Managing your data for the benefit of your organization is not a destination but a journey. It’s a journey that ultimately matters to your organization’s bottom line and success in making data informed decisions within today’s competitive fundraising environment.

In this interactive workshop, we’ll cover basic data governance concepts and models. These concepts and models will be leveraged to develop a roadmap for tackling some of the challenges that you and your fellow attendees are facing in starting up or maturing your data governance program.

CASE collects institutional-level data on philanthropy from around the world. Institutions which submit CASE surveys have access to this data, which they can leverage to identify and benchmark against similar institutions. In this session, we demonstrate how to use R to access SQL databases, create visualizations, and conduct regression analysis on philanthropy data. Upon completion, participants will be able to use data for storytelling and strategic thinking.

Personalization matters, and in today's world of AI-driven technology, truly personalizing the donor experience is possible for any size organization. Join this session to see how new technology can help you change your outreach from "send everything to everyone" to producing a truly tailored experience for each donor. Hear key metrics from millions of communications about the power of personalization and how it can more than triple open rates. And hear how St. John's University transformed engagement with new technology that allows for personalization at scale. It's time to do donor engagement smarter, and if you're ready to ignite the passion of individual supporters using new tactics, this session is for you.

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.

At our educational institutions, we use assessments and remediation for better student outcomes.
So, how can you improve your data outcomes?

In this session – we’ll cover how to leverage the data quality life cycle to improve your data.

By grading your data (on consistency, validation, and recency) and remediating the elements that aren’t passing the grade – you can ensure that every data outcome is "better".

We created dashboards allowing a pro-active approach to successfully collecting pledges. By becoming aware of the high risk pledges, Fundraising professionals were are able to focus on the high risk donors, to properly steward and sustain their affinity to the university. Thus, mitigating the risk and increasing the chance that the donor will fulfill their pledge.

As personalized and curated experiences continue to take over digital and commercial spaces, donor engagement teams must continually innovate to keep up. All-in-one solutions can provide small teams with scalable platforms and comprehensive tools that drive results, but they often come with limitations in product development that don’t meet the needs of individual clients. Over the last year, Georgia State University’s Donor Engagement and Communications team has used data analysis, strategic engagement and personalized outreach to develop an internal donor experience program focused on long-term results: the acquisition and retention of mid-level donors with the aim of carrying them through the “murky middle” and establishing them as our next generation of major donors.

Much of the conversation about improving predictive models focuses on finding the optimal model technology, such as linear regression, logistics regression, classification trees, etc. We argue that there should be just as much focus on Exploratory Data Analysis (EDA) techniques by exploring, diagnosing and transforming the data - before, during and after the modeling process. We will also demonstrate how to apply different Exploratory Data Analysis (EDA) techniques to find optimal transformation and identify outliers.