Data-Driven
Grant Making

Where data-driven innovation could drive impact throughout the grant making cycle

Our work suggests that there are four areas in need of innovation across the grant making cycle

Identifying shared questions and priorities

There is a need for new approaches in the way common questions and priorities are developed among communities, the public sector, and philanthropies.

Iteration and adaptive experimentation

There is a need for new methods and applications to encourage a culture of learning and experimentation.

Coordination and transparency

There is a need for new approaches to collecting information across the philanthropic sector to better coordinate investment decisions.

Impact assessments and accountability

There is a need to better understand how investment decisions are impacting the communities they intend to serve.

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The responsible use of data-driven innovations have growing potential to drive impact across each of these areas. However, it is important to note that the use of data by itself cannot solve these issues. Data must be combined with lived experience and many other factors to drive innovation. 

The responsible use of data and analytical methods can provide value in supporting philanthropies’ decision cycle in four ways

Backward Looking

Situation analysis

Descriptive

What happened?

Cause and effect

Diagnostic

Why did it happen?

Forward Looking

Forecasting

Predictive

What will happen?

Experimentation (What if?)

Prescriptive

What should happen?

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Specifically, data-driven grant making can:

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Help philanthropies understand populations

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Build increased transparency and accountability

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Initiate evidence-based funding models

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Develop new approaches for experimentation

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Evaluate the impact of investments

Using data in the grant making cycle

There is no shortage of data-driven methods and tools available online. The challenge is determining which ones to use and for what purpose. Through our integrated grant making cycle map, we provide a list of emerging data-driven methods from the field and outline the philanthropic functions they could correspond with. Learn more by visiting the map.