"So this model can work as effective both in developed countries as well as undeveloped countries," said Shiffman, who is now president of Giant Oaks, a consulting firm that provides big data analytics for national and homeland security interests.
Shiffman suggests useful data in less developed countries can be found in "economic data -- what people are buying in stores, what cars are they driving, what kind of phones are they using, refugee flows, the direction of their move, mobile use."
Both Shiffman and White believe traditional data and civil society patterns can be as valuable for data analytic forecasts as cyber data that's more prevalent in developed countries.
"In the case of Syria, we look at trends, where the business leaders gather, what they talk about, where are the religious leaders; we follow sermons, political and religious statements, public meetings, statements in commerce and business areas," White said, suggesting that through this accumulation of information, they see connections in Damascus, Turkey and elsewhere and can project connections within the region.
CSO's analysis of large data and civil society enabled the Syrian opposition to build mass-communications and improve internal and external communications networks and develop civilian leadership capacity for if there's a change in government.
Introducing new narratives
In a post-Gadhafi Libya, White said the United States and its allies were under the impression that "militias" were one of the big problems in that country.
As a result, White and his team "dug down and looked at who are these militias? Where are they located? And how is the pattern of violence manifested?" through the study of human patterns and civil-society data-gathering.
"Libyan streets became our study ground, as did civil society and social media," White said.
By using large data analysis in a strategic effort to advance stability in Libya, "it was not your typical police, but rather the Libyan civil society that was the antidote to the militia violence," White said.
By looking at social networking, open sources and the civil society, analysts got a better picture of Libyans' attitude toward Americans, which helped "understand trend lines against Americans --- that potentially led us to learn that Libyans were simply horrified -- Libyans were actually horrified -- by the killing of Ambassador Chris Stevens," White said of the U.S. diplomat who was killed in a terror attack on the American diplomatic compound in Benghazi last September.
Moving forward, White suggests that by looking at these reactions, instead of a military and counter terrorism approach, we can build-up "myth-busters" and thus introduce new narratives that can help prevent violence, not just in only Libya, but the region at large.
Thousands of miles away from where the Arab Spring bloomed, CSO is applying similar methods in Kenya, where more than 1,000 people were killed and 350,000 displaced after the 2007 elections. Last year White and his team integrated similar patterns of large data analysis to help Kenyans prevent violence during last month's elections.
North Korea is another conflicted area that remains a pivotal threat not only for the United States but also for the rest of the world. Both Abdollahian and Raphael Carland, director of partnerships and communication at CSO, say the area lacks readily available "information clouds" that make up the first layer of large data analysis. But by using its analytical tools, it's possible to fill in data gaps by correlating conditions in comparable environments.
Preventing another Boston
There is also strong potential in using large data analytics in the United States to help forecast terror attacks like the bombing at the Boston Marathon two weeks ago.
"Absolutely we can make this work domestically in the U.S., however the challenge is that people guard their privacy from the government very closely, so then we might face privacy-issue challenges when running human patterns and large data in the States," Shiffman said.