Yes, I made it to blog 5!!! Woohoo!!!!!!! This is my most exciting blog yet...so stay tuned.
Some of the interns are working on a Modernization project for the Concurrency ICE site, and I thought I’d help them out with some of the data visualization. An intern sent out a survey for all Concurrency employees to take regarding what changes they would like to see on the site. One of the questions asked: “How important would these features on the new Concurrency Intranet Homepage be to you as an employee on a scale of 1 to 5?”
The options listed were:
Company Updates/News
Yammer Feed
Employee Directory
Training resources and information links
IT Support links
Company Events Calendar
Employee policy documentation and resource links
Concur/Expense reimbursement links
To visualize these responses, I created tables for every option. The following table represents the results of how Concurrency employees feel about a Yammer feed on the home page.

To make this table stand out, I chose to apply conditional formatting by highlighting the maximum value. You can do some conditional formatting with a few simple clicks in Power BI, but this required hard coding in DAX. The following code represents a new measure called "yammer". This works by first creating a variable called "yam". The ALLSELECTED() function returns all the rows in the Yammer Feed column. Next, I created a variable called "themaxvalue". This is counting the number of users who responded 1 through 5. Lastly, I present an IF() function that finds the maximum count and highlights it yellow.
.JPG.aspx)
When I apply this measure to conditional formatting like below,
I am able to create the following tables. For each table, the measure that you use stays the same, despite the type of question that you are analyzing.
.JPG.aspx)
This type of conditional formatting is really nice when you want to focus on a specific part of your data, like the minimum, maximum, or average. This is also really nice to use when you want to highlight a part of data on a histogram or scatter plot.