Most visuals on dashboards try to do too much. A chart lists every slice, a legend asks the reader to decode colors, and the title tells us only the topic. By the time the room agrees on what the picture says, the meeting is over. Part 4 is about a simpler habit: choose the chart for the question, spotlight what changed, and tell the reader exactly what decision the data supports. That is how visuals move real work forward. 

This approach is built on a well-documented body of practice. Foundational work in perception and visualization explains the “why” behind clear scaffolding and mark choices, including Tamara Munzner’s Visualization Analysis and Design and Colin Ware’s Information Visualization: Perception for Design. Practical guides translate those principles into day-to-day communication, such as Stephanie Evergreen’s Effective Data Visualization and Cole Nussbaumer Knaflic’s Storytelling with Data. Finally, accessibility standards remind us to design visualizations for more people, not fewer, and the latest Web Content Accessibility Guidelines (WCAG 2.2) offer clear direction on titles, labels, and non-color cues that improve readability for everyone.   

In Parts 1–3, you mapped your data landscape, translated prompts into answerable questions with the Question-to-Query Path, and wrote KPI specs so the math is stable. In Part 4, you will turn those decisions into visuals that people can read at a glance and use in their next meeting. Here’s what this post covers and how to use it in your next meeting. 

What you will learn 

Over the course of this post, we’ll cover how to: 

• Select the appropriate visualization for the question you want to answer. 
• Help your audience see the story the visualization is telling.  
• Run a quick accessibility check so your story is readable and inclusive. 
• Plan your story before you build your dashboard. 

Choose the Chart for the Question 

Remember Part 2, when we focused on writing data questions? Those questions form the foundation for selecting the right visualization. Research shows that when people come to dashboards, they are looking for answers to specific questions, and when we design with those questions in mind, we get better results. Always start with the question you want to answer before choosing a visualization. If you are ranking programs, a bar chart wins. If you are comparing change over time, a line chart wins. If you are showing how parts add to a total, a waterfall or diverging bar chart wins. If you want to see whether two things move together, a scatterplot wins. And when you feel tempted to add a second axis, pause and split the view into small multiples instead. You will get a clearer story and fewer debates about interpretation. 

Here are some common questions and the best fit visualizations to answer them. 

And that’s just a sample of some of the common questions visualizations can answer. For a more complete list (or if you’re just as excited about visualizations as we are), check out A friendly guide to choosing a chart type from Datawrapper.   

Now that you’ve selected the appropriate chart based upon the question you want to answer, let’s look at how to make it better.  

Spotlight What Changed  

A chart is not a mural. Remove heavy gridlines, keep the axis at zero for bars, and label directly on the marks so readers do not have to chase a legend. The cleaner your chart is, the easier it will be for people to understand what is being presented. When possible, have titles that express what the chart answers instead of the topic. For example, “Enrollment yield down 2.3 percentage points” is a much less ambiguous title than “Enrollment Yield.” This is especially important for static chart images in presentations. Add one or two callouts exactly where the change occurs. If your chart compares to a baseline, label percentage points and percent change so the math is unambiguous (you haven’t forgotten this from Part 3 yet, have you?).  

Here’s a short list of some ideas to make your data story obvious and better for data-empowered decision-making.  

• Title states the takeaway, not the topic.
• Subtitle names the window and baseline. Include term, census date, and time zone when it matters.
• Callouts sit on the exact points that changed. 
• Units, denominator, and sample size are visible. Label percentage points and percent change. 
• Important slices are highlighted when appropriate, and everything else is de-emphasized. 
• Consider adding a one sentence caption that summarizes the answer.  
  • [Outcome] is [up/down/no change] by [X pp, Y%] vs [baseline]. Likely driver is [driver]. 
  • For example: Enrollment yield is down by 2.3 percentage points (8%) vs last year at this time. Likely driver is decrease in applications stemming from two vacant admissions counselor positions.

By shortening the time it takes for people to understand the chart, you can help decision-makers move more quickly to action. Ultimately, that’s the goal of any visualization–to help people get the answers they need to make an impact.  

Make It Accessible by Default 

Accessible charts are better for everyone. Although it is often associated with buildings and construction, universal design is a principle that is applicable to many areas of life. When you’re carrying something, which is easier to navigate: a door with a ground level entrance or having to climb half a dozen steps to access the door? The same applies to our visualizations. When we ensure our charts are clearer, it’s easier for everyone to see the story.  

Here are some quick things to check to ensure your chart is accessible for everyone. 

• Color is not the only cue. Use labels, markers, or patterns to differentiate categories or groups. 
• Ensure the contrast is sufficient. Aim roughly 4.5 to 1 for text and key lines. 
• Fonts are readable. Use at least 12–14 pt on screen and 10 pt in print. 
• Place direct labels on marks. Avoid legend only decoding, when possible. 
• Axis titles include units. Ticks are not crowded. 
• Interactive charts support keyboard focus and sensible ARIA labels. 
• Alt text describes the message and not only what type of chart it is. 
  • [Chart type] of [measure] by [dimension] from [start] to [end]. Main takeaway: [what changed]. Largest values: [X]; smallest: [Y]. 
  • For example: Line chart of first-year fall-to-fall persistence by cohort year from 2018 to 2025. Main takeaway: the rate fell 2.1 percentage points year over year after peaking earlier in the series. Largest values: 2022 at 82%; smallest: 2025 at 78%. 

These habits align with common accessibility guidance and make your charts better for everyone, not just some. Now, let’s put all of the pieces together.  

Plan the Story Before You Draw 

Now that we’ve discussed all the pieces, let’s put them together. Two minutes of planning now will save twenty in edits later. Name the audience, write the one-sentence takeaway, pick the chart, list the slices you will actually discuss, and outline the annotations you need. Decide on the action and the revisit date first. Then build. 

Use our Data Story Brief to plan before you open your visualization tool. It mirrors the Question-to-Query Path from Part 2 and the KPI Spec Card from Part 3, so the definition, the question, and the story stay in sync. 

Fix an Existing Chart in Five Steps 

Now, you may be thinking, “This is great, but I have so many charts that need help. What can I do to make them better?” For existing charts, you may not have the time or resources to start from scratch. However, you can improve almost any busy visualization with a short makeover. Here are five quick steps you can do today. Use them when the question and data are already right, but the chart is hard to read. 

1. Ensure that the chart matches the question being asked. If not, change to the appropriate chart. 
2. Fix scaffolding: start axis at zero for bars; correct time order; unified bins; remove 3D and heavy gridlines. 
3. Label directly on marks and minimize reliance on legends. 
4. De-emphasize backgrounds or highlight what changed. 
5. Add a one sentence caption to summarize the answer. 

How To Get Started 

Download our Data Story Brief and add it as a discussion point for your next data team meeting.  

Consider starting by: 

1. Give the Data Story Brief a try in at least two different meetings.  
2. Find at least one collaborator who can help you gain traction at your institution. 

You don’t have to do everything at once. Start small and remember that perfect is the enemy of good. Give some of your ideas a try and refine as you go. Building small habits now can offer big returns as the academic year continues.  

If this still feels overwhelming or you’d like a little extra support, don’t hesitate to reach out to your Strategic Solutions Manager. Our Client Experience Team (CET) is always here to partner with you and help bring your reporting and data analytics goals to life. 

Where the Field Guide Goes Next 

This is the fourth post in our six-part series on Data Literacy. Telling a clear data story turns abstract numbers into actionable decisions, building trust in the data usage across campus. While each installment builds on the last, you can use Part 4 on its own and see immediate results. 

Join us in two weeks for Part 5: Safe Data Journeys-Governance and Ethics!