Data visualization design is more available than you might think, especially when an estimated 149 zettabytes of data will flood our world by 2024. Here’s the good news: you don’t need groundbreaking topics or original datasets to get started.
So what is data visualization, exactly? It transforms raw data into visual representations of data that reveal patterns and insights you’d otherwise miss. How to visualize data isn’t about mastering complex tools right away. The data visualization process starts simply.
We’ll walk you through defining your goals and understanding chart types. You’ll also learn data visualization design principles that work. Ready to learn data visualization? Let’s take a closer look!
Getting Started: Define Your Visualization Goals
Before you touch any tool or select a single chart type, you need to answer three critical questions. These questions shape every decision in the data visualization process that follows.
Understand Your Target Audience
Who will look at your visualization? This question matters more than you realize. Your audience’s priorities guide your dissemination mode, graph type, and formatting choices.
Charts for policymakers require a different approach than visuals for program implementers.
Match your visualization to the viewer’s information needs by asking: What are they looking for? Think over their decision-making context. What information do they already have? What additional insights can your charts provide? Do they have time to explore an interactive dashboard, or should you design a one-page handout they can understand at a glance?
Your audience’s familiarity with data plays a vital role, too. Unfamiliar data in complex chart types risks overwhelming viewers. Use innovative visualizations only when your audience knows the data well. Take ternary plots, to cite an instance. Soil scientists read them with ease, but lay people struggle to extract insights without guidance.
Test your design by having someone from your target group view the chart without explanation. Watch them reason through what they see. If they draw incorrect conclusions or seem confused, you need to change your design, simplify the data displayed, or pick a different chart type.
Determine What Questions You Need to Answer
What specific questions should your visualization answer? Get clear on this before diving into creation.
Ask yourself:
- Who is my audience?
- What questions do they have?
- What answers am I finding for them?
- What am I trying to say?
- What conversations might my visualization inspire?
The right way to visualize data is the way that best helps you answer your question. Informative visualizations depend on understanding which insights you’re trying to highlight. Your questions filter out unnecessary data and keep you focused on what matters.
Set Clear Objectives for Your Visuals
The three main goals of data visualization are communication, discovery, and analysis. Communication uses graphs or tables to present information in an understandable manner.
Discovery uncovers hidden patterns that aren’t apparent from raw data alone. Analysis provides insights for decision-making purposes.
Define why you’re creating the visual. What do you want people to learn or do after seeing it? Maybe you’re comparing performance over time, showing progress toward a goal, or making an investment case. Your purpose helps you choose the right format when it is clear.
Platforms like zebrabi.com can help you on how to execute with smarter data visualization.
Learn Data Visualization Fundamentals
The ability to select the right chart separates effective visualizations from confusing ones. Each chart type serves a specific purpose, and knowing which one to pick comes down to understanding what story your data needs to tell.
Common Chart Types and When to Use Them
Bar charts handle comparisons between categories. Use them when you’re asking “which is bigger?” rather than “how is it changing?”. They work well to compare revenue in different regions, website traffic by referrer, or spending by department. Line charts excel at showing trends over time. Stock price changes over five years or monthly website views become clear through connected data points that present one continuous development.
Scatter plots reveal relationships between variables and show whether one variable predicts the other or if they change independently. Bubble charts add a third dimension to scatter plots by varying bubble size.
Histograms group data into bins and display the distribution in distinct categories. Pie charts add detail to other visualizations, but shouldn’t be your main focus since viewers struggle to compare information without creating their own context.
Understanding Data Relationships
Network graphs uncover complex connections through nodes and edges. Nodes represent individual entities while edges show relationships between them. Correlation measures how two quantitative variables relate, but note that correlation never proves causation.
A scatter plot might show sales increasing with marketing spend, yet this doesn’t explain why they’re related. Pearson’s correlation coefficient ranges from -1 to 1, with values closer to 1 or -1 indicating stronger relationships.
How to Read and Interpret Visual Data
Start by identifying what the visualization shows. Read all labels, titles, and units. Look for patterns like increases, decreases, or the biggest and smallest values. Compare visual information with surrounding text to find details not mentioned elsewhere.
Watch out for aggregation levels too, since data that is aggregated appears more causal than raw data points.
How to Visualize Data: A Step-by-Step Approach
Getting your hands dirty with actual data beats reading about visualization theory any day. Here’s how the data visualization process unfolds when you’re ready to build something ground.
Start with Simple Data Sets
Simple data sources are CSVs you can use right away, without extensive cleanup. Look for files smaller than a few megabytes. Kaggle Datasets offers data about movies and clothing that you can filter to show only CSVs.
Our World in Data lets you browse by topic, with download buttons below each chart to grab full data as CSVs. Your digital life works too. Apps track steps taken, hours slept, messages sent, and songs listened to.
Clean and Organize Your Data
Data cleaning fixes or removes incorrect, corrupted, duplicate, or incomplete data within datasets. Remove unwanted observations first. Duplicate observations happen most of the time during data collection. Opportunities to create duplicate data multiply when you combine data sets from multiple places. Irrelevant observations don’t fit the specific problem you’re analyzing.
Fix structural errors next. Strange naming conventions, typos, or incorrect capitalization cause mislabeled categories. ‘N/A’ and ‘Not Applicable’ might both appear, but they should be analyzed as the same category. Handle missing values with care since many algorithms won’t accept them. You can drop observations with missing values or input missing values based on other observations, though both approaches carry risks.
Create Your First Visualization
Open your chosen tool and connect to your cleaned data. Drag and drop dimensions and measures to explore what you’ve got. Start with one simple chart rather than trying to show everything at once. Platforms like zebrabi.com make this step simple with user-friendly interfaces.
Iterate and Refine Your Design
Your first visualization helps you understand the data. Subsequent iterations help your audience understand it. Create multiple views to compare how each one frames the story. Each version answers a different question and reveals what matters most.
Share and Gather Feedback
Show your visualization to someone unfamiliar with the data. Ask them what they notice, what conclusion they draw, and what questions remain. You’re close if their interpretation matches your intent. Iteration has revealed a gap worth fixing if not.
Best Practices for Smarter Data Visualization
Alberto Cairo said it best: “The purpose of infographics and data visualizations is to enlighten people”. That principle guides every choice you make.
Focus on Clarity Over Complexity
Your brain holds only 7±2 chunks of information in short-term memory. Overloaded visualizations overwhelm viewers and make it difficult to identify important insights. Edward Tufte counters the “more details equals more authority” mindset with this: “Graphical elegance is found in the simplicity of design and the complexity of data”. Stakeholders care more about clear messages than pretty styles. Time and attention spans remain limited, so decrease cognitive load wherever possible.
Choose the Right Tool for Your Skill Level
Match your tool to your expertise. Beginners benefit from drag-and-drop interfaces with simple menus. Advanced users need customization options and data connectivity. Platforms like zebrabi.com bridge this gap and offer a user-friendly design without sacrificing power.
Follow Design Principles That Work
Actionable titles work best. “50% of Retail Investors Are Focusing On Dividend Investing in 2023” beats “Retail Investment Trends”. Whitespace should be applied with purpose. Elements across multiple visuals need alignment. Backgrounds should stay simple. Data labels work better than legends.
Learn from Examples and Communities
Edward Tufte’s trailblazing work on information design deserves your attention. Information is Beautiful by David McCandless offers great examples. Nathan Yau’s Flowing Data provides tutorials worth following.
Develop a Consistent Visual Style
Once blue represents one group, use blue for that group everywhere. Consistent color schemes, fonts, and chart types prevent confusion. Random formatting changes imply that you didn’t intend.
Conclusion
You’ve got everything you need to start visualizing data effectively. Clear goals come first, then pick the right chart types and prioritize clarity over complexity. Your first visualization won’t be perfect, and that’s fine.
Simple datasets are the best place to start small. Different approaches need experimentation. Real users can provide valuable feedback.
Platforms like Zebra BI make this experience smoother, but the secret is actually getting started. Your data has stories to tell. Let’s bring them to life.