There are a wide range of Visualisations in Power BI to help those viewing the published report to understand the data they are portraying quickly and efficiently. The chart options have already been looked at in an earlier blog so here we will be looking at all the other options available including tables, gauges, KPIs and maps and finding out when each one is useful. The package also has monthly updates so this list is regularly growing.
There are 2 types of table:
Table: A standard table layout with headings in the top row and data row by row below. If using this then try not to show too much data. It works well in conjunction with a filter, for example to show the top 5 of something.
Matrix: Also referred to as a crosstab and similar to a pivot table in Excel. One or more fields are entered into the rows and one or more into the columns and where these cross over a calculation is performed. Useful to produce a text summary of data.
There are 3 types of map visualisation:
Map: A city, country or country can be marked on the map with the size of a bubble representing the size of the data. Alternatively, latitude and longitude figures can be used instead.
Filled Map: This works best for locations which have a shape like a county of country. The map is shaded based on this data.
ArcGIS map for Power BI: A more sophisticated mapping tool.
Gauge: Normally used to show whether targets have been reached or not using a diagram that looks like a gauge.
Card: Shows top level data for an individual field. This could be the total sales, first employee etc.
Multi row card: Similar to a card but can shows multiple fields in one graphic.
KPI: Standing for Key Performance Indictaor this is another visualisation that can show whether targets have been met or not.
Slicer: A user friendly filter that can take the form of a list or drop down box for text or a timeline where dates are involved.
R script visual: Has a programming component to create the visual.
Python Visual: Has a programming component to create the visual.
Key Influencers: It analyses your data, ranks the factors that matter, and displays them as key influencers. It allows you to see which factors affect the metric being analysed and contrast the relative importance of these factors.
Decomposition Chart: 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.
Q&A: Used to explore data using intuitive, natural language capabilities and receive answers in the form of charts and graphs.
PowerApps for Power BI: enables everyone to build and use apps that connect to business data.
Get more Visuals: Access 100’s of other visualisations. So if the options here don’t quite do what you want then get searching.
Always consider which visualisations are the easiest for your audience to understand. This often means that the most common types of visualisation are most popular for a very good reason. With the wide range of visualisations in Power BI there is the scope to produce easy to read dashboards which look visually appealing.