Kelly Brown Director of the D5 Coalition explains the need for institutional philanthropy to include more cultural and technical competence and diversity when working with data. Silos need to be broken down as many foundations and nonprofits track and share data differently or not at all and despite data collection organisations still cannot speak reliably about impact. In addition, the data nonprofits collect about whom they serve and how is often unclear or incomplete and there is little systematic data around equity-focused investments. It follows that collaboratively improving the data collection capacity of the social sector is important.
Paul Connolly from Bessemer Trust talks about how most funders are not adequately tapping into existing data and knowledge on proven strategies to better inform their grantmaking sometimes due to lack of donor confidence. Although some big foundations are extremely influential 75 percent of contributions to US-based nonprofits are actually from individual donors, and the wealthiest 30 percent make three-quarters of these donations. He says that institutionalized philanthropy still concentrates on accountability at the expense of learning and suggests that bigger foundations join forces to develop shared metrics and better knowledge-distribution systems so smaller donors and nonprofits can learn more about what works and why as well as track their performance.
This post by Nicole Wallace in the Chronicle of Philanthropy gives examples of how nonprofits like The Humane Society, World Vision and Donor Choose are utilising data to be more effective. However, not all organizations are prepared to fully exploit data’s promise. In many cases, nonprofits cannot find the money and personnel to collect and analyze data and some experts say the charity world and Big Data aren’t a perfect match. For example, that while a business knows every product it made last year, when it sold, and to whom, charities, don’t which makes it difficult to apply wholesale the same data techniques that corporations use to solve social problems. He says that as a result organizations that are rigorous in the way they use information could actually be at a disadvantage when they’re compared with groups that are not as good and so this knowledge needs to be more widespread.
In this LSE post Prasanna Lal Das of the World Bank argues that the data revolution is already a fact of life even while traditional experts still hope for a gradual evolution and largely dominated the conversation. He says that changes to the supply and demand of data is already restructuring privileged hierarchies of knowledge. Greater capacity building, a more central role for national statistical offices, increased standardization of data collection efforts, smarter partnerships, and more resources for data agencies has been the focus of much discussion. However, much of the data (over 90%?) in the world now comes from ‘machines’ and the imbalance is likely to grow as machines gather more data and become better at analyzing this data and making sense of it. In addition, he says that amateur data hackers are powerful because of context familiarity and democratised and commodified data tools. He therefore asks if the new hackers aren’t the real data revolutionaries and if the real data revolution would not come from below.
Beth Kanter explains about why nonprofits do not necessarily need to have a trained data scientist, graphic designer, loads of time and expensive tools to visualize their data and tell a compelling story. She describes how Excel spreadsheets and Microsoft PowerPoint or Publisher can help and lists other useful infographic and data visualization tools.