Our personal and digital interactions are being recorded in data warehouses around the world. In developed markets we see a value exchange for this “big data;” individuals elect to trade personal information for life-enhancing services (better deals, more precise GPS directions, improved credit access, etc.) However, the proliferation of personal data now has individuals rightly concerned that their information is overexposed: that companies and governments know too much.
But this post is not about “big data” in developed markets. It is about “big data” in developing markets, and the cost of being underexposed and therefore underknown.
Too often basic information on the poor – who is poor, where do they live and move, how do they manage their social and financial lives? – is scarce, while the costs associated with being underknown are significant. Without formal financial histories, creditworthy individuals cannot access loans when needed most, vaccine workers cannot determine what percent of a region they have immunized from a disease, and relief organizations cannot anticipate where people move when catastrophic events occur. Overall, the quality and efficiency of providing services to the poor suffers.
But the developing world mobile explosion can change this.
Today we launch our “Using Mobile Data for Development” report, where we investigate the promise and challenges associated with mobile data analysis. We observe that few technologies have diffused as fast and as far as the mobile phone – especially amongst the poor. Whereas ten years ago few in the emerging world had access to mobile devices and service, now over 70% of individuals either own or have access to a mobile phone in most African and South Asian countries. Billions who were previously digitally invisible now exist in data warehouses around the world.
Among other things, mobile phone data allows us to understand how populations move and interact with one another and how individuals manage their social networks and financial lives. Studies show the potential to use this data to track pandemics like malaria, create alternative credit scores for low-income individuals, improve adoption of agriculture or finance innovations, and locate displaced populations after disasters. These innovations, while untested, represent a potential step-change improvement in supporting the lives of the poor.
Despite our excitement, several questions must be addressed. How can we smoothly extract and analyze data without disrupting mobile operator systems? How much impact will these analyses have on the ground? And, most importantly, how do we expose and analyze data in safe, privacy protected ways?
We invite commercial and philanthropic players to partner with us as we answer these questions and pursue this opportunity.
To read the full report, click here. To read an article about our research in Fortune, click here. To learn more about the Gates Foundation's Financial Services for the Poor Strategy, click here.
 Researchers used mobile phone data in Kenya, Tanzania, Namibia, Mexico and other cites to identify the dynamics of human carriers that drive parasite importation between regions. A number of studies show potential to create high resolution real time models of the spread of malaria and other diseases.
 Financial product innovation: Several companies have looked at using mobile phone data to estimate an individual’s credit worthiness or complement financial products in other ways.
 Data driven marketing to drive uptake and behavior change: Researchers from MIT worked with a mobile operator dataset, and found a 13x improved conversion rate in VAS product purchase when customers were targeted using mobile phone metadata and social network analysis compared to a control group. These customers also retained the service sold at a 98% rate, compared to just 37% in the control group. These kinds of techniques might be applied to drive adoption or behavior change in mobile money or agricultural applications.
 Post disaster tracking: Researchers in Haiti after the 2010 earthquake used mobile phone data to track displaced populations in real time, alerting relief agencies where to send aid