The term “Big Data” has become a popular trope among practitioners and scholars of ICTD and international development more generally. Big Data usually refers to big datasets where the sheer volume of data challenges available platforms and algorithms for storage and analysis. Big Data often aggregates and analyzes information to make comparison between regional and political units, for instance the state. What country has the highest income or what region has the lowest unemployment rate? Thus, while the sampling unit may be the individual the unit of analysis is a country; large amounts of data are sampled at a small unit but analyzed at a big one.

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 amount of data can be enormous. Instead, Small Data refers to instances where the sampling unit and the unit of analysis are similar. For instance, if the data is composed of individually sampled datum than the comparative analysis is between the individual. Small Data empowers local actors with actionable intelligence while also assisting national actors with a better understanding of their internal diversity. In addition, Small Data can be sourced informally and dynamically via the crowd, leveraging grassroots contributors and social media. Small Data seeks a unit of analysis as close to the sample unit as possible and focuses on tools that empower local actors; it develops systems of analysis that add value directly to the sample.

Example research questions from the UNU-CS Small Data Lab include:

  • What new data acquisition and analysis platforms are needed in order to provide reliable and actionable real-time intelligence in fast-changing contexts?
  • How can local communities inform and empower themselves through adaptable data visualizations and locally relevant data analytics?
  • How can community based data best inform national decision-making and integrate into global UN datasets?
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