Giving Compass' Take:

• Existing early warning systems are often ineffective in predicting malnutrition because they prioritize food insecurity and report nutrition crises as they are already happening. New projects are emerging which aim to capture and analyze large data sets to identify trends that can inform farmers and decision makers and help address some of the most pressing food system issues.

• How can donors help fund more innovative data projects such as these?

Here's another example on efforts to end malnutrition. 

Dr. Mercy Lung’aho barely survived her birth. She was anemic, like her mother, and weighed under a pound. Her life was at risk because of a disease that plagues one in nine people worldwide and one in four people in Sub-Saharan Africa: malnutrition. Lung’aho, a Nutritionist and Research Scientist with the International Center for Tropical Agriculture (CIAT), is now at work developing a Nutrition Early Warning System (NEWS) that could help predict and prevent malnutrition.

“If Google can do driverless cars, and we can tell a coffeemaker to start brewing while we’re still asleep,” says Lung’aho, “we can do something about malnutrition.”

The underpinning of NEWS is machine learning, a sector of artificial intelligence that develops systems able to process large data sets and learn from those data sets in order to locate patterns. NEWS will first compile and analyze data related to food and nutrition in sub-Saharan Africa. Then, based on the analyses, the system will deploy algorithms to locate patterns and trends that could identify impending nutrition threats, like climate shocks or economic strife, and provide decisionmakers with opportunities to intervene.

Read the full article on ending malnutrition through data by Anna Short at Food Tank.