Without the right visuals, your Power BI report is redundant. To showcase powerful insights you need to understand when and how to use different visuals so that you can avoid wasting valuable time on building reports that don't make an impact.
In this guide you will not only learn the basics about how to use standard Power BI visuals but also details around sizing visuals, using them in a layout, use cases, and examples.
Additionally, we'll walk through the process we use to decide which visuals to use to showcase difference insights, allowing you to correctly apply and design visuals in your reports, so that you are implementing best practice and driving clear insights.
For reference, all the Power BI reports and visuals used in this handbook were built using the Numerro Toolkit, allowing for an effortless build/design process and ensuring we met design best practice standards.
Part 1 - What are Power BI Visuals
Part 2 - Types of Visuals
Part 3 - Positioning of Visuals
Part 4 - Standard Power BI Visuals
Part 5 - Custom Power BI Visuals
Part 6 - Tips for Using Visuals in Power BI
Part 7 - Visuals in Action | Example Power BI Report
Part 8 - Visuals in Action | Try Free Demo Template
Visuals are simply a visual (picture) representation of your data, and are the most important part of any Power BI report as they are responsible for bringing your data to life.
Visuals help you to tell a better data story, enabling your users to simply and easily identify and understand the patterns in your data.
There are many ways to show your data through visualization.
When choosing your visual you need to consider what type of information your insight is looking to show.
The type of visual you chose to depict your data will depend on: the data you wish to communicate and what you want to say about that data.
Most visuals can be divided into the following 6 categories.
Comparison visuals compare data between different categories.
Use Case: Income across fiscal years.
Data over time visuals represent the spread of data over a period of time and are displayed to identify trends or changes.
Use Case: Sales performance over time.
Correlation visuals are used to find whether there is a relationship between two or more variables.
Use Case: Price and demand.
Distribution visuals are used to show how often values occur in a dataset.
Use Case: Distribution of orders.
Part-to-whole visuals show the breakdown of elements that add up to a whole.
Use Case: Profit by product segment.
Ranking visuals showcase an ordered list based on a unique data point and are used when the position of the element is more important than its relative value.
Use Case: Leads by sales stage
Where you position your visuals in your report is critical.
A consistent layout and grouping relevant metrics together will help your audience understand and absorb the data quickly. The correct layout ensures your dashboard is easy to understand and has a logical flow between different insights, which is important as users tend to process information from top to bottom.
Grouping relevant metrics together, such as KPIs, adds further to the logical report flow and the ease of user insight interpretation.
Referring to the dashboard example below, we can begin to segment our dashboard to ensure the correct layout is implemented.
When determining where to allocate each of your visuals, it's best practice to follow the guidance below:
The top of a dashboard should include high-level insights represented as visuals such as KPI's or Gauges and are best kept to 2-3 squares as demonstrated by the red section in the canvas grid above.
The middle of a dashboard should represent trend-based data including activity-based metrics, and visuals that demonstrate data over time. This section is best suited for larger visuals and is best kept to 4-6 squares as demonstrated by the purple section in the canvas grid above.
The bottom of a dashboard is reserved for granular metrics such as specific KPIs, or Tables, and is best kept to 3-4 squares as demonstrated by the blue section in the canvas grid above.
The sizing of your visuals will depend on the level of detail you want them to display.
The greater the detail that your insight presents, the larger you want your visual to be so that users can distinguish the finer details.
For example, when visualizing an insight that compares multiple data categories, you would opt to have a larger visual in comparison to an insight which is visualising a singular number.
Power BI has numerous options for how you can visualize your data.
Below we are going to explore in more detail each of the standard Power BI visualizations.
For reference, each of the visuals shown below were built-in Power BI using the components from the Numerro Toolkit.
The bar/column chart is one of the most popular choices of visual because you can easily scan them to find information quickly.
The chart is used to compare several items in a specific range of values, with each bar/column representing a category and the length of the bar/column being proportional to the value it represents.
For example, using a column chart to show monthly sales performance throughout the fiscal year.
The line chart is a simple chart that shows changes over time by using data points represented by dots that are connected by a straight line.
For example, using a line chart to show the revenue per month for the fiscal year.
A scatter chart uses dots to loom for a relationship between two different variables each represented on their own axis. Scatter charts are great for looking at correlations in your data. If the dots fall along a curve or a line then they are correlated, the better the correlation the tighter the dots will be to the line.
For example, using a scatter chart to show the correlation between sales and quantity by state.
A funnel chart is used to visualize a linear process that has connected sequential stages. The value of each stage in the process is indicated by the funnel's width as it gets narrower. This type of chart can be used to highlight problem areas in an organization's processes.
For example, funnel charts are commonly used to represent stages in a sales process.
A table is used for quantitative comparisons, enabling you to see and compare detailed data and exact values.
For example, using a table to show last year's sales and this year's sales side by side, and adding conditional formatting to see whether there were increases or decreases in the sales.
A matrix is similar to a table and acts much like that of a pivot table. The matrix visual can be a powerful tool when cross-highlighting one field of data with another to determine totals and subtotals.
For example, using the matrix to cross-highlight the sales by region and quarter.
A card is the simplest of all the Power BI visuals, containing only a single number.
The number you choose to represent in this visual should be of importance such as the number of opportunities in the pipeline or the amount of closed business.
A multi-row card builds on from the standard card visual and allows you to combine several singular cards into one visual.
Similar to the card, the multi-row card is best used to represent a number of KPI's or high-level metrics.
For example, to highlight the total sales for each product category, demonstrating both the category name and it's corresponding value in a slick and compact format.
A slicer is a visual filter, where you choose the data type and the report automatically filters the visuals accordingly.
Filters are great for grabbing quick snapshots of data relating to a specific field. For example, using a year filter alongside a line chart to allow users to quickly move between different time stamps of their data.
A gauge consists of a circular arc which shows a singular value that measures your progress towards a KPI or goal. The line on the arc represents the target or goal and the shading represents the progress made towards it. The value inside of the arc shows the progress value.
For example, to show the progress your team is making towards your revenue goal.
A map visual helps you to answer questions about locations and distances, helping you to understand the distribution of the data.
The map is great to use when you want to interact with the data and quickly compare, such as looking at sales forecasts across the US for computers to drill down into which states are missing their targets.
A 100% stacked column/bar chart shows the relative percentage of numerous data series in stacked columns or vars. The total of each stack always equals 100%. This visual shows a part-to-whole relationship and can display how the different proportions change over time.
For example, to show the region sales performance broken down into individual product categories.
A clustered bar/column chart is an extension of a bar chart.
The chart represents and compares numerous categories instead of one.
The categories are arranged and grouped side-by-side which makes it easy for the user to interpret differences in each group as well as the same category across different groups.
For example, comparing sales and profit by month.
A bubble chart is used to look at three different variables and the relationship between them. The bubble chart is created by modifying the Power BI scatter chart.
Each dot (bubble) relates to a single data point with the variables' values for each point represented by the vertical/horizontal position and size of the dot.
For example, the number of sales and quantity by state, split by region, and using average order value as a size representation.
The area chart is similar to the line chart but is used to show the magnitude of change between 2 or more data points, with the area between the axis and the line filled with colors; showing the volume of values.
Area charts are a great way to show the volume of a trend across time such as sales revenue per month.
A stacked bar/column chart is an extension of the basic bar chart, showing comparisons between categories of data and the ability to break down to compare parts of the whole.
Each bar is a whole and is segmented to represent the different categories of that whole.
For example, regional performance revenue split by different product categories.
A stacked area chart is an extension of the basic area chart, displaying the evolution of the values of different categories. Each category's values are shown on top of each other, which means the user can see the total of the category and the importance of the group.
For example, looking at revenue and profit per month.
A ribbon chart is similar to the stacked column chart, but the order of the categories on each of the stacks varies. This type of chart is great for helping users to quickly identify which data category has the highest rank.
For example, revenue split by product segment per month.
A shape map is used to show relative comparisons of regions on a map using a single variable across a color scale. Using this type of visual can help users quickly identify which regions are performing and which are not.
For example, sales by state.
A donut chart is used to show how different values contribute to the total value, with the size of each piece in the donut representing the proportion of each category.
Donut charts are best used when visualizing the split of data, such as calculating the split or orders between product categories. The donut provides both a visual representation of this split, but also supports with additional value and percentage figures.
For example, total number of orders split by region.
A treemap is used to display large quantities of hierarchically structured data, using nested rectangles. This type of chart allows you to show different perspectives of the data by displaying the rectangles as different sizes and colors.
For example, product segments total revenue.
This chart combines a column chart and line chart on the same graph. The chart will display one variable as a line chart and the other variable as a column chart, each having its own y-axis.
For example, sales and profit per month shown as a column chart, with a line chart to display the orders per month.
A waterfall chart helps the user to understand the cumulative effect. It is often used to show users how the initial value decreases or increases based on a series of values, which then leads to a final value. The values in a waterfall chart can be positive or negative.
For example, waterfall charts are often used to explain changes in performance.
A decomposition tree visualizes data across multiple dimensions, aggregating the data and then enabling you to drill down into your dimensions in any order.
For example, total sales drilled down into categories and subcategories.
Below we have explored some of the top Power BI custom visuals.
The Gantt chart is a popular custom visual to use.
It is one of the most used project planning tools, which enables you to visualize your project plan to communicate and monitor your progress.
A Gantt Chart shows tasks or activities along a timeline, comprised of a Data grid that lists what is to be completed and a Visual representation of these tasks as bars where each bar is representative of the length of the task.
The Gantt chart is a great visual to use if you are looking to improve your project management.
The Bullet chart is a variation of a bar chart, that was developed to replace gauges and meters. It's a good replacement for gauges as it saves space on your report as it can be oriented vertically and horizontally based on the available space.
This chart presents information in a way that is easily understandable as well as combining multiple measures into a single visual.
The Radar Polar chart is used when you are looking to visualize multivariate data.
This chart is great when you want to visualize comparisons of quality data as you can easily compare different attributes along their own axis and see the overall differences. The Radar Polar Chart is especially effective when you are looking to compare the performance of one thing to that of the group's performance.
The Linear Gauge allows you to visually compare target and actual values, which is great when you are looking to visualize a KPI.
The bars color displays how much progress has been made towards the targets set such as year over year.
The Variance Chart gives you the capability to compare benchmark and performance data with variance. This can be visualized in percentage and absolute terms.
The chart integrates into one visual, enabling you to analyze the comparisons of two different values across three visualizations (variance chart, variance percentage, and comparison chart).
The Horizon Chart enables you to analyze any time-series outliers as well as any patterns, using diverging colored bands.
This chart is great as it's an easier way to visualize multiple stacked area or line charts sliced horizontally by the different categories.
The Chord Chart visualizes the inter-relationships between entities. The different connections between entities show that they share something in common.
The nodes are arranged along the circle, using arcs or Bézier curves, to connect the relationships between points to each other. Each connection is assigned a value which is shown proportionally by the size of the arc or curve. To help with comparisons, you can use color to group the data into different categories.
This chart is great for comparing between different groups of data or similarities within a dataset.
The Sunburst Chart visualizes hierarchical data through a series of rings, with each ring relating to a level in the hierarchy. The center circle is the root node from which the hierarchy moves outwards.
The rings are divided by their hierarchical relationship to the parent slice. Each slice's angle can be made proportional to a value or divided equally under its parent node.
Slanted text can cause readers to misinterpret information. Alternate between a bar/column version of your visual to display text in a clear format.
Without a clear baseline, small changes in values can appear misleading. Where applicable, add a zero baseline to provide more context when comparing insights.
Order visuals to support readers in understanding the hierarchy amongst data, as un-ordered visuals add unnecessary complexity.
Prevent over-stretching and projecting graphs over a 45-degree angle, as doing so can warp visuals and overemphasize changes in data.
To help you understand the above guidance we are going to walk through a step by step process of how we built the example report below following the guidance above; whilst using the Numerro Toolkit to support with the build and design process.
Identified the insights we wanted to drive and matched them to the correct visual based on their data type (using the guidance from the 'Visual Types' and 'Standard Power BI Visuals' sections above).
Decided on our insights and how to visualize them correctly, we then positioned them into a logical layout and sized them according to the level of detail presented. We used the 'Positioning of Visuals' and 'Sizing of Visuals' sections of this handbook to support with this process.
Used the Horizontal Canvas Grid from the Numerro Toolkit to ensure the correct alignment and structure when building our report.
Dragged and dropped our chosen visual components on the Canvas Grid and populated them with their relevant data and text, whilst resizing where necessary. Note that we use components as they have design best practices automatically integrated into them, meaning we don't have to worry about repeatedly formatting our visuals and report.
Added the finishing touches, including a theme, in this case 'Theme 1 - Light' from our collection of 24 Themes in light and dark. As well as including relevant icons from our icon set to provide further context, and going through our visual tips checklist to ensure we're following best practice.
We hope you enjoyed this guide and that you use it's guidance to support you when using Power BI visuals to build your own well-designed reports that drive clear data insights for you and your organization.
Additionally, if you're looking for an easier and faster way to build great looking reports that reap the benefits of design best practice, you may be interested in leveraging a design toolkit and the benefits it brings.