Apra DAS Group Discussion Recap: What Does “Big Data” Mean for Fundraising Analytics?

By Brett Lantz, University of Michigan

The following is a recap of the group discussion that centered on “What Does ‘Big Data’ Mean for Fundraising Analytics?”, as part of the Data Analytics Symposium (DAS) at Prospect Development 2019. 

The session “What Does ‘Big Data’ Mean for Fundraising Analytics?” focused on defining the concept of big data, identifying key roadblocks and next steps for implementing big data strategies at our home organizations. 

We identified several key takeaways:

Big data allows us to create more donor-centric relationships. Using data on specific interests and experiences treats people as individuals rather than clusters.

Artificial intelligence uses big data to tailor communications to individuals. As opposed to a more traditional modeling approach, which creates much larger segments of individuals treated exactly the same.

Big data does not necessarily imply external data. Although we often think of big data as being from social media sources like Twitter and Facebook, there are numerous internal sources of “big data.” For example, there may be donor interests buried in the text of visit notes, gift agreements, email clicks, etc.

Big data grow exponentially. They provide a wealth of detail about individuals and their behaviors, but this can quickly become unmanageable. For example, a database of one million potential donors, solicited 10 times per year is 10 million communications. If each communication tracks three engagement behaviors (e.g., open, click thru, and shares), this is potentially 30 million data points in a single year — which is beyond the capabilities of a desktop computer and requires specialized cloud infrastructure to analyze.

Staffing big data projects is often more difficult than anticipated. Due to the sheer volume of data, big data projects require programming experience outside the typical data science skillset. One attendee noted that their organization began a big data project with a budget for one data analyst, who was quickly overwhelmed. Instead, a data engineer is typically required for big data work, whose role focuses more on computer science than business analytics and storytelling.

Big data can be “creepy” if taken too far. Prospective donors expect us to know their publicly available data, but we should avoid using sources that they consider private.

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