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This article lists visualizations available in Power BI. We'll be adding new visualizations, stay tuned!
And check out the Microsoft AppSource, where you'll find a growing list of Power BI visuals you can download and use in your own dashboards and reports. Feeling creative? Learn how to create and add your own visuals to this community site.
Visualizations in Power BI
All of these visualizations can be added to Power BI reports, specified in Q&A, and pinned to dashboards.
Area charts: Basic (Layered) and Stacked
The basic area chart is based on the line chart with the area between the axis and line filled in. Area charts emphasize the magnitude of change over time, and can be used to draw attention to the total value across a trend. For example, data that represents profit over time can be plotted in an area chart to emphasize the total profit.
For more information, see Basic Area chart.
Bar and column charts
Bar charts are the standard for looking at a specific value across different categories.
Multi row cards display one or more data points, one per row.
Single number cards display a single fact, a single data point. Sometimes a single number is the most important thing you want to track in your Power BI dashboard or report, such as total sales, market share year over year, or total opportunities.
For more information, see Create a Card (big number tile).
A combo chart combines a column chart and a line chart. Combining the two charts into one lets you make a quicker comparison of the data. Combo charts can have one or two Y axes, so be sure to look closely.
Combo charts are a great choice:
- When you have a line chart and a column chart with the same X axis.
- To compare multiple measures with different value ranges.
- To illustrate the correlation between two measures in one visual.
- To check whether one measure meets the target which is defined by another measure.
- To conserve canvas space.
For more information, see Combo charts in Power BI.
The decomposition tree visual lets you visualize data across multiple dimensions. It automatically aggregates data and enables drilling down into your dimensions in any order. It is also an artificial intelligence (AI) visualization, so you can ask it to find the next dimension to drill down into based on certain criteria. This makes it a valuable tool for ad hoc exploration and conducting root cause analysis.
Doughnut charts are similar to pie charts. They show the relationship of parts to a whole. The only difference is that the center is blank and allows space for a label or icon.
For more information, see Doughnut charts in Power BI.
Funnels help visualize a process that has stages, and items flow sequentially from one stage to the next. One example is a sales process that starts with leads and ends with purchase fulfillment.
For example, a sales funnel that tracks customers through stages: Lead > Qualified Lead > Prospect > Contract > Close. At a glance, the shape of the funnel conveys the health of the process you're tracking.Each funnel stage represents a percentage of the total. So, in most cases, a funnel chart is shaped like a funnel -- with the first stage being the largest, and each subsequent stage smaller than its predecessor. A pear-shaped funnel is also useful -- it can identify a problem in the process. But typically, the first stage, the "intake" stage, is the largest.
For more information, see Funnel Charts in Power BI.
A radial gauge chart has a circular arc and displays a single value that measures progress toward a goal. The goal, or target value, is represented by the line (needle). Progress toward that goal is represented by the shading. And the value that represents that progress is shown in bold inside the arc. All possible values are spread evenly along the arc, from the minimum (left-most value) to the maximum (right-most value).
In the example above, we are a car retailer, tracking our Sales team's average sales per month. Our goal is $200,000 and represented by the location of the needle. The minimum possible average sales is $100,000 and we've set the maximum as $250,000. The blue shading shows that we're currently averaging approximately $180,000 this month. Luckily, we still have another week to reach our goal.
Radial gauges are a great choice to:
- Show progress toward a goal.
- Represent a percentile measure, like a KPI.
- Show the health of a single measure.
- Display information that can be quickly scanned and understood.
For more information, see Gauge Charts in Power BI.
Key influencers chart
A key influencer chart displays the major contributors to a selected result or value.
Key influencers are a great choice to help you understand the factors that influence a key metric. For example, what influences customers to place a second order or why were sales so high last June.
For more information, see Key influencer charts in Power BI
A Key Performance Indicator (KPI) is a visual cue that communicates the amount of progress made toward a measurable goal.
KPIs are a great choice:
- To measure progress (what am I ahead or behind on?).
- To measure distance to a metric (how far ahead or behind am I?).
For more information, see KPIs in Power BI.
Line charts emphasize the overall shape of an entire series of values, usually over time.
Use a basic map to associate both categorical and quantitative information with spatial locations.
For more information, see Tips and tricks for map visuals.
The combination of ArcGIS maps and Power BI takes mapping beyond the presentation of points on a map to a whole new level. The available options for base maps, location types, themes, symbol styles, and reference layers creates gorgeous informative map visuals. The combination of authoritative data layers (such as census data) on a map with spatial analysis conveys a deeper understanding of the data in your visual.
For more information, see ArcGIS maps in Power BI.
Used to associate both categorical and quantitative information with spatial locations.
For more information, see Azure Maps visual for Power BI.
Filled map (Choropleth)
A filled map uses shading or tinting or patterns to display how a value differs in proportion across a geography or region. Quickly display these relative differences with shading that ranges from light (less-frequent/lower) to dark (more-frequent/more).
The more intense the color, the larger the value.
For more information, see Filled Maps in Power BI.
Shape maps compare regions on a map using color. A shape map can't show precise geographical locations of data points on a map. Instead, its main purpose is to show relative comparisons of regions on a map by coloring them differently.
For more information, see Shape Maps in Power BI.
The matrix visual is a type of table visual (see Tables in this article) that supports a stepped layout. A table supports two dimensions, but a matrix makes it easier to display data meaningfully across multiple dimensions. Often, report designers include matrixes in reports and dashboards to allow users to select one or more element (rows, columns, cells) in the matrix to cross-highlight other visuals on a report page.
The matrix automatically aggregates the data and enables drilling down into the data.
For more information, see Matrix visuals in Power BI.
Pie charts show the relationship of parts to a whole.
Power Apps visual
Report designers can create a Power App and embed it into a Power BI report as a visual. Consumers can interact with that visual within the Power BI report.
For more information, see Add a Power Apps visual to your report.
Similar to the , the Q&A visual lets you ask questions about your data using natural language.
For more information, see .
R script visuals
Visuals created with R scripts, commonly called R visuals, can present advanced data shaping and analytics such as forecasting, using the rich analytics and visualization power of R. R visuals can be created in Power BI Desktop and published to the Power BI service.
For more information, see R visuals in Power BI.
Ribbon charts show which data category has the highest rank (largest value). Ribbon charts are effective at showing rank change, with the highest range (value) always displayed on top for each time period.
For more information, see Ribbon charts in Power BI.
Scatter, bubble, and dot plot chart
A scatter chart always has two value axes to show one set of numerical data along a horizontal axis and another set of numerical values along a vertical axis. The chart displays points at the intersection of an x and y numerical value, combining these values into single data points. These data points may be distributed evenly or unevenly across the horizontal axis, depending on the data.
A bubble chart replaces data points with bubbles, with the bubble size representing an additional dimension of the data.
Both scatter and bubble charts can also have a play axis, which can show changes over time.
A dot plot chart is similar to a bubble chart and scatter chart except that it can plot numerical or categorical data along the X axis. This example happens to use squares instead of circles and plots sales along the X axis.
For more information, see Scatter charts in Power BI.
By definition, high-density data is sampled to create visuals reasonably quickly that are responsive to interactivity. High-density sampling uses an algorithm that eliminates overlapping points, and ensures that all points in the data set are represented in the visual. It doesn't just plot a representative sample of the data.
This ensures the best combination of responsiveness, representation, and clear preservation of important points in the overall data set.
For more information, see High Density Scatter charts in Power BI.
A slicer is a standalone chart that can be used to filter the other visuals on the page. Slicers come in many different formats (category, range, date, etc.) and can be formatted to allow selection of only one, many, or all of the available values.
Slicers are a great choice to:
- Display commonly used or important filters on the report canvas for easier access.
- Make it easier to see the current filtered state without having to open a drop-down list.
- Filter by columns that are unneeded and hidden in the data tables.
- Create more focused reports by putting slicers next to important visuals.
For more information, see Slicers in Power BI.
The Smart narrative adds text to reports to point out trends, key takeaways, and add explanations and context. The text helps users to understand the data and identify the important findings quickly.
For more information, see Create smart narrative summaries.
A standalone image is a graphic that has been added to a report or dashboard.
For more information, see Add an image widget to a dashboard.
A table is a grid that contains related data in a logical series of rows and columns. It may also contain headers and a row for totals. Tables work well with quantitative comparisons where you are looking at many values for a single category. For example, this table displays five different measures for Category.
Tables are a great choice:
- To see and compare detailed data and exact values (instead of visual representations).
- To display data in a tabular format.
- To display numerical data by categories.
For more information, see Working with tables in Power BI.
Treemaps are charts of colored rectangles, with size representing value. They can be hierarchical, with rectangles nested within the main rectangles. The space inside each rectangle is allocated based on the value being measured. And the rectangles are arranged in size from top left (largest) to bottom right (smallest).
Treemaps are a great choice:
- To display large amounts of hierarchical data.
- When a bar chart can't effectively handle the large number of values.
- To show the proportions between each part and the whole.
- To show the pattern of the distribution of the measure across each level of categories in the hierarchy.
- To show attributes using size and color coding.
- To spot patterns, outliers, most-important contributors, and exceptions.
For more information, see Treemaps in Power BI.
A waterfall chart shows a running total as values are added or subtracted. It's useful for understanding how an initial value (for example, net income) is affected by a series of positive and negative changes.
The columns are color coded so you can quickly tell increases and decreases. The initial and the final value columns often start on the horizontal axis, while the intermediate values are floating columns. Because of this "look", waterfall charts are also called bridge charts.
Waterfall charts are a great choice:
- When you have changes for the measure across time or across different categories.
- To audit the major changes contributing to the total value.
- To plot your company's annual profit by showing various sources of revenue and arrive at the total profit (or loss).
- To illustrate the beginning and the ending headcount for your company in a year.
- To visualize how much money you make and spend each month, and the running balance for your account.
For more information, see Waterfall charts in Power BI.
Visualizations in Power BI reportsPower BI Visuals Reference from sqlbi.com, guidance for picking the right visual for your data
Which type of visualization is best in Power BI? ›
- Card. The card visual is the simplest, containing only a single number. ...
- Table. The table visual is used for quantitative comparisons, enabling you to see and compare detailed data and exact values. ...
- Matrix. ...
- Map. ...
- Line Chart. ...
- Area Chart. ...
- Donut. ...
In Power BI, we have two types of map visualization - bubble maps and shape maps.What are the limitations of visuals in Power BI? ›
Power BI visuals can get up to 30,000, but it's up to the visual authors to indicate which strategies to use. The default limit is 1,000, but the visual creator can change that up to a maximum of 30,000.What are the types of visualization? ›
The most common are scatter plots, line graphs, pie charts, bar charts, heat maps, area charts, choropleth maps and histograms. In this guide, we've put together a list of 32 data visualizations.What are the three categories of data visualization? ›
We posit that there are three main categories of explanatory visualizations based on the relationships between the three necessary players: the designer, the reader, and the data.Which is better for data visualization? ›
Some of the best data visualization tools include Google Charts, Tableau, Grafana, Chartist, FusionCharts, Datawrapper, Infogram, and ChartBlocks etc. These tools support a variety of visual styles, be simple and easy to use, and be capable of handling a large volume of data.What are the disadvantages of visual? ›
Some of the disadvantages of visual communication are not all of the concepts can be taught through visual communication, just providing graphs will not communicate everything, it can be distracting as well, since the audience may not listen to the speaker, rather they would be engrossed in the visuals.What are the 4 main type of visuals? ›
There are a variety of different types of visual communication, which can be broken down into four main categories: graphic design, photography, illustration, and video production. Each has its own set of advantages and disadvantages.What are the 4 levels of visualization? ›
These stages are exploration, analysis, synthesis, and presentation.What is data visualization list 4 techniques? ›
A: The visualization techniques include Pie and Donut Charts, Histogram Plot, Scatter Plot, Kernel Density Estimation for Non-Parametric Data, Box and Whisker Plot for Large Data, Word Clouds and Network Diagrams for Unstructured Data, and Correlation Matrices.
What are the two basic types of data visualization? ›
There are two basic types of data visualization: static and interactive. Static visualizations are something like an infographic, a single keyhole view of a particular data story.What is data visualization and its types? ›
Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.Which type of visualization should you use? ›
Line charts are best used when trying to visualize continuous data over time. Line charts are set against a common scale and are ideal for showing trends in data. You might also add a trend line or a goal line to illustrate performance in a certain period against a set benchmark.What is the most important part of data Visualisation? ›
The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets. The term is often used interchangeably with others, including information graphics, information visualization and statistical graphics.Why are visuals so powerful? ›
Why is visual communication so powerful? It isn't just because of the pretty pictures; it's straight-up science. The brain absorbs and synthesizes visual information faster than any other stimuli, making visual content an incredibly effective medium.Why using visual is important? ›
Visuals highlight the main points you are trying to communicate in an efficient and interesting way, helpingelps the viewer connect those main points with contexts that are relevant in their own lives, thereby strengthening their memory's connection to the information.What is an advantage of using visual methods? ›
Visual methods enhance the richness of data by discovering additional layers of meaning, adding validity and depth, and creating knowledge. They add to traditional methods by capturing more detail and a different kind of data than verbal and written methods.What are the different types of data visualization charts? ›
- Bar Graph.
- Column Chart.
- Line Graph.
- Dual Axis Chart.
- Area Chart.
- Stacked Bar Graph.
- Mekko Chart.
- Pie Chart.
There are two basic types of data visualization: static and interactive. Static visualizations are something like an infographic, a single keyhole view of a particular data story.What are the four types of data visualizations? ›
What are the main types of data visualization? The main types of data visualization include charts, graphs and maps in the form of line charts, bar graphs, tree charts, dual-axis charts, mind maps, funnel charts and heatmaps.
What are the 4 most commonly used types of chart? ›
There are several different types of charts and graphs. The four most common are probably line graphs, bar graphs and histograms, pie charts, and Cartesian graphs.What are the three functions of visualization? ›
The utility of data visualization can be divided into three main goals: to explore, to monitor, and to explain. While some visualizations can span more than one of these, most focus on a single goal.What are data visualization methods? ›
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.