Collibra Platform Reviews

We primarily use the solution for when the customer needs data governance to establish cases. We're choosing the product in order to meet the requirements of the customer. That includes requirements such as establishing a governance council if the customer does not have one. It's for data management and the solution handles data quality and metadata management, et cetera. 

Before implementing data governance, the customer had a pain point of identifying the data and providing data useful to their business. That was the biggest challenge. There were also many data quality jobs where the customer wanted to find and identify the data in terms of where the data is sitting and who was responsible for it, et cetera. We were able to identify data sources, which applications were available within the organization, the domain level, and more to clarify exactly what was happening, where reports were sitting, who was connected to what, where data was coming from, and more. It also helps clarify job roles and showed access to identify where pain points were to get an overall picture of the data of the entire organization. 

The ability to pull together data lineage is one of the most valuable aspects of the product. 

The transparency was great. the business was able to come in and see the data and what exactly was happening in a way they couldn't before. The company could see job roles, data sources, and policies. 

The quality and visibility of the data links are great.

They used to rely on third parties, however, now, for example, they've brought in their own data quality tool. 

The beauty of the solution is that you can customize it quite a bit.

The technical support could be better.

Previously, it was a challenge to integrate tools like MuleSoft into the product. However, they've gotten better. 

There needs to be more security and compliance embedded in the product in the future. 

I've used the solution for close to seven years. 

The stability is great. I'd rate it at around a nine out of ten in terms of reliability. There are no bugs or glitches, and it doesn't crash or freeze. 

The scalability is quite good. I'd rate the ease of expansion at an 8.5 out of ten. 

We weren't overly pleased with the technical support. They need to provide a better quality of service to the customer. 

Neutral

I'm also familiar with Informatica Axon. I find that it is a little stronger in the data quality department. However, it is not as easy to deploy. 

It's not too complex to set up initially. It's pretty easy if you are doing a basic setup. However, it does depend on the client's requirements. You can customize a lot of things, which is great. 

We handled the setup ourselves in-house. I mostly did the implementation myself. I have seven years of experience and am quite comfortable with it. 

We have witnessed an ROI while using this product. The customer typically sees huge returns. 

The solution isn't overly expensive for bigger customers. Typically, the cost is handled by the customer, not me, and therefore iI can't speak of the exact pricing. 

I'm not sure which version of the solution we are on right now. We're on the cloud deployment, meaning we are likely using the latest iteration.

I'd recommend the solution to others. It's a straightforward product. 

I would rate it nine out of ten overall.  

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.

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.

I have done implementations of Collibra for three to four years now.

Performance-wise, Collibra is better than other catalogs in the market. It is quite stable.

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.

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.

Positive

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.

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.

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 data lineage capability is valuable as it shows how different sources are connected and how data flows, which is crucial for projects like migrations.

Moreover, data lineage visualization in Collibra Catalog aids our data governance initiatives. It's very important for identifying impacted reports during updates, making it crucial for decision-making due to its ability to link business and technical terms.

I'd like to see more integration with other reporting sources like Qlik Sense, beyond the currently supported ones like Tableau and Power BI.

I have been using it since 2019. It has been a long time. 

Collibra updates the job server every quarter, so it's a stable solution that requires quarterly updates.

I would rate the stability a nine out of ten because it's quite good.

It's scalable. We create different configurations for each data source to minimize latency and ensure efficient data governance. This decentralized approach makes it highly scalable.

I would rate it a nine out of ten because it's quite good. It supports multiple authentication methods, not just username and password. It supports mechanisms like Windows authentication or any other supported source, so there's no issue.

We make it available to everyone in the company. Moreover, most users are on the client side during implementation.

The customer service and support have improved, and Collibra provides very good solutions now. They have a knowledge base that can answer our queries, suggesting solutions before we need to contact support.

The setup is quite easy and takes about 30 minutes to an hour if you have everything you need. Configuration isn't very difficult.

Deployment involves creating a job server in the client-hosted environment and configuring it to connect to all data tools, like MySQL, Oracle, or SQL Server. Then, we establish a connection between the job server and Collibra. 

Data governance instances and the job server send requests to the actual data sources. Metadata gathered from the sources is passed to Collibra through this setup.

We're moving towards cloud solutions, so Collibra Data Intelligence is on the cloud. Collibra Edge can be server-based, but the job server can also be on-premises. It supports both setups, but the direction is towards cloud-only solutions.

One person can maintain the solution, and we need one service account for it.

Collibra offers a per-user licensing model.

Overall, I would rate the solution an eight out of ten because we can connect to multiple sources or create custom connectors, making it a good solution.

I highly recommend it.

Collibra Governance is used as a data governance tool to manage your business metadata and technical metadata from various systems. Customers also use the tool to provide a data cataloging option, create a data lineage, and automate some of the business processes.

The solution's most valuable feature is data cataloging. The solution's data cataloging feature is mainly used for data discovery. Nowadays, data cataloging enables the data marketplace to share data within enterprises. That's another enablement that Collibra Governance does.

When you have a huge amount of hierarchical data to manage, the solution's performance tends to decrease.

I have been using Collibra Governance for six to seven years.

Sometimes, when we face an issue, we resolve it by ourselves. On other occasions, we contact the solution's technical support team. I wouldn't say that the technical support is excellent. The support team struggles to resolve the issues 60% of the time. However, they are able to resolve issues the remaining 40% of the time.

Neutral

Many of our customers I spoke to felt that the solution is a bit highly-priced.

I sometimes recommend Collibra Governance to our customers based on their requirements and environment. Most customers look forward to having a data lineage function, but I haven't seen any tool that satisfies the customer requirements, including Collibra Governance.

The solution has primarily enabled some of the compliance reporting through data lineage reporting. When you have data cataloging with complete metadata enrichment and identification of PII and non-PII, it helps customers control and manage them properly.

If a customer does not have the appetite to procure Collibra Governance because of the pricing models, we may recommend a different product with cheaper prices. Collibra is working on building a new product for AI governance. I want to see how Collibra's data governance and AI governance will relate to each other.

Most of the customers we deal with are looking for a tool to resolve the data lineage challenge. However, none of the products in the market seem to be resolving the customers' data lineage issues. Vendors like Collibra, Alation, and Informatica could work on it and provide a more comprehensible solution to the customer.

Overall, I rate the solution an eight out of ten.

We've used it for cataloging or defining our business data entities and their associated attributes or data elements. Where appropriate, we are defining their sources and their modes of derivation. Rising above that, we are also using Collibra for the definition of our business metrics that are calculated from those data elements and the derivation definitions associated with them.

We have folks in the field doing our clinical research operations, and they have the opportunity to refer to Collibra when they are examining a particular report. When they have a question about how something in that report is derived, they can pull a metric that is defined fully in Collibra. They have the opportunity to drill back into Collibra to see that.

It is a big library. It has a long list of data assets and their descriptions and lineage. It's fairly two-dimensional. For my use, it just has lists of things. There might be others in my organization who are tying the data elements, metrics, and other things together, but I don't rely on that, and I don't leverage that. For me, it is a fairly flat library for information.

What I use it for is fairly rudimentary, and I don't have any complaints about it. I haven't tried to stretch the boundaries at all, but it would be nice if there were capabilities built within the system to somehow help enforce the quality and consistency across related elements that are built in the catalog. It could have intelligent capabilities built in to help maintain the quality of the data and information, such as natural language processing, machine learning, and so on. 

I've been exposed to it for about seven years, but I have been using it more heavily over the past couple of years.

I've not seen or heard of any problems with its stability.

I haven't seen any impediments there. So, I have no concerns about scalability. It could be because I haven't been this involved in pushing the boundaries, but I've seen it accommodate new layers without hearing of any issues.

It is open to everybody in our organization. We have more than a thousand users. So, there are more than a thousand people out there who have lately used Collibra, but most of them have used it in the reference mode or read-only mode. In terms of their roles, we've got everybody from data engineers, data analysts, and systems developers to business analysts and business end-users. 

I've never called them, and I don't hear anybody complaining about it.

As far as I know, this was our first venture about seven years ago into data cataloging and data governance.

I wasn't involved in the setup, but I can see that the maintenance of information within the system is quite straightforward.

My guess would be that we took care of most of the things in-house.

I'm only involved in the use of it. My advice would be to make sure that the content is of quality because it tends to mushroom into lots and lots of information that can easily get out of sync. It should only be entered into by an organization that has the discipline behind it to maintain the information at a high level of quality. Otherwise, you end up with a lot of information that seems useful, but in effect, it could be more harmful than it's worth if things get out of sync. It takes an organization to maintain this kind of solution. The content is dependent on the quality of the maintenance of the content.

I see a small chunk of it, and I would rate it an eight out of ten.

I use the solution to link the technical and business terms and see the lineage between different technical terms. To track data flow, we start on the report level if there is a compliance requirement. We can see which data set a particular resource is using. We can see how the data table for the data set is created. We go back and trace the source. We try to define it from the source to the target end to end.

The product increases trust in the data. We know what is affected and where we need to update things if we change something.

The diagrams are good for data visualization. It is for technical and business terms. The technical lineage is for technical terms and data. Earlier, we used to maintain the data manually. Now, everything is automated. Sometimes, we still need to verify whether the lineage created automatically is correct or not because it might have a lot of information that is not needed. It is very easy to integrate Collibra Lineage with other tools. It provides APIs. It can be for any language like Java and Python. We can use the template provided in the marketplace.

We have to pay separately for each connector. The product must be cheaper. There must be a common license for the product.

I have been using the solution since 2016.

The tool is stable. It provides good column-level and view-level lineage. I did not find stored procedures in the product. In the documentation, the product says that it is possible for some sources, though. I have not worked on every source that the product supports.

The tool is scalable. The solution is used by the people who use Catalog in our organization. It is available to everyone. We have to configure it once. Once configured, it will be available to everyone in a read-only mode. We need two developers for maintenance.

The support team is common for Catalog and Lineage. When we raise a concern, we must mention what our issue is. It is diverted automatically to the right team.

The setup is similar to the setup of Catalog. Catalog is a pre-requisite. It must be installed and configured first. Once the Catalog is installed, we must run the script for Lineage on the same server by configuring the details we want to scan. We can read the documentation and follow it easily. If we get stuck somewhere, the support team will help us. It is pretty straightforward.

The tool helps trace the lineage of complex environments.

The licensing cost depends on our requirements.

I will recommend the tool to others. It saves time spent in creating lineage manually and maintains it over a long time. Overall, I rate the solution a nine out of ten.

We use the product for classification based on the different regulations and compliance requirements.

The platform is customizable. In comparison, Informatica Axon has predefined facets. Meanwhile, Collibra lets us create custom assets. It helps us build a powerful workflow mechanism where there can be any stewardship, including issue management governing assets using policies, regulations, or business rules. They are integrated very well. It has a lot of connectors with different source systems and efficient cataloging features.

There could be more integration similar to Snowflake, for instance. They do support integration, but complete lineage is not possible. Certain implementations, including lineage maps, could be user-friendly. At the moment, there needs to be more lineage between data sources. It needs improvements in terms of attribute connectivity during data transformation.

We have been using Collibra Governance for seven or eight years.

It is a stable platform.

Collibra Governance is suitable for enterprise businesses. It is a SaaS product. Thus, the end users don’t have to maintain scalability or maintenance.

The technical support team is responsive.

Positive

I have worked with Informatica Axon and Data Catalog. In comparison, Collibra has better workflow management features. I recommend it for technical usage as well. However, Axon is more user-friendly for creating lineage diagrams. It has ready-to-use templates for deployment.

The initial setup process is complicated. However, once you get good training and understanding of the system, it is very straightforward. Some of the configurations require support. It takes around a week or two to complete all the configuration for the existing ecosystem.

For Informatica, we have to buy cataloging and governance separately. Meanwhile, Collibra offers one structure for both. We purchase its annual license. There is an extra cost for additional users.

I rate Collibra Governance an eight out of ten.

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.

The tool's overall functionalities need to improve since, nowadays, many tools, from a business perspective, are easy to use.

I have experience with Collibra Catalog. I am an end-user of the tool. My company is a customer of the product.

Stability-wise, I rate the solution a seven out of ten.

Scalability-wise, it is a good solution. I rate the solution's scalability a seven and a half or eight out of ten.

Collibra Catalog is continuously being used in our company in as many ways as we can incorporate from various partners on the data elements and data landscaping, so we are slowly building it out.

Though unsure, I feel that the initial setup of Collibra Catalog is pretty complex over the system.

I think Collibra Catalog is one of the better products out there. The ability to PnP in the back-end and get all the updates is amazing in the solution. Collibra has its own rules engines and has incorporated other rules engines as well.

Collibra Catalog is simple to use and user-friendly for those who are not technically inclined since it is easy to find while also easy to see data lineage diagrams.

I rate the overall product an eight out of ten.