Dashboard design: principles, visualizations, and best practices

Good dashboard design is the difference between data your team acts on and data they scroll past. This guide covers the design principles, visualization choices, and structural decisions that make dashboards easier to read, faster to interpret, and more useful for the people who rely on them.

Why does dashboard design matter?

A dashboard's main purpose is to communicate information faster. A good dashboard doesn't just put data in one place — it gives viewers an immediate sense of what is happening, what matters, and what is good or bad for the business.

That clarity comes from deliberate design. Colors signal positive or negative performance. Size and placement establish hierarchy. Labels add context. When design elements work together, the viewer spends less time parsing data and more time deciding what to do next.

Without that structure, even accurate data creates friction. Leaders end up pasting numbers into a spreadsheet, asking someone to pull a report, or piecing together a picture from four different tabs — none of which is the point.

How to design effective dashboards

1. Define your users and their objectives

Before you build anything, get clear on who will use the dashboard and what they need to know. That answer shapes every design decision that follows: which metrics to show, which visualizations to use, how much detail to include.

Ask these questions before you start:

• Who is your audience? Identify who will use the dashboard and what their role is.
• What is their objective? The more specific, the better. A marketing team might want to track monthly email subscriber growth. A finance team might want to understand what's driving expense increases.

Avoid building a one-size-fits-all dashboard. Focused dashboards reduce the need for filtering and segmentation, delivering the right information at a glance.

Understanding objectives also helps you choose the right dashboard type. There are four:

Strategic

Strategic dashboards track KPIs tied to overall business performance — financial health, market position, revenue growth — to keep leadership aligned with long-term objectives.

• Audience: Directors and executives
• Purpose: Measuring overall business performance
• Example: An executive dashboard showing performance against targets across all areas of the business

Operational

Operational dashboards monitor real-time data against KPIs to show how ongoing operations are performing — by department, process, or initiative.

• Audience: Managers and their teams
• Purpose: Measuring the real-time performance of a specific business area
• Example: A sales leaderboard comparing each employee's performance against targets

Analytical

Analytical dashboards compare data across multiple timeframes to surface trends, patterns, and root causes. They support forecasting and goal-setting.

• Audience: Analysts and executives
• Purpose: Analyzing historical data to support business decisions
• Example: A traffic dashboard breaking down sessions by region, channel, and keyword

Tactical

Tactical dashboards track progress for specific departments, projects, or initiatives over a defined period.

• Audience: Department heads, project leads, and managers
• Purpose: Measuring the long-term performance of a specific business area
• Example: A social media dashboard summarizing follower growth and losses over a month

Once you've defined your audience and dashboard type, go one level deeper. Knowing your viewers' experience with data, their preferences for certain visualizations, and where they'll be viewing the dashboard will guide decisions around color, labeling, and display format.

2. Identify key metrics

With your objective defined, narrow down the metrics that support it. Ask:

• What can you use to measure success?
• What processes contribute to progress? How are they measured?
• What information would help identify problems or opportunities?

MetricHQ is a useful reference for exploring KPIs and metrics by category or service, including definitions, formulas, and example visualizations.

3. Choose the most effective visualizations

Data visualizations carry the dashboard's core function: getting the data story across immediately. The wrong visualization obscures meaning. The right one makes the implication obvious without explanation.

Each visualization type answers a specific question:

• Bar graphs: Which data point performed best in its category?
• Line graphs and area graphs: How did a data point change over time?
• Pie charts, doughnut, and stacked bar graphs: How much does each component contribute to the whole?
• Gauges: How far is the value from the target?
• Scatter plots: What is the relationship between two variables?
• Bubble charts: What is the correlation between three or four variables?
• Heat maps: Which data point had the greatest magnitude?
• Geographic heat maps: How well does one metric perform per region?
• Tables: What are the exact values of each data point?

Line graphs

Main question: How did a data point change over time?

Line graphs track how a value changes across a continuous variable — almost always time. The slope of the line reveals trends, patterns, and shifts. Multiple lines let you compare independent series within the same timeframe.

Caption: The Klipfolio Support Ticket Breakdown Dashboard uses a multi-series line graph to compare monthly tickets by trial, customer, partner, or other.

When to use a line graph

• When tracking trends, patterns, and changes over time
• When comparing change across multiple independent series

When not to use a line graph

• When comparing discrete variables

Area graphs

Main question: How did a data point change over time?

Area graphs shade the space between the line and the x-axis, adding visual weight that communicates volume. Where a line graph shows movement, an area graph shows magnitude.

Caption: The Klipfolio Cash Flow Dashboard uses an area graph to visualize the volume of income, cost of goods sold, expenses, and net profit over one quarter.

Overlapping shaded areas can show how one series contributes to a whole — for example, daily sales volume with a layer underneath representing sales from new customers. That said, multiple independent series become hard to read in an area graph. Shaded areas overlap, conceal lower values, and imply cumulative relationships that may not exist.

When to use an area graph

• When communicating the volume of a single series
• When tracking a series' contribution to a whole

When not to use an area graph

• When tracking multiple independent series

Bar graphs

Main question: Which category is performing best?

Bar graphs use rectangular bars to compare discrete data points. Bars can scale vertically or horizontally. Color-coding adds a second dimension, distinguishing subgroups within a category. Stacked bar graphs divide each bar into components to show how each part contributes to the total.

The main constraint is space. The more values you compare, the harder each bar has to fight for room. Limit bar graphs to 10 data points.

!Klipfolio Support Ticket Breakdown dashboard showing ticket volume trends by type and source, with a multi-series line graph comparing monthly tickets by trial, customer, partner, and other categories

Caption: The Support Tickets Dashboard uses a two-colored bar graph to compare tickets per day and average tickets per day per month across a one-year period.

When to use a bar graph

• When comparing two or more values in the same category
• When ranking performance

When not to use a bar graph

• When comparing more than 10 data points
• When representing continuously changing data

Percentage graphs

Main question: How much does each component contribute to the whole?

Pie charts, doughnut graphs, and stacked bar graphs all show proportions. Pie charts are the most familiar. Doughnut graphs remove the center of the circle, making size differences between segments easier to read. Stacked bar graphs use a rectangle instead of a circle, which takes up less space and makes proportional differences more legible at a glance.

!Klipfolio Support Ticket Breakdown dashboard showing ticket volume trends by type and source, with a multi-series line graph comparing monthly tickets by trial, customer, partner, and other categories

Caption: The Klipfolio Support Ticket Breakdown Dashboard uses a doughnut graph to measure tickets by type.

Multi-bar stacked bar graphs go a step further, simultaneously comparing performance across categories and the contributions of components within each one.

Caption: The Klipfolio Customer Retention Dashboard uses a stacked bar graph to measure the contributions of expansions, new users, and cancellations on direct and partner MRR.

Limit percentage graphs to six components. More than that, and size differences between segments become too small to read reliably.

When to use each percentage graph type

• Pie chart: When comparing percentages of a whole
• Doughnut chart: When you need more visual clarity than a pie chart provides
• Single-bar stacked bar graph: When space efficiency matters
• Stacked bar graph: When comparing contributions to performance across multiple categories

When not to use a percentage graph

• When comparing more than six data points
• When you are not comparing contributions to a whole

Gauges

Main question: How far is the value from the target?

Gauges shade a defined shape — rectangular or radial — to show how a value sits within a range. Some use needles to pinpoint the exact position. Others use color to indicate progress against a target or a maximum.

Caption: The Klipfolio Sales Leaderboard Dashboard uses rectangular gauges to measure employee performance.

Gauges work best when targets and maximums are clearly defined. The visual contrast between the filled and empty portions makes remaining distance to a goal immediately obvious.

When to use a gauge

• When targets and maximums are clearly defined

When not to use a gauge

• When targets and maximums are not significant to the dataset

Scatter plots

Main question: What is the relationship between two variables?

Scatter plots place data points on a two-dimensional plane. Position on the x-axis measures one variable; position on the y-axis measures another. Unlike line graphs, scatter plots do not connect data points — the pattern of their distribution reveals the relationship.

Scatter plots are useful for identifying positive trends, negative trends, nonlinear relationships, and outliers. They also confirm when two variables have no meaningful relationship at all.

When to use scatter plots

• When identifying correlations between two variables
• When relationships between variables are nonlinear

When not to use scatter plots

• When identifying relationships between three or more variables

Bubble charts

Main question: What is the relationship between three to four variables?

Bubble charts extend scatter plots by adding size as a third dimension. Each data point's position on the x- and y-axes represents two variables; bubble size represents a third. A fourth variable can be encoded using color.

Caption: The bubble chart on the Klipfolio Account Health Dashboard uses bubbles to represent accounts, with x-axis position signifying account age, y-axis position signifying percentage of monthly active users, bubble size signifying number of users, and color signifying risk level.

Always include a legend to explain what bubble size represents. Without it, viewers may try to read bubble size against the axis scales, which creates confusion.

Avoid bubble charts when the dataset includes negative values — size cannot go below zero. If large bubbles risk obscuring smaller ones, use a colored scatter plot instead, keeping data point sizes uniform and encoding the third variable through color.

When to use bubble charts

• When identifying correlations between two variables with a meaningful third dimension
• When relationships between variables are nonlinear

When not to use bubble charts

• When negative values appear across all variables
• When the size of the dataset risks overplotting

Grid heat maps

Main question: Which data point had the greatest magnitude?

Grid heat maps use color intensity to communicate value across a two-axis grid. Darker colors typically indicate higher values. The pattern of color draws the eye toward outliers and clusters without requiring the viewer to read individual numbers.

Grid heat maps answer the same core question as bar graphs — which data point performed best — but handle larger datasets more efficiently. Because color communicates value rather than bar height, many more data points fit in the same space.

Caption: The Klipfolio Email Marketing Performance Dashboard colors cells on a grid to communicate newsletter performance.

The trade-off is that color is a less intuitive indicator of scale than size. Include a legend, and consider adding numerical values in each cell for precision.

When to use grid heat maps

• When comparing values across large datasets

When not to use grid heat maps

• When the dataset contains fewer than 10 data points

Geographic heat maps

Main question: How well does one metric perform per region?

Geographic heat maps color a map to show how a metric is distributed across regions. Some shade exact areas where trends occur; others fill within political borders.

Use geographic heat maps when location is genuinely relevant to the objective — for example, a retailer tracking customers by region to understand proximity to stores. If location adds little insight or data is only available for a few regions, a bar graph is usually a better choice: size is more intuitive than color, and bar graphs take up less space.

When to use geographic heat maps

• When comparing trends across regions

When not to use geographic heat maps

• When location is irrelevant to the objective
• When data is only available for a few regions

Tables

Main question: What are the exact values of each data point?

Tables display precise values. They are useful when exact numbers matter more than visual patterns, when organizing non-numerical data, or when grouping lower-priority information that does not need a dedicated visualization.

Caption: The Campaign Performance Dashboard uses a table to communicate overall performance across all channels.

The Campaign Performance Dashboard, for example, uses a table to show total spending, users, leads, trials, and wins. All metrics belong to a single group — totals across all channels — and require no visual comparison. The table keeps the layout compact while preserving the precise numbers decision-makers need.

When to use tables

• When presenting precise numbers is necessary
• When organizing non-numerical data
• When presenting lower-priority data where space efficiency matters

When not to use tables

• When visualizing relationships between data points is critical to the dashboard's purpose

Mixed visualizations

Dashboards let you combine visualization types to cover what no single chart can do alone. The Digital Marketing Dashboard, for example, pairs a geographic heat map to show where sessions and leads originate with a bar graph to clarify volume differences between top regions. Each chart reinforces the other.

Caption: A combined map chart and bar graph from Klipfolio's Digital Marketing Dashboard.

The risk with mixed visualizations is clutter. Every chart added competes for space and attention. Include only what directly serves the dashboard's objective.

4. Add comparison values

A number without context tells you very little. Comparison values — targets, prior periods, trailing averages — are what turn a data point into a signal.

Common comparison types include:

• Comparison against a set target (current Customer Satisfaction Score vs. target): Estimates remaining work.
• Comparison against a preceding period (today vs. yesterday): Benchmarks current against recent performance.
• Comparison against a prior equivalent period (this week vs. same week last year): Assesses whether strategies are holding up or whether external factors are at play.
• Comparison against a trailing average (today vs. average of previous 30 days): Helps spot outliers and assess ongoing strategy effectiveness.
• Comparison against a projection (current cumulative total vs. expected total at this point in time): Checks whether forecasts are tracking accurately.
• Comparison against another metric (new account activations vs. cancellations): Supports qualitative reads on overall direction.

If you cannot identify a meaningful comparison for a metric, it probably does not need one.

5. Visualize your information hierarchy through size and placement

Size and placement communicate importance. The eye gravitates toward the largest elements first, then follows the viewer's natural reading direction — left to right, top to bottom for most of the world.

Put the most important information in the upper left quadrant. Secondary information should sit lower and take up less space.

Caption: The Klipfolio Monthly Sales Dashboard opens with a card for daily sales stats in the upper left, then breaks down more specific information — new accounts, accounts by region, Monthly Recurring Revenue by region, account target progress, and expansion progress.

6. Group related data on a single dashboard

Related metrics are easier to find when they sit together. When viewers have to jump between unrelated panels to piece together a picture, the dashboard is doing extra work for them instead of less.

Organize panels in a logical sequence. Ask: what would the viewer want to know next? Place panels with stronger relationships closer together.

Caption: Klipfolio's Executive KPI Dashboard.

The Executive KPI Dashboard, for example, groups revenue year-to-date, debt-to-equity, return on equity, net profit margin, and gross profit margin. Debt-to-equity and return on equity share a column; net profit margin and gross profit margin share another. The placement makes interrelated figures easier to find and compare.

7. Use color strategically

Color shapes the viewing experience before the viewer reads a single number. Used well, it draws attention to what matters. Used carelessly, it distracts or misleads.

Do: Use neutral colors for regular data, bold colors for outliers. Desaturated colors work for the majority of your visualizations. Reserve saturated colors for changes and indicators that need immediate attention.

Do: Use color to create emotional associations. Red signals danger and negative performance. Green signals growth and positive performance. Yellow and orange sit closer to red on the color wheel — use them sparingly to flag minor problem areas. Klipfolio defaults to blue for neutral data, reflecting associations with reliability and trust.

Do: Use consistent color schemes. If green means positive performance on one chart, it should mean the same thing everywhere. Inconsistency confuses the viewer and erodes trust in the numbers.

Don't: Use more than five distinct colors on a single page. Beyond five, contrast becomes visual noise. Where related data points need differentiation, use varied saturation of the same color rather than introducing a new one.

Don't: Use bold colors for backgrounds. The data should be the focal point, not the background. Use dark or light gray to keep attention on what matters.

8. Make text easy to read

Labels and titles that are hard to read slow the viewer down. Follow these principles:

• Use sans-serif fonts. They remove unnecessary decoration and improve readability on screens.
• Contrast text against the background. Low contrast forces the eye to work harder.
• Scale text to reflect importance. Larger text for titles and key metrics; smaller text for labels and secondary values.

9. Use interactive elements to add depth without clutter

Interactive elements let you include supporting detail without crowding the main view. The most useful types are:

• Tooltips show supplementary text when viewers hover over or tap a component. Use them to surface precise values for data points on a graph.
• Filters let viewers limit displayed data by time range, metric, or other parameters — useful when the same dashboard serves multiple use cases.
• Drill-down features let viewers click into a data point to see a more granular breakdown. A sales-by-region map, for example, might let users click a country to see performance by province or state.
• Drill-through features link from one dashboard to another, letting viewers jump from a high-level view to a more specific report.

10. Design for the display

Where a dashboard is viewed shapes what it can show. Design for the device, not just the data.

Mobile and tablet

Space is limited. Prioritize critical information only.

• Keep layouts simple and minimal
• Use short metric titles
• Use menus and filters to manage complexity

Browser

Browsers offer the most flexibility. Viewers sit close to the screen and can engage with detail.

• Design for responsive layouts so the dashboard remains legible when the window resizes
• Use this format for dashboards that support deeper analysis, since browser viewers are typically at their desks and able to focus

TV display

TV dashboards are often viewed from a distance, sometimes as ambient displays in a workspace or as a reference point in a meeting room. The viewer is not always focused on the screen.

• Trim content so key numbers are readable from across a room
• Use larger font sizes
• Remove axis labels that are implied or redundant
• Dark mode — light text and visuals on a dark background — improves legibility at a distance

11. Schedule regular maintenance

Dashboards update data automatically, but the structure, metrics, and design do not update themselves. As goals shift, teams change, and new questions emerge, dashboards that once served their purpose can become stale or misleading.

Set a regular maintenance cadence. Review performance, check that displayed metrics still align with current objectives, and meet with relevant stakeholders to stay in sync with organizational priorities.

12. Collect and act on user feedback

The people using a dashboard every day will notice what works and what does not. Their feedback is the most reliable signal for what to improve.

Schedule regular check-ins or surveys with the main users of each dashboard. Ask what is working, what is confusing, and what information they still have to find elsewhere. Integrate valid, actionable feedback into your next update cycle.

A dashboard built around user needs earns consistent use. One that ignores feedback gets bypassed — and when that happens, people go back to pasting numbers into a spreadsheet or asking someone to pull a report.

Build better dashboards with Klipfolio

Good dashboard design reduces the cognitive load between raw data and confident decisions. When hierarchy, color, and visualization choices all work together, your team spends less time figuring out what the numbers mean and more time acting on them.

Klips gives you everything you need to build dashboards that work: 30+ visualization types, custom themes, Excel-like formulas, and over 130 data source connectors. Scheduled refreshes keep numbers current without manual effort. Granular sharing options — TV mode, public links, embeds, and scheduled PDF delivery — mean the right people see the right data without having to go looking for it.

For examples of effective dashboard design in practice, explore Klipfolio's 90+ interactive dashboard examples. Each one is live, built for a specific use case, and worth borrowing from.