Schematically, NGOs dedicate resources to implement programs that serve their cause. To finance these resources, they must obtain funds from donors.
They face two main efficiency challenges:
Using data science can help these organizations ensure that they are spending their time, money and energy where it is most needed, and in the most effective way possible. Similarly, data science can assist them as they need to demonstrate results to attract donors.
Data science allows them to :
Some examples of actions to be taken:
An analysis of the donors database should ultimately lead to a better knowledge and understanding of donors. This knowledge and understanding can then be used to both:
When allocating resources to a program, NGOs will want to identify where those resources will be used most usefully. This "usefulness" criterion is unique to each NGO, but it is by analyzing the options available under this criterion that the allocation of resources should be made. Having the necessary data and analyzing it is what will allow NGOs to better target their approaches to achieve their goals.
Similarly, monitoring these indicators with the dashboards will allow them to make sure that their actions remain effective over time, and thus to communicate the results to donors.
Finally, data science can provide operational assistance in the implementation of NGO programs. For example, predictive techniques can be used to estimate future needs, optimization techniques can be used to channel resources in the most efficient way, and computer vision techniques can be used to analyze satellite images and identify geographic areas to be served. An article in Wired details how satellite imagery has been used in Togo to target the distribution of economic aid to the populations that need it most: https://www.wired.com/story/clever-strategy-distribute-covid-aid-satellite-data/
And of course, like all organizations that have data, NGOs are concerned by the fundamental issues of developing a data strategy, implementing data governance and growing a data culture, as well as tackling the issues related to data quality.