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AD3ventures in Mapping
Administrative data – data from civil registration and vital statistics systems, and sectoral (health, education, judicial, social protection) management information systems – is rich with insights on citizen needs and priorities. Though timely and disaggregated administrative data can complement official statistics, it comes with its own data quality, availability, and use challenges.

AD3 ou une aventure cartographique
Par le biais d'AD3, nous avons développé un tableau de bord qui se concentre sur le climat, les prix du marché, les maladies du bétail, le niveau des fleuves et les précipitations annuelles, entre autres thèmes. Si le tableau de bord lui-même est un référentiel d'informations utile, la méthode d’élaboration des cartes et d’itération des commentaires des utilisateurs a permis d'identifier les bonnes pratiques en matière de visualisation des données.

Designing Data Visualizations: Merging Best Practices and Design Thinking
DG has been co-designing data visualizations with partners and stakeholders for over a decade. Thinking about the ways people process information is crucial to developing easy-to-understand data visualizations. In this post, we examine best practices for incorporating user-centered design into our data visualization outputs.

All In Your Business: Talking Data Governance and Privacy at Development Gateway
Our work is at the intersection of open data, technological development, and strategic advising to improve data use. We see growing questions about how we as a global community manage, share, reuse, and store data that is integral to our existing and future work. This blog is the start of a conversation we want to have.

What’s Your Story and How Can Data Help Tell It?
For as long as Development Gateway has specialized in data, we have also specialized in data visualizations. In that time, we have discovered the pitfalls and learned ways that data visualizations can increase data use. In this post, we look specifically at selecting the right type of visualization for the story you want to tell.

The Building Blocks for Successful Data Visualization Tools
In 2020, we sought to answer a pivotal question: what are the good practices and lessons learned from the many existing women’s, children's, and adolescent’s health data visualization tools? In partnership with UNICEF, DG worked to identify good practices, as well as to determine any differences for emergency-focused data visualization tools, using COVID-19 as a test case.

Sourcing Fertilizer Data in Sub-Saharan Africa
In advance of the first VIFAA country dashboard launch next week, we will explore the importance and source of accurate and reliable data for each of the indicators. This is a crucial step in making data available in a way that stakeholders can use to inform their decisions.

Understanding Fertilizer Data
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.

COVID-19 Is Not Gender Neutral
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.
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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?