Scaling Data Skills for Multidisciplinary Impact
Colloquium Concept Note - 2023
Join the NEMISA 2023 Colloquium to explore how data skills can drive social and economic impact, bringing together stakeholders from government, academia, industry, and civil society.
Data is today a new and desirable commodity that needs to be an important part of massive digital skilling efforts (World Economic Forum, 2022). The proliferation of data and the affordance of modern digital technologies has therefore triggered many organizations to contemplate data skills as part of their core skills needs. Increasingly, smart governments and organisations require data skills to gain insights and foresight, secure themselves, and for improved decision-making and efficiency.
However, data skills are scarce (Markow et al., 2017; Radovilsky et al., 2018), and even more challenging is the inconsistency of the associated training programs with most curated for the Science, Technology, Engineering, and Mathematics (STEM) disciplines (Saltz et al., 2018). Nonetheless, the interdisciplinary yet agnostic nature of data means that there is an opportunity to expand data skills into the non-STEM disciplines as well (Gascó-Hernandez & Schaupp, 2022; Jie et al., 2020).
The NEMISA 2023 Colloquium on “Scaling data skills for multidisciplinary impact” seeks to bring together government, international organisations, academia, industry, organised labour, and civil society to deliberate on how data skills can be scaled in South Africa for social and economic impact. Some of the questions that the events will engage on include, but are not limited to:
Importance of Data Skills and Challenges
1. How can data skills be scaled into government and organisations to produce greater insights and decision-making?
2. How can use data skills to share the limited resources that are available, especially in marginal contexts?
3. How can rural and local communities benefit from gaining data skills?
4. What case studies can we adopt for scaling data skills?
5. What career pathways are necessary to scale data skills?
6. What are the likely forms of resistance that can be expected when attempting to scale data skills and what are the solutions?