tswd-portfolio-templates
| home page | data viz examples | critique by design | final project I | final project II | final project III |
Wireframes / storyboards
Visual 1: NCAA Boom
Visual 2: NCAA Historic Gap
Visual 3: WNBA Attendance Growth
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User research
Target audience
1. The Newcomer
People who may recognize players like Caitlin Clark or Angel Reese but lack context about the WNBA or pay equity.
- Why they matter: They test whether the story is clear and accessible.
- Goal:Move them from unaware about this issue to informed by showing the growth and impact of investment in women’s sports.
2.Superfans
WNBA fans, or sports fans, or those familiar with pay equity conversations.
- Why they matter: They evaluate credibility and can tell if this tells the honest story of pay inequity and where data might not make sense.
- Goal:Provide insightful feedback, while also getting more specific critique to this issue area.
3. Data-focused Audience
People interested in data visualization but not necessarily sports.
- Goal: Get unbiased feedback from people with no sports knowledge but have knowledge on data visualizations and can provide honest, precise critique that comes from experience.
Interview script
List the goals from your research, and the questions you intend to ask.
Interview Script
| Goal | Questions to Ask |
|---|---|
| Understand first impressions | At a glance, what is the first thing you notice about this visual? What is this visual about? |
| Evaluate ability to identify trends and key data | Can you define a trend that’s going on here? On a scale of 1–10, how easy was it to find the key data points on this graph? What are your main takeaways from this visual? Is there something on here you wish you had more information on? |
| Assess clarity and design (aesthetics) | Are the sizes of text, lines, and shapes a good size, too big, or too small? Do the colors fit well with the story? Are the images distracting? Do you think this is an appropriate visual to showcase the information? If not, would there be another graph that would help you understand this better? |
| Evaluate understanding of the story and impact | Can you identify what the main story or issue of the graph is? What kind of story is it telling? Does this change the way you think about the WNBA, women’s sports, or pay equity? |
Interview findings
Detail the findings from your interviews. Do not include PII. Capture specific insights where possible.
Text here!
User Feedback Table
| Questions | Interview 1 (briefly describe) | Interview 2 | Interview 3 |
|---|---|---|---|
| At a glance, what do you think of this visual (Visual 1)? | I really like the background image of the arena, but it feels distracting. | I think there are multiple stories here. I see that the women’s NCAA viewership surpasses the men’s, but I also see a gap before that, so I am not sure what I am supposed to focus on. | Maybe making the star at the intersection bigger would help that point stand out more. |
| What story does this tell? | I can see that you are comparing women’s vs. men’s basketball, and from this visual it looks like women’s popularity is increasing while men’s stays around the same level. | ||
| Can you identify a trend that is going on here? | |||
| What are your main takeaways from this visual? |
Identified Changes for Part III
For Part III, I have already made revisions to Visual 1 (NCAA Boom) based on feedback from my preliminary interviews.
I split the original visual into two separate visuals that use the same base design but highlight different stories.
In the first version, I focus on the crossover point where women’s NCAA viewership surpasses men’s. I added an annotation at the intersection and increased the size of the star marker to make that moment more visible and easier to interpret.
In the second version, I focus on the historical gap in viewership. I used arrows and annotations between the two lines to draw attention to the difference in viewership over time. I also kept the larger star at the intersection to maintain consistency across both visuals.
These changes were made to address feedback that the original visual was trying to tell multiple stories at once. By separating them, each visual now communicates a single, clearer takeaway.
Research Synthesis
| Research synthesis | Anticipated changes for Part III |
|---|---|
| Viewers found the background image visually appealing but distracting from the data. | Simplify or remove the background image so the focus stays on the data rather than decorative elements. |
| Viewers were confused because the visual was telling multiple stories at once (crossover vs. historical gap). | Split the original visual into two separate visuals, each focused on a single story. |
| The crossover point where women’s viewership surpasses men’s was not immediately clear. | Emphasize the intersection by increasing the size of the marker and adding a clear annotation. |
| The historical gap between men’s and women’s viewership was not clearly highlighted. | Add arrows and annotations between the lines to explicitly show and explain the gap over time. |
| Viewers needed clearer guidance on what to focus on. | Use stronger annotations and visual cues to direct attention to the key takeaway in each version. |
Final thoughts: The biggest takeaway from these interviews was that clarity matters more than complexity. My original visual tried to show too much at once, which made it harder to interpret. I will take this advice forward as I start to compile my other visuals together.
Moodboards / personas
If you did this optional part, include details here. Otherwise remove this section
Text here!
References
List any references you used here.
AI acknowledgements
Chatgpt was used for proper formatting on github and grammatical errors in text.