Power BI Visualizations and Charts

Visualising large amounts of data on a single screen enables organisations to identify patterns and gain actionable insights to support business decisions.

Among the many visualisation platforms available, Microsoft Power BI is a well-known business intelligence tool that lets you model, analyse, and visualise data. You can create many graphs and charts in Power BI to simplify data presentation in reports and dashboards.

This guide explores different Power BI visualization and charts, their uses, and which visual type to use for different scenarios.

Table of Contents

• Data Visualisation - An Overview
• Power BI Visuals - An Overview
• List of Power BI Visualisations and Charts
• AI Visuals
• Copilot Narrative Visual
• Visual Calculations
• Filter and Interaction Controls
• How to Create a Power BI Visualisation?
• Custom Visualisation Types
• Which Power BI Visual Should I Choose?
• FAQs
• Conclusion

Data Visualisation - An Overview

Data visualisation involves representing data through charts, graphs, maps, and tables. It helps you identify patterns, compare values, and communicate findings. It is a quick way to communicate insights to end users. You can explore different scenarios by making small changes.

You can use data visualisation to interpret complex data more quickly than raw tables alone.

Microsoft Power BI Visuals - An Overview

Power BI visuals are graphical representations that you can use to analyse and communicate data in Microsoft Power BI. They convert raw data into charts, graphs, maps, tables, and other interactive formats.

List of Power BI Visualisations and Charts

1. Area Charts

An area chart builds on a line chart by filling the area between the line and a baseline. It shows trend changes in values over time. It can also highlight overall trends.

Example:

You can use an area chart to compare the number of items sold this year and last year in a fiscal month, as shown below:

2. Line Charts

Line charts commonly show trends over time or changes across an ordered sequence. Each point on the line represents a data value for a given category or time period.

Line charts display plotted values and help users identify trends, fluctuations, and differences between series. You can display exact values using data labels or tooltips. You can use line charts to display trends over time, such as daily, monthly, or yearly changes.

Example:

A line chart can show the number of items over time, as shown below:

3. Bar Charts

Bar charts use horizontal bars to compare values across categories. They can display counts, sums, averages, percentages, and other measures. They can display positive and negative values with bars extending in opposite directions from the zero baseline.

Example:

You can use a bar chart to show the number of vehicles sold in different categories in a particular year, as shown below:

4. Column Charts

Column charts are similar to bar charts. They use vertical bars to compare values across categories. Clustered column charts allow you to compare multiple series side by side within each category, whereas stacked column charts show each series as part of a total.

Example:

A column chart can show the number of loans disbursed by a financial company for different purposes in a particular year, as shown below:

5. Combo Charts

A combo chart combines column and line charts to compare related measures in a single visual. For example, a combo chart can display monthly sales as columns and profit margin as a line.

6. Pie Charts

A pie chart is a circular chart that divides a whole into proportional slices. Each slice represents a category’s proportion of the whole. Pie charts are appropriate for showing how few categories contribute to a whole. They help users understand the data quickly.

Example:

A pie chart can show loans disbursed by a financial company for different purposes in a particular year, as shown below.

7. Doughnut Charts

A doughnut chart is similar to a pie chart but has a hollow centre. For example, you can use a doughnut chart to show a part of a whole, as shown below.

8. Gauge Charts

A gauge chart is also known as a speedometer-style or dial chart. It uses a needle or indicator to show a value on a dial. The gauge chart represents one needle value by reading the data against the axis or coloured ranges.

A gauge visual displays a value against a defined scale, with minimum, maximum, and target values. You can use separate gauges to compare multiple measures.

Example: You can use a gauge chart to compare the current value with its target, as shown below.

9. Funnel Charts

A funnel chart visualises values across sequential stages of a process, such as leads, qualified leads, proposals, and closed deals. It helps reveal how values decrease or change between stages.

Example: You can use a funnel chart to display process progress, as shown below.

10. Scatter Charts

Scatter charts use dots to represent the relationship between two numerical variables plotted on the x-axis and y-axis. A scatter chart displays the relationship between two numeric variables. It can reveal associations, clusters, and outliers, but it does not establish causation.

11. Bubble Charts

Bubble charts show data as circles. A bubble chart is a scatter chart in which the x- and y-axes represent two numerical measures, while bubble size represents a third measure. Colour may encode an additional category or measure.

Example: You can use a bubble chart for analysis purposes, as shown below:

12. Waterfall Charts

A waterfall chart shows how sequential positive and negative contributions change an initial value into a final value.

Example: You can use a waterfall chart to show increases and decreases across categories, as shown below:

13. Ribbon Chart

You can use a Ribbon Chart to show how category rankings change over time. It uses a column chart with ribbons to highlight rank changes.

14. ArcGIS Maps

ArcGIS Maps is a map-based visual that helps you analyse location-based data and identify geographic patterns, clusters, and relationships.

It is an interactive map that helps display business data. It supports location analysis using points, boundaries, and geographic areas. It is useful for analysing sales by region, customer locations, store performance, and demographic data. Esri provides the ArcGIS visual, so you must accept Esri’s terms of service before using this visual.

15. Treemaps

Treemaps display hierarchical datasets using nested rectangles. Treemaps represent hierarchical categories as nested rectangles. Rectangle size represents a measure, whereas colour represents a category or another measure. Each rectangle's size depends on the selected measure's values.

For example, a clothing sales analysis might use the hierarchy department → category → product.

Power BI treemaps divide categories into rectangle sections called leaves. Rectangle size can represent sales amount or units sold, depending on the selected measure.

16. Shape Map

Shape Map displays data using geographical or custom region boundaries. It represents data using filled shapes, with colour intensity indicating the magnitude of a measure.

It also supports built-in maps and custom TopoJSON maps. You can use this map for comparing values across regions, territories, states, countries, or other defined areas.

17. Matrix Visual

A Power BI matrix visual organises data into rows and columns and supports hierarchical row and column groupings, subtotals, and drill-down. It can display one or more measures across categories, depending on the analysis.

18. Tables

A table displays detailed records or measures in rows and columns. Use a table when readers need exact values, record-level details, or several fields side by side. You can also use a matrix when you need hierarchical groupings and subtotals.

19. KPI Visual

Use the KPI visual to show a key business metric's performance against a target. It helps you quickly determine whether performance is improving, declining, or meeting expectations.

20. Azure Maps Visual

Azure Maps visual is a built-in Power BI visual that uses Microsoft Azure Maps to display and analyse location-based data on an interactive map. It replaces the legacy Filled Map and Bubble Map visuals.

It is the best tool for advanced, interactive geographic analysis and multiple map layers. It supports visualising sales by region, store locations, customer distribution, delivery routes, or geographic trends.

It supports bubble locations, filled regions, heatmaps, custom tile layers, traffic, and satellite imagery. It also doesn't support interactive reports in Power BI Report Server and isn't available in sovereign clouds.

21. Card Visual

The Power BI card visual displays multiple values in a single visual. You can use it in dashboards and reports to show important KPIs such as total sales, profit, customer count, and others. Most importantly, the card visual replaces the earlier multi-row card.

The card visual supports category interactivity. So click a card to cross-filter other visuals on the page. It adds fixed-size options for precise dimension control in pixel-perfect layouts. You can use a card for visualising headline totals, related KPIs, or a navigation-style summary at the top of a page.

22. Scorecard (Metrics) Visual

A scorecard helps organisations track progress toward strategic goals by monitoring KPIs and targets. Power BI Desktop labels A scorecard visual as metrics, but it is still in the preview stage.

You can add a scorecard or individual metrics to a Power BI report and edit them from Desktop. Use a scorecard when you need to manage business goals with named targets. You can monitor metrics such as revenue, profit, customer satisfaction, and sales growth using scorecards. Scorecards help assign responsibility for specific metrics to individuals or teams.

23. Python Visual

A Python visual enables you to create customised data visualisations using Python scripts. You can use this visual for advanced statistical analysis and complex data visualisations. It allows you to use Python libraries to analyse data and identify patterns. You can mainly create regression plots, correlation visualisations, and other analytical graphics using Python visuals.

24. R Visual

An R visual allows you to create customised data visualisations using the R programming language and its statistical analysis packages. You can use this Power BI visual for advanced statistical modelling, data exploration, and specialised charts.

You cannot embed R or Python visuals through 'Embed for your customers' or 'Publish to web ' since Power BI ended support for this in May 2026.

25. Paginated Report Visuals

The Paginated Report visual lets you embed a paginated report in a Power BI report. You can design Paginated reports for highly formatted documents that display detailed data across multiple pages.

26. Q&A Visual (Retiring Soon)

The Q&A visual lets users ask questions about their data in natural language and receive answers as charts, tables, or other appropriate visuals.

The Q&A visual is now retiring, and Microsoft has moved its timeline from December 2026 to February 2027. Instead, you can use Copilot for Power BI for natural language questions and ‘Prepare Data for AI’ to develop semantic models.

AI Visuals

AI visuals in Power BI use AI capabilities to help you analyse data, identify patterns, and generate insights. The following are the four AI-powered features:

• Key Influencers: Identifies factors that have the strongest influence on a selected metric. You can use this type to get answers to 'what affects X?' questions. Results may be less reliable when the data is insufficient or doesn't meet the visual’s analytical requirements.
• Decomposition Tree: Uses AI-assisted analysis to break down a measure across different dimensions and identify contributing factors. The AI split option automatically selects a dimension for further analysis. You can explore contributing factors interactively, but the appropriate depth depends on the data and analysis.
• Anomaly Detection: Identifies unusual values or patterns in time-series data. It can identify anomalies in supported line charts and may explain detected anomalies. You cannot rely on this AI visual for noisy data, so avoid using it for critical anomaly monitoring.
• Smart Narratives: It automatically generates editable text summaries of a visual or page. It may generate inaccurate summaries, so review the results before publishing. It doesn't need Copilot capabilities.

Copilot Narrative Visual

Copilot Narrative Visual uses a large language model (LLM) to generate a text-based summary of data in a report or visual. It helps users quickly understand important trends, changes, and insights without manually interpreting every chart.

The narrative visual updates itself when the underlying data or report filters change. It is useful for creating executive summaries and explaining dashboard insights.

Requirements:

• The administrator must enable Copilot in Microsoft Fabric and enable the tenant switch before using Copilot.
• They must ensure paid Microsoft Fabric capacity (F2 or higher) or Power BI Premium.
• Trial SKUs are not supported, so administrators must use paid SKUs.
• The Copilot report view in Power BI Desktop should be at least the January 2025 release or later.

Example:

A sales report shows that revenue increased by 18% in Q2. The Copilot narrative visual might summarise this as follows: ‘Sales increased by 18% in Q2, with the North region being the main contributor.

Visual Calculations

Visual calculations are DAX-based calculations created directly on a visual. They became generally available in 2026. They calculate values using the data already displayed in that visual. The results depend on the visual's structure and the data displayed.

They let you perform visual-level analysis without creating separate measures in the semantic model. They support visual calculations such as running totals, moving averages, and percentage differences. You can also compare values across rows, columns, or categories using visual calculations.

For example, you can calculate a cumulative total on a bar chart without creating a dedicated DAX measure. You cannot use relationship-dependent  DAX functions with this approach. For example, you cannot use functions such as RELATED, RELATEDTABLE or USERELATIONSHIP.

Filter and Interaction Controls

Let’s take a look at the Power BI slicers and interaction controls here.

1. Slicers

A slicer is an interactive report-filtering visual, not a chart type. You can select values or ranges in a slicer to filter data displayed in connected report visuals.

The input slicer allows you to type or paste values to filter data. It supports partial matches and multiple values. It can be useful in enterprise reports where users filter data by product codes, employee IDs, or order numbers.

Additionally, the slicer visual supports a vertical list, tile, dropdown, and date picker. The button slicer supports flexible visual formatting so you can create visually appealing filters. The list slicer (preview) is a vertical list that supports hierarchies, search, and conditional formatting.

2. Field Parameters

Field parameters allow you to dynamically change the fields or measures displayed in a visual without creating separate visuals. Field parameters can work with slicers to provide interactive field selection. It also reduces the need to create multiple similar visuals. You can combine multiple fields or measures in one parameter.

For example, a single chart can allow users to switch between Sales, Profit, and Quantity using a slicer.

How to Create a Power BI Visualisation?

Power BI provides built-in visuals to compare categories, track trends, filter reports, and display geographic data. You can create Power BI visuals to meet your business requirements.

You can add multiple fields to the visual’s axes or other field wells depending on the visual type.

Select a visual, assign the relevant fields, and resize or reposition it on the report canvas as needed. You can also switch to a different visual type using the visualisations pane.

• First, install Power BI Desktop.
• Open Power BI Desktop and connect to a data source.

• Transform and model the data as needed.

• Add a visual to the report canvas.

• Assign the relevant fields and measures to the visual's data roles.
• Configure formatting, titles, labels, and interactions.
• Validate the results and publish the report.

Custom Visualisation Types in Power BI

• Bullet Charts: They compare a primary measure against a target and qualitative performance ranges. They can be an alternative to gauges when precise comparisons are important. They are useful for comparing one or more measures against targets and performance ranges. Bullet charts may be displayed vertically or horizontally, depending on the custom visual.
• Histogram: A histogram chart looks similar to a column chart. It shows the distribution of a numerical variable across bins, rather than across time periods. Each bar shows the frequency in each interval.
• Dot plot: A dot plot compares values using dots along a common scale. It lets you compare values by magnitude, with each value represented by a dot. It plots values along a common x-axis.
• Radar Chart: A radar chart plots multiple measures along radial axes to compare profiles. It works best with a limited number of measures on a common or carefully normalised scale.
• Sunburst: The sunburst chart visualises hierarchical data using concentric rings. Each ring represents a level of hierarchy. Segment sizes represent values or proportions, depending on the visual’s configuration.
• Synoptic Panel: Some custom synoptic visuals allow users to map data to predefined regions of an imported diagram, floor plan, or image. You can colour or highlight these areas to represent the associated data.
• Tornado: A tornado chart uses back-to-back horizontal bars to compare two groups or show how sensitive an outcome is to changes in input variables.

Other custom visuals you may come across are the Aster Plot, Calendar Charts, Chord, Stream Graph, Hexbin Scatter Plot and Enhanced Scatter Plot.

Which Power BI Visual Should I Choose?

The table below shows the business requirements and the relevant Power BI visual.

Business Requirement	Relevant Power BI visual
Comparing values with  categories	Bar chart and Column chart
Showing trends over time	Line chart and Area chart
Comparing actual results with setpoints	KPI visual, Card with a reference label and Gauge
Show several KPIs side by side	Card
Analysing process stages	Funnel chart
Identifying relationships between  variables	Scatter chart and Bubble chart
Analysing many dimensions interactively	Decomposition tree
Showing geographic  boundaries	Filled map and Azure maps
Identify likely drivers of a metric	Key Influencers
Explain results automatically with no Copilot	Smart narrative
Explain results automatically with Fabric	Copilot narrative visual
Display points or regions on a map	Azure Maps
Display custom geographic regions	Shape map
Display exact record-level values	Table
Showing part-to-whole	Doughnut, Pie chart and Tree map
Display grouped values with subtotals	Matrix

What's New in Power BI Reports

Below are the latest features included in the Power BI reports.

• Translytical task flows: Automate record updates and add annotations. They help create workflows that will trigger actions in other systems. You can automate adding, editing, and deleting data using Translytical task flows. You can make API requests and store notifications using the Translytical task flows.
• Modern visual defaults (Preview): This preview offers an updated base theme with subtitles, uniform padding, a grey canvas background, and more. It improves chart presentation with smooth lines, slicers, and buttons, eliminating the need for manual formatting.
• Design ribbon: The design ribbon has all the necessary report styling tools. Therefore, you can quickly open page size, page background, and other tools for your current page. You can also customise report defaults and download theme files directly from the ribbon.

Frequently Asked Questions

1. Can beginners learn Power BI visuals?

Yes, beginners can learn Power BI visuals with a basic understanding of data concepts. They can start with basic charts, tables, cards, and slicers, then gradually learn interactive dashboards, formatting, and AI-powered visuals.

2. What is the difference between a slicer and a filter in Power BI?

A slicer is an interactive visual that allows users to filter report data directly. You can apply filters at the visual, page, or report level through the Filters pane.

3. What is the difference between a dashboard and a report in Power BI?

A dashboard is a single-page canvas in the Power BI service that can display pinned tiles from one or more reports or semantic models. A report can contain multiple interactive pages and visuals based on a semantic model.

4. Can I create custom visuals in Power BI?

Yes. You can import supported custom visuals from Microsoft AppSource or develop your own using the Power BI visuals development tools. However, custom visual availability may depend on your organisation’s policies and security settings.

5. What is the difference between a card visual and a KPI visual?

A card visual displays a key value, such as total revenue or the number of customers. A KPI visual compares performance against a target and can show progress or status over time.

Conclusion

In this guide, we explored different types of Power BI Visualisations and explained how to create custom visuals. You have learned which visual type to use for different data visualisation requirements.

If you want to learn more about Power BI visuals, you can join a Power BI course at MindMajix. By the end of the training, you will gain a deep understanding of different Power BI visuals and how to use them effectively in real-time scenarios.