Data Analytics — Advanced
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Zen and the HeART of Data Mining
Presenter: David Robertson, Syracuse University
The world is not perfect, and neither is our data. When analyzing data, it is critical to acknowledge error and discover the tools that highlight those levels of correlations interacting among the variables. The first step in data mining and predictive modeling is doing everything possible to create models that minimize this error. This session will use Microsoft Excel as analytical engine — a powerful tool available to all.
Data for Sale: Collecting and Using Data in Prospect Research
Presenter: Lawrence C. Henze, J.D., Target Analytics, a Blackbaud Company
In a March 2011 cover article, Time Magazine explored the collection and sale of personal data in the U.S. This brand new presentation provides an in-depth look at the sources and details of data available to non-profit organizations and vendors in the U.S. and Canada. We will also discuss how the data can be applied for planned giving purposes in particular. Topical areas that will be addressed:
Three (3) learning objectives and/or products for this presentation:
Wealth-X Demo: Domestic and International Research Focus
Presenter: Brian Gonzales, Wealth-X
Whether you are a small shop or a large one, often times the research demands can be overwhelming. Wealth-X employs nearly 200 full time researchers that cover 157 countries and 30+ languages. Having been spun out of Forbes, we not only have expertise in researching international individuals but individuals that come from private companies as well. This demo will cover our service offering and how Wealth-X is like adding an army of researchers to your team at a fraction of the cost.
Implementing an Electronic Wealth Screening
Presenters: Mary Gatlin, University of Oregon, Karen Prater, University of Oregon
Join members of the University of Oregon’s Prospect Management and Analytics team as they discuss their electronic screening experience. Hear how UO determined the need for an electronic wealth screening and the steps they took to bring it to their shop. Analysts will outline their process from start to finish including pre-proposal actions through their vendor selection process, data selection, verification and results implementation.
Using Game Theory in Analytics
Presenters: Gregory Duke, Niagara University, Rachel Link, Buffalo State College
The house always wins but how do they do it? Casinos, sports teams and business analysts among many others win when they successfully apply a combination of principles of probability and human psychology to their work. In this session, presenters will demonstrate how they have successfully used these techniques, collectively known as game theory, to improve their work as researchers and enhance their interactions with gift officers and other development staff. This session will teach you to play the game and win.
Presenter: Tanya Ford, Taylor University
Data Visualization in Support of Fundraising: A Discussion on an Emerging Trend
Presenters: Emily Walsh, University of Arizona Foundation and Kimberly Priebe, University of Chicago
Data visualization: It’s a term you have probably been hearing about on an increasingly regular basis in our industry over the past several years. What does this emerging trend in visual reporting mean for us as a profession? Is data visualization or visual reporting something I should be doing in my own office? How can I apply it to my job? How can I do it effectively? Are my peers doing it? If so, has it been effective? This webinar will provide participants with an overview of two recent articles published in APRA Connections, “Data Visualization in Support of Fundraising” and “APRA Membership Survey Results: Data Visualization in Support of Fundraising”. The webinar will provide: a brief overview of some techniques that we have found useful for producing impactful and meaningful data visualizations, an analysis and discussion of the industry survey results and possibilities for future benchmarking, and time for Q & A and open discussion.
Changing Face of Prospect Development
Presenter: Bruce Wenger and Andrew C. Schultz, Bentz Whaley Flessner
In the age of Big Data, analytics is changing the way we approach prospect development. By focusing on propensity, carefully crafted predictive models are identifying those prospects that “will” make a major gift not just those that “could.” This however is only the beginning. Analytics is already providing insights into which gift officers will be successful and how many visits may be necessary to close a gift. Annual funds are focusing resources on what the next ask should be and through what channel the ask should be made based on data rather than tradition. Join us for an exploration of the role data science is playing in philanthropy and a look at what may be coming
Deciphering the Information: A Case Study
Presenter: Tanya Ford, Taylor University
Who are your best prospects? The Data Analysis and Prospect Identification Project focused on deciphering the Taylor University Advancement database to use in identifying promising major and principle gift prospects. This case study provides a practical illustration from start to finish of the analysis of the data, application of the findings and review of the results that can be adapted to create informed prospect management strategies for your organization. Focus will be on understanding the importance of your data by shifting the spotlight off of capacity for a while.
Predictive Analytics 102:A look under the hood of a statistical model.
Presenters: Mike Laracy, Rapid Insight; Chuck McClenon, UT Austin
In this session Chuck McClenon from UT Austin and Mike Laracy from Rapid Insight will discuss some of the statistical concepts behind predictive modeling. From demystifying R-squared to understanding how hypothesis testing and p values work, this session will give attendees a deeper understanding of the basic concepts that enable us to build predictive models. The Rapid Insight® Analytic Suite will be used at the end of the webinar to demonstrate some of these concepts.
Learning Objective 1: Attendees will acquire a deeper understanding of the mathematical concepts and statistical theory behind modeling.
Learning Objective 2: Attendees will gain knowledge that can be applied to their modeling efforts and data analytics in general.