Posts categorized Explainer

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Catalyzing Use of Gender Data

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 16, 2020 Global Data Policy
Data Use, Explainer, Results Data
What Do We Mean by “Gender Data?”

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.

March 6, 2020 Global Data Policy
Data Use, Explainer
Three Recommendations for Stronger Data Ecosystems

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?

March 13, 2019 Global Data Policy
Explainer
The Common Denominators of Administrative Data and Official Statistics

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. 

February 26, 2019 Data Management Systems and MEL
Explainer
Understanding National Data Ecosystems

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.

February 21, 2019 Data Management Systems and MEL
Explainer
Artificial Intelligence: A Silver Bullet or Scrap Metal for Global Development?

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

February 6, 2019 Global Data Policy
Explainer, Innovation
Mapping the Path Toward Collaborative Research

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

November 9, 2018 Agriculture
Explainer, Open Data
GDPR and its Connection to the Open Data Movement

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

October 22, 2018 Global Data Policy
Explainer, Open Data
What does Data Interoperability Require in Practice?

A few months ago, under the mSTAR project funded by USAID, DG and our partner Athena Infonomics (AI) set out to understand the underlying structure of the data currently being collected and managed by Feed the Future implementers, and how to best support them to open up and share their data through digital tools and best practices.

May 30, 2018 Agriculture
Explainer, Open Data
Achieving Sustainable Development Data: Where, and How?

When it comes to implementing Agenda 2030, country partners have shared two main data and digital pain points: knowing where Sustainable Development Goal (SDG) data will come from, and how to ensure this information is useful beyond reporting....

March 16, 2017
Explainer