What Are the Best Visuals for Power BI?

Choosing the right visual in Power BI can be the difference between a confusing report and a dashboard that drives real business decisions. With dozens of options available, from basic bar charts to AI-driven visuals, knowing which one to use for your specific data story is essential. This guide will walk you through the most effective visuals in Power BI and explain exactly when and how to use them to make your data clear and actionable.

Start with Your Goal, Not the Chart

Before you even drag a visual onto the canvas, take a moment to ask yourself one question: "What am I trying to show?" The "best" visual isn't about looking flashy, it's about communicating a specific insight. Most data stories fall into one of four categories:

• Comparison: You want to see how different items stack up against one another. Are sales in the East region higher than in the West? Which marketing campaign had the best performance? This is about comparing magnitudes.
• Composition: You need to show the parts that make up a whole. What percentage of our website traffic comes from organic search versus paid ads? This is about showing proportional shares.
• Relationship: You're looking for a connection or correlation between two or more variables. Does increased ad spend lead to more revenue? Is there a link between customer tenure and product purchases?
• Distribution: You want to understand how data points are spread out over a range. How many deals are in each stage of our sales pipeline? What are our most common order sizes?

Keeping these goals in mind will naturally guide you to the perfect visual for the job.

The Essential Power BI Visuals You'll Use Every Day

While the full library is extensive, a handful of core visuals will handle most of your day-to-day reporting needs. Mastering these is the first step toward building effective dashboards.

Bar and Column Charts

The undisputed workhorses of data visualization, bar and column charts are the best choice for comparing values across different categories. A column chart displays categories along the horizontal axis and values along the vertical axis, while a bar chart flips this arrangement.

When to use them:

• To rank items, like top-selling products or best-performing sales reps.
• To compare a metric across discrete categories, such as monthly revenue by region.
• Use a column chart if you have a handful of categories. Use a horizontal bar chart if you have long category names or many categories to display, as a vertical list is easier to read.

Example: You want to see which product categories generated the most revenue last quarter. A simple column chart instantly shows which bars are tallest, making it easy to spot your top performers.

Line Charts

When you need to show how a value changes over time, a line chart is your go-to visual. By connecting data points over a continuous interval (like days, months, or years), they are perfect for spotting trends, seasonality, and patterns.

When to use them:

• Tracking a metric over a continuous period, such as daily website sessions or monthly sales figures.
• Comparing trends for multiple series, like showcasing website traffic from different sources (Organic, Paid, Social) on the same chart.

Example: To track your website's user growth over the last year, a line chart will clearly show the upward or downward trend and help you identify months with significant spikes or dips.

Pie and Donut Charts

Pie and donut charts are built to show composition - the parts of a whole. They represent a total value (100%) and break it down into slices that show the proportion of each category. Donut charts are a slight variation with a hole in the middle, which can be useful for adding a KPI card with the total value.

When to use them:

• Showing a percentage breakdown for a small number of categories.
• When the sum of the components represents a complete, meaningful total.

A word of caution: Use pie charts sparingly. The human eye isn't great at comparing the size of angled slices. If you have more than five categories, a bar chart is almost always a clearer option.

Example: A pie chart is effective for showing the percentage of sales from three different store locations, making it immediately clear which location contributes the most to the total.

Tables and Matrices

Sometimes, you just need to see the raw numbers. Tables and matrices are perfect for displaying detailed data in a structured grid format. A table shows data in rows and columns, while a matrix allows for more complex layouts with subtotals and the ability to drill down into hierarchical data (e.g., viewing data by Year > Quarter > Month).

When to use them:

• Providing precise numerical values for lookup and comparison.
• When you need to display multiple metrics for each category (e.g., a product list showing units sold, revenue, and profit margin for each item).
• Matrices are ideal for creating pivot table-like reports.

Example: A finance department might use a matrix to show a profit and loss statement, with expense categories as rows and time periods as columns, including quarterly and annual totals.

Scatter Charts

Scatter charts are the ideal visual for exploring the relationship between two numerical variables. Each point on the chart represents an instance where the x-axis value and y-axis value intersect, helping you spot correlations, clusters, and outliers in your data.

When to use them:

• To determine if two metrics are related (e.g., does marketing spend correlate with sales revenue?).
• Identifying outliers that don't fit the general pattern.
• You can also add a third numeric dimension using bubble size, turning it into a bubble chart.

Example: A marketer could plot ad spend on the x-axis and conversion rate on the y-axis for various campaigns. This would reveal if higher spending generally leads to a better conversion rate and highlight specific campaigns that are overperforming or underperforming.

Maps

If your data has a geographical component like state, country, or even latitude and longitude, using a map visual brings it to life. Power BI offers several map types, including a standard Map for showing points and a Filled Map for shading regions based on value.

When to use them:

• Visualizing sales, customers, or performance data by geographic location.
• Analyzing regional distribution and spotting spatial patterns.

Example: A sales manager could use a filled map to see which states have the highest sales volume, with darker shades of color representing higher revenue. This immediately focuses attention on key territories.

Advanced Visuals for Deeper Insights

Once you've mastered the basics, Power BI has some specialized visuals that can uncover even deeper insights.

Treemaps

Treemaps are excellent for displaying hierarchical data as a set of nested rectangles. The size of each rectangle represents its value, making it easy to compare parts of a whole across multiple levels of a hierarchy simultaneously. They are a great alternative to pie charts when you're dealing with a larger number of categories.

Example: A treemap can visualize website traffic by source, medium, and campaign all in one view. You could see a large rectangle for "Organic Search," which is then subdivided into smaller rectangles representing different landing pages.

Funnel Charts

Funnel charts are perfect for visualizing a process with sequential stages and identifying drop-offs. They show how many items move from one stage to the next, making it simple to pinpoint bottlenecks in your process.

Example: Analyzing a sales pipeline is a classic use case. A funnel chart can show the number of leads at each stage: Initial Contact, Qualified, Proposal Sent, and Deal Won. The narrowing of the funnel clearly illustrates conversion rates between stages.

Key Influencers Visual

This is one of Power BI's unique AI-powered visuals. The Key Influencers chart analyzes your data to find the single biggest factors that drive a specific outcome. It automates statistical analysis that would otherwise be very time-consuming.

Example: You could use it to understand what influences customer satisfaction. The visual might automatically identify that customers who received their order within two days are 3x more likely to leave a positive review, giving you a clear, actionable insight.

Dashboard Design Best Practices

Choosing the right visual is only half the battle. How you present it matters just as much.

• Keep it simple: Don't clutter your dashboard with unnecessary visual elements. Avoid 3D effects, shadows, and distracting background colors. Let the data be the star.
• Use clear labels: Make sure your charts have descriptive titles, axis labels, and data labels where needed. A viewer should be able to understand a chart's purpose at a glance.
• Leverage color thoughtfully: Use color to highlight important information, not just to decorate. Stick to a limited, consistent color palette and be mindful of color contrast for accessibility.
• Tell a story: Arrange your visuals in a logical flow on the dashboard. Start with high-level KPIs at the top, followed by trends and more detailed breakdowns below. Guide your user through the information.

Final Thoughts

Mastering Power BI visuals is about understanding the story your data is trying to tell and choosing the simplest, clearest chart to communicate it. By focusing first on your goal - whether it's comparison, composition, relationship, or distribution - you can confidently select the right tool from your toolkit, from a simple bar chart for ranking to a funnel chart for process analysis.

Knowing which visual to use is an important skill, but the process of connecting data, configuring reports, and building dashboards in tools like Power BI can still take hours. At Graphed you’ve simplified this entire workflow. By connecting your data sources and just describing what you want to see - like “create a dashboard comparing Facebook Ads spend vs. Shopify revenue by campaign for the last 30 days” - our AI analyst instantly builds the right report with the right visuals, getting you from data to decision in seconds, not hours.