What Chart Should I Use for My Data? The Complete Decision Guide

"What chart should I use?" is the most common question in data visualization. The answer depends on three factors: your data type, your message, and your audience. This guide gives you a clear framework to make that decision every time.

The Chart Selection Framework

Before diving into specific chart types, understand this fundamental principle: charts are answers to questions. The chart you choose depends on what question you're trying to answer.

The Four Questions Charts Answer

1. How do things compare? → Bar charts, column charts
2. How have things changed over time? → Line charts, area charts
3. What's the composition? → Pie charts, stacked bars, treemaps
4. What's the relationship? → Scatter plots, bubble charts

Match your question to the right category, and you've narrowed your options significantly.

Quick Decision Flowchart

Use this rapid decision process:

Step 1: What's your primary goal?

• Comparing values → Go to Comparison Charts
• Showing change over time → Go to Trend Charts
• Showing parts of a whole → Go to Composition Charts
• Finding relationships → Go to Relationship Charts
• Showing distribution → Go to Distribution Charts

Step 2: How many variables?

• One variable → Simple charts (bar, line, pie)
• Two variables → Dual-axis or scatter plots
• Three+ variables → Advanced charts (bubble, radar, heatmap)

Step 3: How many data points?

• Under 10 → Most chart types work
• 10-50 → Consider line charts or grouped bars
• 50+ → Use heatmaps, scatter plots, or aggregate first

Comparison Charts: When You're Comparing Values

Bar Chart / Column Chart

Use when:

• Comparing discrete categories
• Showing rankings
• Displaying survey results
• Values don't need to sum to 100%

Best for:

• Sales by region
• Performance by employee
• Revenue by product line
• Survey response counts

Example data:

Choose bar over column when:

• Category names are long
• You have more than 6-7 categories
• You want to emphasize the ranking

Grouped Bar Chart

Use when:

• Comparing multiple series across categories
• Showing before/after or year-over-year
• Displaying survey results by demographic

Best for:

• Q1 vs Q2 sales by region
• Men vs women survey responses
• Budget vs actual by department

Limitation: Gets cluttered beyond 3-4 series or 6-7 categories

Horizontal Bullet Chart

Use when:

• Showing progress toward a goal
• Comparing actual vs target
• Displaying KPI performance

Best for:

• Sales quota achievement
• Project progress tracking
• Performance dashboards

Trend Charts: When Time Is Your X-Axis

Line Chart

Use when:

• Showing continuous change over time
• Tracking trends and patterns
• Comparing multiple series over time
• You have more than 7-8 time periods

Best for:

• Monthly revenue trends
• Website traffic over time
• Stock price movement
• Temperature changes

Important: The x-axis should be continuous time data. For categorical time (Q1, Q2, etc.), column charts often work better.

Area Chart

Use when:

• Emphasizing the magnitude of change
• Showing cumulative totals over time
• Creating visual impact for presentations

Best for:

• Total users over time (cumulative)
• Revenue growth visualization
• Market size expansion

Warning: Avoid overlapping area charts—they're hard to read. Use stacked area charts instead.

Stacked Area Chart

Use when:

• Showing how composition changes over time
• Tracking multiple series that sum to a total
• Visualizing market share evolution

Best for:

• Revenue by product line over time
• Traffic sources over time
• Budget allocation changes

Sparklines

Use when:

• Showing trends in limited space
• Embedding charts in tables or text
• Providing context without detail

Best for:

• Dashboard KPI indicators
• Stock tickers
• Report headers

Composition Charts: When Parts Make a Whole

Pie Chart

Use when:

• Showing parts of a whole
• You have 5 or fewer categories
• Values sum to 100%
• One segment dominates or is the focus

Best for:

• Market share (with dominant player)
• Budget allocation overview
• Simple survey results

Avoid when:

• Segments are similar in size
• You have more than 5-6 categories
• Precision matters more than overview

Donut Chart

Use when:

• Same criteria as pie charts
• You want to display a central metric
• You need a more modern look

Best for:

• Progress indicators (75% complete)
• Single KPI with context
• Modern dashboards

Stacked Bar Chart (100%)

Use when:

• Comparing composition across categories
• Showing how percentages differ by group
• You need precise segment comparison

Best for:

• Survey responses by demographic
• Portfolio allocation comparison
• Customer satisfaction by region

Advantage over pie: Much easier to compare the same segment across multiple groups.

Treemap

Use when:

• Showing hierarchical part-to-whole
• You have many categories (10+)
• Size differences are significant

Best for:

• File storage breakdown
• Budget by department and sub-department
• Market cap by sector and company

Waterfall Chart

Use when:

• Showing how a value builds up or breaks down
• Explaining variance between two numbers
• Walking through financial changes

Best for:

• Profit bridge (revenue to net income)
• Year-over-year variance explanation
• Cost buildup analysis

Relationship Charts: When Correlation Matters

Scatter Plot

Use when:

• Exploring relationship between two variables
• Identifying clusters or outliers
• Each data point represents an individual case

Best for:

• Price vs. sales relationship
• Height vs. weight correlation
• Marketing spend vs. revenue

Reading the chart:

• Points trending up-right = positive correlation
• Points trending down-right = negative correlation
• Points scattered randomly = no correlation

Bubble Chart

Use when:

• You need to show three variables
• Size represents a third dimension
• Comparing entities with multiple attributes

Best for:

• Countries: GDP (x) vs Life Expectancy (y) vs Population (size)
• Products: Price (x) vs Rating (y) vs Sales Volume (size)
• Competitors: Market Share (x) vs Growth (y) vs Revenue (size)

Heatmap

Use when:

• Showing patterns in large datasets
• Visualizing correlation matrices
• Displaying activity over two dimensions

Best for:

• Website activity by hour and day
• Correlation between multiple variables
• Geographic density data
• Cohort analysis

Distribution Charts: When Spread Matters

Histogram

Use when:

• Showing frequency distribution
• Understanding data spread
• Identifying patterns (normal, skewed, bimodal)

Best for:

• Age distribution of customers
• Transaction value distribution
• Response time analysis

Box Plot (Box and Whisker)

Use when:

• Comparing distributions across groups
• Showing median, quartiles, and outliers
• Statistical comparison of categories

Best for:

• Salary distribution by department
• Test scores by class
• Performance metrics comparison

Special Purpose Charts

Radar Chart (Spider Chart)

Use when:

• Comparing multiple attributes
• Showing performance profiles
• Entities have 5-8 measurable dimensions

Best for:

• Product feature comparison
• Employee skill assessment
• Competitor analysis

Warning: Radar charts are often misread. Use only with audiences familiar with them.

Funnel Chart

Use when:

• Showing stages in a process
• Visualizing conversion or dropout
• Values naturally decrease through stages

Best for:

• Sales funnel analysis
• Recruitment pipeline
• Website conversion funnel

Gantt Chart

Use when:

• Showing project timelines
• Visualizing task dependencies
• Tracking schedule progress

Best for:

• Project management
• Product roadmaps
• Event planning

Sankey Diagram

Use when:

• Showing flow between stages
• Visualizing transfers or conversions
• Energy or resource flow

Best for:

• Budget flow analysis
• Customer journey mapping
• Website navigation paths

Chart Selection by Data Type

Categorical Data (Names, Labels)

• Comparison: Bar chart, column chart
• Composition: Pie chart, treemap
• Comparison across groups: Grouped bar, heatmap

Time Series Data (Dates, Periods)

• Single series: Line chart, area chart
• Multiple series: Multi-line, stacked area
• Composition over time: Stacked bar, stacked area

Numerical Data (Continuous Values)

• Distribution: Histogram, box plot
• Relationship: Scatter plot, bubble chart
• Correlation: Heatmap

Hierarchical Data (Nested Categories)

• Structure: Treemap, sunburst
• Flow: Sankey diagram

Common Mistakes in Chart Selection

Mistake 1: Pie Charts for Everything

Pie charts are overused. They only work when:

• Parts sum to 100%
• You have 5 or fewer segments
• Exact values aren't critical

Better alternatives:

• Bar chart for comparison
• Donut with central KPI for single metrics
• Stacked bar for comparing composition across groups

Mistake 2: Line Charts for Categories

Line charts imply continuity. If your x-axis is categorical (regions, products), the line suggests a connection that doesn't exist.

Fix: Use bar charts for categorical comparisons.

Mistake 3: 3D Charts

3D effects distort perception and make accurate reading impossible.

Fix: Always use 2D charts. If you need visual interest, use color and typography.

Mistake 4: Too Much Data

More data isn't better visualization. Know when to:

• Aggregate (daily → monthly)
• Filter (top 10 only)
• Split into multiple charts

Mistake 5: Wrong Chart for Audience

A scatter plot perfect for analysts may confuse executives. Consider:

• Executives: Simple bars, single KPI donuts
• Analysts: Scatter plots, heatmaps, detailed views
• General public: Pie charts, simple lines, pictograms

Quick Reference Table

Practical Examples

Example 1: Quarterly Sales Report

Data: Sales figures for 4 products across 4 quarters

Wrong choice: Pie chart (doesn't show time dimension)

Right choice: Grouped column chart or line chart

• Grouped columns if comparing products is primary
• Line chart if showing trend is primary

Example 2: Customer Survey Results

Data: Satisfaction scores (1-5) from 500 respondents

Wrong choice: Line chart (implies continuity)

Right choice:

• Bar chart showing count per rating
• Histogram showing distribution
• Single number (average) with donut showing satisfied vs unsatisfied

Example 3: Marketing Budget Allocation

Data: Spending across 8 channels totaling $500K

Wrong choice: Pie chart (too many segments)

Right choice:

• Treemap (shows hierarchy and relative size)
• Horizontal bar (shows ranking clearly)
• Stacked bar if comparing to previous period

Let AI Choose for You

If you're still unsure, modern AI chart tools analyze your data and suggest the optimal chart type. Tools like ChartGen.ai examine:

• Data structure (categorical vs. numerical)
• Number of variables
• Data distribution
• Common visualization patterns

The AI suggestion isn't always perfect, but it's a great starting point that you can refine.

Conclusion

Choosing the right chart isn't about memorizing rules—it's about understanding what story your data tells and picking the visual that tells it most clearly.

Remember:

1. Start with your question, not your data
2. Simpler is almost always better
3. Match complexity to your audience
4. When in doubt, use a bar chart

Ready to create your chart? Try ChartGen.ai—paste your data and see intelligent chart suggestions in seconds.