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Listening to What Data Users *Say* They Need… but Giving Them What They *Actually* Need

Oct 27th, 2020 / Data Visualization / , ,
A conversation with Zach Bowders of the Data + Love podcast on how to give data users what they actually need.

I recently had the chance to talk with Zach Bowders on his podcast, Data + Love. Zach is a data analyst, a Tableau Public Ambassador, and passionate about data visualization and data storytelling.

We sat down at night after our kids were in bed (8 pm for Zach and 9 pm for Ann!) and talked about how to distinguish between IT and data professionals, how to narrow what users *actually* need, understanding your audience and how to speak up when data isn’t useful.

Watch Our Conversation

Listen to the Podcast

Prefer to listen? Download the episode here

Distinguishing between IT Professionals and Data Professionals

We discussed an idea Zach brought up in a previous podcast: The difference between a traditional IT role and a data professional.

Those working in the traditional IT role typically receive instructions or a request. Let’s pretend they’re being asked to make a dashboard. Sometimes it’s a specific request with lots of planning, and you can tell the person making the request really thought things through. They might’ve even requested a specific chart type, like “Make a bar chart about xyz topic.”

Those working as a data professional also might receive those requests. The value-add of the data professional is that you’re supposed to push back a little bit. Professionally and respectfully, of course. You read between the lines of what’s requested and then give them what they actually want and need. The value you can add is to ask more questions and dig deeper. You might ask what the data’s going to be used for and what types of actions will be taken based on that data.

Zach added, “A data professional is not more skilled than the IT worker. It’s not to say one is better than the other, just that they’re different roles.”

You could easily put together something that is technically correct and absolutely worthless,” he explained.

Designing Three Dataviz Ideas to Narrow Down What Users Actually Need

Here’s what I recommend:

  • You listen to the request for data. You’re listening, you’re nodding, not interrupting, asking clarifying questions.
  • You come up with three ideas. I call these “ideas,” not “makeovers.”
  • Idea #1: The original.
  • Idea #2: Slightly different.
  • Idea #3: Very different. (This is usually the winner, and what people actually need).  

For example, I recently worked I worked with a transportation agency. Their original graph was a pie chart with seven or eight slices representing their revenue sources. It was 3D, had a separate legend, and used tiny font… All of the usual challenges.

Here are the ideas I presented to them:

  • Idea #1: An easier-to-read pie chart. We developed a pie chart with one dark slice and the rest grayed out. We also used direct labels in lieu of the separate legend.
  • Idea #2: A bar chart to make it easier to compare each revenue source.
  • Idea #3: An icon array focusing on how one-third of their revenue comes from ticket sales.

Understanding Who Your Audience Is

Zach and I both agreed that understanding your audience is paramount.

“One of the things I find most important in terms constructing a data viz is your understanding of who the audience is – who is this for?” Zach said. “Because if you don’t know who it’s for, it has to be for everyone. And if it’s for everyone, it’s really for no one. It’s impossible to satisfy all audiences, to bridge all gaps.

Zach said that people been pictorially expressing ideas for a very long time and that a big part of the process of taking something visual and explaining it to someone is the idea of closure. He explained that, “In the sense that in everything you create, there’s a silent partner that you have (a second creator) which is the audience. So, when you’re constructing your visualization, you and the audience are both pouring meaning into it. You’re not always going to be able to stand next to it, hold their hand and explain exactly what it means.”

He went on to share an example about a grocery store aisle with two-liter bottles. You can see part of the image on each one but you can’t quite see what they are. But it’s enough from your life experience to put together, “Oh! It’s a row of Pepsi logos.” You don’t see a single complete logo, but you can take the incomplete thoughts and knowledge of grocery stores to figure out what it is.

Zach said that this is true of when you’re working on a data visualization project. The people at the organization you’re working with have an understanding of their organization and their data, but also in terms of visual language in general. 

He said that the better you can understand their level of literacy, confidence, and competence with the data, the better you can leverage their own creative ability that they’re bringing to it to figure out what you don’t need to say.

“Part of it is, what you need to bring to it. And the other part is, what can you leave out?” said Zach. “If you’re working on some geographic data and people are wanting to know what sales look like for the United States, what’s the real question they’re asking? Do they really want to see all 50 states? They may, and that may be valid, but if they’re really wanting to see the winners to losers, you may be able to get away with two bar charts showing the top five and bottom five states. That might be exactly what they need. The temptation would be to show all 50 states. They don’t need that. They know there’s 50 states. You don’t have to remind them of that. For the most part, you can leverage many of those ideas as use them as a silent partner to help you get your ideas across better.”

Technical vs. Non-Technical Audiences

One of the most important things to understand about your audience, is not just their titles or roles but whether your audience is technical or non-technical.

A technical audience is people who love data and want to dig deeper into it (both inside and outside of work). They love decimal points and tables. Data isn’t a chore for them. In my experience, this is about 1% of the population.

About 99% of the world is likely non-technical. They don’t like data, find it to be a chore, and would rather do something else. They’re very smart, highly educated, and hard-working, but they have a skill other than data.

If you can identify which you are and which your audience is, you can then recognize when there might be a difference. This helps you determine the right chart type and the right dissemination format. Whether your audience is technical or non-technical informs all design decisions.

Connect with Zach Bowders

Data + Love Podcast: https://www.buzzsprout.com/739517

Twitter: @ZachBowders

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