Tricia Aanderud

Fiona McNeill
Senior Manager, Product Marketing for Financial Services
Red Hat

Fiona McNeill is a Senior Manager, Product Marketing for Financial Services, at Red Hat. Before joining Red Hat, she was the Executive Director of the Cognitive Computing Consortium and an independent marketing consultant. While at SAS, she was the Global Product Marketing Manager  where she oversaw the product marketing and messaging for specific SAS technologies. With a background in applying analytics to real-world business scenarios, she focuses on the automation of analytic insight in both business and application processing. During her 20 years at SAS, she worked with organizations across a variety of industries, understanding their business and helping them derive tangible benefit from their strategic use of technology. McNeill received multiple innovation awards at SAS, and when focused in the customer intelligence market, was identified as a Pioneer and one of the Most Influential People by  Prior to SAS, she was a member of IBM Global Services.

McNeill has published both in academic journals and several business publications, conducted education seminars, and presented at both academic and industry conferences over the course of her career. She received her MA in Quantitative Behavioral Geography from McMaster University, examining inter-temporal time dependence in consumer purchasing behavior, and she graduated cum laude with a BSc in Bio-Physical Systems from the University of Toronto.

By This Author

Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World

By Carlos Andre Reis Pinheiro and Fiona McNeill

A practical guide to deploying mathematical and statistical models when performing analytics. Packed with case studies on the entire analytical process using telecom and insurance companies based in Brazil and Ireland, Heuristics in Analytics provides CFOs, chief marketing officers, directors of marketing, and business managers with an insider guide to deploying mathematical and statistical models when performing analytics.