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

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

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

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

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

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

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.

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.

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.

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. 

We are looking forward to using the AI match capability. We are using several Smart Data Connectors, as well as the Reporting Manager and the Workflow Manager.

We are customizing our own installation of the erwin Data Intelligence Suite by adding fields as extended properties that do not already exist, that are of value to us, as well as changing the user-defined fields that are in the Data Intelligence Suite. We're renaming them so that we can put very specific information into those user-defined properties.

The customization and the ability to add information is extremely valuable to us because there is no tool on the market that is going to be able to accommodate, out-of-the-box, everything that every customer will use. Being able to tailor the tool to meet our needs, and add additional metadata, is very valuable to us.

Also, in terms of the solution's integrated data catalog and data literacy when it comes to mapping, profiling, and automated lineage analysis, it is incredibly important to have that business glossary and understand what the data is — the definitions of the data — so that you use it correctly. You can't move data when you don't understand what it is. You can't merge data with other data unless you know what it is or how to use it. Those business definitions help us with all of that: with the mapping and with being able to plan the movement of one data element into another data element. The data lineage, understanding where the data is and how it moves, is very critical.

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.

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.

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.

The most valuable features are lineage and impact analysis. In our use case, we deal with data transformations from multiple sources into our data warehouse. As part of this process, we need traceability of the fields, either from the source or from the presentation layer. If something is changing then it will help us to determine the full impact of the modifications. Similarly, if we need to know where a specific field in the presentation layer is coming from, we can trace it back to its location in the source.

The feature used to fill metadata is very useful for us because we can replicate the data into our analytics as metadata.

The most valuable features include: 

• The mapping facilities
• All of the mapping controls workflow
• The metadata injection and custom metadata properties for quality of mappings
• The various mapping tools and reports that are available
• Gap analysis
• Model gap analysis
• Codesets and codeset value mapping 

We use the codeset mapping quite a bit to match value pairs to use within the conversion as well. Those value pair mappings come in quite handy and are utilized quite extensively. They then feed into the automation of the source data extraction, like the source data mapping of the source data extraction, the code development, forward engineering using the ODI connector for the forward automation.

Smart Data Connectors to reverse engineer and forward engineer code from BI, ETL or Data Management Platform is where we're gaining most value. The capability is such that it's only limited by one's imagination or ability to come up with innovative ideas, to automate every idea that we've been able to come up with. We have been able to apply some form of automation to that. That's been quite good.

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.

The data mapping manager is the most valuable feature.

The interface is easy to use. I also like erwin's automatic data classification and data quality checks. It provides excellent visibility into stable data and data in motion. We have a clear view of ETL processes through data lineage, and a feature called the data mind map. 

erwin provides the necessary traceability. It's easy to assign tasks and keywords. Data owners can manage their data and assign access to various teams. 

The data catalog dashboard enables us to classify sensitive data and comply with regulations, which is crucial in Saudi Arabia. Every government organization must abide by the rules on personal data. I rate the data catalog dashboard nine out of 10. 

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.

• 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.

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.