How to Drill Through in Power BI

One of the great options available in Power BI is the capability to “drill through” to another report page. In this manner you can focus on a particular entity such as a customer, internal division, supplier or any other dimension of importance.

Different users have different data needs. When designing a dashboard typically the Key Performance Indicators (KPIs) are aggregated at a high level on the initial visualization. This offers executives and management types a “bird’s eye view” of performance.

Personally, I am a fan of BANs (look up the term in a dashboard design context if you are not familiar) when I want to highlight key takeaways.

BANs

Subsequent lower level dashboard pages can offer analysts and others the ability to either explore data with additional interactivity or simply display a static detailed report. The point is to start at a high level and allow your user to drill to a more granular level of data.

In this video I demonstrate the use of the drill through functionality in Power BI. In this scenario, you are the Chief Supply Chain officer trying to gauge your Perfect Order Percentage KPI for several internal divisions. When it’s time to sit down with your four division mangers to discuss their performance on this metric, you want the ability to start at a high level and then drill through to a static report based upon their respective internal divisions or on a specific shipping error.

DrillThrough

Drill through on “In Full Delivery” error category

Do not try and cram every visualization, chart, table or gauge under the sun into a dashboard! Take advantage of drill through functionality and tailor your data presentation for specific user groups. This general concept applies to any data visualization tool, but if you’re using Power BI then this video will help you understand the specific steps required to enable drill through functionality.

I’m frequently questioned where I obtain mock data for my scenarios. My secret source is mockaroo.com which is a great starting point for developing test data.

As always, do great things with your data.

Anthony B. Smoak, CBIP

 

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Check out other Power BI videos of interest definitely worth your time:

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Build a Tableau Parameter Action Dashboard

This video is inspired by Filippos Lymperopoulos who has a great article on parameter action concatenation. Definitely check out his article here. I am a hands on person who learns from building and sharing, therefore I put together this video to explore, tweak and hammer home the concept.

In this video we’ll build a Sales Analysis dashboard in Tableau using Parameter Actions and the Concatenation Aggregation functionality. The great thing about this tip is that you can use it across multiple data sources. This is a must see!

This approach relies upon the use of two different data connections. In this manner our data tables are completely un-joined without an established relationship. We have a data-set comprised of a customer list and one comprised of customer transactions across three years.

The key to linking the data sets together relies upon the following calculated field which creates a de-facto set that we can use to highlight customer purchases:

Tableau Concat Calc Field

When we setup a concatenation parameter action on our dashboard, the very act of selecting a [Customer Name] will add that Customer Name to the parameter named [Selected Customer]. This will cause all selected customers to resolve to TRUE, which allows highlighting of the sales bar charts related to the user selected customers.

Tableau Concat Parameter Action Thumb1

In the screenshot above, notice the selected [Customer Name] values on the left hand side are also concatenated together at the bottom of the dashboard (i.e., Franciso Hernandez, Jose Garcia, and Terrye Marchi). All of their respective purchases are also highlighted in the middle of the dashboard.

Tableau Concat Calc Field Label

The above calculated field is used to only show the [QTY] purchased for the user selected customer and is placed on the bar chart label.

Feel free to interact with the viz and download the workbook on Tableau public:

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All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Top and Bottom 10 Products by Sales Using RANKX in Power BI

In this video we’ll venture outside of the default Power BI TOPN functionality used to isolate the top and bottom N values in a visual. Because you’re an astute follower of my blog and YouTube channel, you want to know more than default functionality. The key to pulling off this feat lies with the RANKX function. By using RANKX to provide a ranking to each row in our data set, we can then determine the TOP and BOTTOM 10 values.

Of course watch the video for further breakdown, but they key to using RANKX effectively in Power BI is to use the ALL function as a parameter. In this way the contents of the entire table are considered for appropriate ranking.

The DESC or ASC parameters will enable the calculation of a rank in either descending or ascending sort order.

And finally the DENSE parameter tells RANKX how ties should be handled in the data. For example if you provide DENSE as a parameter, if 10 values are tied with a rank of 1 then the next value will receive a rank of 2.

Power BI Top 10 and Bottom 10 Thumbnail

Here is an example use of RANKX that will return a ranking of Sales Price by Product, that ranks the results in descending order (highest Sales receive the lowest ranks) and ties are in contiguous order. Watch the video to determine how to calculate the BOTTOM 10 ranking and to use RANKX to enable the top or bottom 10 values in a visualization.

ProductRank = RANKX(ALL('Company Sales Data'[Product]), calculate(sum('Company Sales Data'[Sales Price])),,DESC,dense)

As a refresher, check out this popular video to build the calendar table referenced in this video: Power BI Dashboard Tutorial: Year over Year Difference Analysis

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All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Create an Interactive Stacked Bar Chart in Tableau

In this post we’ll use the new parameter action functionality in Tableau to create an interactive stacked bar chart. When we select a dimension from the viz, we can drill down into a sub-dimension. Parameter actions are new to Tableau as of version 2019.2. In the video we use an NBA data set which you can download in a workbook if you visit my Tableau Public viz.

This neat little trick relies upon the creation of a parameter named [Team Name Parameter] and two dimensions.

  • Dimension #1 is the [Team Name]
  • Dimension #2 is the [Player Name]

There is an implied hierarchical relationship between [Team Name] and [Player Name] as teams are naturally comprised of players.

We will also create a calculated field named [Player Drill] that is defined as such:

IF [Team Name Parameter] = [Team Name] THEN [Player Name]
ELSE [Team Name]
END

When we place the [Player Drill] calculated field on color on the marks card and setup our parameter actions (watch the video for instruction), the user selection of a [Team Name] on the bar chart will feed the [Team Name Parameter]. This causes all of the players on the selected team to be displayed with their respective breakdown of points scored.

Bonus: There is a FIXED LOD lesson in this video as well as a Star Wars reference which I’m sure you’ll have no issue finding. Here’s a hint:

It’s really not complicated so don’t let all the words here confuse you. Just watch the video and do some great things with your data! Interact with the viz below:

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All views and opinions are solely my own and do NOT necessarily reflect those my employer.

This video was definitely inspired by Kevin Flerlage who has a great blog post on the uses of parameter actions in Tableau. Check it out here.

Create a Customizable Heat Map in Power BI

In this video we’ll learn how to create a customizable heat map in Power BI without using the prepackaged downloadable visual. A heat map (or heatmap) is a graphical representation of data where the individual values contained in a matrix are represented as colors. A heat map helps draw your eye to the most and least popular areas within the matrix. The cells contained within the table either contain color-coded categorical data or numerical data, that is based on a color scale.

Matrix

Wrong Matrix

I have some fun in the video with a dashboard that I constructed using a publicly available data set from Microsoft, but in the lesson we’ll create the following:

Heatmap

Make sure to watch the video, download the data set and follow along with the instructions.

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All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Tableau Sales Dashboard Tutorial using Table Calculations

In this Tableau data visualization tutorial, we’ll learn to use the LOOKUP table calculation function to return sales revenue for the same day last year. A number of different techniques are used in the creation of this dashboard.

I designed this dashboard solely as a teaching exercise to help you understand the LOOKUP function and how to show the same date last year in a separate column.

  • As we learned in a previous video Tableau Table Calculations Simplified, (make sure to watch this video afterwards for more clarity), we’ll compute using specific dimensions and then use “At the level” to make sure our LOOKUP table calculation is performing correctly.
  • The “Show Missing Values” option is selected to fill in date gaps in the data set that do not exist. Ensuring 365 dates per year are present in the visualization enables the offset (i.e., -1) in the LOOKUP calculation to arrive at correct sales revenue from the same day in the previous year.
  • You’ll learn that we can filter on a table calculation by using another table calculation. Filters based on table calculations do not filter out underlying data. Instead, the data is hidden from the view, allowing dimension members to be hidden from the view without impacting the data in the view.

Tableau Order of Operations

Observe the Tableau filter order of operations above. Applying a dimension filter before the Table Calculation filter removes underlying data which affects the proper functioning of Table calculations. Typically, Table Calculations only work on values that are visible in the view. By applying a table calculation (which is last in the order of operations) you preserve underlying data but filter out data from the view.

Interact with this dashboard via the picture link:

You need to read these posts and watch these videos for additional information:

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All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Tableau Sales Dashboard Tutorial: Year Over Year Comparison

In this Tableau data visualization tutorial I used a technique shared by Tableau Zen Master Ryan Sleeper to “equalize” dates across the same axis. This date equalization calculated field enables year over year, quarter over quarter, month over month, week over week and same day last year comparisons.

MAKEDATE(2018,MONTH([Your Date]), DAY([Your Date]))

Equalizer 2

Call in The Equalizer for this Analysis

It’s a pretty clever way of preserving the same month and day of date values across many years and updating their respective years to one common year.

For example, all dates would retain their current month and day but would share the year value of ‘2018’. In this manner, data points from various years can be stacked on top of each other for comparison purposes.


Additionally, by creating a parameter value for a specific date part (i.e., year, month, week, etc.,) the user has control over the level of comparisons in the visualization.

You’ll have to watch the video to see the details. Again, thanks to Ryan Sleeper for sharing this tip with the Tableau community which enabled me to apply it to my dataset and share it with you in video form.

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.