there are shining examples of little things here and there. But we're on that road.’

Big data can be used to identify patterns and signatures associated with growing instability and conflict. It can also pinpoint the exact causes. This enables governments to implement conflict prevention strategies and stop violence before it has a chance to escalate. There are now a number of initiatives focused on analyzing data from all kinds of sources in order to do this. Organizations such as the US Defense Department, the International Peace Institute, and the CIA have all launched programs in recent years that scrape public data from sources like social media, market data, world news, and so forth, and analyze it for indications of impending conflict.

The UN in particular has an unparalleled database of the world’s socio-economic and political history, and by blending it with contextual understanding - on-the-ground information collected by those actually in unstable communities. According to McKinsey, it is getting this on-the-ground information that is the primary challenge. Unstable communities need people that are credible, trustworthy, and unbiased - something that is always difficult to find in regions prone to instability. Without such people, however, big data is essentially useless.

Perhaps the most famous way that big data has been proven to predict wars is agricultural data. Researchers have, for example, managed to link the extreme drought in Syria between 2006 and 2009 with the unrest in the region that started in 2011, escalating the ‘Arab Spring’ movement across the region. In a report by Peter H. Gleik, he notes that,’Between 2006 and 2009, around 1.3 million inhabitants of eastern Syria were affected by agricultural failures. An estimated 800 000 people lost their livelihoods and basic food supports… By late 2011, the UN estimated that between two million and three million people were affected, with a million driven into food insecurity’. This data, alongside historical evidence of droughts coinciding with conflict, suggests that areas where droughts are occurring should be a particular concern for forces like NATO looking to maintain stability.

The use of big data in conflict early warning systems undoubtedly improves the ability to predict when a conflict might flare up. However, they can only go so far. The most important thing is to formulate a response that deals with the issue, and factors such as lack of political will often stand in the way of anything being done. There is a real disconnect between formal early warning analysis and executive decision-making processes. In truth, it is unlikely that data-driven early warning systems will ever really eradicate violent conflict. It will take a lot more from humans to do that. But, when paid attention to, at least it should help minimize the impact and keep them at a more manageable size.


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