erwin Data Intelligence by Quest Reviews

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.

Our work involves data warehousing and we originally implemented this product because we needed a tool to document our mapping documents.

As a company, we are not heavily invested in the cloud. Our on-premises deployment may change in the future but it depends on infrastructure decisions.

The automated data lineage is very useful. We used to work in Excel, and there is no way to trace the lineage of the data. Since we started working with DI, we have been able to quickly trace the lineage, as well as do an impact analysis.

We do not use the ETL functionality. I do know, however, that there is a feature that allows you to export your mapping into Informatica.

Using this product has improved our process in several ways. When we were using Excel, we did not know for sure that what was entered in the database was what had been entered into Excel. One of the reasons for this is that Excel documents contain a lot of typos. Often, we don't know the data type or the data length, and these are some of the reasons that lineage and traceability are important. Prior to this, it was zero. Now, because we're able to create metadata from our databases, it's easier for us to create mappings. As a result, the typos virtually disappeared because we just drag-and-drop each field instead of typing it. 

Another important thing is that with Excel, it is too cumbersome or next to impossible to document the source path for XSD files. With DI, since we're able to model it in the tool, we can drag and drop and we don't have to type the source path. It's automatic.

This tool has taken us from having nothing to being very efficient. It's really hard to compare because we have never had these features before.

The data pipeline definitely improved the speed of analysis in our use case. We have not timed it but having the lineage, and being able to just click, makes it easier and faster. We believe that we are the envy of other departments that are not using DI. For them to conduct an impact analysis takes perhaps a few minutes or even a few hours, whereas, for us, it takes less than one minute to complete.

We have automated parts of our data management infrastructure and it has had a positive effect on our quality and speed of delivery. We have a template that the system uses to create SQL code for us. The code handles the moving of data and if they are direct move fields, it means that we don't need a person to code this operation. Instead, we just run the template.

The automation that we use is isolated and not for everything, but it affects our cost and risk in a positive way because it works efficiently to produce code.

It is reasonable to say that DI's generation of production code through automated code engineering reduces the cost from initial concept to implementation. However, it is only a small percentage of our usage.

With respect to the transparency and accuracy of data movement and data integration, this solution has had a positive impact on our process. If we bring a new source system into the data warehouse and the interconnection between that system and us is through XML then it's easier for us to start the mapping in DI. It is both efficient and effective. Downstream, things are more efficient as well. It used to take days for the BAs to do the mapping and now, it probably takes less than one hour.

We have tried the AIMatch feature a couple of times, and it was okay. It is intended to help automatically discover relationships and associations in data and I found that it was positive, albeit more relevant to the data governance team, of which I am not part. I think that it is a feature in its infancy and there is a lot of room for improvement.

Overall, DI's data cataloging, data literacy, and automation have helped our decision-makers because when a source wants to change something, we immediately know what the impact is going to be downstream. For example, if a source were to say "Okay, we're no longer going to send this field to you," then immediately we will know what the impact downstream will be. In response, either we can inform upstream to hold off on making changes, or we can inform the departments that will be impacted. That in itself has a lot of value.

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.

Improvement is required for the AIMatch feature, which is supposed to help automatically discover relationships in data. It is a feature that is in its infancy and I have not used it more than a few times.

There is room for improvement with the data cataloging capability. Right now, there is a list of a lot of sources that they can catalog, or they can create metadata upon, but if they can add more then that would be a good plus for this tool. The reason we need this functionality is that we don't use the modeling tool that erwin has. Instead, we use a tool called Power Viewer. Both erwin and Power View can create XSD files but you cannot import a file created by Power Viewer into erwin. If they were more compatible with Power Viewer and other data modeling solutions, it would be a plus. As it is now, if we have a data model exported into XSD format from Power Viewer, it's really hard or next to impossible to import into DI.

We have a lot of projects and a large number of users, and one feature that is missing is being able to assign users to groups. For example, it would be nice to have IDs such that all of the users from finance have the same one. This would make it much easier to manage the accounts.

We have been using erwin Data Intelligence (DI) for Data Governance since 2013.

The stability of DI has come a long way. Now, it's very stable. If I were rating it six years ago, my assessment would definitely have been different. At this time, however, I have no complaints.

We have the enterprise version and we can add as many projects as we need to. It would be helpful if we had a feature to keep better track of the users, such as a group membership field.

We are the only department in the organization that uses this product. This is because, in our department, we handle data warehousing, and mapping documentation is very important. It is like a bible to us and without it, we cannot function properly. We use it very extensively and other departments are now considering it.

In terms of roles, we have BAs with read-write access. We also have power users, who are the ones that work with the data catalog, create the projects, and make sure that the metadata is all up-to-date. Maintenance of this type also ensures that metadata is removed when it is no longer in use. We have QA/Dev roles that are read-only. These people read the mapping and translate it into code, or do QA on it. Finally, we have an audit role, where the users have read-only access to everything.

One of the tips that I have for users is that if there are a lot of mapping documents, for example, more than a few hundred rows for a few hundred records, it's easier to download it, do it in Excel, and upload it again.

All roles considered, we have between 30 and 40 users.

The technical support is good.

When erwin took over this product from the previous company, the support improved. The previous company was not as large and as such, erwin is more structured and has processes in place. For example, if we report issues, erwin has its own portal. We also have a specific channel to go through, whereas previously, we contacted support through our account manager.

Other than what we were doing with Excel, we were not using another solution prior to this one.

We have set up this product multiple times. The first setup was very challenging, but that was before erwin inherited or bought this product from the original developer. When erwin took over, there were lots of improvements made. As it is now, the initial setup is not complex and is no longer an issue. However, when we first started in 2013, it was a different story.

When we first deployed, close to 10 years ago, we were new to the product and we had a lot of challenges. It is now fairly easy to do and moreover, erwin has good support if we run into any trouble. I don't recall exactly how long it took to initially deploy, but I would estimate a full day. Nowadays, given our experience and what we know, it would take less than half a day. Perhaps one or two hours would be sufficient.

The actual deployment of the tool itself has no value because it's not a transactional system. With a transactional system, for example, I can do things like point of sale. In the case of this product, BAs create the mappings. That said, once it's deployed, the BAs can begin working to create mappings. Immediately, we can perform data cataloging, and given the correct connections, for example to Oracle, we can begin to use the tool right away. In that sense, there is a good time-to-value and it requires minimal support to get everything running.

We have an enterprise version, so if a new department wants to use it then we don't need to install it again. It is deployed on a single system and we give access to other departments, as required. As far as installing the software on a new machine, we have a rough plan that we follow but it is not a formal one that is written down or optimized for efficiency.

We had support from our reseller during the initial setup but they were not on-site.

Maintenance is done in-house and we have at least three people who are responsible. Because of our company structure, there is one who handles the application or web server. A second person is responsible for AWS, and finally, there is somebody like me on the administrative side.

We used to calculate ROI several years ago but are no longer concerned with it. This product is very effective and it has made our jobs easier, which is a good return.

We operate on a yearly subscription and because it is an enterprise license we only have one. It is not dependent on the number of users. This product is not expensive compared to the other ones on the market.

We did not buy the full DI, so the Business Glossary costs us extra. As such, we receive two bills from erwin every year.

We evaluated Informatica but after we completed a cost-benefit analysis, we opted to not move forward with it.

My advice for anybody who is considering this product is that it's a useful tool. It is good for lineage and good for documenting mappings. Overall, it is very useful for data warehousing, and it is not expensive compared to similar solutions on the market.

I would rate this solution a nine out of ten.

We use DI for Data Governance as part of a large system migration supporting application refresh and multi-site consolidation. Metadata Manager is utilized to harvest metadata which is augmented with custom metadata properties identifying rules criteria which drive automated source to target mapping. Custom build code generation connector then automates forward engineering code generation groovy. We've developed a small number of connectors supporting this 1:1 data migration. It's a really good product that we've been able to make very good use of.

This use case is a one-time system conversion solution not having life after the migration. Value is in the acceleration, accuracy, quality, and completeness of the migration source to target mapping and generated data management code.

Use case action is the extraction and staging of the source application data targeting ~700 large objects from the overall application set of ~2,400 relational tables. Each table extract has light join and selection criteria which are injected into the source metadata. The application itself is moving to a next-generation application that performs the same business function. Our client is in health and human services welfare administration in the United States. This use case doesn't have ongoing data governance for our client, at least at this point.

erwin DIS has enabled us to automate critical areas of data management infrastructure. That's where we see the benefit, in the acceleration of speed as well as the acceleration of quality and reduction of costs. 

erwin DIS generation of data management code through automated code engineering reduced the time it takes to go from initial concept to implementation for what we're in progress with right now. There is not a production delivery as of yet. That's still another year and a half out. This is a multi-year project where this use case is applied.

erwin has affected the transparency and accuracy of data movement and data integration quite a bit through the various report facilities. We can make self-service reporting available through the business user's portal. erwin DIS has provided the framework and the capability to be transparent, to have stakeholder involvement with the exercise the whole way along.

Through business user's portals and workflows, we're able to provide effective stakeholder reviews as well as then stakeholder access to all of the information and knowledge that's collected. The facility itself gives quite a few capabilities into user-defined parameters to capture data knowledge and organization change information which project stakeholders can use and apply throughout the program. Client and stakeholders utilize the business user's portal for extended visibility which is a big benefit.

We're interested in the AIMatch feature. It's something that we had worked with AnalytiX DS early on to actually develop some of the ideas for. We were somewhat instrumental in bringing some of that technology in, but in this particular case, we're not using it. 

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.

The UI just got a real big uplift, but behind the UI, there are quite a few different integrations that go on.

One big improvement we would like to see would be the workflow integration of codeset mapping with the erwin source to target mapping. That's a bit clunky for us. The two often seem to be in conflict with one another. Codeset mappings that are used within the source to target mappings are difficult to manage.

Some areas we found take time to process such as metadata scans, some of the management functions at a large scale do take time to process. That's an observation that we've worked with erwin support to a degree, but it seems that's just an inherent part of the scale of our particular project.

We're in our second year of using DI for Data Governance.

Erwin's latest general release has addressed performance of metadata sources having greater than 2,000 objects. Our use has 3 metadata sources each having ~ 2,400 relational objects. DIS provides good capability to organize projects and subject areas with multiple sublayers. All mappings have been set to synchronize with scanned metadata. Our solution had built over close to 2,000 mappings over 20K mapped code value pairs. So far so good, scanning and synchronizing metadata and reporting on enterprise gaps take some time to process but not unreasonable considering the work performed. 

Erwin support is pretty good. We've had our struggles there and I've gone through a lot of tickets. I'd rate them an eight out of ten.

There have been a couple of product enhancements, one of which I've not been able to get traction into and that was with regard to code set management and workflows. There's some follow-up that I have to do there. That doesn't seem to be a priority. It seems we have to have a couple of different discussions usually or deep dive to determine the problem understanding for a resolution. Sometimes that takes a little bit longer than I would like but all in all, it's pretty good.

We had erwin involved in the implementation. 

I don't think that it can be stood up quickly with minimal professional services. There's quite a bit of involvement. The integration of the solution into an environment ecosystem has challenges that take some effort especially if you're building new connectors. There's a good bit of effort in designing, preparing, planning, and building. It's pretty heavy as far as its integration effort.

The client is thrilled with higher quality, lower-cost products, and the services.

The financial model will be different. There is the cost of this software but there are offsetting accelerations through the automation as well as cost and efficiency. Don't be afraid of automation and don't get hung up on losing revenue due to automation. What I've seen is that some financial managers resist automation that results in a reduction of labor revenue. These reductions are ideally overcome through additional engagements, improve customer satisfaction, quality, add-on support, whatever the case, automation is a good thing.

The fact that this solution can be hosted in the cloud does not affect the total cost of ownership. The licensing cost is the same whether I use the cloud or on-prem. It may be the partner agreements but we do get some discounts and there's some negotiated pricing already in place with our companies. I didn't see that there was a difference in cloud license versus on-premise.

We haven't integrated Data Catalog and Data Literacy yet. Our client is a little bit behind on being able to utilize these aspects that we've presented for additional value. 

My advice would be to partner with an integrator. erwin has quite a few of them. If you're going to jump into this in earnest, you're going to need to have that experience and support.

The biggest lesson I have learned is that the only limitation is the imagination. Anything is possible. There's quite a strong capability with this product. I've seen what you can come up with as far as innovative flows, processes, automation, etc. It's got quite strong capabilities. 

The next lesson would be in regards to how automation fits within a company's framework and to embrace automation. There are some good quality points to continue with, certainly within the data cataloging, data governance, and so forth. There's quite a bit of good capability there. 

I rate erwin Data Intelligence for Data Governance a nine out of ten. 

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.

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 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.

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.