The contemporary and increasingly popular notion of data for development is largely framed around the use of Big Data in developmental contexts, as well as for supporting social indicators monitoring. Big Data usually refers to big datasets where the sheer volume and velocity of the data challenges available platforms and algorithms for storage and analysis. Often this data is aggregated, analyzed and packaged to inform macro level (e.g. national or sub-national) decision making and action – what country has the highest income or what region has the lowest unemployment rate?

In contrast, we are exploring Small Data and sustainable development. Within the UNU-CS Small Data Lab we do not work with small datasets; indeed the volume of data can be enormous. Instead, Small Data refers to data that is processed at its finest granularity (i.e. the sampling unit and the unit of analysis are similar). For instance, in the context of social indicators monitoring, if the data is composed of individually sampled datum then the comparative analysis is between the individuals. Small Data empowers individuals and local actors with actionable insights while also assisting national stakeholders with a better understanding of the complex and diverse social phenomena. Small Data can be sourced informally and dynamically via the crowd, leveraging grassroots contributors, citizen generated data, and social media. Within the Small Data Lab we study the interplay between the various data sources, for example between social media and traditional mass media, or between grassroots and national development metrics, to provide a more balanced and holistic understanding of the nuanced social phenomena.

The Small Data Lab aims to generate research outputs and inform policy towards the mainstreaming of Small Data approaches for sustainable development, and to empower individuals and community level actors with relevant ICT artifacts.

Small Data & SDGs
The Wisdom of Multiple Crowds During Critical Events

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