Challenge #11: Identify Logical Groups

[GeneR]

Here is this week’s challenge, I would like to thank everyone for playing along and for your feedback. The link to the solution for last challenge #10 is HERE. 

The use case:

A manufacturing company receives customer complaint data on a daily basis from their call centers about the medical parts they distribute to their customers. The company monitors these comments to understand which parts and part groups have the highest complaint rate. This helps the company prioritize which parts to focus on from a development standpoint.

In this exercise, take the customer complaint data and identify which bucket the complaint falls within. The complaint can fall into multiple buckets and needs to be flagged as these complaints take highest priority. Create an aggregate view of which buckets or bucket pairings have the highest # of complaints.

This is only a subset of data so all records will not be assigned to buckets and can be ignored.

Update: As of 9/26/19, this challenge start file and solution were modified.  Your solution will not match those posted by other Community members prior to this date. 

[TaraM]

This article has been updated with 2 different solutions.

[alex]

I love comparing all these solutions and learning more about how to utilize different tools.  I'd like to see an explanation on what the search macro is doing.  I've attached a screen shot of my workflow in the spoiler window.  It works great if you know how many words you are looking for.  On my machine, it finished in under 3 seconds as compared to 10 secs for solution 1 and 12 secs for solution 2. 

Spoiler

In looking at the Solution 1, I learned that I could've eliminated the sort and multi-row tool I used and used the concatenation option in the first summarize tool.  I tested that approach on my workflow and it also worked and took the same amount of time.  I found the tricky part to be how to introduce the search items if you don't know how many you will have.  I also included a test to ensure each complaint ticket only had one row of data to be on the safe side.  I'm happy to send a packaged workflow to anyone who wants it.

[Martin_Bosak]

 Hi, 

I have also tried to do this faster. I used full cross join and then just filter items which contain search.

And it runs very quickly.

Spoiler

[markp201]

Playing catch up here with the weekly challenges.  Similar solution to Martin's.

Spoiler

[SeanAdams]

similar solution to the 2 provided - differences in approach in the spoiler below

Spoiler

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[MarqueeCrew]

I am pleased with my results 🙂

Spoiler

Cheers,

Mark

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[NicoleJohnson]

My solution, similar to others.

Spoiler

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[Laurap1228]

I wasted a lot of time on this weeks challenge trying to figure out how to parse the complaint field. In the end, I gave up and found a solution that didn't involve parsing the text.

Spoiler

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[estherb47]

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