10 things about Tableau 10: Filtering across data sources

31.10.2016

10 things about Tableau 10: Filtering across data sources

A new week means a new edition of my blog series titled “10 things about Tableau 10”. This time we’ll be looking into cross data source filtering, which previously has not been available out of the box. It’s a feature which simplifies handling multiple data sources. Let’s take a look at how it works!

Cross Data Source Filtering

In the past, creating filters that work across data sources would’ve required the use of calculated fields. Though possible, the extra amount of work required was always a bit of a nuisance. Thankfully that’s a thing of the past!

In this week’s example, I’m connected to a sample data set that includes sales for my (imaginary) technology company. I’ve already joined another data set from the same source, which has information about my sales personnel. Now this is all well and good, but as my company operates globally, the Sales Director I’m working for is interested in the performance of the entire company. I need to add data for the United States to get a complete view of how the company is doing.

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Here I’m adding in my US sales data set to complement the European sales organisation’s data. Why not use a Union, you ask. Unfortunately my data sources have different amount of columns, so a union won’t work. But as Tableau supports cross data source filtering, having the data in two separate data sources is fine.

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Now that I have both data sources connected to Tableau, I can go forward an link up the data sets with the appropriate dimensions. In the Relationships menu, I decide which of my data sources is the primary and which one is the secondary.

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To add a relationship, select the Custom radio button, and Add a new Field mapping. Since field names don’t always match in data sources, it’s a good idea to always check the relationships. Tableau however does do a pretty good job of guessing for you — in this case Tableau has recognised the dimensions that match automatically.

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Now that the data sets have been joined, I can go forward and visualise the data. I’ve built two simple maps with my sales regions — North, South, West and Central (Check out my last post about using custom territories to add some extra detail to your maps!). I’ve also added a Year selector which is connected to my European sales viz.

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I know that my U.S sales data also includes data about the Order Date and I want to use that information to compare sales. With the cross data source filtering option, I can set the European Sales date dimension to apply to any related data source. This allows for any data source using a related dimension to be filtered on. If you look closely, even though I’ve selected ‘2012’ as the Order Date year, only the values in Europe have changed.

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Now when we look at the map of the U.S, the sales figures have changed for 2012. Pretty neat.

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Until next time!
Are you a data driven organisation? What does it mean to be Data Driven? Come and find out at the Bilot Breakfast Club on the 24th of November 2016, “HOW CAN YOUR ORGANISATION BECOME DATA-DRIVEN?”. Click on the link and sign up today!

 

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Blog writer

Karri Linnoinen

Bilot Alumni