We use Collibra Catalog for data catalogs, data classifications, and all aspects of data categorization. My focus is on metadata, particularly how easy it is to extract an application's metadata and integrate it into Collibra Catalog.
Collibra Data Intelligence Platform
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Great overall data cataloging tool
What do you like best about the product?
The search capability. It is so nice having the capability of global search and being able to find exactly what I’m looking for. It saves so much time.
What do you dislike about the product?
Data classification is not as sophisticated as I was led to believe. It appears to be column name and regex based. I thought it was more sophisticated and used AI to identify patterns, etc.
What problems is the product solving and how is that benefiting you?
We primarily use it to organize and classify all of our customer PII in support of our State Privacy (such as CCPA) programs.
Very intuitive product
What do you like best about the product?
Very intuitive and easy to be configured
What do you dislike about the product?
There are some missing but important functionalities without any reason
What problems is the product solving and how is that benefiting you?
Data documentation and lineage
Collibra in Practice: Governing Data at Scale
What do you like best about the product?
Centralized data governance: everything is consolidated in one place, which makes search, documentation, and traceability much easier.
Business glossary: extremely useful to align technical and business teams around a common language.
Customizable workflows: practical for managing validation steps and governance processes.
Integration with other tools (ETL, BI, technical catalogs, etc.): helps build a real ecosystem around data.
Customizable Metamodal: relationships between objects (datasets, reports, rules, policies, etc.): provides good visibility into the data value chain
Business glossary: extremely useful to align technical and business teams around a common language.
Customizable workflows: practical for managing validation steps and governance processes.
Integration with other tools (ETL, BI, technical catalogs, etc.): helps build a real ecosystem around data.
Customizable Metamodal: relationships between objects (datasets, reports, rules, policies, etc.): provides good visibility into the data value chain
What do you dislike about the product?
Apparent complexity in onboarding: significant improvements have been made with the new interface (the previous one was clearly outdated)
Configuration: extremely powerful, but can quickly become technical and somewhat rigid without expertise.
Configuration: extremely powerful, but can quickly become technical and somewhat rigid without expertise.
What problems is the product solving and how is that benefiting you?
I see Collibra as the enterprise-wide mega data catalog that brings together all glossaries and dictionaries, with clearly defined roles and responsibilities, and full integration of the data quality layer.
DQ Lead from an international corporation reviewing the Collibra DQ Rapid Start Experience
What do you like best about the product?
Scalability. Previously my organisation used stored procedures automatically triggered on a pipeline to analyse the DQ of data entering the data lake. This meant for every new rule we need to write a new stored procedure each time. Now I can create jobs with multiple rules (and use template rules) which is far quicker than before.
What do you dislike about the product?
The tool is quite buggy. I am not an infra expert but it seems every 2-4 weeks I have to engage premium support and my infra department to figure out why jobs are not running. Additionally I find when new releases happen there are often times bug fixed but also bug introduced, especially the most recent 2025.02 release.
What problems is the product solving and how is that benefiting you?
The DQ problem at my organisation was that we had no central system to use for DQ. There was no central process and all DQ activity was essentially reactive and ad hoc. However, now with Collibra this is not the case. We can proactively monitor DQ, have a central system where the datasets, rules and results are all in one place, I can provide access to different jobs and rules to different people.
The benefit to me is ease of use. As I mentioned we use to use stored procedures which required new stored procs being written for every new rule we wanted. Now I can do all of the above in one system which is scalable.
The benefit to me is ease of use. As I mentioned we use to use stored procedures which required new stored procs being written for every new rule we wanted. Now I can do all of the above in one system which is scalable.
Automated metadata management saves time and effort while ensuring high reliability
What is our primary use case?
What is most valuable?
Collibra Catalog allows us to automate metadata management, significantly saving time, effort, and finances. We connect Collibra to an application, enabling it to read and extract metadata, which is more efficient than manual processes. Additionally, Collibra Catalog is scalable and its servers have high uptime standards, making it reliable.
What needs improvement?
More automation and artificial intelligence involvement are necessary. Reducing required employee involvement and enhancing ease of use are vital. Users often find it challenging to utilize data governance tools, with ease of use ranked as an important criterion by 2028 standards.
For how long have I used the solution?
We have been using Collibra Catalog for more than a year.
What was my experience with deployment of the solution?
For Collibra Catalog, because it's a Software as a Service, it's easy. It takes no time to launch since it's just a subscription. However, configurations and customizing for business functions is where challenges arise.
What do I think about the stability of the solution?
There are no stability issues. The servers are very good, and the uptime standards are high, so it's always available, up and running.
What do I think about the scalability of the solution?
Collibra Catalog is scalable for our environment.
How are customer service and support?
I have contacted Collibra support, and I rate it an eight out of ten. The support is good but not fantastic.
How was the initial setup?
The setup technology-wise is very good. The problem lies in feeding the actual business data and ensuring it works as a process and workflow.
What's my experience with pricing, setup cost, and licensing?
Currently, the pricing is at an average market rate. However, there are plans to increase license rates. Overall, it is a reasonable and average rate.
What other advice do I have?
Users should focus on business-focused objectives and not go for large projects. Implement in iterations, prioritizing smaller items. I rate the overall solution eight out of ten.
Not just a governance tool
What do you like best about the product?
It is highly customisable and easy to implement. The support team is great
What do you dislike about the product?
Because of it's high customisation level we, as a company had to restrain ourself on what to use out of the box and what to build ourself
What problems is the product solving and how is that benefiting you?
It centralize information that were scatered in various sources. It put faces on technology, governance and data.
Data Product creation
What do you like best about the product?
Creation of metadata repository which helps in data product creation which can be made ahoppable by using dataops methodology.
What do you dislike about the product?
At times you have to use custom connectors to connect to collibra as it doesn't supports direct connection with some tools.
What problems is the product solving and how is that benefiting you?
Creation of data products and making them ahoppable for the other users in the organisation.
Data management made efficient and effective for our team members
What do you like best about the product?
What I appreciate the most about Collibra is how it can enforce data governance policies. With the platform we can define and monitor our data stewardship practices, that helped us enormously in improving data quality across our organization. Also very valuable is the customizable reporting, which provides us with details on how data is being used and if compliance is occurring. This also provides us with a level of control which allows us to stay on top of our regulatory requirements and data governance is more efficient.
What do you dislike about the product?
Sometimes, Collibra’s customization of workflows can be tricky, specifically when you need to change some process according to your needs. There’s leeways, but not always enough to the level of leeways that we need to get. Furthermore, there is also an awkward amount of manual steps to do when managing large datasets, that don't move too quickly. However, these challenges have sometimes curbed our workflow, but we are getting used to taking a different approach.
What problems is the product solving and how is that benefiting you?
Among the solutions challenged with the inconsistent data, across departments, Collibra has shined. It also enabled us to set clear data governance rules that all teams are forced to adhere to, to ensure consistency in the data. The platform also allows for tracking and enforcing compliance making audits far easier and less time consuming. This has allowed Collibra to improve our data management process overall, and make sure we're complying with regulations much better.
Facilitates data quality monitoring and AI governance with a complete suite of tools
What is our primary use case?
What is most valuable?
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.
What needs improvement?
Collibra is working on making the latest version more user-intuitive. The previous versions were easier for technical resources to use, however, not so much for business people who are not tech-savvy. If it can become more user-intuitive and work on integrating with communication platforms like Slack or Teams, it would significantly help business users.
For how long have I used the solution?
I have done implementations of Collibra for three to four years now.
What do I think about the stability of the solution?
Performance-wise, Collibra is better than other catalogs in the market. It is quite stable.
What do I think about the scalability of the solution?
Collibra is pretty robust and scalable, given the way they have developed their product and the connectors they have established. People can easily add resources to it.
How are customer service and support?
Their support team is good. However, it depends on which tier you are in and what kind of support you have, which is related to your licensing. Enterprise-grade customers get good customer support.
What's my experience with pricing, setup cost, and licensing?
Pricing is definitely one of the costliest in the data catalog market. It competes with the likes of Informatica in terms of price. It is best suited for established Fortune 500 companies.
What other advice do I have?
I would give Collibra a nine out of ten. For bigger organizations, it is worth the money as a tool. However, smaller organizations can get value out of some other tool, and for them, Collibra may not be the best choice.
Saves a lot of time for IT
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
Except for data quality, everything is perfect. Once you have your data systems integrated with the Collibra platform, and when you set up the frequency of refreshing the metadata from that data system, it happens automatically. It is based on how you define that data refresh.
What needs improvement?
If the price is a bit reduced, that would be better. Improving the data quality landscape much better in the platform would be beneficial because the data quality landscape hasn't matured to function well compared to other competitors.
For how long have I used the solution?
I've been working in Collibra for close to two years.
What do I think about the stability of the solution?
From one to ten, I would rate the stability of the solution as eigh out of ten.
What do I think about the scalability of the solution?
I would rate the scalability of the solution as eight out of ten, which indicates no issues with scalability.
How are customer service and support?
I would rate the technical support from Collibra as seven because to work in the Collibra platform, especially if you are going to do it in your own cloud services, they ask for specific consultants to work in our company. So it's not entirely independent for us to work on it when it comes to the IT landscape.
Which solution did I use previously and why did I switch?
I worked in Alation and Informatica before, and I moved to Collibra right now. Informatica is a fully mature data management platform, which provides different landscapes like master data management, data governance, and data quality, wherein Collibra provides only data catalog or governance. I prefer Informatica since it is a completely mature platform in terms of data management aspects.
How was the initial setup?
The setup is simple. It depends on in which platform you're going to deploy it, whether in the cloud or on premise, and if in the cloud, whether it's your company's cloud or Collibra's cloud.
What about the implementation team?
They wanted us to collaborate only with the specific consulting providers to work on the Collibra platform, especially when it is in our own cloud services.
What other advice do I have?
A suggestion for Collibra would be to improve collaboration with many different other technologies, as that would make it a frontrunner in the data catalog service provider space. It would be beneficial for Collibra to integrate with business intelligence tools and show us good data lineage mapping.
I'd rate the solution eight out of ten.
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