More Than a Buzzword: Why Data Matters to Prospect Development

As prospect development professionals continue to seek innovative ways to gain better insight to their donors, data analytics is becoming an increasingly important tool. Having a clear understanding of how to use data in a meaningful way helps researchers identify trends over time and ultimately allow their organization to excel. We spoke with Renée M. P. Teate, data scientist and keynote speaker at this year’s Apra Data Analytics Symposium, to learn why data science is important to the field of prospect development.

Connections: Why are you passionate about data science?

Teate: Data science is a process of discovery and can be applied to any industry. Businesses and all kinds of organizations are collecting more data than ever before and want to use their data to answer questions about their customers, competitors, machines, samples, subjects, students, constituents, etc. I love exploring a dataset and uncovering insights no one knew before. And now with machine learning techniques becoming more accessible, and new data science educational resources freely available online, there are some advanced analytics that take those insights to the next level. But, there are also dangers and pitfalls that come with data science approaches, and I seek to understand and spread the word about those, too.

Why is data science important to the prospect development profession?

It's important for the prospect development profession for the same reason it's important for other professions: Prospect development is all about gaining insights into potential donors, and learning everything you can about your existing constituents to better understand your donor base. It's a profession that already heavily uses data — both collecting it and buying it from vendors — and you want to make the most of those resource investments. It's an exciting time in the non-profit fundraising domain for data analysts, because most institutions have the transactional database systems in place and, in many cases, a massive amount of data is sitting there waiting to be analyzed. Organizations that don't have as much data yet are exploring what types they should be collecting and how they should be storing and making the most of it, so prospect development is not a profession averse to data-driven decision making. The people who work in this profession also understand the need for privacy, data governance and sound practices to protect your donors; it's closely connected to their data points as people and not just numbers in a file, which is a good thing.

What do you hope Apra members take away from your keynote presentation?

I hope that sharing my path from being a data analyst writing SQL code to pull and filter contact lists, to a data scientist using machine learning to discover patterns and trends in a university's data will be helpful for data-driven attendees. My hope is that attendees will take away a better understanding of what data science is, what you can do with it in this industry, and how to go about learning what you need to know to apply data science approaches to your institution's data to gain new insights.


Renée Marie Parilak Teate is the host and creator of the Becoming a Data Scientist Podcast and the popular @becomingdatasci Twitter account. She got her start in advancement at James Madison University, and now does data science at HelioCampus, a higher education analytics startup. She is a graduate of James Madison University and the University of Virginia, and lives in Harrisonburg, Virginia. Before officially becoming a data scientist, Renée worked with databases for over 10 years as a relational database designer, SQL developer and data analyst.

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