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Buyer's Guide
Data Governance
September 2022
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Manoj Narayanan - PeerSpot reviewer
Practice Director - Digital & Analytics Practice at HCL Technologies
Real User
Top 5Leaderboard
Metadata harvesters, data catalogs, and business glossaries help standardize data and create transparency
Pros and Cons
  • "erwin has tremendous capabilities to map right from the business technologies to the endpoint, such as physical entities and physical attributes, from a lineage standpoint."
  • "Another area where it can improve is by having BB-Graph-type databases where relationship discovery and relationship identification are much easier."

What is our primary use case?

Our clients use it to understand where data resides, for data cataloging purposes. It is also used for metadata harvesting, for reverse engineering, and for scripting to build logic and to model data jobs. It's used in multiple ways and to solve different types of problems.

How has it helped my organization?

Companies will say that data is their most valuable asset. If you, personally, have an expensive car or a villa, those are valued assets and you make sure that the car is taken for service on a regular basis and that the house is painted on a regular basis. When it comes to data, although people agree that it is one of the most valued assets, the way it is managed in many organizations is that people still use Excel sheets and manual methods. In this era, where data is growing humongously on a day-to-day basis—especially data that is outside the enterprise, through social media—you need a mechanism and process to handle it. That mechanism and process should be amply supported with the proper technology platform. And that's the type of technology platform provided by erwin, one that stitches data catalogs together with business glossaries and provides intelligent connectors and metadata harvesters. Gone are the days where you can use Excel sheets to manage your organization. erwin steps up and changes the game to manage your most valued asset in the best way possible.

The solution allows you to automate critical areas of your data governance and data management infrastructure. Manual methods for managing data are no longer practical. Rather than that, automation is really important. Using this solution, you can very easily search for something and very easily collaborate with others, whether it's asking questions, creating a change request, or creating a workflow process. All of these aspects are really important. With this kind of solution, all the actions that you've taken, and the responses, are in one place. It's no longer manual work. It reduces the complexity a lot, improves efficiency a lot, and time management is much easier. Everything is in a single place and everybody has an idea of what is happening, rather than one-on-one emails or somebody having an Excel sheet on their desktop.

The solution also affects the transparency and accuracy of data movement and data integration. If people are using Excel sheets, there is my version of truth versus your version of truth. There's no source of truth. There's no way an enterprise can benefit from that kind of situation. Bringing in standardization across the organization happens only through tools like metadata harvesters, data catalogs, business glossaries, and stewardship tools. This is what helps bring transparency.

The AIMatch feature, to automatically discover and suggest relationships and associations between business terms and physical metadata, is another very important aspect because automation is at the heart of today's technology. Everything is planned at scale. Enterprises have many data users, and the number of data users has increased tremendously in the last four or five years, along with the amount of data. Applications, data assets, databases, and integration technologies have all evolved a lot in the last few years. Going at scale is really important and automation is the only way to do so. You can't do it working manually.

erwin DI’s data cataloging, data literacy, and automation have reduced a lot of complexities by bringing all the assets together and making sense out of them. It has improved the collaboration between stakeholders a lot. Previously, IT and business were separate things. This has brought everybody together. IT and business understand the need for maintaining data and having ownership for that data. Becoming a data-literate organization, with proper mechanisms and processes and tools to manage the most valued assets, has definitely increased business in terms of revenues, customer service, and customer satisfaction. All these areas have improved a lot because there are owners and stewards from business as well as IT. There are processes and tools to support them. The solution has helped our clients a lot in terms of overall data management and driving value from data.

What is most valuable?

  • Metadata harvesting
  • business glossaries and data catalogs

In an enterprise there will already have been a lot of investment in technology over the last one or two decades. It's not practical for an organization to scrap what they have built over that time and embrace new technology. It's important for us to ensure that whatever investments have been made can be used. erwin's metadata managers, metadata hooks, and its reverse engineering capabilities, ensure that the existing implementation and technology investments are not scrapped, while maximizing the leveraging of these tools. These are unique features which the competition is lacking, though many of them are catching up. erwin is one of the top providers in those areas. Customers are interested because it's not a scrap-and-rebuild, rather it's a build on to what they already have.

I would rate the solution’s integrated data catalog and data literacy, when it comes to mapping, profiling, and automated lineage analysis at eight out of 10. erwin has tremendous capabilities to map right from the business technologies to the endpoint, such as physical entities and physical attributes, from a lineage standpoint. Metadata harvesting is also an important aspect for automating the whole thing. And cataloging and business glossaries cannot work on their own. They need to go hand-in-glove when it comes to actual data analysis. You need to be able to search and find out what data resides where. It is a very well-stitched, integrated solution.

In terms of the Smart Data Connectors, automating metadata for reverse engineering or forward engineering is a great capability that erwin provides. Keeping technology investments intact is something which is very comforting for our clients and these capabilities help a client build on, rather than rebuild. That is one of the top reasons I go for erwin, compared to the competition.

What needs improvement?

I would like to see a lot more AI infusion into all the various areas of the solution. 

Another area where it can improve is by having BB-Graph-type databases where relationship discovery and relationship identification are much easier. 

Overall, automation for associating business terms to data items, and having automatic relationship discovery, can be improved in the upcoming releases. But I'm sure that erwin is innovating a lot.

For how long have I used the solution?

We have been implementing erwin Data Intelligence for Data Governance since the 2017-2018 time frame. We don't use it in our company, but we have to build capabilities in the tool as well as learn how best to implement the tool, service the tool, etc. We understand the full potential of the tool. We recommend the tool to our customers during RFPs. Then we help them use the product.

HCL Technologies is one of the top three ID service organizations in India, with around 150,000 employees. We have a practice specifically for data and analytics and within that we cover data governance, data modeling, and data integration. I lead the data management practice including glossary, business lineage, and metadata integration. I have used all of that. 

We are Alliance partners with Erwin and have partnered with them for three or four years.

We serve many clients and we have a fortnightly catch up with erwin Alliance people. We have implemented it in different ways for our customers.

What do I think about the stability of the solution?

It is stable. 

What do I think about the scalability of the solution?

It can scale to large numbers of people and processes. It can connect to multiple sources of data within an organization to harvest metadata. It can connect to multiple data assets to bring the metadata into the solution. From a performance standpoint, a scaling standpoint, we've not seen an issue.

How are customer service and technical support?

We are Alliance partners, so whenever we go to clients and there are specific instances where we lack thorough knowledge of the erwin tools, we touch base with erwin's product team. We have worked together to tweak the product or to give our clients a seamless experience. 

We have also had their Alliance team give our developer community sessions on erwin DI, usages, and PoCs. We've done collaborated multiple times with erwin's product presales community.

How was the initial setup?

It's really straightforward. There are user-friendly tools so that a business user can very quickly access the tools. It's easy to create terminologies and give definitions. Even for an IT person, you don't need to be an architect to really understand how data catalogs work or how mapping can be created between data elements. They are all UI-driven so it's very easy to deploy or to create an overall data ecosystem.

The time it takes to deploy depends. Product deployment may not take a lot of time, between a couple of days and a week. I have not done it for an enterprise, but I'm assuming that it wouldn't be too much of a task to deploy erwin in an organization.

The important aspect is to bring in the data literacy and increase use throughout the organization to start seeing the benefit. People may not move from their comfort zone so easily. That would be the part that can take time. And that is where a partner like us, one that can bring change management into the organization and hand-hold the organization to start using this, can help them understand the benefits. It is not that the CEO or CTO of the organization must understand the benefits and decide to go for it, but all the people—senior management, mid-management, and below—should buy into the idea. They only buy into the idea if they see the benefit from it, and for that, they need to start using the product. That is what takes time.

Our deployment plan is similar across organizations, but building the catalog and building the glossaries would depend on the organization. Some organizations have a very strong top-down push and the strategy can be applied in a top-down approach. But in some cases, we may still need to get the buy-in. In those cases we would have to start small, with a bottom-up approach, and slowly encourage people to use it and scale it to the enterprise. From a tool-implementation standpoint, it might be all the same, but scaling the tool across the organization may need different strategies.

In our organization, there are 400 to 500 people, specifically on the data management side, who work for multiple clients of ours. They are developers, leads, and architects, at different levels. The developers and the leads look at the deployment and actual business glossary and data catalog creation using the tool for metadata harvesting, forward engineering, and reverse engineering. The architects generally connect with the business and IT stakeholders to help them understand how to go about things. They create business glossaries and business processes on paper and those are used as the design for the data leads who then use the tool to create them.

What was our ROI?

We struggle when it comes to ROI because data governance and data management are parts of an enterprise strategy, as opposed to a specific, pinpointed problem. An organization might be able to use the overall data management strategy for multiple things, whether it's customer satisfaction, customer churn, targeted marketing, or improving the bottom line. When we clean the data and bring some method to the madness, it creates a base and, from there, an organization can really start reaping the benefits.

They can apply analytics to the clean data and have right ownership of the data. The overall process is important as it is the base for an organization to start asking: "Now that I have the right data and it is quality compliant, what can I deduce from the data?" There may not be a dollar value to that straight away, but if you really want to bring in dollar value from your data, you need to have the base set properly. Otherwise it is garbage in, garbage out. Organizations understand that, even though there is no specific increase in sales or bottom-line improvement. Even if that dollar value is not apparent to the customer, they understand that this process is important for them to get to that stage. That is where the return on investment comes in.

What's my experience with pricing, setup cost, and licensing?

The solution is aggressively priced. We can compete with most of them. 

It is up to erwin and its pricing strategy, but if the Smart Connectors—at least a few of them which are really important—can be embedded into the product, that would be great. 

But overall, I feel the pricing is correct right now.

Which other solutions did I evaluate?

There are a number of competitors including Informatica, IBM, Collibra, Alation; multiple organizations that offer similar features. But Erwin has an edge on metadata harvesting.

What other advice do I have?

It is a different experience. Collaboration and communication are very important when you want to harvest the value from the humongous amount of data that you have in your organization. All these aspects are soft aspects, but are very important when it comes to getting value from data.

Data pipelines are really important because of the kinds of data that are spread across different formats, in differing granularity. You need to have a pipeline which removes all the complexities and connects many types of sources, to bring data into any type of target. Irrespective of the kind of technology you use, your data platform should be adaptive enough to bring data in from any types of sources, at any intervals, in real-time. It should handle any volume of data, structured and unstructured. That kind of pipeline is very important for any analysis, because you need to bring in data from all types of sources. Only then you can do a proper analysis of data. A data pipeline is the heart of the analysis.

Overall, erwin DI is not so costly and it brings a lot of unique features, like metadata hooks and metadata harvesters, along with the business glossaries, business to business mapping, and technology mapping. The product has so many nice features. For an organization that wants to realize value from the potential of its data, it is best to go with erwin and start the journey.

Disclosure: My company has a business relationship with this vendor other than being a customer: Alliance Partner
Solution Architect at a financial services firm with 10,001+ employees
Real User
Top 10
Solid multi-ingestion tool but with poor exception handling
Pros and Cons
  • "You can create a lot of ingestions based on the file levels or based on the time."
  • "The major pain point with Zaloni is that their exception handling is not good. If any event happens, it doesn't tell you at which point it failed and it doesn't tell the operations team how they should take corrective actions unless you call Zaloni and then identify the issues. That is one issue."

What is our primary use case?

Zaloni is actually a big data platform management tool. It is extensively used. They have different connectors. You can start ingesting your batch and you can also use real-time streaming, but we haven't used that component. We have used it mostly for batch ingestions.

It's not a single product. It has multiple pieces within itself. I think most of them are plug-and-play.

We are using the enterprise version of Zaloni.

How has it helped my organization?

The benefits of Zaloni are that it is readily deployable, so you will have the solution within the tool itself. It's a matter of how you need to integrate and how you can establish your connectors because it has a lot of connectors built-in. Then you run your data pipelines in that span of time.

It's commercial, off the shelf, with minimal configuration, and within three months you can go to the production if you are handling small-scale of data. But if it's huge figures, then it takes more time.

Another good advantage with Zaloni is that it has a very good schema evolution process but the process is very tedious to run on the tool. But once you go through that difficult phase, then it has a very good building. For example, if you want to ingest all the data, it has the ability to automatically find out the right schema at that time and place, and then it picks up that schema and runs the data accordingly. The historical data integration is another very good feature. You can ingest data whenever you want from the historical data.

What is most valuable?

In terms of most valuable features, Zaloni has different components. One is its ingestion aspect where you can create a lot of ingestions based on the file levels or based on the time. You have listeners actually continuously listening on certain poles, and as the data arrives it will start picking up. That is the one use-case that we have used.

Another one is batch ingestion where you can set up your timer. At the set time, it will check whether the file is present, then it will start the data pipeline and pick up the jobs to trigger.

Another feature is that it is time-based. That means the advantage with Zaloni is you can have both set up - either on data arrival or schedule-based, or you can have both, mix and match.

Once the ingestion starts, it has the connectivity to the rest of the big data components. You can store the data on S3 or wherever you want. It has different connectors to connect to on-premise and cloud. We were using on AWS, so our primary storage was the S3 buckets for the information. It uses the Hive as well. Without big data components, it cannot work on its own. You need to have big data installed fast, and Zaloni works on top of it.

It has Bedrock architecture which is a component that actually manages these schedules and all other activities. Bedrock is another tool within Zaloni itself.

There is one more component called Metadata Management. It's called EMDM I guess, but I don't remember exactly what the component is called. It allows you to manage your metadata management. It's kind of a business metadata. You can go ahead and write whatever fields that describe the data, then it manages them.

What needs improvement?

The major pain point with Zaloni is that their exception handling is not good. If any event happens (an event is when the job stops in the middle of the process), it doesn't tell you at which point it failed and it doesn't tell the operations team how they should take corrective actions unless you call Zaloni and then identify the issues. That is one issue.

Another issue is that sometimes your jobs fail and if you run it a second time, it will go through.

A third area it could be improved is the deployment process. When you want to deploy anything, it has a lot of manual processes. For example, you have to create your password in an encrypted format, and then you have to use a lot of manual deployment process. They should actually be building something else, like using Jenkins or automating their process. I have suggested to them that they have to improve their deployment process because they want everyone to run a manual deployment. It takes a lot of time, about half a day, for any single deployment. Then test it, then it might not work, and then reverting back is not easy because of the manual deployment process.

I think in recent versions they added a lot of upgrades and additional features including a lot of integrations. Before it was just AWS, later they extended it to Azure. I'm not sure how they have extended it to GCP.

It has some built-in features and a lot of improvements now because the UI and the features were not easy to navigate. Regarding showing the metrics, it's okay. I will say it's neither easy nor hard, there is whatever is required.

Lastly, on the governance side, it's not very good. We faced some issues with the Ranger version. Ranger was an authentication tool on big data and Zaloni had some compatibility issues with the Ranger at that time. Later they said those are all going to be resolved, but at that time it had some issues. I'm not sure whether it was working with the Sentry or not.

For how long have I used the solution?

I have been using Zaloni Data Platform within the last 18 months.

What do I think about the stability of the solution?

Stability-wise, it's good. I don't see many issues, except one thing - once a week from a job user to event. But after that, if you resubmit your job, then it goes through. So I don't see an issue once you establish it. It's a good product to continue with.

So when you say maintenance it means daily operations is the one thing that I can look at. The other one is version upgrades and upgrading the security patches. Because of the employers, we relied on the Zaloni team for any maintenance activities, like version updates.

Our team needs to handle daily operations. I was one of the team managing daily operations like running and making sure the cluster is up, jobs are running perfectly, and the data is updated for the next business day and available for business users.

What do I think about the scalability of the solution?

In terms of scalability, we were the first one to implement it with the scalability. We tried and tested the scaling with the AWS. It has good scalability. When it comes to auto-scaling Zaloni the only thing is the underlying cluster should have the scalability.

We implemented it in AWS, and once we defined the threshold, I think we were able to run on five different instances. It is auto-scaling enabled on the AWS server.

That was the platform with Zaloni itself. Initially we implemented a big data solution, massive data, almost one petabyte of data. The entire need was to ingest it into big data and Zaloni was the fastest product to test and implement into production.

But before that, there were some attempts to try to build their own clusters without any tools or anything, but those were not successful. Then they had to go and buy Zaloni and then implement the solution.

Because of the complexity involved, my employer was trying to switch to other platforms because they wanted to try different tools rather than sticking to Zaloni because of its difficulty in managing and the version upgrades, because every time you need to have somebody from Zaloni look into the issues.

They were identifying different tools and experimenting with it.

How are customer service and technical support?

Their support is very good because it has a dedicated support team for our employer. They were able to respond. If you call anytime, 24/7, people are available and they made sure that things are taken care.

They are quite responsive in that.

Which solution did I use previously and why did I switch?

I had experience with similar solutions, but Zaloni is something different. They market it as a big data management tool but it's not really because it handles only part of all the data.

If you take the example of Cloudera, it makes your job easier to manage your clusters because it has a lot of built-in features and a lot of UI features so you can add a node at any time. You can decommission the node, you can balance the cluster. But Zaloni doesn't have many of those capabilities. It's more like you can look at it as an ingestion tool. Actually, a little superior to ingestion because it has some metadata management and you don't have to procure another license for that.

On the governance side it doesn't help, but with additional features you can manage your data confidentiality, with data plug-and-play solutions. You can encrypt all the data or you can encrypt only certain fields in the database. You can do it while ingesting or you can encrypt once it is ingested. It has the ability to encrypt the data throughout your pipeline.

How was the initial setup?

In terms of initial setup you need to actually get Zaloni support to do that. We had evaluated the different tools like Talon and other ingestion tools. Even AWS has one of the tools, I forgot its name. We evaluated how we can use these tools to simplify this process.

Whatever workflows you're creating you'll have to create on Zaloni only. So if you create outside of the Zaloni, it doesn't know anything about that so you have to have a scheduler built-in to the platform itself. It has the ability to integrate your data pipelines, then you can deploy the code within that. You have the ability to manage your metadata.

What was our ROI?

I would say there was a return on investment. I would say within two years, but it depends how well you can sell your data. It depends on how organizations look at it. It changes from everyone's perspective.

How is your organization really interested in selling the data and making money by exposing the data to the APS? Then definitely you can get the revenue much faster.

But in my organization, that's not the model, because the model is that they wanted to give data to business users so that they can play around with the data with less effort.

What's my experience with pricing, setup cost, and licensing?

I don't know how the licensing works, to be honest, but it's quite expensive. We paid around 150 to 200 grand, per yearly basis. There is no special pricing charges. It's just licensing regardless of the number of users and regardless of how much data you're processing. They don't have all the different licensing structures.

Which other solutions did I evaluate?

We are thinking about Talon, which is a similar product. There are a lot of different products that can be used. In a different organization, Apache can also be used. That is an open-source. And if you are really rich, then you can go Pentaho Data Integration, a similar tool, that is much easier. Informatica big data component is another one.

What other advice do I have?

I would recommend Zaloni. But before choosing they should evaluate different options so they know which one is better. Even though we have similar products, some products are good in one aspect, some are not good in others.

Take the example of Informatica. Informatica is very good for database warehousing platforms. But when it comes to big data and when you have to input the data into big data then it has a lot of difficulties. Then I had to use another component that was better in handling the data.

So it's based on the need. What is your objective and where are you pulling out the data? If it's just simple, you're pulling the data from only RDBMS then you can rely on Zaloni or in any other product and then you can use it right away, out of box. You don't need any other tools as such. But maybe you are specialized in one them and you have a lot of restrictions, like in financial institutions, when you're not allowed to get the data from any data source. What they do is offload the data from the database and then they put it in some server. Then you can access the data from that server. There are lots of layers for building it to manage the security. If that is the case here, then you have to look at which one suits you best.

There are areas of improvement. One is especially the manual deployment process that was in place. That's one of the biggest challenges. Also when you're creating the entities it could have been done much more easily than it was.

On a scale of 1 to 10, 1 being the worst and 10 being the best, I would rate Zaloni a seven.

Which deployment model are you using for this solution?

Public Cloud
Disclosure: My company has a business relationship with this vendor other than being a customer:
SuChatla - PeerSpot reviewer
Sr Manager - Enterprise Data Office at a healthcare company with 10,001+ employees
Real User
Top 20
Shows the whole history of data elements, though connectors could be improved
Pros and Cons
  • "I like the lineage feature the most because I don't think there's any other tool that actually depicts the data flow from multiple sources and the connectivities between every data element inside those sources."
  • "The connectors are not very sophisticated. They can do, for example, Informatica and Tableau, but the connectors themselves could be improved."

What is our primary use case?

I've been working with multiple companies, but with two of the companies we have been using Collibra mostly for data governance. With these companies, our use case is all about metadata governance, lineage, and data-related policy management. We're doing policy management directly inside Collibra and we're also using it for issue management on the analytics side.

If someone has a data concern, they just call me in and then put that concern into Collibra as a front-end UI for the data stewards and data scientists, and we start processing them.

How has it helped my organization?

We have benefited greatly from Collibra's data governance reporting. If we want to know more about a specific data element, we can use Collibra to get a picture of the whole history of it.

For example, who is the business owner for it? Where is the data coming from (especially when you have different sources which come through) and who was all touching it? And if I wanted to add a rule, like a business rule or a data quality rule for that particular data element, how or where do I keep it? It's like one central place, but for all these items.

What is most valuable?

I like the lineage feature the most because I don't think there's any other tool that actually depicts the data flow from multiple sources and the connectivities between every data element inside those sources.

I don't think there's any other solution where you can view multiple systems and multiple sources and data places and you can just write it down. It's a lot of work to initially organize but there's no other tools to do lineage like Collibra does it.

What needs improvement?

The connectors are not very sophisticated. They can do, for example, Informatica and Tableau, but the connectors themselves could be improved.

I recently got a subscription for another 600K for Collibra for one more year, so the author licenses are not used much. And they keep changing the UI platform; that can also be improved.

From an administration perspective, I like the white-glove onboarding part of Collibra. That was actually nice and I really liked that. For administration in general, I like that you can use Collibra however you want. It's more raw and easily adaptable.

So you can cook it or you can steam it or you can make changes to it in a lot of different ways, but it would also be nice if there were an already available analytics tools like Tableau at hand. Though it is easily adaptable and you'll have a completed end product which you can really leverage.

For how long have I used the solution?

I've been using Collibra Governance for five or six years.

What do I think about the scalability of the solution?

In terms of scalability, it's more like adopting; it's more like a shark. You have to keep feeding it and then it will grow. It depends on how many systems you're using. I worked for a union bank earlier when we set up Collibra and we were able to push in 3000, 30,000, 30,000 data elements. It's great when all the data is available because the team had been doing data analysis for more than a year prior to getting onto Collibra.

At my current company, the data analysis started at the same time along with the data governance and I think I hardly have 300 data elements. So it works on however much you feed it.

And if you have a huge data dictionary and business glossary already available, well and good. Instead of putting it in an Excel sheet, you can put it on Collibra and then you can actually walk through it. But if not, then you have to start feeding it, and it might take at least two years until you get proper food for the tool.

How are customer service and technical support?

The technical support is okay, definitely not bad. I think they have a 24 hour SLA, but again, it's a data governance tool, so if it breaks and it's not available for a day or so, it's not going to create any business loss. It's more of an understanding kind of tool, and if the SLA is a bit delayed it won't be much of a problem.

The only comment I have is that some of the technical support teams in privacy, security, infrastructure, etc., could be more available during US timezones. That would have made our onboarding process easier.

How was the initial setup?

We went through setup with the white-glove onboarding program. I actually gave feedback to Collibra as well, because the process is a little unusual, but I appreciate it.

The one thing I found a bit difficult when properly onboarding with Collibra and setting it up is that some of the Collibra teams we're working with, like in the security, privacy, and infrastructure teams, are in the European timezone and not the US timezone. Because of this, it becomes a little uncomfortable. It would be great if they could change things around so that there's also somebody available in the US.

It's not just one single technical support team when you are setting up Collibra; you have a lot of different puzzle pieces to work with. That's what the white-glove onboarding is all about. So it actually takes five to six weeks to completely set up, from starting with the solution to getting the software installed and all the nodes set up.

Whether it's on-premises or online, in both cases the whole setup takes five to six weeks and in this time frame I also need to have the company-related IT support people available. And it's just hard for me because most of Collibra's support teams are on Europe time. It could even take up to eight weeks.

What about the implementation team?

Regarding implementation, we need to have the role-setting, we need to have the workspace in the UI in the front end, we need to build the communities, the groups, etc. So it's more like a whole structure that you have to build, and it's a lot of work.

It's more raw, so you can change it however you want. But the thing is, there's not much of a guideline and it depends on your company and organization as well. So you have to ask, how do you want to do the structure? Then you first have to find the communities, and you'll have to set up the groups and the UI, and what comes back, and it's just more about adopting the software to your needs.

Our data officer was very interested in doing it. So she's fully on. And we had an administrator, a developer and the business. We had around three or four business owners to set up the first part before we adopted the rest of the businesses. Of course I was there, too, and there was one more project manager. All in all, we implemented Collibra with only about eight people. As for ongoing maintenance, we only require one administrator.

What was our ROI?

We have not seen ROI yet. Again, it's more like a dictionary. You buy a dictionary at home, so whenever you want it, you use it. What is the value of getting the dictionary? I don't know. It depends on your talent. If your team does not have good talent, then the dictionaries are more useful. It gets easier to navigate. And if you don't have the dictionary, it's going to be hard.

What's my experience with pricing, setup cost, and licensing?

I think they have a trust issue.  I did not like the way they recently went through the process. They were like, "Finish this SOW first, only then will we sign the other SOW." Or, "Finish this code." I didn't like that much.

And they're also very hard. They don't negotiate much: The first price is the first price. We tried our vendor management team contracts that our negotiation people use, but they did not negotiate at all, nothing at all. The very first price they quoted, they almost always stuck to the same price, within 95-98%. Always the same price; hardly anything went down. So that's one thing. They shouldn't do that.

Generally, when all the vendors quote, first they quote and then we start negotiating it. They might then reduce the quote or just provide a different way of getting around. Collibra were very rigid cost-wise, so they should improve that or maybe come up with some plan on how to negotiate.

Which other solutions did I evaluate?

I think we considered Informatica and one or two others that I can't remember off the top of my head. Informatica was the actual challenger to Collibra before we finalized the cost and everything.

It was cheaper, and it was another good one from an analytics perspective. But we know that, industry-wise, Collibra is number one from a data governance perspective. That's one of the reasons why we went with Collibra, even though the rest of the tools' setup cost and maintenance were cheaper.

What other advice do I have?

It's a very niche product. It's nice to use and easy to promote. You don't have to have all the user licenses - you can also get the author licenses. If you have 10 author licenses, you can get up to 50,000 consumer licenses. It's nice to know you have a mobile component in that regard.

If you're doing a lot of training as well, you need to do proper training with your data team, and with your business team, try to use it as a business tool instead of a technical tool. Employ it as much as you feed it, because then it's that much more useful.

And then having the business rules, the data governance and data quality rules, everything in one place, is nice to have. If you try to utilize it, the data lineage is number one, because there's awesome capability in it. So just try to use it and you'll start loving it.

I would rate Collibra a seven out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Buyer's Guide
Data Governance
September 2022
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