Drastic cuts to donor aid in early 2025 are projected to have lasting consequences for nutrition in the coming decades. Governments and implementers need to do more with less. Timely and accurate food and nutrition data are needed to guide strategic choices about how...
Where should countries invest along the nutrition data value chain? A multisector perspective from Nigeria
During our recent assessment in Nigeria, DataDENT and our local partner NAHI found significant variability across the 14 Ministries, Departments and Agencies (MDAs) included in Nigeria’s National Multisectoral Food and Nutrition Action Plan (NMPFAN) in whether and...
Making qualitative insights more accessible: Using AI to understand key informant interviews
An essential part of improving the availability and use of data in the countries where DataDENT works is understanding the needs and priorities of diverse national and sub-national actors engaged with the nutrition data value chain. We recently carried out multisector...
Revisiting the nutrition data value chain: An updated framework to promote data use
For the last seven years DataDENT has championed the nutrition data value chain (DVC) as the guiding framework for our initiative. It is rare to see a presentation by a DataDENT team member that does not start with the DVC. DataDENT did not create the idea of a DVC or...
Using process maps to guide the scale-up of national nutrition information systems in Ethiopia
Moving multisector coordination from theory to practice is complex, and it is important to learn from country experiences. A multisector nutrition information system (NIS) supports coordination by synthesizing data from diverse data sources and across different...
Who needs nutrition data? Reflections from DataDENT in Nigeria
DataDENT and our local partner Nutrition, Agriculture and Health Initiative (NAHI) are close to finishing an assessment among national and sub-national actors implementing Nigeria’s National Multisectoral Plan of Action for Food and Nutrition (NMPFAN) 2021-2025. Our...
Data literacy in a world with both too much and too little data
In a data-driven world, individuals and organizations grapple with a paradoxical challenge: navigating an environment characterized by both data overload and data scarcity. This dichotomy creates unique hurdles for data literacy and informed decision-making in...
Developing pathways to cross-sector coordination of nutrition data in Nigeria
In October 2022, participants in the Nigeria National Nutrition Data and Results Conference coalesced around the need for improved nutrition data coordination and stronger capacity for data use across sectors and institutions. They called for development of a...
Ensuring readiness to monitor the reach of new nutrition interventions: A call to invest in coverage indicator development and validation
When a new nutrition intervention is introduced, stakeholders need to monitor the progress of scale up. However, there is often a lag between the introduction of an intervention and its integration into national data systems, including household surveys and...
Is AI the answer to our nutrition data problems?
“Nutrition needs a data revolution” – Global Nutrition Report, 2014 Is Artificial Intelligence (AI) the long-awaited answer to the call for a nutrition data revolution? AI is transforming the ways we can collect, collate, analyze, translate and use data. However,...
Data for Food and Nutrition: What does it really cost?
Drastic cuts to donor aid in early 2025 are projected to have lasting consequences for nutrition in the coming decades. Governments and implementers need to do more with less. Timely and accurate food and nutrition data are needed to guide strategic choices about how...
Where should countries invest along the nutrition data value chain? A multisector perspective from Nigeria
During our recent assessment in Nigeria, DataDENT and our local partner NAHI found significant variability across the 14 Ministries, Departments and Agencies (MDAs) included in Nigeria’s National Multisectoral Food and Nutrition Action Plan (NMPFAN) in whether and...
Making qualitative insights more accessible: Using AI to understand key informant interviews
An essential part of improving the availability and use of data in the countries where DataDENT works is understanding the needs and priorities of diverse national and sub-national actors engaged with the nutrition data value chain. We recently carried out multisector...
Revisiting the nutrition data value chain: An updated framework to promote data use
For the last seven years DataDENT has championed the nutrition data value chain (DVC) as the guiding framework for our initiative. It is rare to see a presentation by a DataDENT team member that does not start with the DVC. DataDENT did not create the idea of a DVC or...
Using process maps to guide the scale-up of national nutrition information systems in Ethiopia
Moving multisector coordination from theory to practice is complex, and it is important to learn from country experiences. A multisector nutrition information system (NIS) supports coordination by synthesizing data from diverse data sources and across different...
Who needs nutrition data? Reflections from DataDENT in Nigeria
DataDENT and our local partner Nutrition, Agriculture and Health Initiative (NAHI) are close to finishing an assessment among national and sub-national actors implementing Nigeria’s National Multisectoral Plan of Action for Food and Nutrition (NMPFAN) 2021-2025. Our...
Data literacy in a world with both too much and too little data
In a data-driven world, individuals and organizations grapple with a paradoxical challenge: navigating an environment characterized by both data overload and data scarcity. This dichotomy creates unique hurdles for data literacy and informed decision-making in...
Developing pathways to cross-sector coordination of nutrition data in Nigeria
In October 2022, participants in the Nigeria National Nutrition Data and Results Conference coalesced around the need for improved nutrition data coordination and stronger capacity for data use across sectors and institutions. They called for development of a...
Ensuring readiness to monitor the reach of new nutrition interventions: A call to invest in coverage indicator development and validation
When a new nutrition intervention is introduced, stakeholders need to monitor the progress of scale up. However, there is often a lag between the introduction of an intervention and its integration into national data systems, including household surveys and...
Is AI the answer to our nutrition data problems?
“Nutrition needs a data revolution” – Global Nutrition Report, 2014 Is Artificial Intelligence (AI) the long-awaited answer to the call for a nutrition data revolution? AI is transforming the ways we can collect, collate, analyze, translate and use data. However,...