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How Nonprofits
Use Data Science
to Improve their Impact

Data for Social Good: 
How Nonprofits Use Data Science
to Accelerate Impact


A lot has changed in the last few years with the advent of big data. nonprofits are no exception to this trend. In this article, we’ll take a look at how nonprofits are using data science to improve their operations and impact. We’ll also look at some of the biggest challenges they face as they embark on this journey.

Nonprofits are increasingly turning to data science to help them better understand their operations, donors, and impact. By using data science, nonprofits are able to make informed decisions that can improve their work and increase their impact in their communities and beyond. How they use data science varies, but areas, where it is frequently utilized, are in their outreach efforts and in measuring the effectiveness of their programs.

Nonprofits that use predictive models to identify patterns in their data that they could not have detected before are a good use of data analytics. For example, an organization may use machine learning to predict how many people will need food assistance next month based on past data. This information can help the nonprofit plan for resources, allocate the budget accordingly, and make more informed decisions about where to assign their time and resources.

In another instance, a nonprofit may use data science to understand which campaigns are most successful in reaching its target audience. This information can help the organization decide which campaigns to continue and which ones to modify or discontinue. That same process can help nonprofits optimize their website for better user experience and increased engagement, when a potential donor visits a landing page or a person deciding where to put their charitable dollars.

The following are real-world examples of how data science is helping organizations produce desired outcomes:

  • The environmental nonprofit Greenpeace uses data analytics to track the movements of ships sailing in international waters. This information helps Greenpeace determine where ships are transporting cargo that could be damaging to the environment.

  • Mercy Corps uses big data to identify trends in global poverty and then develops targeted interventions to address those issues. 

  • The Open Philanthropy Project (OPP) uses data to evaluate the effectiveness of grants it makes. OPP's data scientists use a variety of techniques to analyze grant data, including machine learning, natural language processing, and data visualization. This data is then used to make decisions about which grants to make and how to allocate resources.

  • The CHARITY: Giving What We Can project uses data to track how individual donors donate to charity. The project collects data on how individuals donate, what type of donation they make, and how much they donate. This data is then used to improve the effectiveness of charity campaigns.

  • Additionally, Data Science for Social Good provides a list of examples of how nonprofits are using data science to improve social welfare

The use case of data science in the nonprofit sector is diverse. Nonprofits have a lot of data that they can use for their missions. They are constantly collecting data about their donors, their volunteers, and their programs which in turn affects how they use data to improve their fundraising, their marketing campaigns, and their operations. Data science helps nonprofit practitioners understand their customers better. With these informed insights, organizations can improve their interactions with constituents and find new ways to use that data to support their mission and vision.

One of the most important benefits of data science for nonprofits is that it can help them identify and resolve problems more quickly. By collecting and analyzing data in an objective manner, nonprofit data scientists can identify possible issues before they become too large or serious to fix.

challenges facing nonprofit data scientists

Despite the many benefits of using data science, there are also a number of challenges that nonprofit data scientists face. One of the biggest challenges is the abundance of new and untested data available to them. This means that they need to be constantly learning if they want to stay up-to-date with the latest trends and techniques.

Another challenge is that many nonprofits have limited resources, which makes it difficult for them to invest in data science projects on a large scale. However, by focusing on specific areas where they see potential value, nonprofits can still make significant progress. Fortunately, there are organizations that specialize in transitioning nonprofits into data-driven, tech-enabled environments. Through these upgrades in technology, a community organization can transform into a high-impact agent of change.

how data science can improve the performance of nonprofits

Data science can help nonprofits reduce expenses, increase efficiency, and improve the effectiveness of their work. By analyzing and interpreting data, nonprofit organizations can make informed decisions that support their missions and values. Understanding how people use your organization’s services creates opportunities for growth while minimizing risk.

The use of data science to collect and analyze information is useful for their operations. It addresses the problems and issues typically faced by nonprofit organizations, which can be used to develop solutions that improve their effectiveness. In practice, a nonprofit could use data science to track the spending of its members and develop a model that could identify where spending is highest and lowest. This information is then used to make changes to their budget or system so that they’re more efficient in providing services.

Another way data science can be used to improve efficiency is by identifying which areas of your organization are most inefficient and need improvement. This information can then be used to come up with new processes or strategies that would help these areas function more efficiently. For example, if you wanted to know how many volunteers were working on a project, you could use data science to develop an algorithm that determined how many hours each volunteer was working on the project at any given time. That data would then allow you to better allocate resources in order to make sure that everyone who needed help was getting assistance.

Improved Effectiveness through Data Science: Using Social Media Analytics.

As one would expect, data science has even found its way into nonprofits' social media, a domain where many organizations fall short. Data analyze their social media accounts in order to determine what messages are being communicated and why they’re being shared (or not being shared). This information is then used as part of an analysis plan in order to identify which messaging campaigns are effective and why. Social media monitoring tools like Sprout Social and SocialBlade can help nonprofits see which pages are posting negative or positive content, so they can adjust their messaging accordingly (or even remove posts altogether).

Numerous nonprofits are looking to use data science to help improve their operations and impact. Improved accuracy of data leads a nonprofit to more accurately track their progress and better decision-making. Improved quality of data can lead a nonprofit to identify patterns and trends that could be used to improve its outreach or programs.



Data science is a powerful tool to improve the effectiveness of nonprofit organizations and their outcomes. Good data helps nonprofit professionals achieve programmatic goals faster and more effectively. When you weave data science practices into the fundamental design of a nonprofit, the organization becomes more efficient, optimized, and impactful – which is tantamount to better experiences for both practitioner and participant. All of these aforementioned elements create a better, healthier organization. 

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Maths + English is a personal blog documenting my journey into data science. Topics I cover include ethics & data, machine learning, data analysis, business intelligence, and behavioral design,

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