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erwin Data Intelligence (DI) for Data Governance Alternatives and Competitors

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RickDe Avila
Data Architect at NAMM
Real User
Top 5
Enabled us to centralize a tremendous amount of data into a common standard, and uniform reporting has decreased report requests

Pros and Cons

  • "The biggest benefit with erwin DI is that I have a single source of truth that I can send anybody to. If anybody doesn't know the answer we can go back to it. Just having a central location of business rules is good."
  • "The solution gives us data lineage which means we can see an impact if we make a change. The ability for us to have that in this company is brilliant because we used to have 49 data stewards from some 23 different groups within six major departments. Each one of those was a silo unto itself. The ability to have different glossaries — but all pointed to the same key terms, key concepts, or key attributes — has made life really simple."
  • "The fact that I sometimes have to go in and out of different applications, even though it's all part of the whole erwin suite, perhaps means it could be architected a little bit better. I think they do have some ideas for improvements there."
  • "There was a huge learning curve, and I'd been in software development for most of my career. The application itself, and how it runs menus and screens when you can modify and code, is complex. I have found that kind of cumbersome."

What is our primary use case?

We're a medical company and we have our own source systems that process claims from multiple organizations or health plans. In our world, there are about 17 different health plans. Within each of those health plans, the membership, or the patients, have multiple lines of businesses, and the way our company is organized, we're in three different markets with up to 17 different IPAs (Independent Physician Associations).

While that is a mouthful, because of data governance, and our having own data governance tool, we understand those are key concepts and that is our use case: so that everybody in our organization knows what we are talking about. Whether it is an institutional claim, a professional claim, Blue Cross or Blue Shield, health plan payer, group titles, names, etc., our case represents 18 different titles. For us, there was a massive number of concepts and we didn't have any centralized data dictionary of our data. Our company had grown over the course of 20 years. We went from one IPA and one health plan to where we are today: in five markets, doing three major lines of businesses, etc.

The medical industry in general is about 20 years behind, technology-wise, in most cases; there are a lot of manual processes. Our test use case was to start from fresh after 20 years of experience and evolution and just start over. I was given the opportunity to build a data strategy, a three-year plan where we build a repository of all sources of truth data used in governance. We have our mapping, our design, our data linkage, principles, business rules, and data stewardship program. Three years later, here we are.

How has it helped my organization?

erwin DI needs the Data Modeler, obviously, to be able to harvest the data directly from an existing database, or even a brand new one as you're designing it. That is a huge step in the right direction, although erwin has been known for that for 30 years. But the ability to take that model and interface it directly to the data governance makes it an easy update. It makes it simple for me to move from a development/design stage, for each environment, and into production, and to update the documentation using the data harvester and the Metadata Management tool and data cataloging module. That really brings it all together.

If I were to note any downside, it's that there are multiple modules and you can't have one without the other if you want to be world-class. But when you have them all, it makes life really easy for something like data profiling of an existing database to know if you want to keep it or not, given that there are so many legacy changes all the way through. The way we do it, when we make a change to a database or we add a database, the model is mapped, we import it, and then we have the data stewards populate any of our descriptions in their glossaries. The tool allows us to see all that instantly, unlike before.

I mentioned we have a data steward program, which is not part of the tool. While the solution has ways of using issues and for requesting data access within it, we're still stumbling with that. Sometimes it's just easier to talk to people. But we find that getting requests, getting data, and updating it, is actually a much easier process now.

In addition, the fact that I can always refer back to a centralized location with executive approval has helped me. 

For our business analysts and data analysts, especially for some of the wannabes and the data steward program, we have been able to centralize a tremendous amount of data into a common standard. One of our mandates was to have a Tableau-type business-intelligence component. We went live with our entire enterprise data warehouse, all the tools, in January of 2019, even though we started in 2016. We spent most of the year in massive amounts of discovery just around our organization's members. We didn't even get to claims or provider-contracting because they are so complex. The tool itself has expedited our getting to brand-new levels we've never seen with our members, because now things are becoming standardized.

People can refer to an inventory of reports and they can see that we don't have the same report in 20 different places, having 20 people support them. Now, there is one report in Tableau with one dataset. That dataset has become a centralized dictionary/glossary/ terminology inside the tool. Anybody who needs to get access to our data can access it. 

It's enables efficiency. Just in our marketing department alone, the number of new ways they have to think about our membership and growth has completely changed. They have access to data to make decisions. 

Executives can now look at what we call a scorecard of our PCP because we now have standardized sales. Everybody knows what they mean, how they are calculated.

Very high-end statistics and calculations are now easily designed. Anybody can go look at them, they know where to go. And if they want something because it helps make their business grow, it's almost a 24-hour turnaround, as opposed to a four-week SDLC process. It has expedited our process. The goal was to build a foundation and then, for the next couple of years, to really expand it. We hit that and I don't think we could've done it without these tools.

Recently we had to bring on a brand-new entity, a brand-new medical group. One of the minimum requirements was that we had to take 10 years of historical data from whatever system they had and to convert it, transform it, map it, and log it into our existing source of truth. We did this about four years ago for an entity, and it took us almost nine months just to get a dataset that somebody could use. This last time, it took us three weeks from start to finish because, outside of the governance tool, we have erwin's Mapping Manager and harvester. It also allows us to do source-to-target, so we have all our target mapping to our own repository, and then we have all our targets to EDW already mapped. Our goal was to bring a 100 percent source of truth. We had a complete audit, from when it came in from outside the building, to a location in the building. Then we would transform it into our EDW to whatever attributes, facts, or dimensions we wanted to. The tool allowed us to do that almost in hours, compared to what used to take months.

Another thing with their DI, not necessarily governance, but some of their other tools — which, of course, all feed back there — is that as soon as we do it, it's available to anybody. Not that a lot of people look at it, because a lot of times they just come and ask us, but the difference is that we're giving them the right answers within minutes. We don't have to tell them, "Well, let me go back and search it for six days."

We have downstream departments, like our risk department which manages our Medicare patients, and makes sure that we are taking care of them, which involves a very data-intensive process. Our ability to bring in historical data from an old system, a different type of a computer system, and convert it to make it look just like ours, no matter what it looked like before, is all because we have a data governance program. People can look at the changes from before and after and determine if they need certain data. 

A year ago, if somebody in our company's "left hand" brought in new data, no one but that left hand would know about it. Today, if somebody brings in data, all my data stewards know about it and they can choose to subscribe to it or not, today or later. And that is a matter of a flip of a switch for them, once we have brought it in and published it to anybody in the company. That's really important, for example, from the point of view of a human being. If someone has been around for 20 years it would be nice if we had all their records. Because of our data governance and what we built, all those records are maintained and associated with that person, and that's huge from a medical point of view. Data governance is helping us become even a better company because we know our data and how to use it.

The fact that erwin DI for Data Governance has affected our speed of analysis is a given. The DBAs are starting to use it more and even some of our executives are wanting to get to it for the data dictionary. It can happen that somebody from one of our departments sends them something and it doesn't make sense to them. Our goal was that if that happened we would try to find out and try to centralize it. We ended up creating our own dashboard reports on our Tableau server and published them to the same parties, so we could get rid of old habits and focus on new ones that have now been validated and verified, with the rules checked. 

The data governance allows us a real-time inventory. Every time there's a new request or a new ask, we put it in there and we track it and we make sure that our attributes are the same. If they're not, we have an explanation with a description for the different contexts in which the data is being used.

In addition, part of our ingest of an ask is that we take a first look at it and we provide as-is documentation so that the functional design can be tracked. That's a huge advantage. That has saved huge amounts of time in our development cycle, either for data exchange or interfacing, or even application development. The ability to just pull up the database, to be able to look at the fields and know what's important and what isn't important, note the definitions — we're able to support that kind of functionality. I'm one of the data architects here, and we work with everybody to make sure that our features and our epics are managed properly. For me to be able to quickly assess something, within a few minutes, to be able to say, "Here's the impact, here's what we have to do," and then hand it off to the full-blown design teams; that saves a month, easily. And that's especially true when there are 10 or 15 requests a week.

As for how the solution’s data cataloging, data literacy, and automation have affected the data used by decision-makers in our organization, on a scale of one to 10, I would give it a seven. It depends on which stakeholder or executive we're talking about. But has it had an impact? Every one of them has brand new reports, reports that didn't exist a year ago. Every one of them now sees data in a standardized format. The data governance tool might not have a direct impact on that, but it has an indirect impact due to the fact that we now govern our data. We treat data as an asset because of the tool. It's not cheap, it's an expensive tool. But my project has a monthly executive steering committee and, for 36 months, they never had a question and never second-guessed anything we did, and they loved any and all tools. So being able to sit with them and say, "Hey, we had an issue," and immediately give them a visual diagram — show what happened with the databases and what somebody may have misinterpreted — is huge; just huge. For everyone from our chief operating officer to our network operations, physicians' contracting, our medical management group, and our quality improvement group, it definitely has impacted the company.

We've only taken it out to about 50 percent of what it can do. There's so much it can do that we still don't do, because we ourselves are maturing into the program. It really has helped when it comes to harvesting or data profiling. For those processes, it's beautiful — hands-down the best so far. I love the data profiling.

What is most valuable?

For me, the biggest benefit with erwin DI is that I have a single source of truth that I can send anybody to. If anybody doesn't know the answer we can go back to it.

Just having a central location of business rules is good. That has come up a lot.

The solution gives us data lineage which means we can see an impact if we make a change. The ability for us to have that in this company is brilliant because we used to have 49 data stewards from some 23 different groups within six major departments. Each one of those was a silo unto itself. The ability to have different glossaries — but all pointed to the same key terms, key concepts, or key attributes — has made life really simple.

It has given us better adaptability in our EDW and a more standardized way of looking at data, as opposed to all of the different formats.

Our experience with the solution's Smart Data Connectors is still limited, but so far we're impressed. For us, it's mostly been reverse-engineering. But because we got to start this whole project from scratch it was really about forward-engineering. One of the advantages is that we wanted to go back through the entire enterprise, and start mapping everything legacy or future. Ultimately, the future is to move to the cloud and rearrange all our data and reprioritize the most important attributes and not have multiple replications of data to our many silos. So the Smart Data Connectors to engineer code have been spot-on. Harvesting reverse-engineering allows us to test and verify some of the things that are odd.

It's got a proprietary format that works really well within all the systems and I can export to any format, including my data profiling, my mapping integrator, and any and all of my governance stuff, whether it's within the business rules, the policies, the dictionaries, or the glossaries. I can export that into a CSV or Visio depending on what it is.

What needs improvement?

There is room for improvement in automation, no question. 

Also, the fact that I sometimes have to go in and out of different applications, even though it's all part of the whole erwin suite, perhaps means it could be architected a little bit better. I think they do have some ideas for improvements there. 

But regarding the data governance tool itself, for me there was a huge learning curve, and I'd been in software development for most of my career. The application itself, and how it runs menus and screens when you can modify and code, is complex. I have found that kind of cumbersome. I had one guy make an error and it costs us a few days because it had an impact on a whole slew of options and objects because he didn't know what he was doing. That was not their fault; it was purely my fault, allowing that to happen. For me, that was a struggle.

For how long have I used the solution?

We started about three years ago when we started our data warehouse project.

What do I think about the stability of the solution?

We haven't had any problems with anything. I haven't had to worry about updates. If anything is done in terms of updates, it has no impact, to my knowledge. Every day, one of three of us on it and we haven't had any problems.

What do I think about the scalability of the solution?

In terms of its scalability, I'm a really simple person. Two plus two equals four. If I know that I can always expand, it's basically a configuration engine or I don't feel like I'm painted into the corner, I feel I'm covered for scalability.

I feel that way about everything with the Data Governance tool. If I need to grow it, it's just a few more licenses. I have to pay a little extra, but I can get another hundred if I need that many. So I have no worries about being able to add users. I'm also not worried about size and space on the system.

So far, I have no limitations in terms of scalability. I'm good with it, but I can't say that I have pushed it to the limit yet. We have 500 people here and I have a hundred licenses. Of those, about 85 are currently active, between our users in the building and our IT department.

It's used extensively by the people who need to use it. I don't think the end-users are using it as much as are the data analysts, the business analysts, or the data stewards. But that was the goal. The data stewards are the ones who should use it. The data stewards are the ones who are managing it. They're the ones who get the requests from the users within their department or their group.

As far as using it more extensively, it's just a matter of if we grow as a company and we need more data stewards, but I think we're in a good place. Everything feeds accurately. As far as visibility and reading dictionaries, that's something that we would want to do more of. As users adopt it, as departments go from a mom-and-pop mentality to "corporate America," I would say our goal over this next year is to double our growth.

Self-service is one of our goals: get people to know how to use it themselves. People here ask IT for a particular report and that report goes to a bunch of different people. The same report is used for different reasons in different groups but no one knows it. With erwin, the goal would be that if they want a report they would be able to go in there, see the BI reports which are inside the Data Governance, determine if it's what they want, and make a request; and that day, they would have it, filtered to their needs. If we had to create a brand-new one, they would know the elements and would put the request into the hopper and we could turn that around. Even today, in some cases, because we still have BusinessObjects and we have SSRS, with some of that stuff it can still take up to six weeks to turn a report around. If they use the new system and they use the data, I can put a Tableau report on their desktop within 48 hours.

How are customer service and technical support?

Their helpdesk ticket itself is pretty great. The team of people, whether it's Tammy or Susan or a few others that my associate has worked with, is very good, no matter what the tool is. It's very comparable to our own helpdesk. You can get stuff done and, in some cases, there are almost too many emails. That's just how good they are.

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

We had Excel and Word documents, but nothing else. 

When we started our data warehouse project we were looking for a data governance tool. That led us to buy their data governance 1.0. They've upgraded to the 2.0 and we're working with that now. We then ended up buying the modeling software, so we got server licenses for some copies for our modeling, which is the foundation. Then we purchased their Mapping Manager harvester, their data integrator, their metadata package, and we've just recently even purchased their architectural software, so we're working with their entire stack.

How was the initial setup?

The initial setup of the software was a combination of straightforward and complex. For me, it was complex, as I'd never seen it and there were a lot of components to their instructions. But we ended up doing it pretty much ourselves, with a few interactions with some of their technical people on the installs. The clouds were, obviously, easy, but the making sure the Data Modeler was there and dealing with the architectural software within our organization wasn't as easy. 

But erwin DI for Data Governance, itself, was pretty straightforward. Adding users required a little learning curve but no one taught me. It was trial and error more than anything. I'm sure we could have had great training from them, but they have good videos and good tools on the web. For us, there was probably less than a 5 percent interface with erwin for our installs, configuration setup, and even configuring the databases to house all the data.

Being that it's a cloud solution, in the beginning, the firewalls became an issue but we got those resolved right away. There really wasn't anything bad. We self-taught; we didn't have a whole lot of Professional Services on this. It's intuitive enough that we run our entire data strategy on it. With a group of three people, we support another 50 developers, systems analysts, and data analysts outside of my group.

We didn't really have an implementation strategy. We didn't know what we were doing three years ago. We just knew that we were tasked to create a data warehouse, and I'd never done one. We tried to do it the right way. If it was something that I'd been doing for 30 years, there would have been more of a strategy for how I would do it.

We had a consultant come in in 2016 and create what they called an IT map of applications and data strategy plan. We called it the Imap. They laid out the groundwork and the framework in which to build this whole thing. One of their areas was data governance. It just so happened that I was doing research and erwin's Data Governance tool came up in my research. I knew erwin's responsibility, so I put in a request. Around Christmas of 2016 it got approved. We didn't expect it because the year was out. That became the first thing we started with. From there, it just kept building with all the other modules. It just kept growing with us.

The advantage erwin had with us is that we were new at it. So even if there was something wrong, we wouldn't have known the difference. But I knew what was right in terms of what the data means to the company and how we run our business. For me, it just fit perfectly. It just kept falling into place. Each time we need to get more money for another license or a different module, I could integrate it pretty simply because it fit the narrative. Maybe that's the best way: the technology just simply fit, purely by accident. I'd love to tell you that I'm such a genius and that I planned this all out, but I didn't. It did help that erwin purchased some companies and ran right along with what we were trying to do.

Because of the erwin Data Governance software, and trying to figure out and follow their MO as far as key concepts, key terms, and attributes were concerned, we took 27 of the most important people in our organization from the president on down, and sat them in a room for three hours so they could define the term "member." Who was a "patient," a "member," or a "consultant," meant a lot. It changed the direction of the company. They didn't even know what data governance was three years ago. Now everybody talks about it.

What about the implementation team?

We worked directly with erwin. If we had any questions, we'd go through their helpdesk. Sometimes we'd have conference calls. A lot of times, they seemed to go above and beyond to help us, especially when it came to the database configurations. We ran into a few things with the Mapping Manager and the harvester, but their team was great. They worked with our DBAs with no questions asked and no hesitation.

What was our ROI?

Our labor costs are half what they would have been. And then there was the lack of quality or the lack of productivity. So it's had a huge impact on all those things. That's why the money is more than recouped.

My three-year plan was to recoup the $3,000,000 we spent in the previous three years, and this year we have already recouped $2,400,000 of it. We have two more years to recoup to break even. I don't know if that's directly related to just the Data Governance. It might be that because of governance it has allowed us to do all the other things. The ultimate goal was to get data into the hands of the decision-makers and have it as accurate as it could be, so they could make better decisions.

We have improved our time for report servicing and our capabilities in turning things around quickly.

One thing we missed in our estimates of cost savings was the reduction in the number of requests or the time it would take to do a request. The issue was that we created a standardized report, and it worked so well that we stopped getting requests altogether. We never thought they would never send a new request, so we saved even more. But that's because we knew the fields, we got it right the first time, and we created standards around governance that allowed us to really simplify our business. I have 29 standardized reports around memberships, providers, and claims, and they are used throughout the organization now. Those are reports we didn't have before.

In terms of time-to-value, I wouldn't say standing it up was quick, because a day would be quick. But it was under a month. It could be set up pretty easily, especially once you understand all the components you need and all their modules. The erwin governance solution is on the cloud while the modeler is on-premise and the suites are also on the cloud. And the fact that it's cloud-based made it simple and straightforward. It was just "boom," we got our logins and we were fine, for the Data Governance software.

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

The whole suite, not just the DI but the modeling software, the harvester, Mapping Manager — everything we have — is about $100,000 a year for our renewals. That works out to each module being something like $8,000 to $10,000.

Which other solutions did I evaluate?

Everybody was evaluating other options. I got in trouble because I picked erwin after I had looked at 20 other things out there. Based on price and the size of our company, and the fact that we were brand-new to this whole endeavor, I didn't want to spend a fortune on something like Informatica, and have a master data management system.

Another big difference between Informatica and erwin is interfacing. Informatica is the top-of-the-line, upper-quadrant for MDM solutions, whereas erwin was more about just managing data and not necessarily manipulating it, moving it, interfacing with it, etc. That was the big difference. But erwin allowed us to get our footprint into it and really learn it. It was just the right solution at the right time.

What other advice do I have?

Our first goals were data literacy and data as an asset. Those were our two big, ultimate goals three years ago. Data literacy turned out to be 10 times more important than a data warehouse. We could look at existing data sets and, just by educating people, it gave them an advantage almost immediately. The fact that the data governance was able to put a framework around data literacy helped us focus on the right answer, even if it wasn't the first one given. In other words, sometimes we'd have the same answer three or four times, and it would shift until we nailed it. But without governance, we would never have done that and we would have stayed the same.

The secret to the success of this project was that we had a vision and we stuck to it. Governance was important to us, no matter how other people might have thought about it. In my very first data steward meeting I was introducing everybody to these brand-new terms they'd never seen, and someone in our analytical group totally derailed the meeting. So be aware that it's not going to be easy, but have a vision. 

And make sure that governance is important or don't bother. It's not something that a lot of people add value to at all. When you say, "Oh, I want governance so we can have a data dictionary and you can go look at it," they'll say, "I don't want to look at it, just give me a report." But the ability for those who need to do that is huge. Have a vision and stick to it and be willing to take a step back, sometimes, to go two forward.

The neat thing is that we've pretty much done all of this with two to three people, for our entire organization. We do have three data teams that are using the Modeler for development — ETL SSIS stuff — but we have a pretty serious "wash, rinse, repeat" standard. If anything is in doubt, we just go back to the business rules and see what our rules are. What are our principles, and are we meeting them?

As far as automating the changes through the environments, it has helped, but not a lot. It's not like it was a silver bullet. We need help there, because there's so much. There's the model, but once you promote that in different environments, sometimes you miss it because you only get three or four days to get out of QA to get it into stage.

Obviously, you mitigate risks with automation. It does have an impact. As a company, we just haven't been able to take full advantage of it now, but that's our hope. We're only into it for about a year-and-a-half, even though we have run with the suite for almost three years. We're still immature. I wish I had everything at the push of one button, the "Easy" button. Some of it's over our heads. We could use some new training and we could use some additional support. erwin has been great with us, but it's also a matter of the appetite and the resources. The biggest issue is that I don't have a team of people doing what a team of people need to do to accomplish what we would like to. It's done by a small number of people on a consistent basis, and not full-time.

The solution's generation of production code through automated code engineering would reduce the time it takes to go from initial concept to implementation, but we're a Microsoft shop and most of all that is done inside TFS or Visual Studio. That's how we manage all our codebase, including release management. That's all done separately and is automated. We're trying to create some interfaces between the two. We just haven't gotten there.

In my three years using erwin, besides actually getting approval for the money to purchase the software, I don't think I've had a struggle with it. They've been great. When we first got on and we had some questions, they got me to the development team in England and set it all up with us without question, no extras. They just tried to make sure it worked.

I would rate erwin DI for Data Governance at eight out of 10. I never give a 10 because I have yet to see perfection. It has some gaps, but I definitely think it's in the top third. As far as rates go, I don't have a lot to compare it with. It's easy now but it took going through a learning curve, but that's the case with any software. Does it need to mature a little? Possibly. But that would be it. With their roadmap, they're buying companies, and changing things, and doing things. I've been pleased.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Sr. Manager, Data Governance at a insurance company with 501-1,000 employees
Real User
Top 5Leaderboard
Lets me have a full library of physical data or logical data sets to publish out through the portal that the business can use for self-service

Pros and Cons

  • "They have just the most marvelous reports called mind maps, where whatever you are focused on sits in the middle. They have this wonderful graphic spiderweb that spreads out from there where you can see this thing mapped to other logical bits or physical bits and who's the steward of it. It's very cool and available to your business teams through a portal."
  • "There are a lot of little things like moving between read screens and edit screens. Those little human interface type of programming pieces will need to mature a bit to make it easier to get to where you want to go to put the stuff in."

What is our primary use case?

We don't have all of the EDGE products. We are using the Data Intelligence Suite (DI). So, we don't have the enterprise architecture piece, but you can pick them up in a modular form as part of the EDGE Suite.

The Data Intelligence Suite of the EDGE tool is very focused on asset management. You have a metadata manager that you can schedule to harvest all of your servers, cataloging information. So, it brings back the database, tables, columns and all of the information about it into a repository. It also has the ability to build ETL specs. With Mapping Manager, you then take your list of assets and connect them together as a Source-to-Target with the transformation rules that you can set up as reusable pieces in a library.

The DBAs can use it for all different types of value-add from their side of the house. They have the ability to see particular aspects, such as RPII, and there are some neat reports which show that. They are able manage who can look at these different pieces of information. That's the physical side of the house, and they also have what they call data literacy, which is the data glossary side of the house. This is more business-facing. You can create directories that they call catalogs, and inside of those, you can build logical naming conventions to put definitions on. 

It all connects together. You can map the business understanding in your glossary back to your physical so you can see it both ways. 

How has it helped my organization?

We have only had it a couple months. I am working with the DBAs to get what I would call a foundational installation of the data in. My company doesn't have a department called Data Governance, so I'm having to do some of this work during the cracks of my work day, but I'm expecting it to be well-received.

What is most valuable?

They have just the most marvelous reports called mind maps, where whatever you are focused on sits in the middle. They have this wonderful graphic spiderweb that spreads out from where you can see this thing mapped to other logical bits or physical bits and who's the steward of it. It's very cool and available to your business teams through a portal. 

Right now, we're focusing on building a library. erwin DM doesn't have the ability to publish out easily for business use. The business has to buy a license to get into erwin DM. With erwin DI, I can have a full library of physical data there or logical data sets, publish it out through the portal, and then the business can do self-service. 

We are also looking at building live legends on the bottom of our reports based on data glossary sets. Using an API callback from a BusinessObjects report from the EDGE governance area in the Data Intelligence Suite back to BusinessObjects, Alteryx, or Power BI reports so you can go back and forth easily. Then, you can share out a single managed definition on a report that is connected to your enterprise definitions so people can easily see what a column means, what the formula was, and where it came from.

It already has the concept of multilanguage, which I find a really important thing for global teams.

What needs improvement?

It does have some customization, but it is not quite as robust as erwin DM. It's not like everything can have as many user-defined properties or customized pieces as I might like.

There are a lot of little things like moving between read screens and edit screens. Those little human interface type of programming pieces will need to mature a bit to make it easier to get to where you want to go to put the stuff in.

For how long have I used the solution?

We have only had erwin DI for a couple months. We brought it in at the very end of last year.

What do I think about the stability of the solution?

So far, I haven't had any problems with it whatsoever. Now, I'm not working on it all day every day. It seems to be just as stable as erwin DM is. I used this tool when it was still independent and called Mapping Manager, before it became part of the erwin Suite. It's lovely to see it maturing to connect all the dots.

Four people are maintain the solution. The DBAs are going into harvest the metadata out of the physical side of the house. Then, I'm working with the data architects to put in the business glossaries.

What do I think about the scalability of the solution?

It is a database. All of the data is kept outside of the client, so it's how you set up your server.

We have five development licenses and 100 seats for the portal. Other than those of us who are logging in to put data in, nobody much is using it. However, you have to start some place.

Right now, the DBAs, data architects, and I are its users.

I'm expecting the solution to expand because the other cool thing that this Data Intelligence Suite has is a lot of bulk uploads. I can create an Excel template, send it to the business to get definitions, and then bulk upload all their definitions. So, we don't need a lot of developer licenses. It becomes a very nice process flow between the two of us. They don't have to login and do things one by one. They just do it in a set, then I load things up for them. I have also loaded up industry standard definitions and dictionaries making it easy to deal with.

How are customer service and technical support?

I haven't interfaced with anybody who is just an EDGE team member. I will say the sales and the installation teams that we worked with were both fabulous.

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

We did not previously use another solution. erwin didn't have a formal business glossary.

How was the initial setup?

The initial setup seemed to be very straightforward. I don't do the installations, but the DBAs seem to find it pretty easy. They got the installation instructions from the erwin team, followed them, and the next day, it was up and running.

We're just following the same implementation strategy that we're doing with erwin DM. We didn't set up the lower tiers because I didn't see that we need lower tiers except for upgrades. We just do lower tiers when we do an upgrade and push to production, then we just drop the lower tier. Other than having to train people on how to use it, implementation has been pretty easy.

What was our ROI?

ROI is a bit hard to come at. There is peace of mind knowing that we now have visibility into the business. To be able to know that I'm instantly pushing all the data definitions out to the business, even though culturally I haven't changed everything so they are looking at it on a daily basis. This is still hard to put a price tag on. I know I'm doing my piece of the job. Now, I have to help them understand that it's there and build a more robust data set for them.

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

You buy a seat license for your portal. We have 100 seats for the portal, then you buy just the development licenses for the people who are going to put the data in.

Which other solutions did I evaluate?

We did evaluate other options. Even though erwin DI got a few extra points from the evaluation to coordinate with the erwin DM tool, we looked at other tools: Alteryx Connect, Collibra, DATUM, and Alation.

We did a whole pile of comparisons:

  • Some of them were a bit more technical. 
  • Some of them were integration points.
  • Customization.
  • The ability to schedule data harvests, because the less you have to do manually, the better.
  • The ability to build your data lineages, then the simplicity of being able to look at those sorts of things to do searches. 

There were a different things along those lines that showed up in the comparison.

Erwin DI checked all the boxes for us. There are some things that they will grow into over time, but they had all of the basics for us.

Collibra scored a little higher on being able to integrate with SAP Financials. In fact, other products scored a bit higher with the SAP integration altogether, because with erwin DI, you need to buy a connection to do some of that.

For the connection with some of our scheduler tools, Alation was able to integrate with our UC4 scheduler. Right now, the EDGE tools don't.

For the most part, the functionalities were exactly the same, e.g., being able to do bulk uploads with high performance, Alteryx, Collibra, and erwin Data Intelligence Suite tied on a lot of things. However, erwin's pricing was cheaper than its competitors.

What other advice do I have?

If you have the ability to pull a steering committee together to talk about how your data asset metadata needs to be used in different processes or how you can connect it into mission-critical business processes so you slowly change the culture, because erwin DI is just part of the processes, that probably would be a smoother transition than what I am trying to do. I'm sitting in an office by myself trying to push it out. If I had a steering committee to help market or move it into different processes, this would be easier.

Along the same lines as setting up an erwin Workgroup environment, you need to be thoughtful about how you are going to name things. You can set up catalogs and collection points for all your physical data, for instance. We had to think about if we did it by server, then every time we moved a server name, we'd have to change everything. You have to be a little careful and thoughtful about how you want to do the collections because you don't want the collection names to change every time you're changing something physically.

What we did is I set up a more logical collection, so crossing all the servers. The following going into different catalogs:  

  • The analytics reporting data sets 
  • The business-purchased applications 
  • External data sets 
  • The custom applications. 

I'm collecting the physical metadata, and they can change that and update it. However, the structure of how I am keeping the data available for people searching for it is more logically-focused.

You can update it. However, once people get used to looking in a library using the Dewey Decimal System, they don't understand if all of a sudden you reorganize by author name. So, you have to think a bit down the road as to what is going to be stable into the future. Because the more people start to get accustomed to it being organized a certain way, they're not going to understand if all of a sudden you pull the rug out from under them.

I'm going to give the solution an eight (out of 10) because I'm really happy with what I've been able to do so far. 

The more that the community uses this tool, the more feedback they will get, and the better it will become.

Which deployment model are you using for this solution?

Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
Data Program Manager at a non-tech company with 201-500 employees
Real User
Top 5
Wide range of widgets enables a user to find information quickly. However, the configuration and structuring of this solution is not straightforward.

Pros and Cons

  • "There is a wide range of widgets that enables the user to find the proper information quickly. The presentation of information is something very valuable."
  • "If we are talking about the business side of the product, maybe the Data Literacy could be made a bit simpler. You have to put your hands on it, so there is room for improvement."

What is our primary use case?

This solution is still an experiment for us. My company is in the process of defining the data governance process, which is not settled right now. We have used erwin DG for the purpose of getting acquainted with data governance from a technical point of view. We want to see how it fits in our organization because data governance is neither IT nor a business matter. It is in-between. We have to put the proper organization in place in order for an IT solution to meet all the requirements. This has been in the works for almost two years now, where we have been formerly under an experiment with erwin DG.

We are not fully using it as we would if it were in production running regular operations. What we have done with the tool is have a metamodel for our data and try to see how it fits with the requirements of our project, businesses, and IT. We have two cases that are fully documented under erwin DG. What we are trying to do right now is to integrate all our regulatory obligations, including laws and regulations at the French and European levels. This would enable us to make a bridge between the businesses and the law.

This is a SaaS solution maintained by erwin.

How has it helped my organization?

This type of solution was key to moving our entire company in the right direction by getting everyone to think about data governance.

What is most valuable?

It is very complete. Whatever you need, you can find it. While the presentation of results can be a bit confusing at first, there is a wide range of widgets that enables the user to find the proper information quickly. The presentation of information is something very valuable.

The direct integration of processes and organization into the tool is something very nice. We feel there is a lot potential for us in this. Although we have not configured it yet, this product could bridge the space between business and IT, then a lot of processes related to data governance to be handled through the tool. This gives it that all in one aspect which shows high potential.

The mapping is good. 

What needs improvement?

If we are talking about the business side of the product, maybe the Data Literacy could be made a bit simpler. You have to put your hands on it, so there is room for improvement.

For how long have I used the solution?

We have been using it for two years (July 2018).

What do I think about the stability of the solution?

The stability is very good.

What do I think about the scalability of the solution?

As far as I can see, it is scalable.

We have approximately 10 people, so we are starting to use it on a small scale. We have data governance people, myself, a colleague in IT, four or five business users, and a data architect.

How are customer service and technical support?

Their support is very good. They have very good technical expertise of the product. 

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

We previously used Excel. We switched to erwin DG because it had the best benefit-cost ratio and showed a lot of potential.

How was the initial setup?

The initial setup was very straightforward. However, if we are talking about the opening of the service and setting up our metadata model, it was not straightforward at all.  

The initial deployment took less than two weeks.

Our implementation strategy is small in scope because we are still in the experimentation phase. We just provide a few users with access for people involved in the implementation. We just let them play with it. Now, we are just adding new use cases to the model.

What about the implementation team?

We used erwin's consultants. We would not have been able to do the initial deployment without them. They did the deployment with two people (a technical person and a consultant), though they were not full-time. 

The opening up of the service by erwin was extremely simple and flawless. It is just that you find yourself confronted with an empty shell and you will have to fill that shell. The configuration and structuring of this is not straightforward at all. This requires modeling and is not accessible to everyone in the company.

What was our ROI?

As an experimentation, we are not fully in production. Therefore, it's absolutely impossible to have a return on investment right now.

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

erwin is cheaper than other solutions and this should appeal to other buyers. It has a good price tag.

Which other solutions did I evaluate?

We are a public company who is obligated to open our purchasing to a wide range of providers. For example, we were in touch with Collibra, Informatica, and a few others.

erwin DG was less complex at first sight and cheaper than other solutions. It also fulfilled what we wanted 100 percent and was the right fit for the maturity of our governance process. It was not too big or small; it was in-between. 

What other advice do I have?

erwin is very good for companies who have a very structured, data governance process. It puts every possible tool around a company's data. This is very good for companies who are mature with their data. However, if a company is just looking for a tool to showcase their data in a data catalog, then I would advise companies to be careful because erwin is sometimes really complex to master and configure. Once it is set up, you have to put your hands in the gears of the software to model how your data works. It is more of a company process modeler than a directory of all data available you need and can access. Industrial companies over 30 to 40 years in age are struggling to find what data they may have and it may prove to be difficult for them to use erwin directly.

What we have done with the lineage is valuable, but manual. For the IT dictionary, automation is possible. However, we have not installed the plugin that allows us to do this. Right now, all the data that we have configured for the lineage has been inputted by hand. I would rate this feature as average.

We have not tested the automation.

I would rate this solution as seven (out of 10) since we have not experienced all the functionalities of the product yet.

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: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
Manoj Narayanan
General Manager - 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
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