Mobile data-driven analytics and solutions have a wide range of applications. From mitigating the effects of climate change, to informing policy decisions during disease outbreaks, the value can be demonstrated for a broad cross-section of the United Nations Sustainable Development Goals. The AI for Impact initiative has examined in detail several applications of mobile analytics, several of which are captured as case studies in this toolkit. The impact that mobile analytics can have has two different dimensions. Firstly, in transforming the options given to populations when affected by a crisis or facing environmental risks. And secondly, by offering Governments and agencies new decision support tools and data driven information to assist in problem solving.
Transforming the options for populations:
- Improved health of the population through disease containment and better targeting of healthcare resources. In India, our partners Airtel and WHO used mobile data to understand the spread of one the world’s most common and deadly infectious diseases, Tuberculosis.
- The ability to plan more effectively and therefore improve the quality of life for those displaced from their homes - whether that be as the result of a sudden crisis or a long-term change, such as that driven by climate change. In Latin America, TEF and FAO demonstrate how mobile analytics can be used to increase communities resilience to climate change.
- Data-derived intelligence may even save lives when made available to support rapid decision-making and response – for instance in the event of a destructive disaster, or an outbreak of a disease. In a ground breaking project, KDDI, OYO, Toyota combined mobile analytics, Internet of Things (IoT) to produce an AI powered decision response system during times of natural disasters.
Enhancing Governments’ Capabilities
- More efficient use of budgets –insights can be used to make more effective decisions about the allocation of resources, planning and response. Mobile networks may offer a more effective and comprehensive way to capture location-related data than other methods of data capture, such as traditional surveys.
- More effective plans – governments and agencies can use mobile analytics tools that create predictive models to mitigate risk, as well as react to events such as natural disasters and disease outbreaks. These can also be used to improve understanding of internal displacement and climate change, enabling public agencies to make better decisions about where to target long term development budgets.
- Improved ability to measure impact and demonstrate results – when decision making is based upon data-derived analytics, that data can be built into measurable impact reports (in addition to mining the data for insights). Mobile big data could even be used as a proxy for development ‘indicators’, such as poverty. Impact reports and indicators improve governments’ ability to track progress against their objectives and global goals (such as the United Nations Sustainable Development Goals or SDGs).
To realise these benefits, analytics and insights must be available to put into action when the challenge demands it. This means that the intelligence extracted from mobile data and other contextual data sources needs to be available in the right form and format, and ‘delivered’ in a timely way to decision-makers.
As well as technically integrating mobile big data analytics into the relevant tools and systems, the right people, processes and partnerships need to be in place to ensure there is a sustainable way of applying the mobile data derived intelligence.
To discover more about how to set up a mobile big data project for successful delivery and maximum impact, read the ‘Ecosystem’ section of this Toolkit.