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November ’22 Updates: Dataset Folders, Classic Matching Models, and More!

By November 7, 2022No Comments

Our second fall release brings enhancements to dataset organization, duplicate identification, and a new merge automation feature. We also added a DIY option to move rules and model from your Sandbox org to Production. 

Read on to discover the highlights and, as always, you can find a detailed list of all our updates and fixes on the DataGroomr Support Portal.   

Keep in mind that many of the updates we make were driven by requests submitted to the Ideas Exchange portal. So, keep them coming. 

Organize Datasets in Folders

This one comes directly from some of our heavy dataset users. As you may recall, DataGroomr allows you to create as many datasets as you need in Trimmr, Brushr and Importr. They are grouped in the navigation menu under the associated Salesforce object. However, this becomes a nuisance as the number of datasets increases.   

To allow users better control, we are introducing the concept of folders.

Supervisr Enhancements

The essence of DataGroomr is to simplify duplicate identification and merging. The new features introduced below continue to improve and expand this functionality. 

Classic Matching Models

DataGroomr relies heavily on Machine Learning to detect duplicates because it is more accurate, maintenance-free, and will detect duplicates that traditional matching methods will just miss. Even though DataGroomr is shipped with pre-trained matching models, there is an option for customers to create their own models based on the unique data that resides in their Salesforce organization to tailor duplicate detection algorithms. This process requires two things. First, duplicate records have to exist in the organization and, secondly, time has to be spent to perform supervised training, in order to provide examples of duplicates and non-duplicates to the system so it can identify matching data patterns. 

Occasionally, customers need to simply find exact matches by a predefined criterion; for example, in fields such as email or phone number. This is where a traditional matching method is faster to set up. For these customers, we are introducing Classic Matching Models. Just like with machine learning, you’ll define the field(s) to match, but there is no training required. DataGroomr will simply find all the exact matches when assigned to a dataset. 

Exclude from Merge

This new feature allows users to define exception conditions for certain records to be excluded from a matched group during merge. It is available within the field merge rules.    

Copy Rules and Models from Sandbox to Prod

In order to copy any of the rules and models created in the Sandbox to production, users had to send a support request. In this release, we have added a DIY option.   

Merge Rules page 

Matching Models page 

What’s Coming in the Next Release?

We are working on a brand-new Lightning component for Salesforce focusing on email, phone and address verification. Please follow these release announcements to stay tuned and share any feedback you may have.   

If there are other features that are important to you, we invite you to submit them to the Ideas Exchange portal or vote on existing features already there. 

As always, we would like to remind you that we offer a free, 14-day trial of DataGroomr. Please feel free to forward this article to your Trailblazer colleagues. They can start the free trial by logging in with their Salesforce credentials. There is no setup required for any user to get a handle on the duplicate management of their data immediately! 

Happy DataGrooming, Trailblazers! 

Ben Novoselsky

About Ben Novoselsky

Ben Novoselsky, DataGroomr Co-Founder, is a hands-on software architect involved in the design and implementation of distributed systems, with over 20 years of experience. He is the author of multiple publications about the design of the distributed databases. Ben holds a Ph.D. in Computer Science from St.Petersburg State University.