World of Workato 2025 | Mike Frasca, Reltio

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Mike Frasca

VP, Product Management

Reltio

play_circle_outline Introduction of Mike from Reltio and discussion on data chaos resolution.

play_circle_outline Building a Robust Data Foundation: Overcoming Legacy Challenges for Effective AI Implementation Across SaaS Platforms

play_circle_outline Strategies for ensuring data quality in real-time environments.

play_circle_outline Workato's integration with Reltio for streamlined data flow and faster onboarding.

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Mike Frasca, Reltio

Mike Frasca

VP, Product Management Reltio

In this World of Workato interview, Mike Frasca, VP at Reltio, joins theCUBE hosts Rob Strechay and Savannah Peterson to discuss how enterprises can unlock the full potential of agentic automation by addressing one of AI’s biggest hurdles: data chaos. Frasca explains how Reltio embeds Workato to unify, cleanse, enrich and standardize customer data across dozens of siloed applications, making it usable in real time for AI-driven decision-making.

The conversation highlights the importance of delivering “gold” quality data to ensure AI agents make bette... Read more

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What is the significance of establishing a strong data foundation to achieve success with AI technologies? add

What are the challenges customers face with managing their data, and how does your platform assist in addressing those challenges? add

What methods are used to ensure the quality of real-time data processing? add

What role does Workato play in the integration of data sources within the platform? add

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Mike Frasca, Reltio

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Savannah Peterson

>> Good afternoon, Workato community, and welcome back to fabulous Las Vegas, Nevada. We're here on the back end of day one of our two days of programming at World of Workato. My name's Savannah Peterson, very delighted to be joined by Rob Strechay, as always. Such exciting stories coming out. Really excited we've got yet another customer-

Rob Strechay

>> Love it....

Savannah Peterson

>> on the stage with us. Please welcome Mike of Reltio. Mike, thank you so much for coming to hang out with us this afternoon.

Mike Frasca

>> Thank you for having me. Really appreciate it.

Savannah Peterson

>> You very clearly brought the energy and enthusiasm from the second you walked up to this news desk, so I am particularly excited about this segment, not just because of your energy, but because we're going to be talking about something absolutely mission critical to achieve success with AI genies and really anything in the tech future, and that is unraveling data chaos and making sure that you've got a really strong data foundation going into these. How are you helping your customers navigate legacy data, new data, all the incoming data from AI and start seeing some of these outcomes achieved?

Mike Frasca

>> Yeah, that's a great question. So today, most of our customers have 40, 50 different SaaS applications that are sitting across their stack. They have data everywhere. They've been in spreadsheets on people's laptops. They've got data and weird databases running in who knows where. So we actually embed Workato in our platform and use that to help us get data into Reltio. But once it's in Reltio, we unify, cleanse, enrich, and standardize that data to make it available for all of these agents so that they get that context, because that's really the challenge that all of these genies and the AI agents are having, is they can make decisions, but if they don't have contexts, they make bad decisions, and that's even worse.

Rob Strechay

>> Yeah. We were just briefly talking before this, and I think one of the things that I've seen... And people talk about master data management and other things around that, or the medallion version, bronze, silver, and gold type data. And to me, there's nothing but gold data should be in AI, when you think about it. How do you guys see that? Because again, you're dealing with a lot of customer 360 and things of that nature where you're having to bring and key off of different things to get that really keyed into that so that, hey, revenue means revenue and customer means customer, and things of that nature.

Mike Frasca

>> Yeah. So I think a few different things that are unique for us. One, we operate everything in real time. So you have this medallion architecture of bronze, silver, gold. That's great when you've got pipelines and you can wait a week for it to go through this process. Most of our customers can't. They can't wait minutes. They can't wait seconds. They're talking milliseconds to get this data available for these models and to operational systems. So we do all of that enrichment and all of that standardization and formatting on the fly, and we do that live.

Savannah Peterson

>> That's complex.

Mike Frasca

>> It is very complex.

Savannah Peterson

>> Yeah.

Mike Frasca

>> It's an exciting field to be in. It's-

Savannah Peterson

>> Yeah. Yeah.

Mike Frasca

>> It is a lot of data to be able to manage through that, but I agree 100 percent. The data that gets to these models in the end has to be at that gold level. Otherwise, once again, you make bad decisions. And then what do you do if you deploy 100 customer service reps that are all worse than your worst human? Right? You've just done nothing but hurt your business.

Savannah Peterson

>> And it costs money.

Mike Frasca

>> Exactly. Brand recognition, brand support, revenue, all of that gets impacted.

Savannah Peterson

>> How do you assure the quality of that data then? Because doing it in real time is a lot different than data at rest. You got to be on your toes and operating with a very high level of confidence. How do you make that happen?

Mike Frasca

>> So we do a few different things to make this available in real time. All of our matching and enrichment all happens in real time, and we work with a bunch of partners that have reference data sets. We have our own reference data set that we have now built into Reltio to be able to look up data against and be able to find matches and find that enrichment. But then the next thing that we do to make this happen in real time is we can start deploying all this data and co-locating it at the source wherever this work load is. So we shave off whole seconds and whole minutes off of the timeframe by putting your data wherever your agents are. So now, you don't need to worry about that transformation on top of everything.

Rob Strechay

>> I was going to say that, kind of just building off of that, what are some of the largest barriers that you see with people who are trying to get to agentic workflows and with their data? And how do you see them when they're trying to scale AI, and what some of the challenges are there?

Mike Frasca

>> Yeah. I think to me, the biggest one is real time. Prototypes work great when you have all data sitting in a lake, and it's all organized and structured and it's been there for a week, and you can run some model against it and get an output. But when you need to make a decision because someone's on the phone with your customer support center, you need to help them rebook a flight because you're an airline. I have a problem, I'm checking into my hotel room, and you've got to help them make a decision on that, you need that real time interaction. And that data sitting in a lake that may be two days old or a week old just doesn't help. So it's great for prototyping, but once you start shifting from prototype to production, you see a lot of customers really fall down and making that transition.

Savannah Peterson

>> I think that's a really good point. And I think speaking from even the community side of folks using your product on the other end or from that traveler who's trying to book a flight that just got delayed, or whatever might be there, that real time experience is also what makes AI real.

Mike Frasca

>> 100%.

Savannah Peterson

>> It's what lets us realize the benefits. It doesn't do us any good if it's just hanging out in a lake and not being able to be applied to our real lives.

Mike Frasca

>> You got... I want to be able to get rebooked on my flight quicker. I want to be able to check my hotel quicker, I want to solve a return quicker, and I want to be able to do this at scale. I was talking to some people earlier today that, I don't know, for the last two years, whenever I've called a customer service line, it's always longer than normal hold times, everything is out of the ordinary busy, but it's always like that. With agents, we can solve that problem. But if they can't really solve your problem, then it might as well not exist. So if we can do this right, if we can get contacts in real time to these agents to make decisions, they could have massive impacts for people across the board in every industry.

Rob Strechay

>> How do you look at it from a perspective of providing really... getting more operational and more agile with AI, and how... really getting AI agility because, to your point, the data is in real time. And that has to be a challenge for you and for your customers for what they're building on top of this.

Mike Frasca

>> So we kind of look at AI in two kind of buckets. We have AI that we're building that's built into our platform to help customers do the things they do within Reltio, organize standard, unify that data. But then we have this other section of AI where our customers are also getting value where they just need the data in that context. So we take a lot of the lessons learned we have around us using our own data to try to provide that to customers. I think the few big things are starting small and scope. Agents do really well. You define small scopes. An agent's not going to replace your CRM. It's not going to replace an entire application, but they replace a piece of it. They replace one workflow. Solve those problems one at a time and start quantifying the business value that you can get out of it, and you can really make an impact the scale is really effective for.

Savannah Peterson

>> Something that those of us who are very technology don't always want to talk about but must acknowledge is the level of risk here. And I can imagine there's quite a bit of risk with data and easing customer fears and ensuring that trusted data is what they're using. What are some of the examples of risk that customers should be thinking about as they go into cleaning up their data or working with you to do that to then achieve these outcomes?

Mike Frasca

>> Yeah. I think I see two major kind of issues that when I talk to customers about. The first one is not having the right context, right? You make a decision, but... We'll say an example. I book a lot for hotels every week. I travel a lot.

Savannah Peterson

>> We feel you.

Mike Frasca

>> I'm status. I'm sure that you know .

Savannah Peterson

>> You're in very welcome company right now. Yes.

Mike Frasca

>> My wife doesn't, but she books all our personal travel. But when she has a problem and she calls in to Marriott or she goes to front desk, I want them to treat her like me. They also want to treat her like me because that's going to have a direct impact on my business with them.

Savannah Peterson

>> Right.

Mike Frasca

>> But if they don't understand the context, then she's just another guest who stays there once a year, and they treat her poorly. That's a huge risk. But if you understand that full context, it immediately goes away, because now they know she's married to me. They know that I'm one of their top loyalty customers. She gets the same experience, and then she's happy. And if she's happy, I'm happy, right? And that saves their business. The other thing that I've seen is on the security side. It is a real challenge and a real risk to start deploying these agents about what they have access to,-

Savannah Peterson

>> Yeah....

Mike Frasca

>> what they can do, what kind of data can they manipulate and change. And I think really defining their security roles upfront is really important for customers.

Rob Strechay

>> When you look at... You kind of hit on it, and I look at some of the technology that you guys deploy out there, and you go down kind of the graph perspective and are able to help people from a data fabric. Kind of help us understand, because what we're seeing is that people still have a ton of data silos in their companies, and there's technologies, it sounds like, that you guys have that are helping break those down so you can bring disparate data together for AI.

Mike Frasca

>> 100%. So we take data from multiple sources of truth. So your CRM, your billing system, your shipping system, your marketing campaign system, they all probably have a different version of you, of me, Mike Frasca, right? They probably have multiple versions of me in some of these systems where I bought things under Michael and some things under Mike, so they don't even know I'm the same person.

Savannah Peterson

>> Different emails. Yeah.

Mike Frasca

>> Different emails, different addresses. So we bring all of that in, and then we manage that. We master that and organize that and match that together. We use lots of AI tools to do that. We have our own AI matching engine that sits on top of... We have enrichment providers that help us match things like addresses and the platform. And then we get that single view of that person, and we can pick the best name, the best address, the best phone number. So now when you go into a CRM, even if they had really bad data to start with, they could get the phone number that came in through billing because I had to have a good phone number for billing, or an insurance carrier from their policy underwriting system. They have great data and underwriting, but marketing probably has bad data. Now, marketing can take advantage of that data from underwriting.

Savannah Peterson

>> Let's talk a little bit about... You get to see lots of different types of customers and interact with a lot of different use cases across your product portfolio and customer portfolio. What are some of the things that are really impressing you in terms of enterprise leadership and how they're approaching the challenges and integrating this new technology?

Mike Frasca

>> Yeah. So I'm working with a company. They basically act as a poison control center for veterinarians, for dogs and cats.

Savannah Peterson

>> Oh, yeah.

Mike Frasca

>> And so they staff and have a bunch of poison experts for vets that other vets called, and then talk to them and ask them, what do I do? I have a dog in my clinic, and they've ate this thing. They're using AI to transcribe that conversation. They're using the context of the dogs and the people that are owning those dogs, getting medical history, just like you would a person, and trying to craft a wellness plan for them, provide that to the poison expert so they can just approve it. They can then service 4 or 5x more calls. That's 4 or 5x more pets that get saved in vets offices. That's amazing.

Savannah Peterson

>> Okay. We are both --

Mike Frasca

>> We love that....

Savannah Peterson

>> dog... Yeah. Yeah. We are big dog lovers and animal lovers over here, so you are absolutely speaking our language. And that's so amazing because you don't know where your animal might get hurt or have poison, or anything like this happen. And to have that full context, to know that Martini's kidneys are a little weak, for example, or something like that within that full wellness profile could be the difference between life and death, to your exact point. Wow, that's super compelling.

Mike Frasca

>> Yeah.

Savannah Peterson

>> So we're here at World of Workato. Talk to me about how working with Workato helps you achieve these goals for you and your customers.

Mike Frasca

>> So we embed Workato in our platform. It's a core part of our platform, and our customers use it to connect all of these disparate sources of data that they have, so their CRMs and their building systems and all of these places. And Workato enables them to connect to them, bring that data, and make that data available in Reltio. So it's really streamlined that process of ingestion and allowed customers to get much more time to value.

Savannah Peterson

>> Ooh, love that.

Mike Frasca

>> They come on board with Reltio. They don't need to go hire a developer, or three developers, or four developers. They can go have a Workato expert build a recipe in a day, and that data can start flowing through. They can do transformations in it. So for us, it's really completely about how fast can we get that data and make that data available to customers, and Workato enables that for us.

Savannah Peterson

>> Or in some cases, saving animal lives faster. I love it.

Mike Frasca

>> 100%.

Rob Strechay

>> Yeah.

Savannah Peterson

>> Yeah.

Mike Frasca

>> That is an... I'm a dog lover too, so-

Rob Strechay

>> Yeah....

Mike Frasca

>> it resonates incredibly well.

Rob Strechay

>> Absolutely. And I think one of the things... Because you also sit in a very interesting spot in that data layer for your customers. Where do you see agentic data and really how people are going to bring intelligent automation to go and solve a lot of these problems into the future? Where do you see this going?

Mike Frasca

>> I see it going everywhere. When I say, in fact, every line of... Every part of business, I mean every part of business, every human interaction that anybody is doing, I think agents will help either improve performance, improve productivity, or be able to help people scale. I talked about a lot of our customers are in the pharma space. They're going through a massive shift moving into consumer direct type integrations. They're not going to go hire 10,000 people to go staff call centers and marketing centers talk to people. They're going to do this all with agents. That's an entire new line of business for them. They're going to build from the ground up with agents. I can't think of a single thing in my lifetime, besides the internet, that I think will have a larger impact on businesses than agents.

Savannah Peterson

>> I think we both agree with you there, Mike. It is definitely one of those times. I've got one final question for you because I know it's a very busy day for you. When we're hanging out at World of Workato next year, what do you hope to be able to say then that you can't yet say today?

Mike Frasca

>> That's a very good question. I've been looking forward to talking about customers that have deployed this live. We have a few that have moved into small POCs and pilots in production, but I think that the agent space is moving so fast and so rapidly, by next year, we're going to be talking about a completely different landscape. And so I'm very excited about all the things that I don't know are going to happen. Because six months ago, I didn't know what MCP was.

Savannah Peterson

>> None of us did.

Mike Frasca

>> Eight months ago, I didn't really... Now, you kind of knew foundational models, and I talked to ChatGPT, but never used it. I'm very excited about all the things that we don't know that are coming on in the next six months because the space is just changing minute by minute. It's super exciting.

Savannah Peterson

>> It is super exciting. What a time to be alive. And I do hope that your wife's travel booking experience becomes as easy and glamorous as yours was status in the meantime as well. Mike, thank you so much for taking the time and for joining us. We can't wait to hear what you have to tell us next time.

Mike Frasca

>> I really appreciate it. Thank you guys so much.

Savannah Peterson

>> Yeah. And thank you, Rob. And thank all of you for tuning in to our two days of absolutely brilliant coverage here in Las Vegas, Nevada at World of Workato. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.