GuideStar and the Foundation Center recently combined forces to create Candid. Find out how the nonprofit is using machine learning, data science and other tech.
Just recently, two powerful entities in the non-profit sector combined forces to create an unprecedented data collaboration. GuideStar and the Foundation Center merged to form Candid. I talked with Brad Smith, President of Candid, about the technology, data standards and research being used to help nonprofits and foundations make informed decisions. The following is an edited transcript of our interview.
Brad: The power of the standardization of data is basically the ability to aggregate it across a sector which is a widely heterogenous sector. When you look at foundations, for example, the Bill and Melinda Gates Foundation...it is light years larger and more complex than the vast majority of the 90,000 foundations in the United States. So they are all very different and all describe what they do very differently so to be able to describe that with a common vocabulary is hugely powerful. When it comes to non profits, there's more than 1.3 million in the US and many more in the world and they all do different activities and so the ability to have data standards to standardize this information allows you to understand and answer a really important question. Like for example, what is the social sector doing about ocean conservation? The standards allow you to answer that question across all these capital flows, all these types of givers and all they types of doers.
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Karen: Most nonprofits are front line service organizations that don't have much of an IT staff or the knowledge to employ new technology. With Candid's help however, these groups don't have to reinvent the wheel...the information they need to learn and grow is at their fingertips.
Brad: The potential for data and technology in this sector is huge. I think it's really the key to helping this sector function as more than just a sum of its parts. The social sector is wonderful in its diversity but it can be very frustrating in it's degree of duplication. We've all heard the story of the feeding program in a city where they duplicate efforts and could be better off if they combined and had one kitchen. Or the ten different foundations that are funding the same organization without even knowing it and not funding the great organization next door. So the ability to begin to rationalize the use of these resources gives non profits the things they need to do their work. And it gets both individual and corporate funds a really quick and easy view of the landscape so they don't have to be the first person in history to fund a charter school. We've never been closer and in part because of the technology. We are using a lot of advanced technology, we have data scientists on staff and we are using machine learning as well. The advantage of being a historical organization is we're able to adapt machine learning quickly because we have such large sets of training information that was curated over years by humans. So you really can train algorithms on human coded data. With the combination of the two organizations, we're gonna double down on that and serve the sectors in ways we think it needs to be served, and do much more than we could do separately up till now.
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