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.

"Join us for an interactive and guided panel discussion on automating the collection of marketing data, processing this data (ingest, deduplication, matching), and analyzing the data collected (visualize, report, insight delivery). In this session, plan to have open, casual dialogue with the panelists and your DRIVE peers on innovative strategies that streamline the automation of marketing communications such as event data, survey feedback, web analytics, social media, or email interactions. Whether you are just starting with your first survey, or you are looking to find solutions to better marketing automation, you will walk away from this session with useful perspectives on automation and practical applications of data collection."

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.

Across industries and in our personal lives, people are exploring the possibilities of generative Artificial Intelligence. In this brave new world, organizations are often independently developing policies around the use of tools like ChatGPT and Bard. Hear about institutional approaches; what peers wish they had known; and outcomes of early projects whether they were successful or not.

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.