We combine data from the International Aid Transparency Initiative (IATI) with machine learning and data science techniques to allow aid professionals to focus less on navigating messy data and more on making the world a better place.
Meet Sarah. She's working to improve access to fresh water in her community in West Africa.
How can she find the information she needs to create the best strategy for expanding her programs to Ghana?
AidSight can answer Sarah's question in three easy steps
Which organizations are already working to improve water systems in Ghana?
Sarah can examine data reported by over 500,000 development projects around the world to find people who are working on the same problems she cares about. But, unlike other tools that can access these datasets, she doesn't have to build complicated queries or read through endless lists of choices to find what she needs.Search Now
Clean water access in Ghana|
How are the organizations I care about connected with each other?
Sarah can't find the best growth strategy without having a clear understanding of the funding, implementation, and collaborative relationships between the development organizations operating in her area of interest. AidSight's network map turns the aid sector's messy data into a visual tool to explore these relationships without a single line of code.See Sarah's Results
How much can I trust the data that each organization provides?
Even though there are standards in place to describe how aid organizations should report data about their work, in practice interpretations of these best practices vary widely. As a result, practitioners like have to read through pages of data and make their best guess about whether or not they can rely on what they're seeing.
AidSight solves this problem with a new framework for evaluating the reliability of an organization's data. We've identified where important information is missing, numbers that may be fabricated, and places where useful data is hidden among pages of filler. All of this is rolled up into an simple letter grade that Sarah can interepret at a glance.
In places where data is missing or incomplete, AidSight also explores the network of related organizations and, where possible, directs users to possible replacement data sources. This approach gives Sarah the best possible opportunity to optimize her expansion strategy, even if the organizations she's researching haven't provided all the data she'd like.See an example dashboard
Thanks to AidSight, Sarah has...
Now, she can reinvest all the time and effort saved by using AidSight into actually carrying out her projects and bringing clean water to the communities that need itTry it yourself