What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
We're in our second year of using DI for Data Governance.
What do I think about the scalability of the solution?
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.
How are customer service and technical support?
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.
What about the implementation team?
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.
What was our ROI?
The client is thrilled with higher quality, lower-cost products, and the services.
What's my experience with pricing, setup cost, and licensing?
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.
What other advice do I have?
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.
Which deployment model are you using for this solution?
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)