Nonprofits with deeper data capabilities see stronger impact, transparency and decisionsDownload infographic #1Download infographic #2Download infographic #3
What are the driving motivations, emerging trends and pressing challenges that nonprofit organizations face today? What methods can they start using to accelerate their impact and achieve full potential?
Advances in technology and rapid digitization of information have made data more accessible, setting a new expectation for the collection and sharing of information. Constant connectivity has transformed the nature of interactions, resulting in pressure for nonprofits to reach constituents in new ways and communicate impact. These movements, coupled with emerging curiosity about artificial intelligence (AI), can paint an overwhelming picture on how to advance in data and analytics.
To assess the current state of data and analytics use in the nonprofit sector, IBM Corporate Citizenship and Corporate Affairs (CC&CA), in collaboration with the IBM Institute for Business Value, conducted a global study through April 2017 (see “Methodology” on page 20 for more details).
We found that organizations that are more advanced in data and analytics practices are more effective in driving performance against mission and achieving internal efficiencies. Despite the benefits of advancement, nonprofits are largely behind the curve, with 67 percent indicating they are in the preliminary stages of the analytics journey.
Respondents indicated that the nonprofit sector faces growing pressures on several fronts. In the past two years, the majority said they are increasingly pushed to advance data and analytics capabilities as part of meeting organizational objectives (see Figure 1).
To make meaningful advances, nonprofits need to commit to becoming data-driven, create a data-centered culture and collaborate effectively with partners, donors and others within their network. Organizations can begin to leapfrog ahead by:
• Capitalizing on advances in technology
• Upskilling staff
• Rationalizing use of internal and external talent
• Aligning funders to support data-driven practices
• Engaging in collaborative ecosystems.