Posts categorized Explainer

Finding reliable agriculture data in sub-Saharan Africa is often difficult. If available at all, data is usually fragmented and tucked away in silos within government ministries or closely held by private companies. It is also significantly delayed or in a format that makes analysis difficult. For stakeholders who need information for decision making, a lack of reliable data is a significant barrier. The Visualizing Insights on Fertilizer for African Agriculture (VIFAA) program is working towards making fertilizer-related data, a key subset of agriculture data, more accessible to stakeholders for decision making.

As the world continues to face the effects of Covid-19, policymakers are turning to data more than ever to understand the scope of the crisis, anticipate its spread, and formulate policy decisions; but gender-disaggregated data are missing from the picture. Knowing what information is being captured and what is not could impact decision-making.

From our experience understanding data use, the primary obstacle to measuring and organizational learning from feminist outcomes is that development actors do not always capture gender data systematically. What can be done to change that?

March is International Women’s History Month. Throughout the next weeks, DG will be publishing a series of blogs that highlight and honor the work that we and others are doing to support the vital role of women. We’re kicking off the series with this post, highlighting the importance of gender data.

Incentives, accountabilities, and fitness-for-purpose influence how (and whether) data are used to drive policy. So what opportunities exist in national data ecosystems that can catalyze systemic change, toward greater evidence-based decision-making?

What does “fit-for-purpose” data actually mean? It depends: on who you ask, and what decision is at stake. For governments and development partners – particularly those who rely on data from country systems for program planning and management – much frustration came from perceived redundancies in statistical and administrative data systems. 

Within the Sustainable Development Goal context of “leave no one behind,” there exists an opportunity – and a pressing obligation – to support better outcomes for children. But much of the change needed must happen at country and local level, through better use of data and evidence in decision-making.

When someone mentions artificial intelligence (AI), it’s easy to conjure up two conflicting images: the first, killer robots whizzing past, replacing human jobs, daily tasks, and social interactions in a post-apocalyptic world; the second, a C-3PO-esque personality revolutionizing our health and food systems. Pondering this, we are also inclined to explore the question, where does

Development actors, ourselves included, talk a lot about the importance of opening up datasets and building interoperability in order to leverage the power of collective data – but often without clarity on what meaningful collaboration and sharing actually requires in practice. For example, what can a livestock project in Nepal and a rice project in Cambodia learn from each

Since this past May, you’ve probably received a flood of company emails updating terms of service and consent requests to give permission to collect your data. You also probably know that this flood is all thanks to the EU’s recent General Data Protection Regulation (GDPR), which has set us abuzz in its heightened protection of