With an undergraduate degree in zoology and a master’s in public health, you wouldn’t expect Amanda Makulec to lead a successful career in data analytics and data visualization. As we’ve seen with multiple guests on the podcast, the path to a career in data analytics is windy and unexpected. It was the intersection of public health and data visualization that got Amanda interested in data visualization as a career. In one of her roles, Amanda was supporting USAID by analyzing open data sets and creating charts and graphs for publishing content. Her team consisted of graphic designers and developers. Designers would basically take her charts from Excel and add more color and add on text to the chart. Amanda found that large enterprises were facing the same challenges as the organizations she was supporting in public health (and enterprises have more money to throw at this problem). Thus began Amanda’s career in data viz.

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How do you tell a data story?

We’ve talked a lot about data storytelling a lot on this podcast. If there is one person who can crisply define what data storytelling is, it would be Amanda. This is Amanda’s definition according to this blog post:

Finding creative ways to weave together numbers, charts, and context in a meaningful narrative to help someone understand or communicate a complex topic. 

We talked a bit about how data storytelling can mean different things to different people (this blog post in Nightingale talks more about this). You might work with a business partner or client who says they want a data story, but all they really want is just an interactive dashboard with a filter. Amanda cites Robert Kosara’s definition of data storytelling in 2014 as one of her favorites:

• ties facts together: there is a reason why this particular collection of facts is in this story, and the story gives you that reason





• provides a narrative path through those facts: guides the viewer/reader through the world, rather than just throwing them in there





• presents a particular interpretation of those facts: a story is always a particular path through a world, so it favors one way of seeing things over all others

Amanda stresses the 3rd bullet point as the most important part of data storytelling. If the audience has to walk away with one analytics fact from the story, what is that fact you want to get across?

<figure class="wp-block-image size-full"><figcaption class="wp-element-caption">Source: Effective Data Storytelling</figcaption></figure>

Getting feedback on your data stories and visualization

One point Amanda brought up during the conversation which I think is worth highlighting is feedback. After you’ve published of launched an analysis, dashboard, or data story, you rarely get feedback on how effective the product was at telling a story. You might get some qualitative feedback like the dashboard answers specific questions or that the findings are “interesting.” But was the visualization actually effective at telling a story?

Amanda likes to ask people what they like and don’t like about her data stories and visualizations. Often people will get frustrate because the key takeaway from the data story is simply counter to what they believe. This leads them to questioning the validity of the data source. But you as the storyteller are simply conveying the signal from the noise in all the data.

During the pandemic, Amanda worked with the John Hopkins Center for Communications to create charts around COVID. Talk about telling an important data story! Amanda is presenting data about a worldwide pandemic while working with an organization that was at the core of reporting on the stats on the pandemic. Needless to say, the data stories and visualizations drew a variety of feedback. Remember seeing stories like this questioning how different entities and organizations were collecting and disseminating data about COVID? Being able to concisely present dense survey data about COVID is probably the toughest data storytelling job I can think of.

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Applying principles of user-centered design to data visualization

Before Amanda starts working on a new dashboard or visualization, she asks several questions about the project:

• Who is going to use the dashboard?





• When are they going to use it?





• What are their needs?

Before designing the dashboard, Amanda likes to borrow from the world of user-centered design to make sure her data visualization meets the goals of the end user. She creates mindset maps to make sure the dashboard is serving the right group. Journey maps also helps with figuring out how often the target audience will engage with the dashboard.

<figure class="wp-block-image size-full"><figcaption class="wp-element-caption">Source: The Interaction Design Foundation</figcaption></figure>

We chatted about data exploration and data explanation. The explanation step is sometimes overlooked by analysts because so much time is spent on the nuts and bolts of creating the visualization. But data explanation is just as important because this helps lead the end user to the key analytical fact of the data story. This means having clear chart titles and annotations so that you’re guiding the user to the key takeaway of the story.

Data tools for building effective data visualizations

I love talking about tools so we spent some time talking about the tools Amanda uses to build her data stories and data visualizations. Amanda talked about understanding the constraints of the data tools so that you know what you can and cannot build with the tool. For instance, Amanda talked about Power BI not supporting dot chart plots before so she didn’t consider Power BI as tool in her toolbelt for telling data stories (if it involve dot chart plots). Other tools like Tableau and Ggplot are great for adding annotations to different parts of the data visualization.

Did you really mean story-finding?

Amanda talks about how some people want more data storytelling but what they really want is “story-finding.” Coupled with data exploration, story-finding is all about finding the trends and outliers in a dataset before doing the actual data storytelling. This graphic from Amanda’s blog post neatly plots some of these common terms we hear in the data viz world and shows how important words are in describing what we want:

<figure class="wp-block-image size-large"><figcaption class="wp-element-caption">Source: Nightingale</figcaption></figure>

I asked Amanda what story-finding projects she’s actively working on and she talked about a project she’s working on with the Data Visualization Society (where she is the Executive Director). Her team has been trying to learn more about the membership (31,000+ people) so that they can create better programming for members. They partnered with the The Data School at The Information Lab to create a survey for members. As Amanda explored the data, she found that members are requesting information about professional development and work in a digital analytics capacity. The Data Visualization Society also issues a challenge to the community to come up with interesting visualizations using the survey data (see results from 2022 here). I really like this one done in Figma which shows which tools are most used by the members (surprise surprise Excel is on the map):

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