• Megan Johanson

Data Literacy Conversation with George Mount

I recently had a great conversation with George Mount, of Stringfest Analytics, about data literacy and data visualization. George does consulting work for companies looking to upskill their data teams and has a book coming out this year called Advancing into Analytics: From Excel to R and Python.


Below is the post George wrote based on our conversation, which was originally published here: https://georgejmount.com/megan-johanson-dataviz-qa/


Data visualization and data literacy: Conversation with Megan Johanson

Perhaps the most common way a variety of audiences interact with data is through visualization — and this has never been more true after a year of coronavirus dashboards, electoral maps, and other artifacts of data journalism. So how should we as data professionals think about data visualization in the wider context of data literacy?


I spoke with Megan Johanson on this and related topics. Megan is Data Analytics and Insights Manager at the Educational Service Center of Central Ohio (ESCCO) and also blogs at meganjohanson.com. ESCCO is an agency offering a variety of professional and administrative services to 30 member school districts, and in that role Megan works with staff to draw and communicate insights from their data.


Here are some of the topics that came up during our discussion. You’ll also find a Q&A section following.


When data wonkery’s not your mission: I’ll admit that I’ve immersed myself so wholly into data nerd-dom that it’s easy to forget some people don’t care to drop the mic on opinions about pie charts or Python. Many highly talented professionals feel deeply about serving some mission, and just don’t know what to make of the data that inevitably comes their way — or even worse, unruly data has gotten in the way of fulfilling their mission.


Megan shows ESCCO staff that using data and conveying emotions are not mutually exclusive. It often takes just a few tweaks of information design and data visualization to make their deeply-held messages resonate with the clients’ intended audience. She pointed to a recent Black History Month post from her blog as an example of marrying data analysis and visualization with establishing an emotional connection and fulfilling a mission.


Excel charts left in the dark ages? Don’t get me wrong, Excel can produce some stellar visualizations… it can just take a lot of pointing-and-clicking to get there. From the so-so default chart settings, it often feels like an uphill battle to work toward sound information design principles, and you can tell the application was built by engineers, not designers.


I really hadn’t considered this incongruence until Megan brought it up: with all of the exciting updates to Excel, it seems like charts are still where they were 20 years ago (and they weren’t that great then). So, if anyone at Excel HQ is reading this… you’ve got two votes here to overhaul charts.


Accessibility in the future of data visualization? Raw data is hardly a sound way to visualize data, but Megan’s seen that it’s often the way it’s presented for accessibility. Visualizations are often designed to be interactive, and for lack of alternative of translating this into an accessible experience, a data dump is presented instead.


Are we excluding potential awesome data analysts and information designers because of the lack of media we choose to work with? How can we account for accessibility in the future of data visualization? It looks like there are some advances in using a combination of sight and sound to do this. Perhaps mixed reality can also help in some ways. Accessibility in data visualization is just another topic that got me thinking after speaking with Megan.


As you’re seeing, Megan and I had a far-ranging discussion on data visualization, working with a variety of clients, and the tools of our trade. Here’s more from Megan on how she understands data visualization in the wider context of data literacy:


Q&A on data literacy and data visualization

Do you ever face objections or misunderstandings about the importance of data visualizations for data literacy, and how do you overcome them? Not so much objections as deprioritization. The challenge is that communicating data effectively is almost never one of the primary job responsibilities so learning around data literacy gets pushed to the backburner. Until people have seen examples with data that they care about, it’s hard for them to understand the impact that a few tweaks to a chart can have on their work. Often, when I can make-over some of their own data, it gets the buy-in and interest to encourage people to think more critically about how they are conveying data to their stakeholders.

How are data visualization literacy needs different for technical, non-technical, and general audiences? For all audiences, you want to make them work as little as possible to understand the main takeaway of the data. The biggest difference between the groups would be the level of detail that each group wants. Technical audiences are likely more interested in details, more decimal places, detailed supporting tables, etc. General audiences want and need less detail. They are more impacted by a few easy to understand findings, likely written out as the chart title, or maybe without a chart or table at all. You can summarize big picture data utilizing icons or images.

What are the hardest things to teach about data visualization and how do you overcome them? That it’s worth the few extra minutes of editing a default chart- and that it isn’t that hard! It can be overwhelming enough to work with data when that isn’t your main job responsibility. To then be told to improve the design of the chart can turn people off.

I don’t want people to think they need to be graphic designers as well as their actual role. A few simple techniques applied to a default Excel chart can make a huge difference: decluttering the chart (removing or turning axis lines light gray, using fewer decimal places, directly labeling the data and removing the legend), using (branded) colors strategically, and using an informative title are the top 3 ways to make the data easier to understand.

With so much work being done online due to the pandemic, are there particular considerations to be aware of when producing and consuming data visualizations remotely? The interest in data since the start of the pandemic has been so high and the need to convey information clearly has been so important, that we’ve seen a lot of great visualizations over the past year. All kinds of people are hungry for data, not just business leaders and data analysts. So, the easier it is to understand, the better off everyone will be. The data visualization creators need to demonstrate data literacy in their visuals, because the average reader may not have much experience reading charts.

Don’t make people work hard to understand. This is true whether remote or not, but the interest in data seems to have increased such that the general population is regularly looking at COVID case charts and deaths and spreading simulations. If people are frustrated trying to understand the COVID trends in their area, it just adds to the anxiety their constantly dealing with. I do think I’ve seen many good examples of clear charts and graphs pertaining to COVID data, which I hope makes the average non-data expert feel comfortable understanding the current situation.

What major or unexpected benefits have your audiences found with a better grasp of data visualization? Seeing patterns in the data that were hidden before. For example, I’ve been working with our communications team and during our data meetings they realized that our internal email open rate was lower than expected. We discussed comparative data and came up a few options to try and increase views, which is critical because these emails for staff often contain important HR or administrative information. The pattern had existed for a while but no one had really observed it before.

Is there anything else you’d like to discuss about this topic? Data visualization and data literacy are things no one was trained on in school. They just come along with many jobs, with varying levels of expectation. No one tells you that you need to spend time formatting your charts and it can be overwhelming to have to teach yourself. But it’s so worth it.

Most people will not read huge tables of data, so don’t present your data that way. Make it easier for them and they will stop getting hung up on irrelevant details and focus on the main idea you’ve identified. If you want people to take action on data, don’t let them waste time trying to understand your table or chart. Putting in a little effort on the front end can help people cut right to the data-informed action discussion.


Growing the mission with data

I’m hoping from this discussion that, regardless of your technical ability or interest level, data can be so powerful in advancing your mission, and it often takes just a few “hacks” of information design to get there.


Thanks again to Megan Johanson for the discussion and Q&A and for offering me and my audience some new perspectives and questions on data visualization. I suggest checking out Megan’s blog for more of her work.


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© 2019 by Megan Johanson.