When should I use each Visualisation in Power BI?

Nov 9, 2023 | Power BI Hints and Tips

Choosing the correct visualisation type to help others understand your data is vital.  The visualisations should be easily understood by those you are creating the report for and normally show higher level summary data rather than the hundreds or thousands of rows of each specific record of data that you find in the underlying data source.  There are many options available which are listed below and most report pages will consist if a mix of these.

Charts

There are currently 18 types of charts available in Power BI Desktop. These are similar to the options available in Excel but Power BI Desktop does not include 3D charting options.  These are a very popular options in reports as you can normally get a good feel for the data at a glance.

  • Stacked Bar Chart – shows a comparison between categories.  Bar charts are plotted horizontally.
  • Stacked Column chart – shows a comparison between categories.  Column charts are plotted Vertically.
  • Clustered Bar Chart – represents discrete values for more than 1 item that share the same category. Bar charts are plotted horizontally.
  • Clustered Column Chart – represents discrete values for more than 1 item that share the same category. Column charts are plotted vertically.
  • 100% Stacked Bar – compare proportional contributions across all categories.  Bar charts are plotted horizontally.
  • 100% Stacked Column – compare proportional contributions across all categories.  Column charts are plotted vertically.
  • Line – tracks changes over a period of time.
  • Area – shows differing trends over time where the data is expressed as a total.
  • Stacked Area – shows differing trends over time where you want to track not only the total value but also the breakdown by groups.
  • Line and Stacked Column Chart – combines a line and stacked column chart.  Useful if you have 2 different sets of data to show on the same chart.  Often the data is different in size so the scales on the axis can be different for the line and the column.
  • Line and Clustered Column Chart – combines a line and clustered column chart.  Useful if you have 2 different sets of data to show on the same chart.  Often the data is different in size so the scales on the axis can be different for the line and the column.
  • Ribbon Chart – used to see which data category has the highest value over a period of time.
  • Waterfall Chart – shows how an initial value can be affected by positive and negative changes in the data and how this affects the closing value.
  • Funnel Chart – used for a linear process that has sequential connecting stages e.g. Sales funnel data.
  • Scatter Chart – used to determine whether 2 sets of numerical values are related.
  • Pie Chart – shows proportions of the whole.
  • Donut Chart – shows proportions of the whole.  Very similar to a pie but with the centre removed.
  • Treemap – shows proportions of the whole.  The area of the rectangle represents the size of the data.

Tables

There are 2 types of table visualisation:

  • Table – shows the data in a standard table layout.  This enable each specific row of data to be viewed but often contains too much detail for a summary report.
  • Matrix – Similar to a pivot table in Excel, the data can be grouped by rows and columns producing a summary table of the data.  However, a chart visualisation of the same data is often a better option.

Maps

There are currently 4 types of maps that you may have access to but it appears that in the long term the Azure Maps will be replacing both the Map and Filled Map options.

  • Map – shows geographical data based on the location of the data as a data point.  A bubble represents the size of the data.
  • Filled Map – shows geographical data based on the location of the data as a shaded area.  
  • Azure Map – more sophisticated than either the map or filled map this allows for a rich set of data visualisations to be added on top of the map in addition to the features available for maps and filled maps.
  • ArcGIS Map – an ArcGIS account needs to be set up for this feature to be used.  It gives access to certain demographic information that you can add that isn’t available directly in the other map types in addition to the standard map functionality.

Cards

There are 3 types of card visualizations available.  They are all used to show high level overall figures such as total sales, average sales, how many sales, when did the last sale occur etc.

  • Card – normally used to show 1 figure only.  If multiple data fields are added to the card then they can only be viewed in multiple columns 
  • Multi-row card – similar to the card but the layout can be set to view the fields row by row if required
  • Card (New) – similar to the card but with a few new formatting features such as easily changing the shape to a rectangle with rounded corners.

Other

There are many other visualisations that can be used.

  • Gauge – uses a circular arc to show progress towards a goal or KPI.  The gauge line shows the goal or target value with the shading representing the progress towards the goal.  The value inside the arc represents the target value.
  • KPI – used to evaluate the current value and status of a metric against a defined target.  The visualisation requires a base measure, a target measure and a goal figure.
  • Slicer – a user friendly way to filter the data.  It is a better user experience than relying on the standard filter pane.  Filtering using the slicer is quicker than using the filter pane and can be performed using a button, tick boxes, a drop down list or a slider depending on the data and preference on how it is viewed.
  • R Script – requires a programming background and should only be used if you are familiar with R script already.
  • Python Visual – requires a programming background and should only be used if you are familiar with Python already.
  • Key Influencers – helps you to see which factors affect the metric being analysed.  e.g. do short term contracts influence turnover more than long term contracts.
  • Decomposition Tree – lets you visualise data across multiple dimensions.  It automatically aggregates data and enables drilling into your dimensions in any order.  It is an interactive visual to conduct root cause analysis.
  • Q&A – allows users to ask natural language questions and get answers in the form of a visual.  e.g. Count the total sales by customer as pie chart.  This can be done by the end user on an adhoc basis.
  • Smart Narrative – provides a quick text summary of visuals and reports.  These can be customised.  It is great to address key takeaways and to point out trends.
  • Paginated Reports – these are ideal for creating sales invoices, receipts, purchase orders and tabular data.  They are optimized for printing and are normally only used for this purpose.
  • Power Apps – enables the ability to build and use apps that connect to business data.
  • Power Automate – One of the key benefits is the ability to automate data refreshes.

In addition to all these visualisations there are many more available from the get more visuals option immediately after the options already mentioned.

Further Reading

If you’ve enjoyed reading about when you should use each visualisation in Power BI, there are some other blogs below that you might find useful:

Want to learn more about Microsoft Power BI? Then email lara@laramellortraining.co.uk to discuss how I can help or have a look at the Microsoft Power BI Desktop Courses I run.