Turning Aid Data into Insight

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Understanding what's really going on with international aid is complex. AidSight makes it simpler.

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.

The Challenge

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?

Web searches aren't precise enough and give her ads rather than answers. Other aid data sources are either too complex, too expensive, or just plain broken.

How AidSight Helps

AidSight can answer Sarah's question in three easy steps

Step 1: Search

Look for collaborators by name, location, or program

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Step 2: Explore

Follow connections between key funders, implementers, and leaders in the field

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Step 3: Validate

See how reliable an organization's data will be at a glance. No statistician needed!

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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

The Result

Thanks to AidSight, Sarah has...

  • Searched for and found possible collaborators for her new water project
  • Explored and identified the connections between these organizations
  • Validated and understood the details of these programs without worrying about incomplete or unreliable data

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 it

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The AidSight Team

Natarajan Krishnaswami

Nick Hamlin

Minhchau Dang

Glenn "Ted" Dunmire