Collibra Platform Reviews

We used Collibra Lineage mainly for end-to-end lineage. Additionally, we wanted to use it for business velocity data cataloging and data quality.

The most valuable features of Collibra Lineage are the multiple options for automation. Additionally, if you have a scenario where automation does not work, you have the option to manually create the lineage. The solution does not force you to always go through an automated way where you have broken lineage and you are stuck without a lineage.

Collibra Lineage could improve connectively. If they can offer more out-of-the-box connectors, which come as in a package compared to you having to go and buy a lot in the marketplace for certain specific connectors.

I have been using Collibra Lineage for approximately two years.

We have no major issues with the stability of Collibra Lineage.

The scalability of Collibra Lineage has been good. We have not seen any problems.

We have four clients that are using Collibra Lineage and a total of 25 users.

The technical support is good. There was not a lead time in terms of their response. They were quite responsive.

The initial setup of Collibra Lineage is straightforward. It is not complex.

We had a partnership with Collibra to do the implementation and we participated in the configuration.

There are different licenses available for Collibra Lineage based on what kind of access you want. If you want, for example, a read-only license they have different ones to pick from.

I would recommend this solution to others.

I rate Collibra Lineage 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.

We are focused primarily on the Data Governance Catalog (DGC) for our data dictionary use. We are not using it for information governance in terms of regulatory compliance, etc. We are focused on business glossary, data catalog, data dictionary, and some workflow processes to help with metadata management and other things.

We are fully updated, and we are using its latest version.

One of the workflows that we're just finishing and rolling out is a certification process. Collibra DGC has an attribute in there called "certified," and it is basically a yes/no choice. We wanted a lot more information about the certification of data sets, so I wrote this workflow, which is probably 30 or 40 steps long. We have a series of criteria that we have to meet in order for a data set to be considered certified. I was able to create attributes for all those and create a workflow that goes hand in hand with data set development so that when somebody competes their work, he or she can pop into Collibra and say, "I have finished this." It logs it and it becomes part of a trust score. It is really nice, and it is making our certification process more robust and the documentation easier to collect and maintain.

I like Collibra's flexibility. I like to be able to modify things for our own use. For example, we've chosen to use Collibra also as a knowledge management tool, even though it is not designed to be a knowledge management tool. That's the beauty of it. It can serve as a knowledge management tool by creating some custom assets specifically for knowledge management. I have a knowledge base domain and a knowledge base article asset type- along with a few other things. I really like the flexibility to be able to extend it in those non-traditional ways.

On the flexibility side, I've created some really nice and very useful custom workflows that have really helped with work processes and productivity. They've really helped us do things easier, faster, and better.

The UI is good if you happen to be an administrator and are familiar with the technical side of the administration. If you're a business user, the UI is not good. It is hard to learn. It is hard for those who are administering it to teach to end-users and it can take hours of training to do it. Because it is difficult and non-intuitive, business users resist using it. It is a battle to get them on board and to keep them engaged because of the UI. On the other hand, Collibra just hired a person specifically to revamp the UI. So, they're dealing with it, but it isn't there yet.

They're working on the lineage harvesting for technical lineages. I don't know this for a fact, but my feeling is that this is new to them. So, they're still developing it and it feels awkward.

I have been using this solution for eight months.

Its stability is very good. Out of 10, I'd probably give it a nine. We've had a couple of little glitches where something happened, but they were minor and we were able to create tickets and get issues resolved within a week and usually within just a day or so.

So far, we haven't had any major problems. We're ingesting metadata from AWS Redshift. We've also got connectors built for S3 but we haven't used them yet. We're importing from Oracle and working on lineage harvesting from DBT, Data Services, and few other places. So far, everything has been really good. We're importing metadata from Tableau, and there are no problems there, either.

In terms of the roles of its users, we've got product managers and solution managers. (A solution manager is the IT coordinator who works with the product manager.) We've also got analysts, engineers, reviewers, editors, data stewards, and data coordinators or custodians. We also have technical stewards, admins, and a group of people we call "normal" who are business users that have read-only access.

In terms of Collibra's usage, I would love to have it used 10 times more than it is, but because of the difficult UI, I am getting resistance from the users. It is hard for them to navigate and learn the interface. Once you know it, it is easy to get around and find what you need. It is just about learning the interface and dealing with some poor choices of how to use the screen real estate. So, right now, the user engagement is lower than I would like it to be. 

Their technical support is very good. On a scale of one to 10, I would give them a seven. We've probably had four or five tickets open and were able to get them resolved quickly. 

It feels like they may be sensitive--possibly they've had some bad reviews regarding their customer service--so it seems like they may be going overboard there. For example, they have scheduled a meeting every month for our team to meet with what they call a customer success manager.  I feel like this is too often.  They're responsive about stuff, but it feels like they're trying too hard.

Prior to this and strictly for our data dictionary, I used Dataedo. They are very good at what they do, but have a much more focused solution.

For knowledge management, I used Confluence and really liked it, but we came up against the issue of access levels for for non-registered users; we didn't want to pay license fees for a large number of "read-only" users. Basically, it is either full (paid) access or free access to "the world" and like everyone else, we have information that we don't want to expose to the world.

Setup was really straightforward. Collibra was very good with holding meetings and being responsive to requests. I was pleased with the setup and configuration process. They were really good at the initial stages of learning and understanding what we were doing.

Those of us who are working on this project also have "day jobs" so we were not focused solely on this. The focused time on it among members of the team was probably less than a hundred hours extended over a period of several weeks.

In terms of maintenance, we have a small, three-person team of people who are actually working on the technical maintenance side. All told, the actual time that we spend in administration at this point is very low--perhaps 20 hours a month spread between three people.

We did it in-house with direct interactions with the Collibra team. Our experience with their team was very good. They have an onboarding process and a roadmap they like to follow. We were just one department with a small team working on the actual setup and use case. Several of their steps didn't apply to our use case so we skipped these and even took some of the other steps out of order simply because it made sense for us. The onboarding team at Collibra was 100% supportive of making those changes and doing things the way we needed instead of the way they ordinarily would have done.

It is substantial, and we pay yearly. 

The thing that made our onboarding really simple was that we understood beforehand our use case and our needs. We understood what our roles were and who needed to be involved. We also understood our communities and our setup structure. We had all of that from the beginning. They mentioned several times that we were extremely well-prepared. So we learned that the more users know about the needs of the department or the company, and the more advance prep work they can do, the easier the onboarding process is.

When we started, the Collibra team sent us two huge Excel files that were basically questionnaires that they wanted us to go through to prepare for onboarding. We already had all of that done. It was finished but wasn't in the same format.  We wrote back and asked, "Do you really want us to fill out these Excel sheets? We've already gathered this information." They said, "Oh, great. We will skip the first two steps of onboarding because you've already got it." That really accelerated the process.

I'd give Collibra a nine out of 10. I am really happy with it though there are few things that I wish were different, especially in the UI.

The solution that I had worked on was related to the technical implementation of metadata for capturing analytics. That said, that particular implementation missed the bus with using it for business use and getting a proper buy-in from the users.

We learned some lessons on a past implementation, and with the new implementation, we're going beyond the data catalog and looking at the OwlIQ, data quality, as well as data lineage. We're really selecting some of the best modules within Collibra's toolset.

Right now, we're just implementing it. We're still in the purchasing process, however, having experience with it, I would say a great feature is its usability on the engagement with enterprise functionality. The crowdsourcing and just making it very accessible is foundational. At the end of the day, it always comes down to terms. For example, we might be saying the same exact term, however, have completely different meanings. It brings to light just the nuances, especially within a larger enterprise organization, a global organization. In organizations of this size, we've realized just how different our terminology views are. It sheds a good light on this and helps clarify. 

The data lineage piece is very useful for us. The ability to understand data flows, where systems and changes originate, is great. A lot of time you might have something on paper that isn't necessarily working in real life. This product brings about the right visibility to have the right conversations between business and IT.

There is a good community setup around the solution that can provide insights. 

It's not necessarily a tool specific, however, with any sort of application, there's an investment as far as the way in which you need to use it. There is a lot of upfront work that has to be considered. That's just a common reality with any software implementation. There's a lot of pre-work. You just don't turn on the lights assume it's going to work exactly as you envisioned. There is input and planning required.

If anything, I would say that the licensing is one area that could get improved. We have basically three roles: an admin, an editor, and a view-only role. It is limiting. For example, we want view-only, however, if we want users to be able to approve workflows, they need editor rights. That makes sense, except it doesn't necessarily meet all the business cases we have. In some instances, you might just need proper approvals, and you are not necessarily asking anyone to edit things. Yet in order for them to approve, they must have edit rights.

The last implementation was very much focused more on IT and capturing more of the IT view of data and even data definitions really focused on data standards, such as how we're going to name the technical fields or how we're going to name the entities. This new deployment is really much more focused on not just the IT side but on the business side and the operational side. It's based more so around analytics and operational governance. I'm hoping to use more of the modules and have a better, more favorable opinion of the solution's capabilities. While overall I have the sense it's good, the last company I was with didn't have the right business partners and it really just became another IT tool, which wasn't helpful to the company as a whole.

The initial setup requires more of a trial and error approach and there isn't too much documentation available to help you figure things out. There needs to be more online support around the sharing of best practices. There are a lot of use cases and people like the tool. That said, you hear a lot of pain points around large amounts of data being ingested and creating backlogs of data that need to be cataloged and there's really no way to prioritize it. 

Ultimately, it's a tool that should help to coordinate a lot of efforts and it would be nice to be able to look at something and understand how another experience could be similar or you can get a lesson learned before you actually make it your own lesson to learn.

This is more of a data governance tool, not necessarily a centralized tool for data cleansing. However, with the data quality module, that's the next evolution that's possible. Looking at data quality issues and then ultimately not necessarily being able to correct them, there's a lost opportunity. Data changes all the time. We're measuring it all the time. It would be advantageous to build this into more of a data quality tool in which users could cleanse data that could go back to source systems. That said, that's encroaching on more of the MDM solution.

I've been using the solution for about two years or so at this point. 

It's largely a good, stable solution. This is not an MDM solution. From a governance standpoint, there are some things that Collibra does better than some of its competitors, however, there's always something about having multiple tools and getting users to accept the multiple tools. It would be great if they could partner with an MDM solution provider to give more of a seamless look and feel.

In the last implementation, I do not recall dealing with bugs or glitches. In this new implementation, it's still too early to tell. 

The scalability potential is all around the framework that's specific to the company. It'd be good to have some general best practices from Collibra's standpoint. That said, is scalable, however, first and foremost, you need to implement it and really look at how the tool is functioning out of the box before you put your own strategy on it. 

Many times though, projects as they go, you're really not afforded that freedom. You might have a specific use case and you're trying to get that implemented so you'll get a quick win from a governance standpoint and so you can continue to incrementally add value. It's a balance due to the fact that, as we're trying to provide a solution, governance is an investment for sure. While there's certainly scalability potential there are structures that need to be in place from a foundational standpoint for it to scale as you need it to.

In the last implementation, there were about 20 users on the product. In that case, it was not that extensively used. Doing a data warehouse migration from Cloudera to Azure, things were collected, however, what was missing was the business definitions and the scenario-based understanding. Due to the implementation the last time, it offered a very flat view of the data. You didn't understand how everything was related or how things were scenario-based, et cetera. You couldn't get a sense of how fields are ultimately connected, and the KPIs that they ultimately built didn't help with understanding. The intention was that it was going to be an enterprise data catalog and it missed that chance. 

I haven't been in touch with technical support. I can't speak to how helpful or responsive they are. 

The implementation is not that easy. All the sell sheets and everything makes it seem as though it's more structured. Here you have this catalog, however, in reality, you have to define the structure including the data that you're going to be collecting, how you're going to define it, what those workflows are, what the user groups are, et cetera. There's a whole change management initiative even beyond just turning it on. 

With any application, whether it's cloud-based, but especially if it's on-prem, there is a level of pre-work that needs to be done. It's not just a turn-it-on type of event. Overall, that's sometimes lost in the process.

Getting it installed and all that is pretty straightforward if you can get a system integrator, or maybe if you have the in-house knowledge, however, it's really the strategy that's behind it that makes for an easy or difficult rollout.

The community is pretty good, however, I haven't necessarily found anything that's like user groups that can help guide implementation. A lot of it is you make a mistake and you have to go back and try to remedy it. There is a lot of trial and error involved.

We handled the entire implementation ourselves, in-house. 

While I do not have a sense of the version number, I would say that we are not on the latest version of the solution at this time. 

I would advise new users looking at getting it implemented to really use the out-of-box features before you overlay your specific strategy on it. Upfront investigations and creating a repeatable framework of how this will ultimately operate are important to the success of the solution. One of the crucial early factors is to get this as part of an operating fabric within your company. There's a lot of pre-work and pre-thought that needs to be in place in that sense. Having well-engaged business folks as part of it will help with the level of success as well. This is not necessarily a big bang type of development and release. It's very incremental. You've got to think backward as far as the user experience - of how things are going to be searched and located - and bring that back to your IT process.

I'd rate the solution at a seven 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.

Collibra Governance is a stable solution.

The solution's data lineage is a little difficult and will not support all the source systems on the database.

I have been working with Collibra Governance since last year.

Collibra Governance is a scalable solution.

Collibra Governance's documentation is clear. We can get all the documents from Collibra University.

Collibra Governance's initial setup is complex.

It takes more than 15 to 20 days to deploy Collibra Governance.

Collibra Governance is an expensive solution.

Collibra Governance is a good tool that has some pros and cons.

Overall, I rate Collibra Governance a seven out of ten.

We use the solution to organize data. The IT team has access to the data and knows where it is stored, but when it comes to legal compliances and various regulations, the business itself does not have control over where the data is stored and how it is stored. Typically, the business wants to have control over where the data is stored, the various data classifications, and data sensitivity. That is the main reason why we use Collibra Governance.

I appreciate the range of features it has. Based on the reviews of some of the other tools we use, we find that the other tools lack in certain areas. If I had to choose a comprehensive tool across all the fields of data governance, I would opt for Collibra Governance.

The most valuable feature is the Technical Lineage. The fact that you can trace the auditability of a particular field or attribute across various systems is very useful.

There are a few things that need improvement. From a usability perspective, customers usually find some areas of the solution a bit complex. It takes a long time for the customers to get used to the UI and the interface. Another thing that the customers complain about when they opt for Collibra is the pricing. There are fees for additional features which mess with the customers' overall budgets.

I've been using Collibra Governance for about two and a half years.

It is a very stable solution. I would give it a nine out of ten. It works very well compared to other solutions on the market.

The scalability of the solution is good. I give it an eight out of ten.

The response time of technical support is typically high.

Neutral

Nowadays, most customers are looking into Microsoft Stack, so we use Microsoft Purview as well, although Calibra Governance is our preferred choice since it's much more stable than Purview.

Up until now, Collibra Governance has been the market leader and is always the first one to add new features. Purview tries to bring out a lot of features as fast as possible, but those features are not very stable and have a lot of bugs. Purview has the upper hand when it comes to the usability of the app system. It is easier for customers to understand the feature list while using Purview. For example, after using both solutions for a few weeks, they usually have a better grasp of Purview than Callibra Governance.

Since it is a SaaS platform in a way, the solution is deployed on Collibra cloud. The deployment is pretty straightforward since Collibra deploys it on its own. It takes about a week to get the core of the system up and running. After, we configure it to fit the specific needs.

On a scale from one to ten, the simplicity of the setup would be an eight.

Collibra Governance's pricing is based on its features. You get what you pay for. There are other solutions that are cost-effective but lower quality. With Collibra, you get a fair-value product if you pay a bit more.

I would not recommend using the Big Bang approach with Collibra Governance. You should understand the bigger picture of what you want to do. Start off with a few systems and four small modules. Get feedback from the business and then continuously evolve it. It is better to have the implementation spread out over a one-year period than have it crammed down within three to six months while trying to implement everything at once.

I would say that Collibra Governance is suitable for small to medium businesses. 

Overall, I rate the solution a nine out of ten.

I primarily use Collibra Catalog for data management and governance. It lets you build data catalogs, making sure you've got the right definitions for business terms so that everyone's aligned. It also ensures that the correct roles and responsibilities are assigned and the correct data stewards are assigned to the correct data set.

Collibra Catalog's best feature is the data quality checker. It has a functionality that displays a tick when the data set is complete and the data is working and matching properly. This is a good way to validate if the data quality is sufficient.

Ai is a critical thing in today's world, so Collibra Catalog could improve its automation to increase the efficiency of the software.

I've been using Collibra Catalog for four to five months.

I would rate Collibra Catalog's stability eight out of ten.

I would rate Collibra Catalog's scalability eight out of ten.

I would rate the ease of the initial setup seven out of ten. Deployment takes two to three days.

Our ROI from Collibra Catalog is about 15%.

Collibra Catalog is fairly priced - I would rate their pricing seven out of ten.

I would recommend Collibra Catalog as one of the best solutions for data governance. I'd give Collibra Catalog a rating of eight out of ten.

We use this solution for workflows and for specific use cases. For our retail and insurance clients, we meet governance requirements by using all the features that Collibra provides. We do most of the work using workflows and connectors. We build connectors where the data will be extracted from a third party tool and  input in Collibra.

We have 40 to 50 team members using this solution. We have three admins who manage this solution. 

Workflow is very helpful and assessment are good features. I really like the structure of this solution and how it is compartmentalized.

There are certain processes that involve a lot of manual work, specifically when searching for attribute assignments. It would be better if we could do this via APIs. 

In a future release, we would like to have rules. We would like to have Regex Expression that scans data and to monitor quality. 

I have been using this solution for four and a half years. 

This is a stable solution. 

This is a scalable solution. 

We do get most of our support questions answered precisely, but not all of them. We have needed to do some online learning. 

We have previously used Informatica and Alation. 

Collibra has a better workflow while Informatica does not offer automation. We rely on the automation that Collibra offers.

In terms of governance, I prefer Informatica because it offers different tools and data. 

The initial setup is straightforward. 

I would advise others to have a meta model before they get into Collibra so that they can use the features that Collibra provides. They should have a proper architecture design ready before starting implementation. 

I would rate this solution an eight out of ten. 

I have been working on Collibra for a data governance assignment. Along with that, we also use Ataccama for data quality purposes. It is a Collibra/Ataccama mixture that we use.

We would like to have out-of-the-box automation. We have leveraged some of the out-of-the-box workflows. Collibra does have the workflows such as the creation of business assets. We designed tools to tag the sensitivity information in Collibra and embedded scripts into the workflow. We then deployed that workflow so the user can upload a sheet with metadata and the tags will get tagged across those columns.

Collibra's lineage is a very powerful feature. We have integrated Collibra with Azure and ADLS storage and we create lineage diagrams using the two tools.

This solution does not have an out-of-the-box connector for the cloud platform. If a client does not want their data sent out of their own server, it will be very difficult to ensure this. You can install Collibra Jobserver and DGC on your machine, but you cannot install a newer solution. This is a restriction for us. 

We have been using this solution for one and a half years.

This is a stable solution. If you have data sources for which Collibra can provide an out-of-the-box connector, then it is stable. You can schedule your job to pull metadata or data out of your source system, with Collibra as a connector. However, if you have something out-of-the-box which Collibra does not support as a connector, that is a challenge. 

If you have Teradata or Oracle as a system, you can build that lineage into thw Collibra platform but if you want to build something on Azure, there is no direct way to do this because they don't support that.

This is a scalable solution. 

Collibra are very supportive. If you need anything, they have a customer support portal where you can raise a ticket and they reply immediately. 

Positive

Collibra does have its own university so when we started this assignment, we went through the courses. After that, it was quite easy to complete the setup. 

This is a very good tool and they have upgraded many features including an ADF connector and ADLS connector. You can write your own connector and customize it in the workflow.

I would rate this solution a six out of ten.