Engine Works

Under the hood of Alteryx: tips, tricks and how-tos.

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Alteryx Auto Insights now supports non-time series data! This new feature also includes more data stories to view in Auto Insights, such as factors in your data that are performing above or below average.


So, what does this mean for you?


This feature unlocks more datasets you can load into Auto Insights, as you can now load datasets without a date column for analysis. This unlocks more use cases you can utilize Auto Insights for, opening even more opportunities to get value from the tool.


Example Use Cases


Use cases that leverage this feature include scenarios where you don’t need to perform change or root cause analysis to understand what’s in your data for decision-making. Some examples include:

  • Customer base analysis – What profile of customers are spending the most on existing products? This can help me tailor marketing messages and target a specific demographic for new product releases.
  • Controls reporting – How many controls have been executed? Which have failed or passed? This can help me identify potential opportunities to improve our company’s policies and address the controls that are failing.
  • Daily snapshot of incidents (from datasets that don’t have historical data) – What proportion of incidents raised are classified as severe? Which product/service are these incidents largely coming from? This can help me identify the high-priority areas my team should focus their workday on today.  


Using Auto Insights for Playlist Creation


I’m an event manager, and I want to host a dance party to generate hype before the release of Taylor Swift’s The Eras Tour tickets sale.


To maximize attendance, I want to curate a playlist of Taylor Swift’s most popular songs to play at the dance party. I also need to figure out how long the party should be so that I can hire out an event space for that amount of time.


Since I don’t need to do any change analysis, I’ve sourced an extract from Spotify that includes a snapshot of how popular, danceable, and long each of Taylor Swift’s songs are. I then loaded this dataset into Auto Insights for analysis to help me make decisions about my event.




When I look at Popularity, Auto Insights shows me a data story that tells me 26 tracks have more than double the average Popularity compared to other tracks. This is a great start to my analysis because I would have had to otherwise create some complex formulas in Excel or another ETL tool to calculate this.




I then narrow down my investigation by filtering for those 26 tracks so that I can find the most Danceable songs. Again, Auto Insights calls out that 15 tracks are above average, which I’ll be curating into a playlist.




Finally, to understand how long this playlist will take to play, I select the Duration in Minutes metric and let Auto Insights calculate the total time for me – 157.28 minutes (~2.6 hours).


These insights have helped me decide that I should book the event space for 3 hours to allow enough time for registration and the event to run. It’s also helped me curate a 15-track playlist for the dance party, all in a matter of minutes!


I hope that from this post, you now have a better understanding of how to use Auto Insights to answer your questions using non-time series data.


If you’d like to analyze this dataset in Auto Insights, you can download the dataset below.


This feature is available to use with any version of the Auto Insights Uploader Tool that is compatible with your version of Alteryx Designer. Click here to download a compatible version of the Auto Insights Uploader Tool.