Amid rapid growth in data complexity and technology advancement, many organizations still struggle to manage, understand, and measurably improve their legacy and evolving data ecosystems. In this hands-on data lab, you will learn how to build flexible data enrichment models, reusable tools and repeatable processes to 1) establish a constituent data integrity index/baseline 2) explore, visualize and measure constituent data integrity 3) identify and prioritize opportunities to improve your overall data quality in systematic and sustainable ways towards insights, equity and efficiency.

I will summarize some of my research findings about bequests and I will explain why I utilized the Panel Study of Income Dynamics (PSID) publicly available dataset. Specifically, I will discuss my research comparing Caucasians and African Americans on their self-rated importance of leaving a bequest to family, charity, and religion, possible reasons for the bequest gap, and what opportunities this presents for education and service to an underserved community. I will wrap up with my study that explores whether lifetime donors to different types of charities also differ in the importance they place on leaving a charitable bequest. As a whole, individuals give more during life, through annual and major gifts, than they do at death. Many of these donors are an untapped resource for planned gifts, some more than others, and this data about which donor types are most likely to be interested in a charitable bequest could inform our conversations about philanthropy.

Amid rapid growth in data complexity and technology advancement, many organizations still struggle to manage, understand, and measurably improve their legacy and evolving data ecosystems. In this hands-on data lab, you will learn how to build flexible data enrichment models, reusable tools and repeatable processes to 1) establish a constituent data integrity index/baseline 2) explore, visualize and measure constituent data integrity 3) identify and prioritize opportunities to improve your overall data quality in systematic and sustainable ways towards insights, equity and efficiency.

RFM analysis is a great first step to analyzing and grouping your donors. By summarizing their giving history and ranking by Recency, Frequency, and Monetary values, you can cluster donors into smaller comparable groups. This can help with identifying possible future prospects, searching for a target donor audience, and allow for more personal communications to each group.

This interactive session highlights the union of two CASE resources: the Alumni Engagement metrics framework and the Advancement Inclusion Index. Join Jenny and Ben as they provide you with outcomes-driven next steps to apply broad-based data informed strategies in engaging diverse communities, regardless of your current organizational diversity, equity, inclusion, and belonging (DEIB) maturity level. You’ll leave with an understanding of resources provided by CASE; examples of how institutions are applying the framework to focus on ever-diversified communities; and how to identify gaps and potential barriers to establish pathways to build enduring and mutually beneficial relationships, drive positive change and foster a sense of belonging for all.