Paul Szyarto, head of the Big Data program at the U.S.'s Rutgers University, said many developing nations are attempting to collect, organize and analyze large, varied data sets to uncover patterns and trends to help address poverty.
For example, countries like Kenya and India are gathering data related to weather patterns and using models to forecast climate variations, which can help farmers adapt agricultural practices, boost crop yields and tackle hunger.
While in West Africa, nations are trying to capture data on previous disease outbreaks, such as Ebola, to predict where an infection may begin, what may cause its spread and identify high risk zones which can be targeted with prevention programs.
But Szyarto told the Thomson Reuters Foundation on Monday that developing nations were not utilizing big data analytics to its full potential, largely due to low investment in infrastructure such as hardware, servers and computers.
"Thanks to technologically advanced countries, there are several models which could be leveraged to analyze data, but most developing countries lack the infrastructure to capture, gather, store and analyze the data being created," said Szyarto in an interview via email.
"Many of these countries lack the technical devices internally and externally to collect unstructured data due to corruption, low operational cash, and plagued poverty."