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...
How can we build capacity for data analysis and translation? Learning from Countdown to 2030
Background DataDENT is working to identify knowledge & skills needed to support strong nutrition data value chain and ways to effectively strengthen the capacity of teams engaged with national nutrition information systems (NIS). We are committed to learning from...
Using validity as a criterion for selecting process indicators for the World Health Assembly 2030 nutrition targets
Last week the WHO hosted the 77th World Health Assembly (WHA). Back in 2012, the 65th WHA endorsed a set of six global maternal, infant and young child nutrition targets with an endline of 2025. In May 2024, a proposal was shared with the Member States about extending...
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,...
Measuring food fortification coverage in large-scale household surveys: challenges and opportunities
Micronutrient deficiencies affect 1 in 3 persons globally and contribute to disease and lost productivity. Large-scale food fortification (LSFF) is a widely used strategy to address micronutrient deficiencies by adding micronutrients to staple foods at the point of...
What knowledge and skills support better use of nutrition data?
We previously reported hearing the following question from our national partners: How do we strengthen capacity to use nutrition data? To help answer this question, DataDENT is taking a critical look at the knowledge and skills underpinning this capacity at the...
Bringing a human-centered approach to strengthening nutrition data value chains
Questions that DataDENT regularly hears from our national partners include “how do we strengthen capacity to use data?” and “how do we improve data quality?” DataDENT’s answer to each of these questions is to start with understanding the desires, needs, and...
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...
How can we build capacity for data analysis and translation? Learning from Countdown to 2030
Background DataDENT is working to identify knowledge & skills needed to support strong nutrition data value chain and ways to effectively strengthen the capacity of teams engaged with national nutrition information systems (NIS). We are committed to learning from...
Using validity as a criterion for selecting process indicators for the World Health Assembly 2030 nutrition targets
Last week the WHO hosted the 77th World Health Assembly (WHA). Back in 2012, the 65th WHA endorsed a set of six global maternal, infant and young child nutrition targets with an endline of 2025. In May 2024, a proposal was shared with the Member States about extending...
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,...
Measuring food fortification coverage in large-scale household surveys: challenges and opportunities
Micronutrient deficiencies affect 1 in 3 persons globally and contribute to disease and lost productivity. Large-scale food fortification (LSFF) is a widely used strategy to address micronutrient deficiencies by adding micronutrients to staple foods at the point of...
What knowledge and skills support better use of nutrition data?
We previously reported hearing the following question from our national partners: How do we strengthen capacity to use nutrition data? To help answer this question, DataDENT is taking a critical look at the knowledge and skills underpinning this capacity at the...
Bringing a human-centered approach to strengthening nutrition data value chains
Questions that DataDENT regularly hears from our national partners include “how do we strengthen capacity to use data?” and “how do we improve data quality?” DataDENT’s answer to each of these questions is to start with understanding the desires, needs, and...