Every month Microsoft publishes a summary of all the new features in Power BI within their Power BI Blog. In the July 2021 post we got the opportunity to introduce our Control Chart XmR for Power BI to the Power BI community. Here you have our contribution to the Power BI Blog.
Years ago, Stacey Barr introduced us to the magic of Control Charts. Magic it is, because it allows everyone to split their temporal data in two: random noise and real signals. And we all are looking for real signals, and don’t want to be distracted by random noise.
Stacey applies Control Charts based on the so-called Wheeler rules (as specified by Dr. Donald J. Wheeler). This is why our first release of the Control Chart XmR supports this set of rules. Obviously, the Wheeler rules are not the only set of rules. A couple of months ago we were contacted by a large manufacturing organization. They use Control Charts to continuously improve their processes, and this helped them in obtaining the highest CMMI maturity level.
They required support for the Nelson rules. Now the Control Chart XmR supports both the Wheeler and Nelson rule sets: the user selects the most appropriate rule set.
Don’t hesitate and try the Control Chart XmR now on your own data by downloading it from the AppSource. All features are available for free to evaluate this visual within Power BI Desktop.
Every month Microsoft publishes a summary of all the new features in Power BI within their Power BI Blog. In the June 2021 post we got the opportunity to introduce our Dumbbell Bar Chart for Power BI to the Power BI community. Here you have our contribution to the Power BI Blog.
Data visualisations play a fundamental role in answering an important data question:”How does result A compare to result B?”. Typical examples of these questions are:
How does the sales of this month compare to the sales of last month?
What is the difference between the number of documents processed this year compared to 2020?
How does the number of planned-visitors compare to the number of unplanned-visitors at our locations?
Key in answering these kind of questions is clearly visualising the difference between the two results. This is the strength of the Dumbbell Bar Chart: showing both values and the difference between them.
The top chart shows the two values (Last Year and Current Year. This allows the user to identify the growing (ie. Adventure Works) versus the shrinking (ie Fabrikam) brands. Optionally the user can include the variance chart (second chart) to increase the emphasis on the difference between the two values.
Don’t hesitate and try the Dumbbell Bar Chart now on your own data by downloading it from the AppSource. All features are available for free to evaluate this visual within Power BI Desktop.
Every month Microsoft publishes a summary of all the new features in Power BI within their Power BI Blog. In the May 2021 post we got the opportunity to introduce our Cycle Plot for Power BI to the Power BI community. Here you have our contribution to the Power BI Blog.
Time series are great to clarify changes over time in measures. The line chart is the favourite chart for this data. But displaying results with a normal line chart can also hide important patterns. This happens when the measure contains seasonality. The Cycle Plot is a special line chart developed to show seasonal time series. It helps you to visualise trends within seasonal data. It has the strengths of common line charts. But without hiding cyclical patterns.
Let’s explain with an example. Say we are looking at items sold over a number of weeks. We expect to sell more on weekdays compared to the weekend. A line chart will show low values during the weekend and higher values during the week. However, it’s hard to tell if sales on Mondays are increasing or decreasing over the weeks. With the Cycle Plot a subplot can be created for each day. You can show the change in sales over time for that day. All the subplots together still show the seasonal pattern as well, as seen in the image above.
Don’t hesitate and try the Cycle Plot now on your own data by downloading it from the AppSource. All features are available for free to evaluate this visual within Power BI Desktop.
Every month Microsoft publishes a summary of all the new features in Power BI within their Power BI Blog. In the April 2021 post we got the opportunity to introduce our visual Merged Bar Chart to the Power BI community. Here you have our contribution to the Power BI Blog.
On the surface the Power BI Visual Merged Bar Chart has a lot of similarities with small multiples. The key difference is the way these charts allow you to compare values. The Merged Bar Chart focusses on comparing multiple measures (like Player Value, Monthly Wage, clause, etc. in the example below) within one specific categorical variable (i.e. Soccer players).
The small multiples focus on segmenting the bars by one or more categorical variables (here: Country of Birth).
If you want to compare a single variable over multiple categories, think small multiples. Looking for comparison of multiple independent measures? Go Merged Bar Chart.
Don’t hesitate and try the Merged Bar Chart now on your own data by downloading it from the AppSource. All features are available for free to evaluate this visual within Power BI Desktop.
Every month Microsoft publishes a summary of all the new features in Power BI within their Power BI Blog. In the March 2021 post we got the opportunity to introduce our visual SMART KPI List to the Power BI community. Here you have our contribution to the Power BI Blog.
One important goal of any well designed dashboard is to inform its readers by creating one overview of all KPI’s. This requires a compact and effective way to display them all together. The SMART KPI List is created specifically for this purpose.
Why do we name it SMART? Because this visual allows everyone to create an overview of their KPI’s that is:
Specific: the red-dot highlights the KPI’s that need immediate attention;
Measurable: A value alone is a weak indicator of performance. A sparkline shows the trend to determine if you are moving towards your goal;
Achievable: By comparing each result with a target you can determine if your KPI has met expectations;
Relevant: Within the sparkline you can add a bandwidth of acceptable results which helps the user to identify “normal” and “abnormal” results in the past;
Time-bound: The sparkline adds the required historic context to each indicator to enrich the indicators signals;
Don’t hesitate and try the SMART KPI List now on your own data by downloading it from the AppSource. All features are available for free to evaluate this visual within Power BI Desktop.
Every month Microsoft publishes a summary of all the new features in Power BI within their Power BI Blog. In the February 2021 post we got the opportunity to introduce our Shielded HTML Viewer to the Power BI community. Here you have our contribution to the Power BI Blog.
Context is king in data visualization. This probably explains the popularity of Power BI visuals that allow users to add HTML formatted content to their reports and dashboards.
Showing HTML formatted content can add great value to your reports, but it may also form a potential security risk. This is why we created the Shielded HTML Viewer: the first and only HTML Viewer for Power BI certified by Microsoft.
The Shielded HTML Viewer (in the image above used in the tooltip of a Dumbbell Bar Chart) is based on a so called allow-list: only those HTML tags and attributes mentioned in this list will be interpreted and formatted accordingly. Anything else will be ignored, so no risk of running harmful HTML codes.
Furthermore, all functionality is available through the standard Power BI interface, so no need to learn a new interface.
Don’t hesitate and try the Shielded HTML Viewer now on your own data by downloading it from the AppSource. All features are available for free to evaluate the Shielded HTML Viewer within Power BI Desktop.
If you really want to get the truth from your KPIs, XmR charts are the best tool to display them. We get clear signals of true change, and it’s easier to set targets and quantify our progress toward them.
In January 2021 I had the opportunity to talk with Stacey Barr, THE specialist in evidence-based leadership and organisational performance measurement. We spoke about the XmR chart in general and about our Power BI Custom Visual named Control Chart XmR.
Watch the replay here and find out:
What an XmR chart is and how it gets the truth out of your KPIs
How the Power BI XmR chart tool works
What it’s like to use the Power BI XmR chart tool on one of Stacey’s real KPIs
Ah, the smell of coffee and nicely sorted charts in the morning is something everyone can enjoy. Sorting your data in Power BI is easy at first sight but it doesn’t always give you the results you first expect, especially with ordinal data. In this post, we zoom into sorting and some of its caveats.
Power BI offers some readily available functionality to sort your charts ad hoc. Simply select the More Options icon in your chart, go to Sort by and select the field on which you want to sort. Easy!
Fig. 1: Sort your graphs via the Sort by option. Here you will also find options to sort ascending or descending.
This works fine for sorting values numerically or alphabetically, such as for nominal values. Things get a little bit trickier when you are working with ordinal values such as the day of the week. By just sorting your chart on the day of the week, you will end up with Friday, Monday, Saturday, Sunday, Thursday, Tuesday, Wednesday.
Nominal data: data in which the variables have no natural order. Examples are binary data (true / false) or names (Dustin, Will, Lucas, Johnathan).
Ordinal data: data in which the variables have natural, ordered categories. Examples include Months (Jan, Feb, Mar, etc.) or questionnaire scores (poor, reasonable, good, or excellent).
Fig. 2: The left column chart has the Weekday names in the Axis bucket, while the right one has Weekday number on the Axis.
As an alternative you can replace the names of the weekdays with the weekday numbers. Creating such a column can be done in your workbook using the following DAX function:
Day of Week Number = WEEKDAY(Date[Date],2)
Mondays are now the first day of the week, the standard in most European countries. Change the last argument of the WEEKDAY function to have Sunday or Saturday as the start of the week. Updating your chart with this newly created column allows you to show the days in the correct order. However, we’ve now lost our labels.
We can fix this problem by sorting our column, based on another column. We therefore must indicate on which column to sort our data. In the Report view in Power BI Desktop, select the column you want sorted. Next, go to Column tools and find the “Sort by column” options. Select the column containing the correct order, and that’s it. (Note: this option is also available in the Data and Model view, it works exactly the same way)
Fig. 3: After selecting your column in the Fields pane, you will see the Column tools tab appear in the Ribbon.
This sort order is used in all visuals whether it’s a bar chart, table or slicer. You may run into visuals that overrule sorting of the data, so keep that in mind when working with some custom visuals.
Fig. 4: The chart has been sorted in the way we want it: it shows the names of the day in chronological order
You can do this trick on any column in your data, even if you’ve created that column yourself using DAX. Do make sure that every value in the column you want sorted, has only 1 corresponding value on which to sort. Power BI will not be able to sort your weekdays if Monday sometimes has number 1 and other times number 2. In other words, both columns must have the same cardinality.
Fig. 5: Make sure that the column on which you base you sorting has the same cardinality as the column to be sorted, or Power BI will not be able to figure the correct order out!
In order to use Sort by Column, both columns must be in the same table. If you have two tables with a relationship between them, it will not be possible to sort a column based on a column in the other table. You would have to merge the two tables to resolve this. Remember to only keep relevant columns after merging as your table size can grow substantially, degrading performance.
Fig. 6: Unfortunately, we can’t perform the sorting based on column from other tables.
Unwanted side effects can occur when using DAX expressions on columns that are being sorted by another column. What makes matters worse is that there is not easy way to see which columns are being sorted on others. Take the following example: we want to calculate the percentage of the total sales for each month. We can do this by creating the following measure:
Monthly % of Year=
DIVIDE (SUM(Sales[Sales]),
CALCULATE(SUM(Sales[Sales]),
ALL(Sales[Month Name])))
In the two tables below, we see what happens to our result as soon as we sort the Month Name column on the Month Number:
Fig. 7: Left: The Month Name column is sorted on itself. We see that our DAX statement correctly calculates the fraction of yearly sales that happened that month. Right: After sorting the Month Name by the Month Number, our DAX statement divides the monthly sales by itself. Surely this is not what we wanted!
All of our fractions are 100% after using Sort by Column! This happens because the SUMMARIZECOLUMNS function groups the data using both the Month Name and Month Number column. We must therefore make sure to clear both involved columns using the ALL function in our measurement.
Monthly % of Year=
DIVIDE (SUM(Sales[Sales]),
CALCULATE(SUM(Sales[Sales]),
ALL(Sales[Month Name],Sales[Month Number])))
The table below show the results that we expected. The monthly sales are now correctly divided by the total sales of the year.
Fig. 8: By making sure that both column filters are cleared in our DAX statement, we now have the expected results
In short: sorting categories in a chart numerically or alphabetically can quickly be done using the More Options menu in your visual. Sorting a column based on another one is done using the Sort by Column option but both columns must have the same cardinality. Finally, keep in mind that your measures may show unexpected behaviour when you’ve sorted on another column.
Every month Microsoft publishes a summary of all the new features in Power BI within their Power BI Blog. In the december 2020 post we got the opportunity to introduce our Strip Plot to the Power BI community. Here you have our contribution to the Power BI Blog.
Most charts will force you to summarize or categorize data before it is displayed. This can hide important details and may be misleading. The Strip Plot shows all your data observations in one go without hiding important details. It shows each data point on a single continuous scale.
The example above illustrates this by showing the number of reported COVID-19 cases per continent (on September 1st, 2020). The bar chart shows the average per continent, where the Strip Plot shows the cases per individual country. In the Strip Plot it becomes obvious the relative high number in Oceania are caused by just two countries (outliers), where the bar chart only shows a very high average. It’s these kinds of details that become visible in the Strip Plot.
Furthermore, the Strip Plot supports all standard Power BI functionality like drilling, selection & highlighting, context menu and full tooltip support. All this functionality is available through the standard Power BI interface, so no need to learn any new interface.
Don’t hesitate and try the Strip Plot now on your own data by downloading it from the AppSource.
December 2020 has been an exciting month for us in Power BI as we have released the Merged Bar Chart. At the same time the long awaited “Small multiples feature” has become available as a preview feature in Power BI. This feature is currently available for bar/column, line and area charts. In this article we zoom in on the small multiples variant of the bar chart and the Merged Bar Chart, to find out how to use them and when to use which.
In short, the Small multiples feature allows you to split up your Bar, Column and Line charts into multiple smaller charts, separated per category. There are numerous use cases for this, such as showing your product sales per region (see below). The feature has been highly anticipated as it gives you a lot of flexibility in your report creation.
Recently, Nova Silva has released the Merged Bar Chart custom visual to the AppSource. On the surface the Merged Bar Chart has a lot of similarities with the small multiples, as it also allows you to show multiple bar charts combined. The key difference is that the Merged Bar Chart allows you to compare multiple independent variables. For example, you can use it to compare the GDP, Life expectancy and population of a range of countries as seen below. Often, we see scatter plots used for this purpose, but this has two downsides: the reader often finds it a complex visual to understand, and it is hard to compare more than 2-3 measures with it. The Merged Bar Chart solves this by grouping multiple bar charts together which helps you to uncover patterns in your data in an intuitive way.
Now, is the Merged Bar Chart still relevant after the arrival of the Small multiples feature? In short: absolutely. Both visuals serve a completely different purpose, so it all comes down to the story you want to tell with your data. There is a place for both of them in the Power BI ecosystem as no individual visual type will solve all of your problems. In the following section we will show some the key differences between the different visuals and explain when to use which.
Let’s compare the Merged bar chart with a Stacked bar chart with Small multiples enabled. The bar chart is a great way to compare multiple categories on a single measurement. You could decide to subdivide every bar into a category via the legend bucket, but this makes it hard to compare the values within a category since they are not aligned. Also, the result is often a colorful mess when you have multiple categories to show.
Below is a more elegant solution using small multiples. We have created an overview of the average player values in the football video game FIFA19 for a few clubs, split out for six selected countries. You are now able to see patterns per nationality much more clearly for these clubs, but you lose the overview of all nationalities combined.
Say instead you want to show the height, weight and income of the world’s eight best players in a single visual. A clustered/stacked bar chart wouldn’t be the right way to go as it doesn’t make sense to combine these measures. This is where the Merged bar chart really shines. With it, you just select the columns you want to include in the comparison and that’s it. You don’t have to worry about the units or scales being different, as each column gets its own axis (although you can choose to plot them all on the same scale).
So: if you want to compare a single variable over multiple categories, think small multiples. Looking for comparison of independent measurements? Go Merged bar chart.
We are excited about these new ways improve your data storytelling and are very happy that our custom visual complements the charts with small multiples. What do you think? Are your reports already filled with tiny graphs created by the small multiples, or are you waiting out on this? Let us know!
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