Strengthening the Skills Pipeline for Statistical Capacity Development to Meet the Demands of Sustainable Development: Implementing a Data Fellowship Model in Colombia
Pete Jones, Jackie Carter, Jaco Renken & Magdalena Arbeláez Tobón
Abstract
The UN Sustainable Development Goals (SDGs) create a global need to deliver on an agenda which relies heavily on quantitative data. This creates a demand for human capital with the requisite statistical and data skills to work on the challenges represented by the SDGs. Increasingly, long-term domestic solutions to the building of statistical capacities are being sought, in order to decrease the dependency on external support and ensure a sustainable flow of qualified professionals.
In this paper we argue for the value of thinking of statistical capacity as a pipeline which needs to begin in the education system, and illustrate how investment in this end of the pipeline can deliver a more sustainable and long-term pathway to building up the holistic skills-base to enable the challenges of the SDGs to be tackled. We describe an existing tried-and-tested experiential learning model called Q-Step based on developing skills in the workplace, and propose that the successes of this partnership-driven model illustrate how ‘data fellowships’ can fulfil some of the unmet capacity needs of the data revolution for sustainable development.
We illustrate our argument through a practical exploration of the development of such a learning model in Colombia. Although there are challenges in ensuring that educational access is equal, we demonstrate that there are significant opportunities and a data fellowships model opens doors for addressing local skills gaps to help deliver the SDGs.
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