Data Mapping
The Data Science Without Borders (DSWB) project uses data mapping to assess the data science readiness of African institutions. Data is collected through surveys, interviews, and document reviews, providing insights into data ecosystems, infrastructure, and training needs. The data is then analyzed to map out the existing data landscape, including dataset availability, governance frameworks, and storage solutions. This helps identify strengths and weaknesses in the institutions’ data capabilities.
DSWB uses models like the Higher Education Data Maturity (HESA) framework to evaluate data maturity, categorizing institutions into different stages. Data mapping also helps identify training needs, such as machine learning, data visualization, and data governance, and prioritizes areas for capacity building. The insights from data mapping are used to develop tailored solutions, collaborating with institutional leaders and data managers to improve data quality, strengthen governance frameworks, and foster a culture of data-driven innovation.
