The Data Product Mindset: Diego Rodriguez
Join us for an insightful discussion between Diego Rodriguez, Product Management Principal at CoreLogic, and Harbr’s Anthony Cosgrove as they explore the evolution of data products and solutions in today's market.
🎙️ In this 23-minute episode, they'll dive into:
- The crucial distinction between data products and solutions
- CoreLogic's approach to delivering data — and value — to customers
- The journey from raw data assets to refined data products- Innovation mindset and AI opportunities in data
- Real-world customer use cases and applications
Whether you're a data product manager, business leader, or data professional, you'll gain valuable insights into how industry leaders are thinking about data quality, productization, and the future of data solutions. And if you'd like to learn more about CoreLogic's innovative use of its Harbr data marketplace, check out the case study.
Transcript
Anthony: Hi, welcome to episode nine of Data Product Mindset. I'm Anthony Cosgrove, the co-founder of Harbor, the private data marketplace platform that enables the discovery, access, use of data products at scale. Today I'm joined by Diego Rodriguez. Welcome Diego. He's a data scientist by background and now a data product manager at CoreLogic.
For this episode, it's going to be really interesting — you are a commercial data product manager at a very large commercial data vendor. I think you guys are just way ahead of the pack as most commercial data product managers are when it comes to data product management. A lot of our users are fairly new to the discipline. They may be doing it internally rather than commercially, which is a similar but different beast. I think your experience around what happens when you try to manage data products at scale is going to be super interesting to everyone.
So thank you very much for joining us. Really looking forward to this one. Why don't we start by telling us a bit more about what you do at CoreLogic and what a typical day looks like?
Diego Rodriguez: One of the exciting things about working at CoreLogic is that every day, every week's a little bit different. I'm heavily involved and invested in managing data products, and I'm also a customer-facing person within the organization, which I think can be really beneficial for product managers. I play both sides of the fence, and being external-facing really helps with managing products, while managing products helps inform how I position them with clients. It's really good to be wearing both of those hats.
Let me take a quick step back and introduce the company for your listeners. CoreLogic is an info services company. It's been around for multiple decades in the United States and in some markets abroad as well. We are a main provider of real estate and real estate-related data, data products and solutions. We're branching more into technology and trying to provide as much value as we can for existing and new clients.
Anthony: Perfect. That's a really nice segue — you've mentioned products, you've mentioned solutions, you've mentioned technology. How do you think about data products? Feel free to give some examples, but I guess for you specifically, how do you think about product versus solution is probably a really interesting question for everyone listening to this.
Diego: My opinion on that is really tied to my experience with CoreLogic and the data that we have. At its essence, CoreLogic data is aggregated from counties. A lot of it is raw data that — if you're thinking about using a food analogy — starts off very unrefined, like flour. We have a lengthy pipeline for processing that data before it gets to our customers. We can insert machine learning, we can insert refinement at different places. Some customers want to buy the flour, and some customers want to buy a more refined product.
Data has many different meanings to me, depending on the customer and what their needs are. The benefit of working with the data we work with is that we help guide its journey. We can figure out how to best position it with customers based on their needs and at the stage of data that it's in.
As a technical person, as an analytical person, I also have the ability to work with refined data and try to manipulate it so that it can be demonstrated to a customer for their needs. That's actually where I spend a lot of my time — helping a customer understand if they were to consume this data, what would that look like? Using their analytical people, using their use cases. So we get glimpses early on in the engagement process of customers' needs and we try to speak to that and help demonstrate what that would look like from our end. I do enjoy wearing the analytical hat as well and playing that role for them.
Anthony: Just thinking about your analogy where you've got some users really interested in quite a raw product — let's say a very large table of data being an example of that. Other customers want something that's a lot more refined and has gone through an algorithm, some analytical process in order to create a different output. Maybe that's still a table, but maybe it's not, maybe it's a visualization, maybe it's a very short list, maybe it's some kind of app. How do you think about product in relation to that? Are each of those things products, or is the product the whole thing and they're different mechanisms for consuming it? How do you define that, and why?
Diego: That's a great question. I think of the product as something that's usable, that meets a specific need. If we're refining an asset for demonstration or evaluation purposes — what we produce is the product. We can pipe the data into a visualization, we can pipe the data product into a report or summary, and we often do that. That's part of our process.
For example, a lot of public record US data is county-based. We're providing summaries of reports at the county level to give users insight into coverage and quality for those respective counties. A lot of our reporting can be aggregated to that geographic level, and that also translates into how we visualize the data.
I would say the visualization is the product. The refined data asset becomes a usable product because we have the ability and the team to manipulate the data for the use case, to demonstrate the use case. We can tailor it to the specific industry that the audience is from, whether it's real estate, finance, insurance.
There's also a lot of data blending, which is important because that's what our customers are doing too. We know that what they license from us may not be the only thing they use to enhance a model. We think about what else they might be consuming, or we think about their needs as being more than one data asset — it could be a blend of our different data assets. So we're often showcasing more than one asset at a time in a visualization, in a summary report, in a demonstration.
Anthony: When you talk about a product, what does that mean for you? What would be the characteristics or what would be the boxes that you would want to tick or have to tick in order to say, this is now a CoreLogic product?
Diego: It's interesting because our general philosophy at CoreLogic is that the data we have is essentially the truth. We want to provide as unrefined of a data asset as possible because customers have different interpretations of things. Like, if you talk to 10 different customers, they might have 3 or 4 different definitions of what a residential property is, and that might initially seem strange because you think of residential property, you think of a house, you think of a single family residence.
But it's not just that. It's also how the county is classifying the land that parcel sits on versus how the property is classified in a loan. Really what a data product is, is an interpretation. Internally, we're aligning, agreeing on certain characteristics of a property and being able to present that point of view to a customer through data.
In a way, we're telling them how we think about it, but ultimately it's a product when they've agreed to either adopt that interpretation or they've provided us with one of their own so that we can help mold the asset into the thing that's going to be most useful to them. A lot of it is collaboration with the clients, really understanding how their definition compares to ours and then manipulating it so that it's more relevant, more useful.
Anthony: Perfect. You've gone through quite a lot there in terms of working with a customer to initially understand how they define a particular data element or particular semantic meaning. You've spoken about trialing and adapting and molding the data to different needs. You've also spoken about bringing different assets together to help the customer get to the outcome that they're looking to achieve. So you're doing a lot of different things. What are the key lessons you've learned while you've been doing all of these things? What are the things you would share with other people and say, that's really important — don't do this or definitely do that?
Diego: I think it's definitely to have a point of view on what data quality means. The data that we have, that we license — we think it's very high quality in terms of what was captured from source, from record, and what we're able to put together and sell. I also think we do an incredible job with modeling. There are some products that CoreLogic has worked really hard to put to market, particularly around the mortgage space.
Understanding what quality means for a product like that can mean different things to different people and also at different levels of the data. Having general awareness of that to communicate outwards, and then also across the organization, sets the right expectation for how the data should be interpreted, understood and communicated to customers or end users.
Data transparency and data ethics is important. Because we encourage our customers to come to our environment and try out our data and use cases and refined products before they license, there's a commingling of our data and their data. We like to be transparent with them about how responsible we are with the data they bring onto our evaluation platform, or what we're doing with the data that we model. There's just a general transparency around data modeling and handling customer data.
I'm in a unique position where I feel like I get to innovate. Creating data products is also innovation, and that's really beneficial for not just our clients, but also the organization. Having an innovation mindset is important, even if it's small scale, even if it's just an internal proof of concept for something larger. You don't know necessarily what that's going to become. I think it's important for data product managers, specifically data product managers or data analysts, to incorporate innovation into their workflow.
Anthony: Just on that last point, I think a lot of data product managers start with some form of innovation. There's usually an idea, maybe there's some trigger event that leads them to start building a data product. They go through that innovation cycle, they get a product out there into the market, people start using it — if they've done their job well, hopefully that's a lot of value that those people are getting. They can see that value, they can build on it. But how do you find the time and the space to continue innovating when you're also running very large commercial products with a large install base already, and as you said, a lot of things that need to be monitored and managed in terms of quality, semantic meaning, and so forth?
Diego: You make it a priority. You put it front and center. Managing our customer exposure to data products is a priority. Making sure they have the best possible experience. Innovation is a priority. If you speak to anyone in CoreLogic, I think they would probably say the same for those two things, especially because we're shifting into more of a technology company.
For me specifically, I have the resources, really a platform, where I can do that. It's the same platform that I use to expose customers to our data assets and data products, is also the platform where I innovate, where I have a team where we innovate. Because of the platform, it makes it easier to do both of those things. And for transparency, that platform is the Harbor platform.
Anthony: If people want to check that, that's called the Discovery Platform by CoreLogic.
Diego: It's taken on a new name in 2025 and it will be called Workbench, but historically, yes, it's the Discovery Platform.
Anthony: So prioritizing it and then making it easy to sort of do both in close proximity to the customer as part of the overall customer experience. Is that fair?
Diego: That is fair, yes.
Anthony: You shared a bit about moving towards being a technology company, you shared a bit about renaming your platform. There's a lot happening in the data product space at the moment. I think a lot of people are now managing data products internally within their organizations and trying to drive that internal value at scale. New data businesses seem to be popping up all the time, built around one or more data products. And you guys obviously have been in that space for a very long time and have prioritized innovation. Where do you see it heading? What's on the horizon? What excites you? What does the future look like?
Diego: As a former data scientist, or I guess still kind of current data scientist, I'm really excited about our organization and just really the industry at large leveraging AI. I think it would be remiss to not mention that, simply because I think it will allow us to either create data products faster, or at least incorporate it in such a way that our users can learn about our data products more succinctly, more quickly and get through that introduction phase a lot more quickly. In the market, I've seen that become more prevalent. I think CoreLogic is looking to incorporate that as well for our customers.
I also think that having bite-sized products is going to be helpful, particularly through a UI. I've seen that interfaces are really helpful for those business users, for those that may not be the most technically savvy people that aren't in programming. So interacting with data through interfaces, I think is going to be pretty big, will continue to be big. And CoreLogic is working on that and providing those kinds of interface-based solutions to customers.
Anthony: Is that about expanding your users or is that about just making it easier for existing users? How do you view that?
Diego: I think it's a little bit of both. We definitely have two different sides of the business — it can be distilled to bulk data and solutions. We can use solutions to demonstrate the value of bulk data, and we can do more with bulk data and pipe that through solutions. For our bulk data audience, they might continue to want to consume via bulk data, but for those solution-based customers, I think the more that we have to offer, even if it's just through new use case based solutions, would allow us to increase the number of customers that we can support.
Anthony: Cool. I have about 15 more questions, but we're out of time. So we'll have to stop there and I'll save them for offline. Diego, thank you very much for joining and sharing your experiences. For those watching, you can connect with Diego on LinkedIn and find out more online. If you're building data products or looking for a data marketplace, check out harbrdata.com. And you can also find all of the previous episodes of the Data Product Mindset podcast there. Thank you very much for listening.
