Cloudingo vs. Fivetran

Cloudingo is great at mass merging account and contact records based off a specific criteria instead of having to do it manually within the record in SF. It also is easy to analyze a record while in a mass merge to see if you need to edit some values. However, having other standard objects would be very beneficial and even having it for custom objects could be helpful.

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Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.

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• Cloudingo displays a single dashboard view of the quality of data you have in Salesforce. You no longer have to guess how much of your CRM is "clean" or "dirty".
• Cloudingo allows you to test functionality in Salesforce Sandbox prior to conducting changes in Salesforce Production. I highly recommend thoroughly testing functionality prior to making changes in your live CRM.
• Cloudingo allows you to mass merge and converts records automatically and manually either individually or in groups. This saves a lot of manual time in trying to keep your CRM's data clean.

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• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

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• I would love to have a multi-user mode. I realize this very difficult though given I as an admin can see records my peers cannot.

• Very difficult to get connectors enhanced if a specific needed object is not supported by them
• Depending on the edition needed and the data volumes, can get quite expensive

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We spend enough money on Salesforce that the cost of Cloudingo when compared with its benefits is extremely small. We've been able to see measurable KPIs move as a result of the work we've done with Cloudingo

Very usable. I have not spent enough time to fully grasp the capabilities. The interface is fairly intuitive, and I think with more time, I would grasp it very well.

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Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.

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It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

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I have had to reach out to Cloudingo support several times. They almost always respond that same day. Often times they will call me directly to fix the problem or walk me through best practices. They even reach out after the fact to make sure that everything worked okay. They are also kind and personable.

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For us it was just a matter of committing the 10 minutes to install it from the AppExchange. It automatically builds out a dashboard based on standard filters and you get a lot of insight from them. We then copied some of the standard ones and pulled two from a filter library that rounded out what we needed. I wish I had done this months before given how little of my time it actually took.

We considered DemandTools CRMFusion. As a small company with a limited budget, it was considerably more expensive than Cloudingo. It also was an installable, on-premise piece of software which is a big drawback. While the reviews of CRMFusion on TrustRadius were very positive, it appeared to have a steeper learning curve as well. So the combination of cloud-based, easier-to-use, and lower price led us to select Cloudingo over CRMFusion.

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We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.

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• Neglected and not used due to destructive automation
• Fear of cleaning up data
• Not wanting a human to do proper cleansing

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• It has been very positive in serving BI team with new source requests
• It has been OK at scaling to match as data volumes as source data size grows

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info