Group by similar name

[JAM9210]

Hi everyone,

I found a couple similar posts but couldn't find one that had a solution for my specific scenario. I am also fairly new to creating workflows from scratch, so I apologize in advance.

I'm trying to identify IDs that have multiple company names and addresses linked to them from a large data set of thousands of records. I was able to group by ID and Company name, however the data could use some cleansing as some of the company names are being counted more than once due to the different naming conventions (see sample attached). How can I clean the data to group by similar company name under the same ID?

I hope my question makes sense.

State	ID	Company
WA	123456789	BIG POPPA
AZ	123456789	BIG POPPA INC
AZ	123456789	BIG POPPA INCORPORATED
NY	123456987	AYE AND BEE INC
TX	123456987	AYE & BEE INC
FL	254170270	BEYONCE
FL	254170270	SOLANGE
CA	313076766	PARKWOOD WAY
WA	313076766	PARKWOOD WY
AZ	678912345	STONEBRICK PL
AZ	678912345	STONERICK PL
TX	876543219	NICKI MONET
AZ	876543219	NICKY MONET
CA	987654321	MACY GRAY
AZ	987654321	MACY GREY
CA	987654321	RIH STUDIOS

[PanPP]

Hi @JAM9210 

Can you provide what the output should look like?

We will need to utilize the Fuzzy match tool in this scenario.

[TimN]

Try Fuzzy Match and Make Group.  Something like this.

[TimN]

Here's a better example if I'm following your problem.  I haven't done this in a while ;).

[JAM9210]

The output should look something like the below. I want to be able to group similar company names (i.e. BIG POPPA INC and BIG POPPA INCORPORATED) and then count how many actual different named companies fall under the same ID. State2, ID2, and Company2 would be the result columns after cleansing the company name field. I also added the last line to have a different company name under that same ID to add to the count:

State	ID	Company	State2	ID2	Company2	Count
WA	123456789	BIG POPPA	WA	123456789	BIG POPPA	2
AZ	123456789	BIG POPPA INC	AZ	123456789	BIG POPPA	2
AZ	123456789	BIG POPPA INCORPORATED	AZ	123456789	BIG POPPA	2
NY	123456987	AYE AND BEE INC	NY	123456987	AYE AND BEE INC	1
TX	123456987	AYE & BEE INC	TX	123456987	AYE AND BEE INC	1
FL	254170270	BEYONCE	FL	254170270	BEYONCE	2
FL	254170270	SOLANGE	FL	254170270	SOLANGE	2
CA	313076766	PARKWOOD WAY	CA	313076766	PARKWOOD WAY	1
WA	313076766	PARKWOOD WY	WA	313076766	PARKWOOD WAY	1
AZ	678912345	STONEBRICK PL	AZ	678912345	STONEBRICK PL	1
AZ	678912345	STONERICK PL	AZ	678912345	STONEBRICK PL	1
TX	876543219	NICKI MONET	TX	876543219	NICKI MONET	1
AZ	876543219	NICKY MONET	AZ	876543219	NICKI MONET	1
CA	987654321	MACY GRAY	CA	987654321	MACY GRAY	1
AZ	987654321	MACY GREY	AZ	987654321	MACY GRAY	1
CA	987654321	RIH STUDIOS	CA	987654321	RIH STUDIOS	1
CA	123456789	XYZ STUDIOS	CA	123456789	XYZ STUDIOS	2

[JAM9210]

Thanks Tim! I think we're on the right track here :)

[TimN · Accepted solution]

Here's how I understand it...

If this works, please mark my answer as the resolution.

Thanks.