TAAS 2023 Speakers
The theme for TAAS 2023 is “Developing Sophisticated Analytics Programs.” Speakers and topics have been selected that address this theme to create a program that focuses on both theory and practice. We look forward to the conversation!
Leveraging Data Analytics to Drive Business Value and Improve Performance
In today’s fast-paced business environment, data analytics has become a crucial aspect of organizational success. Leveraging data analytics to drive business value and improve performance can help organizations gain valuable insights into their operations, identify new opportunities, and improve operational efficiency. We will discuss the importance of data strategy, data quality, data preparation, and data visualization in effective data analytics, common data analytics techniques, measuring the ROI of data analytics initiatives, and communicating the value of data analytics to key stakeholders.
As the Executive director of Decision Science and Analytics department at University of Texas MD Anderson Cancer Center, Gokul oversees campaign management office, prospect development, business solution, reporting and analytics teams and he is part of philanthropy division senior leadership team. He is building an end-to-end analytics solution team to provide business insights, responsible for leading the development and implementation of new technologies and data driven strategies to inform and improve decision-making and provide empirical assessment of performance at the institutional, divisional and team levels with respect to constituent engagement and philanthropy. He taught data science, database management and visual analytics for professional MBA and MS in business analytics program at University of Iowa college of business and he’s also a meditation trainer. He has significant experience with advanced analytics, managing complex data projects and leveraging technology to drive efficiencies and effectiveness, AI/Machine learning, data management/governance and strategic leadership experience driving divisional change through the use of analytics.
Md Rezwan Islam
Uncovering Actionable Insights for Customer Service Improvement: A Text Mining Approach to identify key themes
The explosion of data in the digital age has created a pressing need for organizations to extract valuable insights from text data. Higher education institutions, in particular, receive a significant amount of text data from various sources, such as alumni surveys, student feedback, and social media. This unstructured text data, which typically comes in the form of open-ended responses, presents a significant challenge for organizations seeking to extract meaningful insights. We will present a case study of North Carolina State University’s advancement shop leveraged text mining algorithms to extract valuable insights from our “Report Request” dataset.
Md Rezwan Islam is a Business Intelligence & Analytics Developer at NC State University’s Advancement Services. With over five years of experience in higher education, Rezwan leads data experiments, analytical studies, and machine learning projects to drive data-driven decision making for fundraising initiatives at NC State. While he enjoys building analytical models and writing codes, his true passion lies in delivering clear and accessible results and insights
Tim Boyd is the Director of Analytics and Data Services within Advancement Services at NC State University. Tim has 10 years of analytics and data management experience within the realm of higher education. As Director, Tim is driving towards comprehensive visual analytics solutions that drive decision-makers towards better insights. He is also very interested in leveraging the power of predictive modeling to uncover new opportunities with philanthropic constituents.
We will examine how the best practices behind DataOps can be translated to the context of Advancement. It is a brief review of the methodologies from which DataOps was derived: Agile project management, Lean Development, and DevOps. These methodologies have each been proven to lead to faster, higher quality software development. Adopting the DataOps methodology could yield similar results for a sophisticated analytics program in the context of Higher Education Advancement.
We’ll, also, outline a framework for adopting DataOps using the University of Texas’ Development Office as an example. This framework includes practical steps for forming a cross-functional DataOps team as well as for changing coding practices.
Michael Tzaperas is an Austin native working at the same university he graduated from. He’s worked as a Data Engineer at SaaS companies to build and monitor ETL pipelines and data warehouses. Michael also managed Data Engineering teams and trained them to adopt DevOps coding practices. He’s bringing both of those experiences to my current position at UT Austin’s development office with a focus on data quality.
Meet Them Where They Are: A New Paradigm for Data – and Prospect-Driven Major Gifts
As Data Science becomes increasingly integrated into fundraising, Prospect Management is often still worked via lists. Ruthie Giles, prospect management expert, will discuss adapting Lead Management technology from the sales profession, and Marianne Pelletier, nonprofit data science expert, will discuss data science techniques to both automate and to boost the effectiveness of using lead management as a Major Gifts portfolio, timing, and suggested gift size tool.
A thought leader in the field of prospect management, Ruthie Giles has worked in prospect research and prospect management since 2000. She has spoken on the topic of prospect management at APRA, CASE and NEDRA conferences, as well as to regional nonprofit professional groups.
Ruthie has held positions at University of New Hampshire, Westfield State University, Amherst College, Mount Holyoke College, Harold Grinspoon Foundation, The Loomis Chaffee School, and the Williston Northampton School. She is a member of the New England Development Research Association’s board of directors. She is the former Board President of the AIDS Foundation of Western Massachusetts and the former Board Vice President for Women in Philanthropy of Western Massachusetts.
Ruthie holds a BA from Smith College, a MBA from University of Massachusetts at Amherst – Isenberg School of Management, and a MS in Nonprofit Management and Philanthropy from Bay Path University. She is also a graduate of the Leadership Institute for Political and Public Impact, through the Women’s Fund of Western Massachusetts.
Marianne Pelletier has over 35 years of experience in fundraising, with the majority in prospect research and prospecting. She is one of the first adopters of donor modeling and data mining techniques, and is now a world-renown leader on this segment of the research profession. Pelletier’s career also includes running an annual giving program and providing software and conversion consulting through the Datatel Corporation.
Pelletier is a graduate of Rockford University and earned her MBA at Southern New Hampshire University. Her recent workbook, Building Your Analytics Shop: A Workbook for Nonprofits, is available both on Amazon.com and on this website.
Deployment and Integration of Machine Learning methods with Continuous Integration/Continuous Delivery
Machine learning methods are increasingly being used to support decision making in fundraising. Insights from these methods need to be integrated into a variety of end-user applications in order for university advancement staff to utilize them in their work. This can be a challenge because the systems and technologies used for machine learning model development are generally different from those used to deliver data to advancement staff. Consequently, separate systems need to be designed to support the automation and deployment of machine learning systems, as well as the integration of their insights to end-user systems including business intelligence (BI) and customer relationship management (CRM) tools.
We’ll follow the approach used by Penn State University’s Prospect Management and Analytics office to deploy Xgboost models, as well as Deep Neural Network based Natural Language processing models. This approach utilizes continuous integration/continuous delivery (CI/CD) through gitlab to provide version control for project code while also supporting code-based automation to the AWS cloud. Machine learning project code is then run in pre-built docker container environments on cloud servers. Finally, the output of these processes is automated to endpoints where they can be made available to the CRM and BI tools. We will outline the approach, provide code resources that can be used to replicate the approach, and discuss some best practices that have arisen from utilizing the approach over the last year.
Drew Wham, Ph.D., currently serves as the Director of Data Science and Analytics in the Office of Prospect Management and Analytics at the Pennsylvania State University. Leveraging his expertise in advanced machine learning techniques and modern technical infrastructure and deployment practices, he is working to transform the organization’s approach to prospect management. Prior to his current role, Drew worked with a team to develop data science-based applications that support student success by providing near real-time data and alerts to instructors and advisors. In addition to his current role, Drew helps shape the next generation of data scientists as an Adjunct Professor at Penn State, where he teaches ‘Statistical Learning through Computation. Drew Wham earned his Ph.D. in Biology from Penn State, specializing in statistical genetics.
Jonelle Bradshaw de Hernandez
Prediction Analytics Process for Determining Foundation Giving Gift Capacity
Jonelle Bradshaw de Hernandez, an accomplished professional with a passion for advancing transformative initiatives at The University of Texas at Austin. As the Executive Director of Foundation Relations and a Research Assistant Professor at the Information role, she plays a pivotal role in collaborating with academic and advancement leaders to strategize and implement innovative fundraising programs that garner significant support from foundations.
At the heart of her research lies a fascination with the convergence of science and technological breakthroughs, as well as risk perceptions and their impact on fundraising and job security. Dr. Bradshaw de Hernandez’s mission is to cultivate a robust U.S. workforce that prioritizes social good and drives positive change.
Before her tenure at The University of Texas at Austin, she held the position of Senior Director of Corporate and Foundation Relations at Stony Brook University in New York. A highly educated individual, she earned her Bachelor of Science in Human Service Studies from Cornell University’s College of Human Ecology, specializing in Social Policy and Community Development. Subsequently, she pursued her passion for organizational psychology, achieving a Master of Arts from Columbia University, Teachers College. Furthermore, she obtained an Advanced Certificate in Instructional Leadership from St. John’s University and successfully completed her Doctorate from Stony Brook University’s College of Engineering and Applied Sciences, focusing on Technology, Policy, and Innovation.