Collibra Platform Reviews

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.

We mostly use Collibra Governance for governance-related tasks like building business glossaries and creating policies.

Collibra Governance comes with a lot of features, and we have used it in one of our projects for metadata management and data lineage. I have used the solution to create governance rules and a centralized repository for business glossaries.

The solution's metadata management is pretty novice and could be improved.

I have been using Collibra Governance for around one year.

I rate Collibra Governance a nine out of ten for stability.

Collibra Governance is more suitable for medium-level and enterprise businesses.

I rate Collibra Governance a nine out of ten for scalability.

The solution's technical support was good, but there was a delay in their response time, possibly because of the time zones.

Positive

The solution’s initial setup is simple.

The solution's deployment will not take more than a week and can be done in five working days.

The solution's pricing is moderate and not very expensive.

Informatica is coming up with some level of data governance. erwin Data Modeler is quickly emerging as a competitor for Collibra in the coming days.

Collibra Governance is deployed on the cloud in our organization.

Overall, I rate Collibra Governance an eight 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.