Collibra Catalog Reviews

I used Collibra Catalog primarily for cataloging all the assets. We created data dictionaries by ingesting data from multiple databases into Collibra Catalog and setting up workflows for review and approval processes.

The most valuable features of Collibra Catalog are its customizability and ease of use. We created data dictionaries both by ingesting our database structures and for non-database sources. We also set up different communities, defining ownership and stewardship with customizable workflows.

The use case for Collibra Catalog is for metadata management.

The best features in Collibra Catalog are apparent when we do the integration and establish when we fetch the data into Collibra. We have an option to store everything into a particular domain under which all the data can reside. This feature stands out because when I worked with Alation and Microsoft Purview, they did not have these capabilities.

Using lineage and Collibra Catalog has helped me overall improve the trust and transparency regarding data origin and transformation.

The business glossary feature in Collibra Catalog contributes to consistent data terminology across my teams because it is easy to use. If you want to search any business term or business-related assets, you can go to the glossary and search for it directly. Instead of using global search, you can navigate directly to the glossary and find the asset more easily.

Collibra Catalog has improved my organization through data cataloging as we were able to find assets more easily. It was organized in a structured way so that everybody can understand where the assets reside and how to navigate them.

The primary use case for Collibra Catalog in my organization is for documenting all the metadata that we have in the company. Apart from that, I am also focusing on many different layers on it, including data lineage, data quality, business glossaries, and access management workflow.

In the company that I'm working for, there are lots of different analytical groups, so it is going to save much more time for IT people.. Previously, when I was working at a more business-oriented company, our primary audience was the business people. This demonstrates that the improvements depend on the different organization structures and how you're defining your data governance goal and vision.

I am using Collibra at one of my clients. We implemented Collibra there as well.

When I initially started with Collibra, it was just a data cataloging platform with governance workflows around it. Now they have acquired a lot of other tools, or they have merged or acquired different platforms. 

It is a complete suite of tools for managing data. We can monitor data quality and take actions on the profiling results obtained by running data quality checks. Collibra helps catalog data assets, monitor the health of data assets, and take necessary actions. If we find data quality issues, it also provides a medium to capture those issues and how to remediate them. 

The workflows allow the creation of custom workflows based on needs. The newest addition in their tool suite is AI governance, which allows cataloging all AI models currently deployed or even in the pre-production stage. It helps document model meanings and the risks involved, thus managing all risks related to AI deployments.

I use Collibra Catalog for cataloging various reports, data elements, and tables, along with schema, associated with all the various attributes of the element itself, like business rules and sensitive data.

The most valuable features of the solution are the ability to PnP with all the various DBs in the background and to ensure that the Tableau is updated and any changes are visual while notifying about the integration to various business rules, data rules, data quality rules, and systems.

Catalog is primarily used for metadata cataloging and enriching it with additional information, such as adding business terms. Then, if lineage support is available in the tool, we can link business terms to technical terms. 

Additionally, we can store sample data. If there's a client requirement, we can link physical metadata with reports or datasets. This allows users who need access to a particular dataset to obtain it by creating a dataset from the Catalog metadata.

For data discovery, we create datasets. The most frequently used datasets are featured on the dashboard. Users can create their own if their requirements aren't met by the most frequently used datasets. We also create data requests, and owners can approve these requests, which adds a process layer to accessing particular datasets.

So, it has been effective for data discovery in our company. 

I use different functionalities within the tool. I use Collibra Catalog for metadata management or Collibra Lineage for data governance.

Once configured, data in the Catalog will be automatically updated, reducing the need for manual maintenance. So, the automation feature has positively impacted our data management tasks.

The product’s primary use case is metadata management. It helps us capture different datasets, including images, glossaries, etc.

Collibra Catalog has significantly enhanced data governance and compliance for our team, primarily through its valuable feature of endpoint lineage enabling visual representation of the data. However, it takes a lot of manual effort for the same. They could introduce more features to align the process.