Data Literacy: To the Synthesizers Go the Spoils

March 26, 2014
Geoff Krall

Geoff Krall

Last month saw the ever-growing MIT Sloan Sports Analytics Conferencewrestle with a new conundrum: what do we do now? This is a recent development in the field of sports analytics. Before, the “statheads” were the underdogs, punching upward at the curmudgeon scouts and sports writers that were caricatured so savagely in Moneyball. Now so-called “data-geeks” is no longer a pejorative. Now, almost every major sports franchise devotes a non-trivial amount of their expenses on a data analysis department. With the ascent of Nate Silver to prominence in the New York Times and now ESPN, the success of Freakonomics (the book, blog, and podcast), and the validation of the successful heavy data mining of recent presidential elections, the geeks have inherited the earth.

Still, these successes (quite literally, data driven) are the result of not simply computational prowess, but the ability to tell a compelling narrative from the data. Nate Silver’s true gift isn’t just his complex algorithms that correctly predicted the electoral outcome 49 of 50 states in 2008 and all 50 in 2012. It’s his ability to explain his methods, data sources, and errors with the richness of a magazine piece. Silver is a storyteller and data are his main characters.tuva

The successful data-divers of the 21st Century will be those that can understand data, analyze it, and communicate the analysis in an applicable and compelling way. Data literate means more than being able to run a linear regression in a TI-83 calculator and spit out the R-squared value. The data literate student is able to pose an interesting question, find the relevant data to answer that question and communicate the analysis in a narrative that compels the audience. The data literate student is able to be critical of data sources and suggest better ways of collecting data and performing analysis. In other words, the data literate student sees right through the slight-of-hand that occurs frequently with data.

This Summer, Tuvalabs and I will be hosting an institute in New Orleans to explore how to create data literate students in a Problem- and Project-Based Environment. In schools with tech-rich environments and classrooms that ideally pose interesting questions every day, Tuvalab’s datasets and tools are essential assets in developing the data literate student. Because it does take more than a compelling storyteller (even a good one) to affect change using data: it takes a compelling storyteller that understands and has wrestled with primary data sources. Tuvalabs paves the path for students to do this, along with facilitators as a guide along the way. While there has never been more data freely available, acquiring relevant data can be difficult: the formats vary, it can be difficult to find, and much of it is under lock-and-key. This is why Tuvalabs has an open-door policy when it comes to going data-hunting. Teachers and students can submit queries of the data they’d like to answer the questions they’d like.

Grantland’s Zach Lowe and Bill Simmons debriefed the 2014 MIT Sloan conference on a recent podcast. Lowe suggested that one of the major undercurrents of the conference was how to make all this data useful. What good is the data if it doesn’t result in a change on the field of play? The folks who can figure out that, they conjecture, will be the ones who succeed in the next generation of sports analysis.

This aspiration can be extrapolated to virtually all professional fields, which now all incorporate data: What good are the IPCC reports from scientists if they cannot affect climate change policy? What good is voter information if it cannot generate turnout? What good is customer satisfaction surveys if they do not result in return business? The data literate student will be able to collect, analyze and narrate data in such a way to affect behavior. We hope you spend some time with Tuvalabs online and in-person to help make that happen for your students.