erwin Data Intelligence by Quest Reviews

Data Intelligence enables us to provide deeper technical insight into our enterprise data warehouse while democratizing the solution and data. 

For more than 10 years, we had built our data systems without detailed documentation. We finally determined that we needed to improve our data management, and we chose Data Intelligence Suite (DIS) based on our past experience using erwin Data Modeler. After researching DIS, we also discovered other desirable features, such as the Business Glossary and Mind Map features that link various assets.

Data Intelligence has provided more profound insights into legacy data movements, lineages, and definitions in the short term. We have linked three critical layers of data, providing us with an end-to-end lineage at the column level.

Our long-term plans include adding other systems to complete the end-to-end picture of the data lineage. We also intend to better utilize the Business Glossary and Mind Map features. This will require commitment from a planned data governance program, which may still be a year or more into the future.

We appreciate the solution's ability to upload source-to-target mappings as well as other types of metadata. We were able to semi-programmatically build these worksheets. The time needed to map each manually would be prohibitive.

Although it was not intuitive, there is a feature where DIS can generate the Excel worksheet as a template. Using this allowed us to discover many other types of metadata we can upload, which is the most efficient way to populate metadata.

We have loaded over 300,000 attributes and more than 1000 mappings. The performance is slow,  depending on the lineage or search.  This is supposed to be fixed in the later versions, but we haven't upgraded yet.

The integration with various metadata sources, including erwin Data Modeler, isn't smooth in the current version. It took some experimentation to get things working. We hope this is improved in the newer version. The initial version we used felt awkward because Erwin implemented features from other companies into their offering.

I have been using Data Intelligence for two years.

Because Data Intelligence is a Java-based solution, initial usage can require patience and reloads to function properly.  

There are many options to scale the repository and webserver application for performance.

Generally, erwin support was highly responsive. However, we did this installation while Erwin was transitioning to Quest. Support was still surprisingly good given that situation.

Positive

This was the first metadata repository tool at this company.

Setting up Data Intelligence is complex. It required a few calls with support to figure out how to confiugre multiple components.

I can't quantify our return in a dollar amount. However, we can now answer how the system works down to the transformation level as needed. Previously, we would need to start a "project" to obtain such information.

Tools like this generally have a low or no cost for "read only" usage. The licensing required to actively update metadata is much more expensive, but we only needed three licenses. Two licenses would likely suffice for most organizations.

I rate erwin Data Intelligence nine out of 10. LDAP integration is provided, but the roles and role integration require some research and setup.

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. 

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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

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. 

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.

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

The stability is very good.

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.

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

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

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.

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.

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

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

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. 

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.

The use cases were for a large federal government agency with 10 smaller agencies that handle all of the metadata for that agency, and all of that metadata is sensitive PHI or PII. It includes Social Security numbers and all of the metadata for the provider, and beneficiary health records. The purpose of the agencies is to prevent fraud, waste, and abuse of American taxpayers' money.

One of the biggest use cases is to do mappings, manual and auto mappings, and data lineage. The data is used by the agency for prosecution when they find fraud and waste.

At a very high level, what the Medicare or Medicaid services want is the ability to ingest their metadata so that there is transparency. They also want it to be up to date, and the ability to interpret it, both technically and for business use cases, meaning business terms, policies, and rules.

They want end-users with different levels of technical acumen to be able to find information easily and quickly. A data store would be a quasi-technical person, like me, who understands enough to retrieve information or a lineage.

A business user would be either a congressman or congresswoman or a project manager who would see visual representations. erwin has a lot of really good data visualization capabilities.

The use cases include being able to quickly look at data and evaluate it on many levels, from the granular level of a column, table, or even a view, to a zoomed-out level to see how many of a certain table or column are in a data set from each agency.

Another use case is to take the data out of legacy tools, like Excel spreadsheets. And in some cases, agencies are still using a mainframe from the 1960s where some of that data is stuck.

The Standard Data Connectors we used were for Snowflake, RedShift, SQL, IBM, and others. All of the standard data connectors worked. One problem that our team ran into was that some of the applications didn't really do the best job of grooming and maintaining their data. One particular system had 1 million tables, which meant there were a couple of million columns. The size of the data was an issue, but the data connectors worked. There were no APIs used, just database connectors.

In terms of seeing the technical details needed to manage the data landscape, when you log in to erwin it's broken down into modules. One of them is Metadata Manager, and that is one of the things I liked about it. It's broken down according to the work you need to do. With Metadata Manager, you immediately see all of your systems and, in our case, The Centers for Medicare & Medicaid Services had many systems. And in the left-hand panel, there was a really good user interface to expand your systems. You can see your environments and what's in them, and then you see your tables, columns, views, and anything else.

In the center of the UI, you can do your work, such as run a lineage, mind map, or look at an impact analysis. It's set up well visually, and it's also set up like old-school computer science with correct folders.

Another work area module, called Mapping Manager, is where you do all of your mapping. It gives you a mirror view of everything that's in your systems and environments, and you can work with that metadata on your mappings. You can also export and publish your mappings and, once you've done your mappings, you can go back into Metadata Manager and run an impact analysis and look at your mind map.

The third module for business users is the Business Glossary Manager where you can create your business terms, policies, and rules, assign them and see how many are spread across which environments. It gives you a visual in addition to the folder structure.

These modules are the strength of erwin's Data Intelligence Suite. People who are non-technical can learn about data governance using this tool, like I did. The tool we're now using instead of erwin, requires too much searching and linking things, like you're using Facebook.

I and the DevOps architect think erwin Data Intelligence is a better product technically because it's more designed for a technical user. But it couldn't pass one penetration test. In the federal government, if there's one problem like that, they're not happy anymore.

Also, really huge datasets, where the logical names or the lexicons weren't groomed or maintained well, were the only area where it really had room for improvement. A huge data set would cause erwin to crash. If there were half a million or 1 million tables, erwin would hang. And then, when the metadata came in, it would need a lot of manual work to clean it up.

I used erwin Data Intelligence by Quest for a year and a half.

The stability issues were around erwin's not being able to handle that huge amount of metadata.

We used their Premier Support and had weekly meetings with them to go over any tickets. There were two people assigned to us. One was their government specialist and the other was their customer service person in charge of their support.

The biggest value of Premier Support is that you are able to verbalize feedback and get input on defects and the fact that you have an open forum. You can communicate with people face to face and collaborate. You can discuss an issue, provide input, and get things resolved.

Positive

I was a technical and business user. The team I worked on stood the erwin Data Intelligence suite up within the MAG (Microsoft Azure for Government) environment. We put it through the penetration test and hooked it up to the LDAP with all the security requirements.

Standing up a metadata governance platform is always going to be complex. It was complex for us because it was being used for the government and we had a lot of penetration tests and high-level cybersecurity requirements. That made it complex.

And maintaining the system is what our team did. Our contract included getting it stood up, integrated, configured, and then ensuring it kept running. It was only available from eight in the morning until seven at night, but that was our job. We bought erwin off the shelf. We weren't working with them on customized features.

Our team was the integration team and we had five people involved.

erwin was at a good price. The federal government wouldn't buy something if the pricing wasn't good. We have to use FedRAMP pricing, so I'm not sure about what erwin's pricing would be "out in the wild," for a regular company. But they do work with you on the price.

The erwin interface was a good balance between technical and visual information compared to some other products that we looked at. The one that we switched to is a "glorified social" solution. It's about socializing the metadata and ways for people to search and create articles. They can also link and rate the veracity of a particular data source and write comments. 

Whereas with erwin, you can actually do things, like create your own lineage and mind maps. That is a really good feature because it is very helpful in seeing which column's tables are related. Also, you can flag them with "sensitive data" and other indicators. You can also customize your own features for the mind map. That was another very robust feature.

Faster delivery of data pipelines at less cost is more a question for the architect than for me, but it is possible if the metadata sources are clean and set up correctly. This is not an erwin-specific topic. My understanding is that a lot of data catalogs are dependent on what is called the "logical name" of the tables and columns. If the data store or the data analyst never labeled or created a correct lexicon for any of their metadata, then it's going to slow down the whole process, whether it's Erwin or Alation or Informatica or Calibra. erwin can make data pipelines faster, but it's dependent on how clean the metadata is and how well it was set up in the first place. And I believe it does save costs because the Medicare & Medicaid Services wouldn't be using it if it wasn't cost-effective.

erwin Data Intelligence is a good platform and I wish we were still using it.

We use Data Intelligence as a metadata repository for our search target systems and to define metadata. We also use the tool to define the mapping between the search and the targets. It enables us to track the flow of data between systems, design data flows, and share flow implementation with developers. Our end-users seldom access Data Intelligence. We use other tools to provide end-users access to our metadata repository. Data Intelligence is used internally for projects and users with project-oriented roles.

Data Intelligence allows us to automate multiple tasks we had previously done manually, such as restructuring the metadata for our purposes, setting up ETL flows, and defining the data tables we create. It also enables us to standardize our approach and our technical processes.

The ability to automate metadata harvesting and ingestion from common industry sources is pretty nice. Data Intelligence helps us better understand the systems we use. We automatically connect to a specific system. For example, we might connect our Oracle Snowflake databases to Teradata Cloud and automatically ingest all the metadata. We transform it and browse through what we can use. Automation also simplifies the mapping processes. We don't need to recreate anything manually. 

Data Intelligence's standard data connectors for the metadata manager are easy to use. We can connect the systems and have everything in place. 

The automation capabilities help us create ETL flows and map the tables in our system. It frees up our staff who would otherwise need to spend time generating all those pieces manually. Data Intelligence lets us connect to those reports and change the metadata automatically. We get a picture of the target lineage, so we can check the dependencies of one data object on another.

The lineage functionality that erwin offers is only a recommendation, and it isn't fully validated. We cannot trust shared data because anyone can modify a work in progress. Based on the latest information in erwin, we can make high-level assumptions and trust the data in the process.

Our project must be validated, and erwin is a non-validated tool. We can only use outcomes that we can validate. We can check the data lineage and see the potential data flows from sources to targets. However, we cannot fully track the data that we have there.

Data Intelligence saves time on data discovery and helps us understand our data through standardization. It helps us connect to data services and simplifies the process. That's one of the significant benefits of using the Data Intelligence suite. It's difficult to estimate how much time it saves us. In the early stages of a project, we need to spend a lot of time on integration. Data Intelligence simplifies and standardizes the mapping. It's hard to say how much because I would need to compare the time spent manually generating the metrics in Excel versus doing it in Data Intelligence. 

In this project, my role is to be a systems analyst, so I'm primarily using Data Intelligence as a mapping tool. I use it for target mapping. At my previous company, we used multiple approaches to implement all of those target flows. It was problematic to manage all the versions of the mapping because we were using different approaches. It was confusing. 

Data Intelligence standardizes everything, visually linking the source, target, and all the documents in the table. This table is Data Intelligence's main advantage. We can better utilize the services we have on the projects. We don't need to spend 10 hours performing repetitive manual tasks.

Everything about Data Intelligence is complex. Though we've used the tool for five years, we're still only using about 30 to 40 percent of its capabilities. It would be helpful if we could customize and simplify the user interface because there are so many redundant things. Some of the features aren't being used. It's challenging to understand everything, especially if you aren't using it daily. 

I have used Data Intelligence for five years.

Data Intelligence is stable. Any errors we've had were attributable to the platform rather than the tool itself. I've never had a serious failure. 

Data Intelligence is pretty scalable. It's easy to increase or decrease the solution's capacity based on your organization's requirements. 

I rate erwin's support a five out of ten based on the one experience I had with them. I haven't interacted with support much, but I contacted them with a minor feature request a couple of years ago. It wasn't a positive experience. When we finalized the implementation, erwin scheduled a call, but no one showed up. 

Neutral

We have Data Intelligence deployed on-prem in our production environment, but we also have an environment for testing different versions of erwin. We are a validated project. Although Data Intelligence is deployed in a production environment, it's hard to mess things up because we are testing it at the end of the day, then rolling everything up. 

We have one person responsible for maintenance and implementation on the server side. They're also responsible for rolling out new features. We can implement this as JavaScript code. erwin gives us the option of creating scripts. To use Data Intelligence effectively, we need someone who knows JavaScript so that we can augment it. 

I rate erwin Data Intelligence an eight out of ten. Before implementing, you should adequately define the processes behind this tool. You need to understand the correct way to gather document metadata, set up a project in the mapping, and define the automation. 

If you do not have the processes sorted out, you will still have a map but won't realize all the benefits. It's all about standardization, so you can have the metadata in place. It's the same with automation. You need to understand what kinds of automation you need so you can implement it and deploy the necessary resources. 

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.

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.

• 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.

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.

We have been implementing erwin Data Intelligence for Data Governance since 2017-2018. 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 the 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.

It is stable. 

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.

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.

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.

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.

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.

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

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.

Initially, we had a data warehouse built on a digital data platform. The first task was to perform data fixes and optimize scripts to prepare data for our Teradata databases. We had to adapt the data types, indexes, etc., and generate the codebase. 

Most of our users are product developers. I'm unsure how many there are, but I believe it's fewer than a hundred. The operations team typically isn't digging deeper into the solution. This is related to how the project is organized. The operations team is primarily interested in what is already in production, whereas Data Intelligence is mostly for software development. 

We have erwin two environments: testing and production. The data fixes are performed in the testing erwin environment. Upgrades are also implemented in the test erwin environment. 

Data Intelligence creates a single source of truth for all of our metadata. This solution is better for data warehousing, but the metadata features speed up our development work. It's easy to create and manage mappings because we can export them to Informatica and pick up the work where we left off.

We are using the connectors for Snowflake and our data warehouse. The data connectors work well. We've never had any bugs or other issues when new versions of the connectors are released. 

The solution allows us to deliver data pipelines faster and cheaper. The alternative is to write the code down from scratch, so it's almost 30 percent faster. 

The data mapping features are helpful because it's critical for our technical analysts to properly mark all the requirements from end to end, from the source to the target. The metadata component is also handy because we can manage all the sources and pieces of metadata.

We can leverage Data Intelligence for data governance. Developers can manage all the data for the entire project. For example, code that is automatically generated must be verified. Data Intelligence helps our developers because they don't need to spend as much time preparing the code. The code is already generated, and they just need to validate it. 

The versioning can sometimes be confusing because we use the publishing feature for the mapping. Technical analysts sometimes have two versions, and they should know that the public version is the correct one. 

I started using Data Intelligence in 2019. 

Data Intelligence is stable. We haven't had any issues with the platform. It's working well. The only complaint I heard from a user is that there was a mapping conflict. Other than that, we haven't had any problems with the platform. 

I think Data Intelligence is scalable, but we've never had that many users. It's working well for us with our current user base. 

I rate erwin's support a nine out of ten. 

Positive

I wasn't involved with the deployment. Data Intelligence doesn't require much maintenance aside from periodic upgrades and adjustments to the server where it is deployed. One person is enough to handle it. 

I rate erwin Data Intelligence a nine out of ten. I would recommend the solution to others. I suggest doing a proof of concept to see if the solution meets your business requirements. You need to ensure you have the data connectors you need.

The three big areas that we use it for right now: metadata management as a whole, versioning of metadata, and metadata mappings and automation. We have started to adopt data profiling from this tool, but it is an ongoing process. I will be adding these capabilities to my team probably in Q1 of this year.

It is improving just a small piece of our company. We are an extremely big company. Implementing this to the company, there is probably a zero percent adoption rate because I think it is only implemented in the development team of our platform. 

If you look at this from the perspective of the platform that we are delivering, the adoption rate is around 90 percent because almost every area and step somehow touches the tool. We, as a program, are delivering a data-oriented platform, and erwin DI is helping us build that for our customers. 

The tool is not like Outlook where everyone in the company really uses it or SharePoint that is company-wide. We are using this in our program as a tool to help my technical analysts, data modelers, developers, etc.

The possibility to write automation scripts is the biggest benefit for us. We have several products with metadata and metadata mapping capabilities. The big difference when we were choosing this product was the ability to run automation scripts against metadata and metadata mappings. Right now, we have a very high level of automation based on these automation scripts, so it's really the core feature for us.

I'm working as a solution architect in one of the biggest projects and we really need to deliver quickly. The natural thing was that we went through the automation and started adopting some small pieces. Now, we have all our software development processes built around the automation capabilities. I can estimate that we lowered our time to market by 70 percent right now using these automation scripts, which is a really big thing.

The second best feature that we are heavily using in our project is the capability to create the mappings and treat them as a documentation. This has shown us the mappings to the different stakeholders, have some reviews, etc. Having this in one product is very nice.

The SDK behind this entire product needs improvement. The company really should focus more on this because we were finding some inconsistencies on the LDK level. Everything worked fine from the UI perspective, but when we started doing some deep automation scripts going through multiple API calls inside the tool, then only some pieces of it work or it would not return the exact data it was supposed to do. This is the number one area for improvement.

The tool provides the WSDL API as another point to access the data. This is the same story as with the SDK. We are heavily using this API and are finding some inconsistencies in its responses, especially as we are going for more nonstandard features inside. The team has been fixing this for us, so we have some support. This was probably overlooked by the product team to focus more on the UI rather than on the API.

We have been using the product for two and a half years.

There have been no issues with the stability from the erwin DI platform. We haven't encountered any problems for the last two and a half years.

It is maintained by another team. erwin is maintained by the team who generally maintains our platform. However, the effort is close to zero because there is nothing happening. Hold the backups and everything is automated by default on our shared platforms on which it is installed. 

It is a Java-based platform. So, if there would be some issues with the performance of this platform, we would probably migrate this to a bigger server. Therefore, it can scale. 

It does not have fancy cloud scaling tools capabilities, but we don't need this. For this type of tool and deployment, it's sufficient.

We have around 40 users. All the roles are very different because half of the developers work with different technologies. One-fourth of the users are technical analysts. The rest of the users are data modelers.

We have used the technical support several times. It's really different based on the complexity of the task. Usually, they meet their SLAs for fixes and changes in the required support time.

We did not previously use another product.

We used this product even before it was bought by erwin. Before, it was a company called AnalytiX DS. Then, after two years ago, erwin bought this company and their product, doing some rebranding. So, we started using this product as version 8.0, then it was migrated to version 8.3. Now, we are using version 9.0. We went through a few versions of this product.

The initial setup was not so simple, but it wasn't hard. If it would be between a one and five, with one being easy and five being hard, I would put it at a two. 

It was a new tool with new features. It had to be installed on-premise. Therefore, we struggled a bit with it. We were using it for quite a complex task, so we needed it to go through areas that would be potentially supported with the tool. The work associated with this initial setup to define that was not so easy, just to go through everything. 

Some companies have an initial packet that they show you everything in a very structured way. When we were implementing this, we really needed to discover what we needed rather than be given the documentation showing that this is here, this can contribute to your use case, and so on. We needed a lot of effort from our side. In comparison, I'm leading some other PoCs right now with other vendors in different areas. Those vendors contribute highly to me being capable to assess their tools, install and use them. 

The deployment took two days and was nothing special. It was just a simple Java application with a back-end database.

Migrating my team to use this tool properly, do some training, putting some capabilities so does people have some reason to use the tool, that took us around three months. Because we are using this for automation, the automation is an ongoing process lasting continuously for these two and a half years because we are adopting and changing to the new requirements. So, it's like continuous improvement and continuous delivery here.

I was involved from the very beginning of the PoC, actively checking the very basic capabilities. Then, I designed how we would use it, leading the whole automation stream around this tool. So, I was involved from the very beginning to the full implementation.

It took us around three months to introduce this tool.

If you count that it takes 70 percent less time to deliver and multiply this by 40 people who work around the development process, this is a big time savings that we can use for more development. From my perspective, there is a very big return on investment for this tool.

The licensing cost is around $7,000 for user. This is an estimation. 

There is an additional fee for the server maintenance.

We evaluated four products and chose erwin. None of the competitors had this out-of-the-box automation feature. This was the biggest thing for me because we were looking for a tool which would allow us to do big scale automation. When I was searching for this tool, my responsibility was to find a tool that could be used in our development process and core automation product. We built the whole development lifecycle and everything. In our platform, we are doing some development around automation capabilities. Usually people have a manual process and they automate some parts of it, we went the other way. We were searching for automation capabilities and built our entire process around its capabilities to use them as much as we could. The key differentiator straight from the very beginning was the automation capability.

Other competitors were showing us that they had an API and we could use that to automate somewhere else. Automating somewhere else means to me that I need to create some other platform, server, etc., then maintain it with some other resources to just make it run. This was really not enough for us. In addition, erwin already had some written automation templates on the PoC level which showed us that they had something that worked. 

At the PoC level, erwin was able to convince the customer (us) that this is the automation, this is how it runs, and you can use it almost straightaway.

I learned how to automate in the data area and how this is very different from any CI/CD development platforms that I was working on before. I learned that we need totally different things to automate properly in the data area. We need very accurate metadata. We need precise mappings reviewed by different data stakeholders. 

I would rate this product as an eight (out of 10). I can imagine some capabilities for this product that would make it even better.