What we asked

What's the best way to describe the term data literacy?

That's a great question. I think different people understand it in different ways. One of the textbook definitions you can find online is that data literacy can read, understand, create and communicate data as information. I like that definition quite a lot because it covers a lot of ground. But I also really like a definition that I found in one of the early research papers published in 2013 on this topic by Javier Calzada Prado and Miguel Ángel Marzal. They define it a little differently and look at it from the point of view of someone trying to understand how information is collected, stored, and communicated. They say that data literacy can be defined as the component of information literacy that enables individuals to access, interpret, critically assess, manage, handle and ethically use data. That was helpful for me as I began to look to launch my business and try to understand how I could help other people effectively speak the language of data.

How did your career intersect with data journalism?

I started in engineering back in the late 90s at UCLA in the Los Angeles area where I grew up. I have a degree in mechanical engineering, and I spent the early years of my career designing products for automotive and medical devices. Then gradually, I started to use statistics and data visualisation more and more to the point where I really saw that it was a very powerful medium to both learn about the world and communicate that to others. I started to write a blog called DataRemixed.com. That allowed me to begin getting connected with others who were excited and passionate about data visualisation, including many data journalists I greatly admired.

I think the big connection happened when I was asked to move up to Seattle to work for Tableau Public, a free offering for anyone who wants to tell interactive data stories on the web. Of course, a huge contingent of Tableau users was at news organisations from all around the world. That led me to many different data journalism conferences where I started to present, train and work with journalists who were preparing interactive graphics about everything from elections to the Olympics to everything in between. And that really led me to learn a lot from them.

Tell us how you've been inspired by data journalists and their work.

Yes, I was inspired by so many data journalists. A handful that comes to mind -- the team that runs La Nación data blog in Buenos Aires includes Momi Peralta, Gabriela Bouret and others. This is just a really inspirational team. They started off with just a small number of individuals who were diving into data for the first time. But they were under a lot of pressure and were uncovering corruption in the political climate in Argentina. They were finding things that were embarrassing for politicians. There were even examples where the government was one of their financial sponsors and they were pulling ads -- if I recall correctly. But they were very dedicated. They would make requests of the government for data and they would deliver them in these boxes of paper and they would gather as many people as they could to sift through them for stories and digitise them. They were devoted to it and they just went after it and were publishing these really important articles that were exposing some corruption -- and at great risk to themselves sometimes. So if that's not inspirational, I don't know what it is.

Journalism has faced a lot of economic challenges in the digital world. One way publishers have approached this is through the attention economy and clickbait. How do you see this impacting the data journalism space?

That's certainly a risk. If the charts and graphs only show one side of the story or only one very biased angle to the story, people might walk away with a very skewed view of the world. Unfortunately, people are much more likely to want to share that. There is a profit motive there, and it runs counter to the best journalists I know who believe in journalism as a public good and try to tell accurate stories, perhaps that embrace multiple views that don't show an overly biased perspective.

Readers who are highly literate are more likely to see through that and understand that bias. At least that's my hope, that if we can help raise data literacy levels across the board, that people will be more likely to recognise when someone might be misleading them or telling an overly biased version of the story. I think it's a challenge connected to it and not in any way dissimilar from the broader human challenge of communicating thoughts, feelings and facts. The charts are just one outworking of that overall communication challenge.

What advice do you have for people to understand data that is inherently uncertain?

There are ways to communicate uncertainty. The chart that has crisp, clean lines and places points in precise spots conveys a notion of precision. But it's possible to make them fuzzier. For instance, you could use error bars or ranges to blur the lines a little bit. Studies show that sketch styles with wavy lines can be perceived as less accurate or precise. In a situation where the data is highly uncertain, that might be appropriate. It might be appropriate to use some design techniques to convey that.

Oftentimes those can even seem more confusing or intimidating to a layperson who may not be well versed in statistical methods. I think some journalists that I've seen out there are trying to find ways to translate those techniques into ways that a reader might not need that technical education to understand. But I do think it's a big challenge.

At Data Literacy, have you developed any courses specifically for data journalists?

We don't have courses specifically designed for data journalism right now, but I designed them right after I left Tableau. So because of that, there are many examples peppered throughout that have journalism style examples. I've found that that's actually interesting for people in business. They like that because it's data about their world instead of about a fake store that's not even a real company. For instance, we talk about deforestation and then learn to visualise that data. I believe that crosses over well into the journalism world. I imagine our courses will be more interesting to someone in data journalism, and that's probably because I've been so influenced by it.

How should those who want to become data journalists approach training?

Should data journalists or those who want to learn that discipline seek out training that's specific to that field? I think the answer is yes. The best learning you're going to get is from individuals like Cheryl Phillips at Stanford University and Christian McDonald at The University of Texas at Austin. Those are individuals that aren't just teaching you the techniques and tools for the field. They're also teaching you the context and the challenges in the newsroom. I had a chance to visit the University of Missouri, and they have a fabulous data journalism programme. I still think you're probably going to get a lot of value out of data journalism at the university level because that's where many senior and experienced journalists go to learn.

But there's also probably some value in just learning the tools through the training you're going to find online. It's just going to be up to you to translate what you're going to learn, which may be about something that isn't really applicable to the news cycle. You're going to say to yourself, 'Well, OK, how can I translate this?' So I wouldn't say don't take data science or data analytics courses that aren't geared toward journalists. I think that you will probably find some better tool based training over in that world. But I do think it's helpful to then go beyond and look at resources that organisations like Hacks/Hackers so you can talk to individuals who are applying those tools in the newsroom. There are so many great, generous individuals in data journalism who are happy to share their knowledge.

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Our next conversation

Our next Conversations with Data podcast will be a live Q&A with Eva Constantaras and Anastasia Valeeva on Tuesday 6 July at 3pm CEST / 9 am ET. The pair will discuss the power of building a hypothesis for data journalism, a topic covered in our latest long read article. The conversation will be our second live event on our Discord Server. Join the live recording on Discord and share your questions with them.

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Onwards!

Tara from the EJC data team,

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