By testing the use of non-traditional data sources, we want to discover new ways to build inclusive and sustainable transport systems. For this purpose we conduct experiments using and combining different types of data. In Bangkok, Thailand, we are experimenting with data from ride hailing services to find out if and to what extent we can gain insights on the urban transport system from the data. In Dar es Salaam, Tanzania, we are building a prototype of an integrated platform that combines different data sources to help decision-makers gain actionable insights into urban transport routes.

In this experiment we test whether we can use our ‘internal data treasure’ to train artificial intelligence models that may serve to improve our services for our target group/project beneficiaries. In a first step we focus on determining how data gathered from GIZ projects in the agricultural sector may be used to train sophisticated crop yield projection models.

The lack of quantitative, reliable and up-to-date data is a major obstacle for evidence-based, high-quality service provision in fragile – frequently inaccessible, target areas. In this experiment the Data Lab will focus on healthcare as one of the most promising sample sectors and in order to try to increase the validity of service coverage projection through the combination of qualitative and quantitative data. This will help to better plan the maintenance of existing healthcare infrastructure and (to a lesser extent) to plan the development of new healthcare facilities.

Using large datasets, such as satellite data or online search information, we are trying to identify positive outliers, often referred to as positive deviants, that fare better than their peers with regards to crucial development outcomes. Once identified, the aim is to understand and later to scale their locally-sourced solutions within their communities.