Cesar T.

hey Sonal G., how can I get the cost of the enterprise version? How can I get in touch to understand the options?

I think I will go with link - define master dataset, link phase against dataset 2, postprocessing, enrich master data, do the same for each dataset...

so for example dataset A - I have Zingg Ai (misses location) dataset B - I have Zing Ai - Germany dataset C - I have Zingg Ai - Spain If I use match, probably the 3 will come in the same cluster if I use link, I should run A vs B, to identify Zingg Ai as Germany and then run A vs C so it is easier to identify that they are not the same company Is this correct?

If I understood, the right way of doing it is matching dataset 1 vs 2. Then when it's finished and cleaned, I match dataset 1 vs 3, and so on?

So it's not trying to match dataset 2 vs dataset 3 for example?

In my case, as I have 5 datasets, the tool will find matches of each dataset against all other datasets?

Hello team, will you be able to support me on this?

yes, I am running link

Hi, happy new year. There are a few issues I am not sure how to handle. I am still getting duplicated clusters - I was able to identify them and deduplicate it, but it took a while. I still get duplicated companies. for example: Cluster 1 - Company A and Company B and Cluster 2 - Company A and Company C. Is this the expected behavior? (not according to documentation right? https://docs.zingg.ai/latest/scoring) I have a list of companies with the same name and different address, that should be treated as different companies. I added manually as a training sample, but it is still being matched in the same cluster in the final output. Any instruction on how to deal with it? Thank you!

Not really, this is before writing the final output: ============================================================ Found 9239 records that appear in multiple clusters Sample of records in multiple clusters: +----------+------------------+--------------------------------------------------------------------------------------------------------------------------------+------------------+ |system |customer_id |all_clusters |max_score | +----------+------------------+--------------------------------------------------------------------------------------------------------------------------------+------------------+ |Salesforce|0015G00001VXX79QAH|[1766152039827:127504, 1766152039827:45071] |0.8929810880954289| |Salesforce|0015G00001VXbmsQAD|[1766152039827:74971, 1766152039827:81991] |0.9956035871827682| |Salesforce|0015G00001VZpBSQA1|[1766152039827:105300, 1766152039827:10894, 1766152039827:55549, 1766152039827:6500] |0.9881827003423975| |Salesforce|0015G00001VZpDnQAL|[1766152039827:139932, 1766152039827:74607, 1766152039827:124202] |0.7169362231998548| |Salesforce|0015G00001VZuuTQAT|[1766152039827:141609, 1766152039827:148174] |0.819534337404906 | |Salesforce|0015G00001Va59OQAR|[1766152039827:141414, 1766152039827:79612] |0.9997608785006721| |Salesforce|0015G00001Va5GoQAJ|[1766152039827:77935, 1766152039827:33293] |0.7672079743675397| |Salesforce|0015G00001YJXZRQA5|[1766152039827:148135, 1766152039827:38454] |0.8880569815816263| |Salesforce|0015G00001YJhImQAL|[1766152039827:148850, 1766152039827:3393, 1766152039827:123630, 1766152039827:123617, 1766152039827:3380, 1766152039827:112567]|0.9960268398654779| |Salesforce|0015G00001YK7okQAD|[1766152039827:77194, 1766152039827:89583] |0.9999920329355999| +----------+------------------+--------------------------------------------------------------------------------------------------------------------------------+------------------+ only showing top 10 rows