Chris Lam gives keynote speech on data literacy | Technical Communication
May 25, 2018

Chris Lam gives keynote speech on data literacy

By Bailey Herring

Dr. Chris Lam recently gave the keynote speech at the University of Central Florida (UCF)'s fourth annual Department of Writing and Rhetoric Symposium.

The 2018 symposium theme was "Identities in the Making: Materials, Methods, and Practices," emphasizing "the roles that disciplinary, professional, and personal practices play in the development of our identities as writers, scholars, teachers, experts, and citizens." Lam was invited to present to the group by the UCF faculty.

Lam spoke about the importance of data literacy and how it relates to being a professional in a variety of fields. In his own words, being data literate is having the ability to consume, produce, and apply various types of data in meaningful ways.

During his speech, Lam provided the example of how notorious MSG, a seasoning salt most often equated with Chinese food, has become. Though studies have shown that MSG does not cause headaches or other adverse affects, many people believe otherwise.

We use critical data literacy as consumers of information by asking questions: (1) what are the strengths and limitations of a particular data source? (2) what do you do when one data source contradicts another? and (3) how do you decide when to act on a particular data source?

"Even in the humanities, we have a skill set that allows us to apply and analyze all kinds of data," Lam said. "You don't need a PhD in statistics to build a case. Other sources can be just as valuable as hard data."

He notes that data literacy is the ability to use alternative data sources like visuals or personal experience to build a case, in lieu of sticking only to traditional quantitative studies. In fact, Lam argues that quantitative and qualitative approaches represent a false dichotomy.

Lam also spoke about the use of critical data literacy for producers of knowledge. In particular, researchers must be explicit about the limitations of their samples and generalizability of their results.