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This post was written by
Scott Schwertly

Scott is the Founder and CEO of Ethos3.


You know all those awesome data visualizations on the New York Times? (We’d link to some, but unfortunately we’ve been cut off cold turkey for the rest of the month. Starting in April, non-subscribers can access only 10 free articles a month… and we’re not happy about it.) Well, it’s Jer Thorp, the data artist in residence at the newspaper, who creates those beautiful visuals. He stopped by TedxVancouver in November 2011 to discuss how we can make data more human, which is a topic we are particularly interested in here at Ethos3.

He starts by joking that he wants to discuss the two most exciting things: data and history. Of course, both can be especially dry and boring, so Thorp gets laughs by playing contrarian. And, he wins points with us by announcing that he’s going to start by telling his story. He shows a picture of himself looking particularly geeky as a kid, and talks about his love for computers and programming. At times he rambles, starting to talk about something and then saying he’s not going to talk about it, but luckily, he only does this a few times in the beginning of his talk.

Thorp uses more visuals than the average TED speaker, which is appropriate in regards to his topic. He shows the audience examples of the data he’s made visual, like the frequency with which certain words appear in the NYT: communism versus terrorism, Iran versus Iraq, crisis versus hope. The data is very beautifully displayed; Thorp himself admits that he’s most concerned about aesthetic value when organizing the data. He says he builds visual tools to help him understand systems, which is precisely what good visuals should accomplish.

Thorp transitions to telling stories about two projects he’s recently worked on. The first is Cascade, a data visualization tool that tracks how NYT content is shared across the Internet. The tool attempts to visualize how content gets from point A to point B to point C, and tracks Twitter activity related to the story as well, to create a well-rounded history of the story (Nieman Journalism Lab has an excellent, detailed article on the tool).

Next, he discusses working on the September 11th memorial, where the names of those who died were not listed alphabetically but according to relationships and connections. Brothers placed next to brothers, friends next to friends, coworkers next to coworkers, and so forth. He worked on creating a software tool to make connections between people, and he furthered his main point that data should represent our lives and tell our stories.

The main problem with Thorp’s talk, a problem that isn’t resolved, is that he doesn’t tell his audience why they should care about what this data can tell us. He discusses OpenPath, a tool that allows you to upload all the location-based data stored on your iPhone and then visualize it, so you can see where you’ve been for years. Thorp shows us when he arrived to New York City, he shows us the Thai restaurant where he ate after his flight, he shows us the moment he met his girlfriend. This is all interesting for him, but he doesn’t explain why the audience should care. He doesn’t address clearly enough an essential part of any presentation: audience benefit. Why should we care? How will this help us?

Of course, the audience will ponder for themselves what this data can do for them, how they could use the information, but as Jerry Weissman says over and over again in Presenting to Win: Don’t make them think. Thorp needs to tell us why we should care, and then convince us.

His main point is clear: “By placing data into a human context, it gains meaning, and I think this is tremendously, tremendously important because these are our histories that are being stored on these devices.” It is essential to place data in a human context, in a story, but at the end of Thorp’s talk, we’re not convinced that it’s tremendously important to track our every move and visualize it on a map. It’s cool, certainly, but Thorp’s main point– that we should think about data in a human context–isn’t proved well enough. His discussion is indeed fascinating, but his support doesn’t substantiate his main point enough to compel the audience to really care.

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