Building Statistics and Data Science Capacity for Development

Citeable URL: https://scholar.colorado.edu/concern/articles/7w62f974t

Abstract

Data-driven decision making for sustainable development requires domain expertise to ask the right questions; high-quality, relevant data; appropriate, nuanced statistical analyses; and the power to make and implement a decision. Statistics enables and accelerates all of these aspects. We propose a new model for building statistics and data science capacity to engage in data-driven development. Statisticians and data scientists must be able to understand the data and projects they are working with on both a deep and broad level and be able to communicate the results of statistical methods and analytical work in ways that provide actionable evidence to those who can use it to positively impact society. Our model for building statistics and data science capacity is to create statistics and data science collaboration laboratories (“stat labs”) that work in the intersections of data-driven development by collaborating with data producers and data decision makers to transform evidence into action. We present lessons learned from the LISA 2020 Network, which has leveraged the collective experiences of more than 30 newly created stat labs in developing countries to build such statistics and data science capacity by focusing on the intersections of data-driven development.

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