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.
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.
