erwin Data Intelligence Reviews

erwin Data Intelligence serves two primary use cases: one is where people use it as a data catalog, which is one of the main ones, and the other is for traceability and lineage.

The functionality of erwin Data Intelligence includes smart connectors, which are data connectors specifically designed for ETL and ELT and reversing that type of code for documenting lineage. This feature is probably the most valuable because it allows for automated reverse engineering of lineage.

Business glossaries have significantly improved collaboration within companies. Metadata is very technical, and understanding the glossaries of business terms is crucial for making sense of it. Currently, one can do manual associations between metadata and business terms, and I am also interested to see if AI can help support creating these relationships.

The dashboard in erwin Data Intelligence is customizable, and you can easily create different views. However, you are confined to the widgets that are already provided, so the customization is somewhat limited.

I have been working with erwin Data Intelligence for about six years.

From my perspective, I would rate the stability of erwin Data Intelligence as an eight or nine out of ten.

Regarding scalability, I would rate erwin Data Intelligence as an eight or nine for its ability to expand.

Quest technical support is very good, as they provide not only a technical help desk but also a data automation team that creates and customizes smart connectors, offering a wealth of skilled support.

My company, being a partner with Quest, can work directly with them for support and purchasing, ensuring access to deep technical assistance from the erwin Quest team.

The initial setup for erwin Data Intelligence is really easy, and the steps are quite quick. The challenge usually lies in the configuration and commissioning to ensure that the right users utilize it effectively.

The main benefits end users receive from erwin Data Intelligence include saving time and money while streamlining processes. Compared to competitor products like Collibra, erwin Data Intelligence is more cost-effective, providing a data catalog and data lineage views without the high costs associated with governance software.

I am not currently working with Collibra Governance or using their product. I work for a company that is a partner with Quest, specifically with erwin Quest products, and I was looking for competitor information, which is why I looked into reviews for Collibra.

I am working with erwin Data Intelligence Suite from Quest.

I am open to answering a few questions about erwin Data Intelligence and sharing my opinion about the product.

Regarding the analytic part of the product, I find it very interesting that erwin Data Intelligence has its own inbuilt reporting toolset, with a new version coming out in January that will be AI-powered. This means you will be able to write analytical reports and questions based on the metadata and data lineage, making it more powerful than the current version, as AI will assist in creating those reports and performing analysis on the metadata.

I find that the time taken to realize value from erwin Data Intelligence can be quite long. Creating a data catalog and developing data lineage takes significant time, and I expect that AI will help accelerate the process of creating data products by streamlining the steps involved.

I can recommend erwin Data Intelligence to other users. I would rate this review as an eight out of ten overall.

My main use case for erwin Data Intelligence is applying it and the DQ Labs in a financial institution.

The features I find most valuable in erwin Data Intelligence cover all the functions, especially the mind maps. The mind maps clarify for the business the related business terms and the relation between the business terms and other technical terms and technical data products, and they are very effective.

The main benefits erwin Data Intelligence provides to me include better decision-making, saving money and time by applying governance policies and procedures on the actual technical data, ensuring decisions are based on KPIs that are governed from source to end, and providing functionality for data classification to prevent unauthorized access through integration with the infrastructure team using DLP methodology.

In my opinion, the analytics part of erwin Data Intelligence is not satisfactory. The name 'Intelligence' is not related specifically to analytics; it is focused on data governance with no advanced analytics, only simple profiling on the data.

erwin Data Intelligence could improve particularly in the UI, as they are using old technologies that are not modern and do not serve the current requirements of the modern market.

I would like to see improvements in erwin Data Intelligence regarding connectors, as they have some limitations, especially with open-source tools. If you need to connect to Postgres or MongoDB, you need to request an ad hoc scanner for that specific purpose, and this ad hoc solution works only once and is not automated or scheduled.

I have been working with erwin Data Intelligence for two years.

I would rate the stability of the product as eight. It is good for small and medium enterprises, but larger enterprises with huge amounts of metadata might face some issues.

Regarding scalability, I believe we faced some issues on-premises. When we tried to connect erwin Data Intelligence to ERP on Oracle Cloud, specifically Oracle Fusion, we encountered many problems and could not achieve successful connections after numerous attempts.

I would rate erwin's technical support around six or seven.

For technical support, they need to improve response time. While the quality is good, the response time is not satisfactory. We often have to escalate and call multiple times to get a response to our cases.

Positive

Before working with erwin Data Intelligence, I compared it with Informatica, which is the top tool in the Middle East, but its pricing and licensing are very high. I find erwin Data Intelligence to be a good replacement for Informatica, although it does not have all the functionalities and quality of that solution. Other replacements include open-source tools such as OpenMetadata, which is a very good tool with a modern stack, and Oval Edge, which provides good functionality at a pricing tier similar to erwin Data Intelligence.

The initial setup process for erwin Data Intelligence is straightforward. I would rate it as medium complexity, neither simple nor complex.

The integration of business glossaries has significantly helped improve collaboration in our organization. We define the business glossaries first during meetings with the business, and then we bulk import them to erwin Data Intelligence once we have defined these terms based on the policy of the organization.

I am satisfied with the customization reports from erwin Data Intelligence. They provide some KPIs and we can build dashboards from a group of KPIs to track governance parts. For example, if we need to build a dashboard for data assets that lack descriptions, we can first build a KPI or metric, and then use these KPIs in the main dashboard, which provides this functionality.

Automated metadata management in erwin Data Intelligence has helped reduce data risk because it has an automated scanner to pull the metadata, and it has some AI capabilities that can provide descriptions for undescribed columns and classification for well-known columns such as personal data or financial data.

I would rate this review an overall eight out of ten.

Our primary use case is that we want to enable self-service for different business teams to be able to find different data. We are using erwin Data Intelligence platform to enable data literacy and to enable different users to be able to find the data by using the data catalog.

It can be hosted on-premise or in the cloud. We chose to run it in the cloud because the rest of our analytics infrastructure is running in the cloud. It made natural sense to host it in the cloud.

I represent IT teams, and a lot of times different business teams want to do data analysis. Before using erwin Data Intelligence Suite, they used to constantly come to IT teams to understand things like how is the data is organized and what type of queries or table they should use. It used to take a lot of my team's time to answer those questions. Some of those questions were pretty repetitive. With erwin Data Intelligence Suite, they can now do a self-service. There is a business user portal using which they can search different tables. They can do the search in different ways. If they already know the table name, they can just directly search for that table name, and they will find the definition of each column there, and that would help them in understanding how to use that table. In some cases, they may not know the exact table name, but they may know, for example, a business metric. In such a case, they can search by using a business metric, and, inside the tool, they can link those business metrics to the underlying tables from which these metrics get calculated. They can get to the table definitions through that route as well. This is helping all of our business analysts to do the self-service analytics, and, at the same time, we can enforce some governance around it. Because we enabled self-service for different business analysts, it has improved the speed. It has easily reduced at least 20% of the time that my IT team had to spend answering questions from different business teams. The benefit is probably even more for business teams, and I think they are faster by at least 30% in terms of being able to get the data that they need and perform their analysis based on that. I would expect at least 25% savings in time.

It has a big impact in terms of the transparency of the data. Everybody is able to find the data by using the catalog, and they can also see how the data is getting loaded through different tables. It has given a lot of transparency. Based on this transparency, we were able to have a good discussion about how the data should be organized so that we can collaborate better with the business in terms of data organization. We were also able to change some of our table structures, data models, etc.

By using the data catalog, we have definitely improved in terms of maturity as a data-driven decision-maker organization. We are now getting to a level where everybody understands the data. Everyone understands how it is organized, and how they can use this data for different business decisions. The next level for us would be to go and use some of these advanced features such as AI Data Match.

In terms of the effect of the data pipeline on our speed of analysis, understanding the data pipeline and the data flow is helpful in identifying a problem and resolving it quickly. A lot of times there is some level of ambiguity, and businesses don't understand that how the data flows. Understanding the data pipeline helps them in quickly identifying the problems. They can solve the identified problems and bottlenecks in the data flow. For example, they can identify the data set that is required for a specific analysis and then bring in the data from another system. 

In terms of the money and time that the real-time data pipeline has saved us, it is hard to quantify the amount in dollars. In terms of time, it has saved us 25% time on the analysis part.

It has allowed us to automate a lot of stuff for data governance. By using Smart Data Connectors, we are automatically able to pull metric definitions from our reporting solution. We are then able to put an overall governance and approval process on top of that. Whenever a new business metric needs to be created, the data stewards who have Write access to the tool can go ahead and create those definitions. Other data stewards can peer-review their definitions. Our automated workflow then takes that metric to an approved state, and it can be used across the company. We have definitely built a lot of good automation with the help of this tool.

It definitely affects the quality of data. A lot of times, different teams may have different levels of understanding, and they might have different definitions of a particular metric. A good example is the customer lifetime value. This metric is used by multiple departments, but each department can have its own metric definition. In such a case, they will get different values for this metric, and they won't make consistent decisions across. If they have a common definition, which is governed by this tool, then everybody can reference that. When they do the analysis, they will get the same result, which leads to a better quality of decision-making across the company.

It affects data delivery in terms of making correct decisions. Ultimately, we are using all of this data to get some insights and then make decisions based on that. It is not so much of the cost but more of the risk that it affects.

Being able to capture different business metrics and organize them in different catalogs is most valuable. We can organize these metrics into sales-related metrics, customer-related metrics, supply chain-related metrics, etc. 

Data catalog and data literacy are really the great capabilities of this solution that we leverage. A data catalog is coupled with the business glossary, which enables data literacy. In the business glossary, we can maintain definitions of different business terms and metrics, and then the data catalog can be searched with them. 

Data lineage is very important to us. It is related to the origin of the data. For example, if a particular metric gets calculated from certain data, how did this data originate? From which source system or table did this data originate? After we have the data, lineage is populated, and some advanced users, such as data scientists, can use this data lineage to get to the details. Sometimes, they are interested in kind of more raw data so that they can get details of the raw table from which these metrics are getting calculated. They can use that raw data for their machine learning or AI use cases.

I used a couple of different Smart Data Connectors, and they were great. One of the Smart Data Connectors that we used was for our Microstrategy BI solution, so it was a Microstrategy Smart Data Connector. Microstrategy is our enterprise reporting tool, and a lot of the metrics were already built in different reports in Microstrategy. So, it made sense to use this connector to extract all the metrics that were already being used. By using this connector, we could connect to Microstrategy, pull all the metrics and reports from that, and then populate our business glossary with those details. This was a big advantage of using the Microstrategy Smart Data Connector. Another Smart Data Connector that we used was the Python Connector. It enabled us to build the data lineage. We already have a lot of ETL kind of processes built by using Python and SQL, and this connector can reverse engineer that and graphically show how the data flows from the source. This work was done by our data engineers or IT teams, but the business teams didn't understand how it is built. So, by giving them a visual representation of that, they became more data literate. They understood how the data flows from different tables and ultimately lands in the final tables.

It can be stood up quickly with minimal professional services. Its installation and configuration are not that complicated, and we can easily and quickly stand it up. We wanted to get faster time to value. So, we did a small professional services engagement to come up to speed in terms of how to use the product. Its installation and configuration were pretty quick, but afterward, for configuring it, we wanted to make sure that we have the right processes established within the tool.

There may be some opportunities for improvement in terms of the user interface to make it a little bit more intuitive. They have made some good progress. Originally, when we started, we were on version 9 or 10. Over the last couple of releases, I've seen some improvements that they have made, but there might be a few other additional areas in UI where they can make some enhancements.

We have been using erwin Data Modeler for quite a while. We have been using erwin Data Intelligence Suite for about one and a half years.

It seems to be easily scalable. I haven't seen any problems so far from the scalability aspect. We have strong support, so whenever I have some issues or there is something for which I need technical support, their support is always there to answer the questions. Their support has been great.

We have a few key users, such as data domain experts, and we have different business areas, such as marketing, sales, finance, supply chain, etc. Each of them has a domain expert who also has an account in erwin to maintain definitions. The rest of the organization kind of gets a read-only view into that. 

We have about 30 people who can maintain those definitions, and the rest of the organization can find the data or the definitions of that data. These 30 people include data stewards or data domain specialists, and they maintain the definitions of different business terms, glossary terms, and business metrics. There are about five different IT users who actually configure data lineages and data catalog definitions. These are the core teams that basically make sure that the catalog and Data Intelligence Suite are populated with the data. There are more than 200 corporate business users who then find this data after it is populated in the catalog. 

I would expect its usage to grow from 200 people to 2,000 people within the next year. When we become more mature at using this data and analytics, we will use the advanced features within the tool.

In terms of the support that I'm getting, I'm able to get all my requests fulfilled. The only thing that happened was Erwin got sold and then Quest acquired them, but so far, I haven't seen any issues because of this acquisition.

When we upgraded the version, we had some issues related to Smart Data Connector not working properly, so we had to log a ticket with them, and they were responsive. They set up meetings with us to go through the problem and helped us in resolving the problem. Their support has been pretty responsive. When we submitted tickets, we got immediate attention and resolution.

The initial setup was straightforward. We only had to work with the Erwin team to get some of the Smart Data Connectors configured properly.

Its deployment was within three months. Installing, configuring, and getting it up to speed wasn't that much of a pain. Getting business users to use the tool and making sure that they are leveraging it for their day-to-day tasks is what takes more time. It is more of a change management exercise.

In terms of the implementation strategy, we worked with Erwin's team. In fact, I hired their professional services as well because I wanted to make sure we get up to speed pretty quickly. The installation, configuration, and some of the cleaning were done with Erwin's professional services team. After my team was up to speed, we retained some of the key data stewards. We included them as part of the planning, and they are now kind of driving the adoption and use of the tool across the company.

We didn't use any company other than Erwin's team. 

You don't need many resources for its deployment and maintenance. You just need one person, and this person also doesn't have to be full-time. Only in the initial stages, you have to spend time adjusting or populating these definitions. 

We are in the early stage of ROI. The ROI is more in terms of the time that we saved from the analysis. If the analysis is much faster, let's say by 30%, we can get some of the insights faster. We can then accordingly make business decisions, which will give the business value. So, right now, the ROI is in terms of being able to be faster to market with some of the businesses.

Smart Data Connectors have some costs, and then there are user-based licenses. We spend roughly $150,000 per year on the solution.

It is a yearly subscription license that basically includes the cost for Smart Data Connectors and user-based licenses. We have around 30 data stewards who maintain definitions, and then we have five IT users who basically maintain the overall solution. It is not a SaaS kind of operation, and there is an infrastructure cost to host this solution, which is our regular AWS hosting cost.

When we were looking for a data catalog solution, we evaluated two or three other solutions. We evaluated data catalogs from both Alation and Collibra. We chose Erwin because we liked the overall solution that Erwin offered as compared to the other solutions.

One of the great features that Erwin provided was the mind map feature, which I did not see in any of the other tools that we used. A mind map gives a visual representation of how the data flows from tables to the metrics. Another great feature was being able to pull the metric definitions automatically from our reporting system. These were the two great positives for us, which I did not see in the other solutions when we did the proof of concept.

We are not using erwin's AI Match feature to automatically discover and suggest relationships and associations between business terms and physical metadata. We are still trying to get all of our data completely mapped in there. After that, we will get to the next level of maturity, which would be basically leveraging in some of the additional features such as AI Match.

Similarly, we have not used the feature for generating the production code through automated code engineering. Currently, we are primarily doing the automation by using Smart Data Connectors to build some data lineages, which is helping with the overall understanding of the data flow. Over the next few months, as it gets more and more updated, we might see some benefits in this area. I would expect at least 25% savings in time.

It provides a real-time understandable data pipeline to some level. Advanced users can completely understand its real-time data pipeline. Average users may not be able to understand it.

Any organization that is looking into implementing this type of solution should look at its data literacy and maturity in terms of data literacy. This is where I really see the big challenge. It is almost like a change management exercise to make sure people understand how to use the data and build some of the processes around the data governance. The maturity of the organization is really critical, and you should make your plans accordingly to implement it.

The biggest lesson that I have learned from using this solution is probably around how to maintain the data dictionary, which is really critical for enabling data literacy. A lot of times, companies don't have these data dictionaries built. Building the data dictionary and getting it populated into the data catalog is where we spend some of the time. A development process needs to be established to create this data dictionary and maintain it going forward. You have to just make sure that it is not a one-time exercise. It is a continuous process that should be included as part of the development process.

I would rate erwin Data Intelligence for Data Governance an eight out of 10. If they can improve its user interface, it will be a great product.

We are a consultant for erwin Data Intelligence by Quest and provide the service to our customers for data access discovery.

The standard data connectors for automation, metadata, harvesting, and ingestion are easy to use.

The solution enables us to deliver data pipelines faster and with a 20 to 30 percent reduction in cost.

erwin provides an immediate view of the technical details required to manage our data landscape. Data transparency is increasing which helps our IT operations.

It delivers up-to-date and detailed data lineage which is important.

erwin helps us with data discovery and understanding of our entire organization's data.

The solution provides visibility into our organization's data for our IT, data governance, and business users which we require to build the compound report.

The asset discovery and collaboration provided by the data quality feature is good.

Its ability to affect data users' confidence levels when they are utilizing data is admirable.

erwin's capacity to tackle challenges associated with data quality and offer the required information for users to make well-informed decisions is effective in overseeing the regional database and metadata.

The data mapping manager is the most valuable feature.

The data quality assessment requires third-party components and a separate license. 

I would like to have better integration around the data quality.

I would appreciate the inclusion of a non-structured database feature in a future release.

I am currently an IT user for erwin Data Intelligence by Quest. 

The solution is stable.

We might encounter problems with disaster recovery while attempting to scale.

The response time for technical support is slow.

Neutral

Compared to Informatica, erwin Data Intelligence by Quest is more integrated and has a better UI, but Informatica has a larger product line geared toward enterprise business. erwin Data Intelligence by Quest is focused on two areas.

The initial setup is straightforward. Before deployment, we engaged in discussions with the data owner and the DBA team. Subsequently, we communicated with the business users, who constitute the general consumer base. We assisted them in defining the business terms through a workshop. 

The deployment took six months and required five people to complete.

We had help from the vendor during the implementation. We are satisfied with their help.

The price is reasonable, and a subscription is required.

I would rate erwin Data Intelligence by Quest seven out of ten.

Before implementing erwin Data Intelligence by Quest, potential users should first determine their use case.

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. 

The Data Intelligence suite helps us manage all our data governance activities around setting up metadata, data lineage, business clusters, and business metadata definitions. These are all managed in this tool. There is definitely extended use of this data set, which includes using the metadata that we have built. Using that metadata, we also integrate with other ETL tools to pull the metadata and use it in our data transformations. It is very tightly coupled with our data processing in general. We also use erwin Data Modeler, which helps us build our metadata, business definitions, and the physical data model of the data structures that we have to manage. These two tools work hand-in-hand to manage our data governance metadata capabilities and support many business processes.

I manage the data architecture plus manage the whole data governance team who designed the data pipelines. We designed the overall data infrastructure plus the actual governance processes. The stewards, who work with the data in the business, set up the metadata and manage this tool everyday end-to-end.

The benefit of the solution was the adoption of a lot of business partners using and leveraging our data through our governance processes. We have matrices of how many users have been capturing and using it. We have data consultants and other data governance teams who are set up to review these processes and ensure that nobody is really bypassing them. We use this tool in the middle of our work processes for utilization of data on the tail-end, letting the business do self-service, and build our own IT things.

When we manage our data processes, we know that there are some upward sources or downstream systems. We know that they could be impacted based on some changes coming in from the source or some related to the lineage and impact analysis that this tool brings to the table. We have been able to identify system changes which could impact all downstream systems. That is a big plus because IT and production support teams are now able to use this tool to identify the impact of any issues with the data or any data quality gaps. They can notify all the recipients upfront with the product business communications of any impacts.

For any company mature enough to have implemented any of these data governance rules or principles, these are the building blocks of the actual process. The criticality is such because we want the business to self-service. We can build data lakes or data warehouses using our data pipelines, but if nobody can actually use the data to be able to see what information they have available without going through IT sources, that defeats the whole purpose of doing this additional work. It is a data platform that allows any business process to come in and be self-service, building their own processes without a lot of IT dependencies.

There is a data science function where a lot of critical operational reporting can be done. Users leverage this tool to be able to discover what information is available, and it's very heavily used.

If we start capturing additional data about some metadata, then we can define our own user-defined attributes, which we can then start capturing. It does provide all the information that we want to manage. For our own processes, we have some special tags that we have been able to configure quickly through this tool to start capturing that information.

We have our own homegrown solutions built around the data that we are capturing in the tool. We build our own pipelines and have our own homegrown ETL tools built using Spark and cloud-based ecosystems. We capture all the metadata in this tool and all the transformation business rules are captured there too. We have API-level interfaces built into the tool to pull the data at the runtime. We then use that information to build our pipelines.

This tool allows us to bring in any data stewards in the business area to use this tool and set up the metadata, so we don't have to spend a lot of time in IT understanding all the data transformation rules. The business can set up the business metadata, and once it is set up, IT can then use the metadata directly, which feeds into our ETL tool.

Impact analysis is a huge benefit because it gives us access to our pipeline and data mapping. It captures the source systems from which the data came. For each source system, there is good lineage so we can identify where it came from. Then, it is loaded into our clean zone and data warehouse, where I have reports, data extracts, API calls, and the web application layer. This provides access to all the interfaces and how information has been consumed. Impact analysis, at an IT and field levels, lets me determine:

• What kind of business rules are applied. 
• How data has been transformed from each stage. 
• How the data is consumed and moved to different data marts or reporting layers. 

Our visibility is now huge, creating a good IT and business process. With confidence, they can assess where the information is, who is using it, and what applications are impacted if that information is not available, inaccurate, or if there are any issues at the source. That impact analysis part is a very strong use case of this tool.

The most critical features are the metadata management and data mapping, which includes the reference data management and code set management. Its capabilities allow us to capture metadata plus use it to define how the data lineage should be built, i.e., the data mapping aspects of it. The data mapping component is a little unique to this tool, as it allows the entire data lineage and impact analysis to be easily done. It has very good visuals, which it displays in a build to show the data lineage for all the metadata that we are capturing.

Our physical data mapping is using this tool. The component of capturing the metadata, integrating the code set managers and reference data management aspects of it with the data pipeline are unique to this tool. They are definitely the key differentiators that we were looking for when picking this tool.

erwin DI provides visibility into our organization’s data for our IT, data governance, and business users. There is a business-facing view of the data. There is an IT version of the tool that allows us to set up the metadata managed by our IT users or data stewards, who are users of the data, to set up the metadata. Then, the same tool has a very good business portal that takes the same information in a read-only way and presents it back in a very business-user friendly way. We call it a business portal. This suite of applications provides us end-to-end data governance from both the IT's and business users' perspective.

It is a central place for everybody to start any ETL data pipeline builds. This tool is being heavily used, plus it's heavily integrated with all the ETL data pipeline design and build processes. Nobody can bypass these processes and do something without going through this tool.

The business portal allows us to search the metadata and do data discovery. Business users come in and present data catalog-type information. This means all the metadata that we capture, such as AI masking, dictionaries, and the data dictionary, is set up as well. That aspect is very heavily used.

There are a lot of Data Connectors that gather the data from all different source systems, like metadata from many data stores. We configure those Data Collectors, then install them. The Data Connector that helps us load all the metadata from the erwin Data Modeler tool is XML-based.

The solution delivers up-to-date and detailed data lineage. It provides you all the business rules that data fields are going through by using visualization. It provides very good visualization, allowing us to quickly assess the impact in an understandable way.

All the metadata and business glossaries are captured right there in the tool. All of these data points are discoverable, so we can search through them. Once you know the business attribute you are looking for, then you are able to find where in the data warehouse this information lives. It provides you technical lineage right from the business glossary. It provides a data discovery feature, so you are able to do a complete discovery on your own.

The data quality has so many facets, but we are definitely not using the core data quality features of this tool. The data quality has definitely improved because the core data stewards, data engineers, data stewards, and business sponsors know what data they are looking for and how the data should move. They are setting up those rules. We still need another layer of data quality assessments on the source to see if it is sending us the wrong data or if there are some issues with the source data. For those things, we need a rule-based data quality assessment or scoring where we can assess tools or other technology stacks. We need to be able to leverage where the business comes in, defining some business rules and have the ability to execute those rules, then score the data quality of all those attributes. Data quality is definitely not what we are leveraging from this tool, as of today.

I have been using it for four or five years.

We had a couple of issues here and there, but nothing drastic. There has been a lot of adoption of the tool increasing data usage. There have been a few issues with this, but not blackout-type issues, and we were able to recover. 

There were some stability issues in the very beginning. Things are getting better with its community piece.

Scalability has room for improvement. It tends to slow down when we have large volumes of data, and it takes more time. They could scale better, as we have seen some degradation in performance when we work with large data sets.

We have some open tickets with them from time to time. They have definitely promptly responded and provided solutions. There have been no issues.

Support has changed hands many times, though we always land on a good support model. I would rate the technical support as seven out of 10.

They cannot just custom build solutions for us. These are things that they will deliver and add to releases. 

Neutral

We were previously using Collibra and Talend data management. We switched this tool to help us build our data mapping, not just field-level mapping. There are also aspects of code set management, where we are translating different codes that we are standardizing to enterprise codes. With the reference data management aspects of it, we can build our own data sets within the tool and that data set is also integrated with our data pipeline.

We were definitely not sticking with the Talend tool because it increased our delivery time for data. When we were looking for other platforms, we needed a tool that captured data mapping in a way that a systematic program could actually read and understand it, then generate the dynamic code for an ETL processor pipeline.

It was through AWS. The package was very easy to install. 

If I use a traditional ETL tool and build it through an IT port, it would take five days to build very simple data mapping to get it to the deployment phase. Using this solution, the IT cost will be cut down to less than a day. Since the business requirements are now captured directly in the tool, I don't need IT support to execute it. The only part being executed and deployed from the metadata is my ETL code, which is the information that the business will capture. So, we can build data pipelines at a very rapid rate with a lot of accuracy. 

During maintenance times, when things are changing and updating, businesses will not have access to their ETL tool, code, and the rules executed in the code. However, using this tool with its data governance and data mapping, the data captured is what actually it will be. The rules are first defined, then they are fed into the ETL process. This is done weekly because we dynamically generate the ETL from our business users' mapping. That definitely is a big advantage. Our data will never be off the rules that the business has set up.

If people cannot do discovery on their own, then you will be adding a lot of resource power, i.e., manpower, to support the business usage of the data. A lot of money is saved because we can run a very lean shop and don't have to onboard a lot of resources. This saves a lot on manpower costs as well.

The licensing cost was very affordable at the time of purchase. It has since been taken over by erwin, then Quest. The tool has gotten a bit more costly, but they are adding more features very quickly. 

We did a couple of demos with data catalog-type tools, but they didn't have the complete package that we were looking for.

Our only systematic process for refreshing metadata is from the erwin Data Modeler tool. Whenever those updates are done, we then have a systematic way to update the metadata in our reference tool.

I would rate the product as eight out of 10. It is a good tool with a lot of good features. We have a whole laundry list of things that we are still looking for, which we have shared with them, e.g., improving stability and the product's overall health. The cost is going up, but it provides us all the information that we need. The basic building blocks of our governance are tightly coupled with this tool.

We are a large company, and we purchase a lot of small, medium, and large companies and roll them into our IT. As a result, we have a lot of challenges with mapping all of these systems. We brought in erwin Data Intelligence by Quest to automate some of the data transformations and get a head start on it.

We map corporate entities to these business units, which means that we deal with a lot of different data sources. The biggest use case we have is mapping things like an older mainframe-type database to SQL Server, which erwin does really well. We're super impressed with it.

erwin's staff were great to work with in terms of customization. For example, if we told them that we'd like customized connectors to generate DDL and SSIS packages, then in a couple of weeks they would usually have those features put in. They were very flexible and willing to make the changes on the fly. We had good direct access to the development staff, and they were great to work with.

The biggest impact for us is that erwin generates DDL extremely quickly. We're able to pull in metadata, map it to a target, generate DDL to create the tables, and generate SSIS packages. Previously, especially going back 10 to 15 years ago, hundreds of hours had to be spent to manually perform these tasks. This solution completely automates it and gets it 90% done. We can then pass it off to a developer to create the items in SSIS.

erwin provides visibility into our organization's data for our IT, data governance, and business users. When we pull in the mapping, we can generate SSIS packages for all our data warehouses. That saves hundreds of hours because it just does it for you. We can then send it to our data warehouse folks, and it's 90% done. In some cases, it's 100% done as far as the mapping goes. The metadata is part of the governance. A part of the erwin Data Integration Suite product is the BUP, which is the Business User Portal. Business users can go in and search in that portal, and they can find all kinds of entities, in terms of call names and so on. It is important to us that all three teams are able to leverage the data, and erwin does it well.

The data catalog dashboard handles the visibility of sensitive data distribution and top data sources quite well. It makes it extremely easy.

The data catalog dashboard is also easy to label and search, which has helped with our data governance.

The Business User Portal or data catalog dashboard does a pretty good job of providing a single view of all of the key attributes needed to manage our data landscape.

I would rank the Standard Data Connectors for automating metadata harvesting and ingestion from common industry sources as the highest piece of the product. It was our big use case, and in terms of being able to generate DDL and SSIS mapping to harvest data, I would rank it very high.

erwin enabled us to deliver data pipelines several times faster and with less cost for the use case we were looking at. We had to either roll out our own solution, invest the time and money there, or get up and running relatively quickly to do what we needed to do with erwin.

One thing that erwin does extremely well in terms of viewing the technical details needed to manage the data landscape, is that once the mapping is done it can generate mapping documents for business analysts and ECL developers to consume. We've been impressed with what it can do.

erwin does impact analysis and data quality assessment as well as any other tool that I've seen.

It delivers up-to-date and detailed data lineage.

We used data profiling in erwin DI, which saves time when it comes to data discovery and understanding the entire organization’s data. I would give it a high rating because it did it faster than some other tools that we've used.

We were fairly impressed with the Smart Data Connectors for reverse or forward engineering to automate the delivery and maintenance of data pipelines. However, our SSIS packages are extremely complex, and we pass a lot of variables. It makes it extremely hard for any kind of reverse-engineering automation. While we were impressed with what it was able to do, it wasn't great for us. The level of distraction that exists within the packages, passing parameters and variables down through several levels of containers, makes it very difficult to consume. In more complex use cases, it's hard to follow it and map all the metadata correctly.

It's a little bit clunky. I think a lot of these features were bolted on, and they don't necessarily transition smoothly in the interface. I would like to see a little more cohesion.

I've been using this solution for about three years.

erwin's technical support was great. They got right back to us when we had any questions.

Previously, we used IBM's Information Governance Catalog. We haven't switched to erwin as an enterprise, but we brought erwin DI in for the metadata mapping automation. It was the only product we found that could really target multiple SSIS from a single platform and had reverse- and forward-engineering capabilities.

The initial deployment was straightforward and took us a couple of weeks. 

This solution requires minimal maintenance. You have to put a little time into the upgrades, but in between upgrades, there is not too much maintenance.

We implemented it ourselves because it was easy to install.

We have seen an ROI, and it is more about speed and that opportunity cost.

The price is reasonable and competitive. When you get into forward and reverse-engineering, the cost could go up. However, if you are a large organization, you would probably be able to access different packages. If, however, you don't need forward and reverse-engineering, then the price is relatively cheap.

We had a very specific use case, and it definitely met our needs. Therefore, on a scale from one to ten, I would rate erwin Data Intelligence by Quest a nine.

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.

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.