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DATARAMA #3 - Interview of Lynn Cherny - Data Analysis Consultant, Dataviz Specialist & UI Designer

DATARAMA #3 - Interview of Lynn Cherny - Data Analysis Consultant, Dataviz Specialist & UI Designer
Lynn Cherny at Datarama #3 on September 19th, Nantes.

Interview with a data mining and analysis specialist

Lynn Cherny is a Data Analysis Consultant specialized in data mining and analysis, customer research, and interface design.

After receiving a Ph.D. from Stanford University in Linguistics, an M.Phil. from Cambridge University in Computer Speech and Language Processing and a B.A. from University of Maryland in Linguistics, she left research to work in industry as a UI Designer and has spent 20 years in various UI/UX roles. She received an academic fellowship teaching Interactive Data Visualization from University of Miami and served as Associate Professor at Emlyon Business School in Lyon, France. She is now consulting for an AI company in London. She is also the author of two books "Wired Women: Gender and New Realities in Cyberspace" and "Conversation and Community: Chat in a Virtual World". She shared her definition of design with us and tells us more about her professional path.

Nantes Digital Week invites professionals and the public to explore digital culture under all its forms

Datarama is a conference-dedicated day, as part of Nantes Digital Week, initiated by Banque Populaire Grand Ouest - LIPPI Connected Environments Chair. During the conference she gave, Lynn Cherny raised the question of what a story actually contains and how important it is to investigate the data before graphically represent it. She also addressed the political aspect of data storytelling: who tells the story? who decides if it’s worth telling?

Among the many data she reviewed and analyzed - from 1928 stories to nowadays movies - she noticed how poorly some protagonists were considered according to their gender or ethnic background. Her works highlight how those stereotypes have passed through time and continue to define the way stories are told today.

She thinks that graphic translations of those data can make it more obvious to people and make them realize that often "the news is not equal in how it treats the topics it’s covering".