10 Data Visualization Checklist Items for Impact

Want to create data visuals that grab attention and drive decisions? Here's your 10-point checklist:

Pick the best chart type
Check data accuracy
Keep it simple
Use colors wisely
Label clearly
Keep scales consistent
Add interaction when needed
Make charts accessible
Tell a clear story
Get feedback and improve

This guide covers everything from choosing the right chart to getting user feedback. Whether you're new to data viz or a seasoned pro, these tips will help you create clear, impactful visuals.

Quick Comparison:

Checklist Item	Why It Matters	Key Tip
Chart type	Matches data to message	Start with your main point
Data accuracy	Builds trust	Double-check sources and math
Simplicity	Focuses on what matters	Cut the clutter
Color use	Makes key data pop	Stick to 6 colors max
Clear labels	Explains at a glance	Write short, clear titles
Consistent scales	Avoids misleading comparisons	Start bar charts at zero
Interaction	Lets users explore	Balance features with simplicity
Accessibility	Works for everyone	Design for color blindness
Storytelling	Guides viewers through data	Organize data to support your message
Feedback	Helps you improve	Ask your audience and make tweaks

Remember: It's all about your audience. Start by defining who they are and what they need. Then use these tips to craft visuals that inform and drive action.

1. Pick the Best Chart Type

Choosing the right chart can make or break your data presentation. Here's how to nail it:

Match Data to Charts

Different data, different charts. Simple as that. Check this out:

Data Type	Best Chart	Example
Time changes	Line chart	Monthly sales
Value comparisons	Bar/column chart	Product sales by region
Parts of a whole	Pie chart (2-3 max)	Market share
Relationships	Scatter plot	Ad spend vs. sales
Geographic data	Map	Customer locations

Start with your main message. What's the point you're trying to make? That'll guide your chart choice.

Make Charts Pop

Want your charts to grab eyeballs? Here's how:

Keep it simple. Ditch the fluff.
Use color smart. Highlight key points with bold colors.
Label clearly. Make titles and axes easy to read.
Scale right. Always start bar and column charts at zero.

Pro tip: Stick to charts people know. Fancy isn't always better.

Take the Financial Times data team. For election results, they use arc or stacked column charts. Why? They show parts of a whole without confusing viewers.

Remember: Your chart should tell a story at a glance. If it doesn't, rethink your choice.

2. Check Data Accuracy

Data accuracy is key for trustworthy visualizations. Here's how to make sure your data is solid:

Double-Check Sources and Math

Verify sources: Track your data to its origin. Use multiple sources when you can.
Review calculations: Double-check your math. Small errors can cause big problems.
Use data quality checks: Compare data across tables and sources. For example:

Check Type	Description
Row count	Do the rows match up?
Column count	Are all columns there?
Total sum	Do sums match across sources?
Min/max	Do the highest and lowest values align?

Fix Missing or Wrong Data

Bad data can mess up your visualizations. Here's what to do:

Spot patterns: Are certain fields always blank?
Pick the right fix:
- Time series data? Use interpolation to fill gaps.
- Independent rows? Maybe drop them if you can't make a good guess.
Be clear: If you've filled gaps, show it. Use dashed lines or different colors for calculated vs. real data.
Keep records: Write down how you handled bad data. It helps if someone needs to check your work later.

3. Keep It Simple

Simplicity is your best friend in data visualization. Strip away the fluff and focus on what matters. Your charts will be easier to understand and pack more punch.

Cut the Clutter

Want more effective charts? Here's how:

Ditch the extras: No 3D effects, grey backgrounds, or heavy gridlines.
Try data labels instead of value axes and grid lines.
Be selective with labels. Only highlight the key points.

Edward Tufte, data viz guru, calls these extras "chartjunk". He says:

"Data graphics should draw the viewer's attention to the sense and substance of the data, not to something else."

Make the Important Stuff Pop

Once you've cleaned house, make your key data shine:

Put crucial info in the top-left. That's where eyes go first.
Use color wisely. It's your secret weapon for highlighting what matters.
Label data directly instead of using legends.

Ann K. Emery, design whiz, has a quick tip:

"Removing leader lines is simple. Click on the lines and hit the Delete key, or right-click on the leader lines, select Format Leader Lines, and click No Line."

Remember, you want your data to speak FAST and CLEAR. As Antoine de Saint-Exupery put it:

"Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away."

4. Use Colors Wisely

Colors can make your data viz pop or flop. It's not just about looks - it's about making your data crystal clear.

Choose a Good Color Scheme

Why does your color palette matter? Simple:

It helps viewers get your data fast
It keeps things consistent across charts
It makes sure everyone can understand, even if they're colorblind

There are three main color palettes for data viz:

1. Qualitative: For categories (up to 10)

2. Sequential: For numbers or ordered stuff

3. Diverging: For data with a middle point that matters

Palette	Use It For	Example
Qualitative	Categories	Types of fruit
Sequential	Ordered data	How dense an area is
Diverging	Data with a midpoint	Temp changes

Use Color to Highlight What Matters

Color is your secret weapon. Here's how to use it:

Make important stuff pop with bright colors
Keep the rest soft
Stick to 6 colors max (don't go crazy)

"Above all do no harm" — Edward Tufte

This quote nails it. Color should make things clearer, not messier.

Quick tips:

Use black for serious topics (like atomic weapons)
Go light for fun stuff (think kids' books)

5. Label Clearly

Clear labels make your data viz easy to understand. Here's how to do it right:

Write Clear Titles and Labels

Your chart's title is your elevator pitch. It should tell readers what they're looking at and why it matters.

For titles:

Keep it short (6-12 words)
Put the main point first
Use simple words

Example: "Q4 Sales Jumped 20% in 2023" instead of "Sales Data 2023".

For axis labels:

Use plain language
Include units (%, $, etc.)
Align text horizontally when possible

"The chart's title is your elevator pitch." - Flourish Blog

Add Helpful Notes

Annotations make your data pop. They explain key points without cluttering your chart.

How to use them:

Highlight important data points
Explain sudden changes or outliers
Keep it brief

Example: On a chart showing dog breed popularity:

Year	Popularity	Annotation
1940	23%	WWII starts
1945	1%	War ends

This simple note explains the change better than a long paragraph.

Tips:

Place notes close to the data they describe
Use arrows to connect notes to specific points
Don't overdo it - a few key annotations beat a cluttered chart

6. Keep Scales Consistent

Consistent scales in data viz? They're a big deal. Here's why and how to nail it:

Show Data Accurately

When making charts:

Start bar chart axes at zero
Use full axes, no skipping
Keep intervals consistent

Comparing quarterly sales across years? Make sure each quarter gets equal space on the x-axis, even if some had zero sales.

Avoid Misleading Comparisons

Changing scales between related charts? Bad idea. Instead:

Use the same scale for similar data sets
No truncated scales that blow up differences
Skip dual scales on one axis

Real-world oops: In 2022, the U.S. Bureau of Labor Statistics got called out for using different y-axis scales in employment charts. Made job growth look bigger than it was. They fixed it fast.

Chart Type	Do This	Why
Bar Charts	Y-axis starts at zero	No visual distortion
Line Charts	Consistent intervals	Spot real trends
Multiple Charts	Sync scales	Fair comparisons

"Don't mix scales and units on one chart. Cut the clutter. Give readers max unbiased clarity." - Nir Smilga, Data Viz Pro

7. Add Interaction When Needed

Interactive charts can make your data more engaging. But don't go overboard.

Include Filters and Drill-Downs

These features let users explore data on their own:

Filters: Focus on specific data points
Drill-downs: Dive deeper into data subsets

The WastewaterSCAN Dashboard is a great example. Users can search their location on a map to see local pathogen levels. They can pan, zoom, and watch data update in real-time.

Balance Interaction and Simplicity

Too much interaction can confuse users. Here's how to strike the right balance:

Do This	Don't Do This
Add clear, intuitive controls	Overload with complex features
Use interaction to reveal key insights	Hide essential info behind clicks
Test usability with real users	Assume all users are tech-savvy

The VACS Explorer gets this right. It uses mini-map multiples to show climate impacts on crops side-by-side. Users can explore deeper without getting lost.

"Interaction allows us to chart our own path in the data." - Stamen

Interactive data viz is powerful:

28% more likely to find timely info than static dashboards
48% of business users can analyze data without IT help

The bottom line? Make data more accessible, not more complicated.

8. Make Charts Accessible

Charts should work for everyone. Here's how to make them better:

Design for Color Blindness

8% of males have Deuteranomaly, the most common color blindness. To help:

Skip red and green together
Go for blue and orange instead
Add patterns or labels to data points

Do This	Don't Do This
Use thick lines, big symbols	Only use color to show info
Add patterns (like stripes)	Use more than 8 colors
Put legends near data	Only name colors

Make Charts Work Everywhere

Your charts should look good on all screens:

Keep it simple for touchscreens
Use big text
Stick to 3-4 colors max

"Good accessibility is just good design for everyone."

More tips:

1. Add alt text

Tell screen readers what the chart shows:

alt="[Chart type] of [data] showing [main point]"

2. Write summaries

Explain the key trends in words.

3. Use proper HTML

Give each chart a heading and intro text.

9. Tell a Clear Story

Data doesn't always speak for itself. Here's how to make your visualizations pack a punch:

Organize Data to Tell a Story

Start with your main message. Then, structure your data to support it:

1. Set the scene

Give context for your data.

2. Introduce the characters

Highlight key data points or trends.

3. Present the conflict

Show changes, comparisons, or surprises.

4. Resolve the story

Offer insights and next steps based on the data.

Check out how Airbnb told their story about unique stays:

Year	% of Unique Stays	Top Unique Property Type
2019	15%	Treehouses
2020	28%	Tiny Houses
2021	40%	Houseboats

This table paints a clear picture: unique stays are booming, with changing preferences each year.

Lead Viewers Through Key Points

Don't leave your audience guessing:

Use a logical flow: Start broad, then zoom in.
Highlight what matters: Use color, size, or position to grab attention.
Add annotations: Explain key points right on your charts.

Take Spotify's year-end Wrapped data. It walks users through their listening habits step-by-step, from total minutes to top artists and genres.

Your goal? Make complex data easy to grasp. As Harvard Business School Professor Jan Hammond puts it:

"Always remember that applying analytical techniques to managerial problems requires both art and science."

10. Get Feedback and Improve

Data visualization isn't a one-and-done deal. Here's how to make your charts better:

Ask Your Audience

Want to know if your charts hit the mark? Just ask. Here's how:

Surveys: Quick questions like "What's this chart telling you?"
Interviews: Chat one-on-one for deeper insights.
Usability tests: Watch people use your charts in real-time.
Online communities: Share on Reddit's r/dataisbeautiful for honest feedback.

Method	Good	Not So Good
Surveys	Fast, lots of responses	Surface-level info
Interviews	Deep insights	Takes time
Usability tests	See real behavior	Can cost a lot
Online communities	Free, diverse views	Might get harsh comments

Make It Better

Got feedback? Use it:

1. Pick your battles: Fix what most people mention or what's really confusing.

2. Small tweaks, often: Don't overhaul everything at once.

3. Check again: After changes, get more feedback.

4. Keep notes: Track what you changed and why. It'll help later.

Feedback isn't about being right or wrong. It's about making your charts work better for your audience.

"Feedback is the breakfast of champions." - Ken Blanchard

Conclusion

Data visualization turns numbers into insights. By following our 10-point checklist, you'll create charts that look good and pack a punch.

Here's the gist:

Pick the right chart
Keep it simple
Use color wisely
Label clearly
Make it accessible

But here's the kicker: it's all about your audience. As Olga Cheban from Coupler.io puts it:

"Always start with defining your audience and the goal of your visualization to ensure it meets their needs."

Apply these tips, and you'll craft visuals that inform and drive action. Whether you're talking to big shots or Joe Public, these guidelines have got you covered.

Want to level up? Get feedback and keep tweaking. Soon enough, you'll be churning out visuals that make people sit up and take notice.

10 Data Visualization Checklist Items for Impact