Basma Albanna
Basma, from Egypt, is a postgraduate researcher in Development Policy and Management. Her thesis title is "Leveraging the positive deviance approach using big data".
On my background
I wanted my research to be at the intersection of data and international development. When I came across the work done at the Centre for Digital Development and particularly the foundational research done by Prof. Richard Heeks in the field of ICT for development, I decided to apply and I was lucky enough to have him as my PhD supervisor. The University of Manchester, in particular, was on top of my list because of its very good reputation and the world-class research produced at the Global Development Institute. I believed that their PhD program is very well suited to the kind of research I want to conduct and it will help me gain the technical know-how and exposure necessary to become a thought leader in the field of data-driven development.
On my research
My research focuses on developing a method that combines non-traditional digital data (e.g. satellite imagery) and traditional data (e.g. interviews) to identify and characterise outperformers in development-related challenges. It builds on the “Positive Deviance” (PD) approach for development, which is based on the observation that in every population there are individuals or communities who, despite facing similar challenges and limitations, achieve better results than their peers. This approach focuses on these outliers (or positive deviants) in order to discover unusual practices and strategies that successfully solve complex problems – particularly where conventional solutions failed.
The growing availability of non-traditional digital data (e.g. from remote sensing and mobile phones) relating to individuals, communities and spaces enables data innovation opportunities for positive deviance. Such datasets can identify deviance at geographic and temporal scales that were not possible before. But guidance is needed on how this new data can be employed in the positive deviance approach, and how it can be combined with more traditional data to gain deeper, more meaningful, and context-aware insights. The data-powered positive deviance (DPPD) method I developed during my PhD research provides such guidance; enabling development practitioners to put knowledge about outliers into action as it uncovers solutions underlying their outperformance. Those solutions could then inform the design of community/policy interventions.
On my motivation
In 2015, I was undertaking a course on design thinking. This course introduced me to the positive deviance approach, which I found extremely intriguing at that time. One year later, I was developing a research proposal to apply for a PhD abroad. I wanted my research to lie at the intersection of my three passions: data, design and development. I was drawn to the field of big data for development which achieves this multidisciplinarity but felt that there has been a bias towards substitution instead of complementarity when it comes to its application. I believed that the biggest value of big data lies in its ability to complement traditional data. So, the starting point for me was to look for already existing development approaches that lend themselves to traditional data and complement this data with big data. At this point, I could not help but remember the positive deviance approach, about which I had learned in the previous year, and the analogy between the positive deviants of this approach and what we refer to as outliers in data. It occurred to me that perhaps big data might bring some new opportunities that could expand the use of positive deviance in development.
On my aspirations
The method I have developed is still at its operational infancy, holding both promises and challenges in its application in development. But I hope that the guidance provided through the case studies and action projects conducted during my PhD research will help it become a standard part of the data-for-development repertoire in the future.