Nonprofit organizations frequently work in areas where connectivity is unreliable, security is paramount and fast action is essential. In such demanding environments, traditional data processing reliant on cloud computing may not be the most effective solution.
With edge computing, however, organizations have a distributed framework to process data locally, where it’s needed. This approach enables nonprofits to gather and analyze data in real time, strengthen protections for sensitive information and improve operational efficiency in complex scenarios.
Here are three examples of how edge computing can benefit nonprofits’ data management.
1. Edge Computing Powers Data Processing for Humanitarian Aid
Nonprofits involved in disaster relief and humanitarian aid must depend on reliable connectivity and quick transmission of critical data regardless of where they are in the world. Because edge computing processes data near to where it is collected, it enables real-time analysis and decision-making without dependence on remote servers.
In 2024, the International Rescue Committee (IRC) leveraged edge computing and artificial intelligence to enhance real-time data processing for humanitarian aid through its Signpost project. This initiative utilizes mobile applications and social media channels to give assistance to areas with limited connectivity.
By deploying AI-powered chatbots on edge devices, the organization assists displaced individuals. Google, Cisco and Tech For Refugees have also partnered up with IRC.
And with the most recent set of Southern California wildfires, a relatively new WIFIRE Edge platform, which uses predictive AI and real-time edge computing, was put to the test. The Department of Homeland Security's Science and Technology Directorate, in collaboration with the University of California San Diego used the platform to gain insights into fire behavior and predict damage which helped firefighting teams stay proactive.
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2. Edge Computing Enhances Nonprofit Data Security
Nonprofits are obligated to protect the data that they manage, which can sometimes be quite vast. Edge computing processes information locally, reducing cloud transmissions and thus minimizing exposure to external threats.
A nonprofit healthcare provider in a remote or underserved area, for example, could use edge devices to collect and process patient data securely.
“Instead of sending data to the cloud, data is analyzed onsite, where it is generated,” writes Ben Cushing, chief architect for health and life sciences at Red Hat, in HealthTech. “By keeping and managing more data at the edge, these deployments reduce the cost, latency and data privacy risk of sending information across what may be multiple third-party networks for connectivity and cloud computing.”
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Edge devices equipped with artificial intelligence data processing and hardened with configurations that improve network reliability and performance, Cushing writes, “can improve patient outcomes by supporting clinical decision-making and cutting down the time it takes providers to diagnose and begin treating conditions — even in the most remote and rugged healthcare delivery settings.”
Zero trust is a key strategy for protecting the many connected devices in IoT edge systems. The cybersecurity approach requires all users and devices to be authenticated and continuously validated.
“The key to security is recognizing the need for an ongoing, holistic security approach, because security goes beyond just implementing perimeter walls,” Red Hat notes. “With security approaches like zero trust, gaps in architecture can be closed.”
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3. Conservation Groups Use Edge Computing to Optimize Resources
Nature conservation organizations must deal with challenges of limited resources while meeting their missions, such as combating deforestation or animal endangerment. Edge computing helps them by enabling real-time data processing in the field for swift decision-making and precise resource deployment.
Conservation X Labs, for example, is a nonprofit that leverages AI-powered technology to detect objects of interest, such as wildlife, and then collect and monitor massive amounts of data on them. The Galapagos Conservation Trust integrates Conservation X Labs’ Sentinel, an AI-powered wildlife monitoring device, with the trust’s own camera traps to identify endemic and invasive species as teams track wildlife behavior.
“Machine learning algorithms can be trained to recognize particular species, meaning the system can be used to alert researchers in near real time to the presence of invasive species,” writes Tom O’Hara, the Galapagos Conservation Trust’s communications manager.
Because data is collected and analyzed onsite, edge technology saves conservation groups the time and expense of traveling to retrieve images from remote sites. Also, there is less need for continuous cloud uploads, which can be unreliable in remote locations.
“The ability to drop a camera anywhere in the world is a game changer for conservation,” says Jes Lefcourt, senior conservation technology director for the Allen Institute of Artificial Intelligence. Lefcourt is director of EarthRanger, a software solution that presents sensor data in a dashboard for easy viewing. “Whether it’s to treat an injured animal or prevent a security incident, Sentinel allows field personnel to have a virtual presence anywhere, no matter how remote.”
