Good morning, good afternoon, and good evening, everyone. My name is Jesse Tipton, field marketing manager at Quest, and I'm your host for today's session. Thank you all for joining us today for our webcast, Speeding Data Product Delivery from Model to Marketplace.
A couple of things to note before we begin. First, please type any questions you have in the Q&A field, and we will answer it at the end of the presentation. Second, today's session is being recorded, and it will be available to view by the end of the week on erwin.com.
You will also receive a follow up email with a link to on-demand recording. With that, let's get started. Our presenters today are Susan Lane, data thought leader and Yetkin Ozkucur, professional service director here at Quest. Welcome, Susan.
Thank you. Hello, everybody. Great to be with you today talking on this really modern fun topic of data product delivery from model to marketplace using Erwin.
We recognize, along with the many clients that we have and our field, that Gartner as well is talking a lot about data products today. And being that the number one issue that we still see-- I was on a TDWI webcast a couple of weeks ago, and the number one issue or priority for catalogs and marketplaces today was to be able to easily find and use data. The number one issue is end user adoption, and I really think that this new method of building products using data is helping in both of those avenues.
It's helping from a standpoint of structuring the data for business value through a product, and it's also helping in a way of being able to shop and share those products. And that's what we're here to talk to you about today. So let's start with what is a data product.
From our perspective, a product is something that really facilitates that end user need or requirement through the use of data. And we see data modelers, data scientists, data analysts, end user data consumers, and decision makers and everybody that's really in that DNA intelligence environment as the primary stakeholder requesting the products and building those products and then shopping and sharing them in the marketplace.
So there's a lot of stats that are out there right now that this is really a viable way to go and something that's going to be around for quite some time. There's three key attributes of a data product, the first being it must be accessible. So it must be available and easier for end users to grab and use.
It must be well-curated so that they understand what that product does and what potential insights they're going to be able to gain from that product. It must have some clear business value and a way to measure that incremental business value around the product itself. And then somebody is owning it, observing it, ensuring that it stays on track with the original intention of what that product was built for.
A really simple example that most of you can relate to is some banking problems. So if you're needing some data to back up, where is a great place to build a new physical branch location? Should that branch have ATM? Should it be a full service branch? How should it be staffed?
If that's the business problem, some of the components of a data product might be what are the data sets, customer addresses, loan histories, external data sets. So maybe I don't have all the data that I need, and I need to go reuse some purchase data or go purchase some data to have more data behind what I'm trying to do. And maybe there's already an AI model that's out there today that I can use for customer segmentation and, of course, being able to understand any logical models, any reports that have already been built to understand those different customer addresses and history of the loan, et cetera.
So, some potential insights could be that you learned from this product is how to adjust staffing accordingly, how to improve the customer experience, how to align my purchases for that customer segment. So this is just a real life example of what a product-- how a product is built, how it's used, and potential insights from a product. So within Erwin and in speaking with Gartner, we just had a meeting with Gartner yesterday, and they see a lot of the same issues out there when it comes to data governance, when it comes to data intelligence, and now when it comes to data products, where we're just standing up these things without any real clear intention and use cases behind it.
So for us at Erwin, it's really important for us to stay use case-driven, and we're a model first company. So if you're really looking to put some structure behind your products, we suggest that you start to iteratively model out those products. So instead of, you know, building that huge enterprise conceptual, logical, or physical model, you can also start building out models that relate specifically to a product, pass them to the catalog, which, there is an Erwin DI catalog.
A lot of people don't know that. But the DI catalog supports all that curation around the inventory of your systems, the data lineage, and also produces code. So if you have new code that needs to join data together to create that product, the catalog can produce that code. Then it's put back to the business to curate that information, to govern it, to associate it to any of those regulations or controls around the data that you need to adhere to.
We observe that data so you can subscribe to that data product and understand when the quality might go bad on that, or maybe when bias or data tends to drift and be alerted to the changes, to the data, to the
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