In this episode, Marc Rind, the Chief Technology Officer for Data in the Banking Business Unit at Fiserv, talks about data exploration and monetization, bettering our financial institutions, prioritizing openness with your data, and so much more.
In this episode, Marc Rind, the Chief Technology Officer for Data in the Banking Business Unit at Fiserv, talks about data exploration and monetization, bettering our financial institutions, prioritizing openness with your data, and so much more.
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Steve Hamm: [00:00:00] Well, Marc, it's great to have you on the podcast today.
Marc Rind: Oh, it's great to be here, Steve. How are you?
Steve Hamm: Oh, doing great. Uh, some of our listeners probably aren't familiar with Fiserv, so it'd be great if you could start by describing the company, its business and its strategy.
Marc Rind: Absolutely. Fiserv is a Fortune 100 company handling all sorts of transactions from both the merchant end as well as in the financial institution and banking industries. Um, basically our clients are made up of over 6 million merchants worldwide handling close to 1.4 billion accounts, um, over 10,000.
Financial institutions call Fiserv their, uh, software and technology provider, and we handle close to 12,000 transactions a second. Um, you're talking about, um, over 40% of all [00:01:00] US merchants, uh, work with our technology solutions. Um, 90% of all US consumers are every day, um, sending data through our various systems.
Um, and then also on the banking side, we have over 150 million deposit accounts that makes up almost 40% of all the direct deposit accounts, um, across all the US banking. So you can imagine, um, there's a lot of data to be had here at Fiserv. I've been here for three years now. Um, and the potential for what can be done with this data is, um, astounding.
So it's very,
Steve Hamm: this is, this isn't just big data. It's very big data.
Marc Rind: It is, uh, very big, very unique and, um, very important data I believe.
Steve Hamm: yeah. No, it's amazing. We, I, we do, you know, encounter companies that are gigantic and essential to kind of commerce and everyday life. But, [00:02:00] but for the consumer, they may not be known at all. They're kind of, they're behind the scenes. So it, it's great to, uh, to talk to you and hear about Fiserv. you are the c t O of data for Fiserv's banking business, one of the, one of the major business units.
So please, uh, describe the business and the opportunity for exploiting data to grow revenues and profits, and, and also how does the business interact with other Fiserv business units.
Marc Rind: that, that's a great question. So, um, in the banking space, um, Fiserv has, has always historically been, um, serving software to, um, banks, which other than the largest Chases banks of America. Um, basically we provide the software solutions that banks run on everything from smaller credit unions community. Um, as well as upMarcet, um, upMarcet banks.
Um, also we have o over 1800, [00:03:00] um, banks who call us, um, their technology provider. Um, that's everything from the banking core. Um, ultimately the, the software that handles, uh, ultimately the GL and account management as well as many of the other, um, products. and solutions, which basically banks count on to run everything from ACHs to um, card management, um, teller systems, you name it.
Um,
Steve Hamm: I wanna make sure I understood you correctly. So, do the very largest banks not use your, your technology or, or
Marc Rind: they use some of our technology. Um, but for the most part, the core processing, the very large, it's the top five, uh, banks. Build or obtain their own core, right? It all ultimately comes down to the core. Um, and beyond the very top, um, of those banks, all other banks [00:04:00] that you see in town around, or even, um, even on a larger scale, uh, look to Pfizer to be their technology solution provider.
Steve Hamm: Okay, I got it. I got it. Yeah, so I, I kind of envision this kind of a graph with kind of a circle with, with around the, the customer around the bank with. With, you know, like stops along the way. Different kinds of services or, or technologies. So that's both, that's both the, the vision of the technology world vision, but it's also the vision of FI serves various business units.
Correct.
Marc Rind: That that's correct. So the, the best way to think about it, um, Is, so within banking, there are many different products and solutions, all producing data that ultimately our clients are looking to pull together to connect and stitch together to get a full view of their customers, right? So if our clients are the bank, they care most of all about those people that [00:05:00] have accounts with them, have loans with them, take mortgages out, you name it, right?
Um, so they are looking. Uh, to remove the friction of getting at their own data. Um, they are looking to, and this is where things get really interesting because of the unique breadth of data that Fiserv has, provide additional data and aggregate, um, anonymized data assets, uh, for insights so a single client can understand how they.
Against competition, right? Where are they having their greatest pain points? Where's their greatest, where are their greatest opportunities in looking at just their own data, but in context against the larger field of data? That, that's what we at Fiserv, are, are starting to pull together, pulling all that data together safely and securely, but then not just connecting.
Provide some context behind the single client's data. This is where you have opportunities. This is where [00:06:00] you do very well in, but then going a step further is not just freeing that data up and getting it to them in a frictionless state, but then even connecting it beyond even banking, right? So understanding, connecting financial transactions of a, of a human being, not just in what's going on within their banks, but also where they swiping their cards.
We have a. Card issuing business, uh, where we issue credit cards on behalf of even the largest financial institutions all the way out to the merchants about where we're swiping those cards. Um, our merchant, um, business GBS is huge. Um, everything from small business to, um, you know, the largest businesses, um, in the country, uh, use Fiserv Technologies one way or another.
So it's. , the opportunity for us is to pull all that data together, centralize it, but then be able to connect it across Fiserv and [00:07:00] then open it, um, uh, back out to our clients to have our clients get their piece of that puzzle, their data, along with enhancements and context that we can provide, um, using the, the data, sharing data as a
Steve Hamm: Yeah. Yeah, yeah. We're gonna get into, into some de detail on that in just a minute, but, so you've been there three years in, in this role, or, or, or other roles.
Marc Rind: No, no in, in this role. So I, um, yeah, before this I was at adp, um, I was there for quite a while and, um, built, uh, their data cloud platform, um, basically pulling all the payroll data together, doing very similar, you know, providing context. It's the same data that ultimately is the national employment report.
Just showing the power of what you can do with massive amounts of data. Um, been here for three years, um, and a lot has been, you know, understanding where our data is, where are the most important pieces of data, um, pulling from [00:08:00] your traditional, um, single tenant implementations to pull all the data together, um, so that we can easily query cross.
Um, cross fis, cross merchants, connect the data easier and then open it up to produce, build products on top of that data, but then also free up the data, um, uh, to get it into our client's hands much easier.
Steve Hamm: Yeah. Yeah. Now you mentioned that you worked for ADP for many years. I think it was 17, Fiserv for three. Um, you know, we, we, we do like to explore kind of management techniques and management approaches here. Uh, we think the. The listeners are interested in that. So what are the key management lessons that you have learned along the way, adp, Fiserv, uh, that you're putting to work now?
Marc Rind: that's, um, that's a great question. I, and I'll, I'll even go back a step. Um, before adp, I [00:09:00] worked@a.com startup back in the 90. . And quite honestly, a lot of the style and what I've learned came from those days, um, where you've got to build a culture of ownership. Like when you're in a small, smaller business, everyone in the room doesn't matter if you're an engineer or head of Marceting, you're all in it to win it.
Right? You all have to understand what everyone's. You cannot be afraid to fail. You have to fail quickly. Um, but taking that mentality of, Hey, I have an idea. This is something that we should be doing, and start building it. Even it works at big companies if you do it the right way, right? You have to, uh, be open and transparent about what it is trying to do.
Um, and the best way to, I think, to answer your question from a managerial. Um, whatever you build, you should be [00:10:00] open, open it up to the rest of, especially at the larger companies. Um, the intent there is, especially if you're in the data space, data has gravity. I, I love that phrase, right? So if you are building solutions that you can open up to say, Hey, here's what we're doing.
We're focused, we're, we're marching towards what we need to build. So for instance, in the banking, We know that direction, but everything we're building, we're intending to open up to the rest of the organization so they can leverage a lot of the pieces that we've built on ingestion and um, data encryption at rest.
And there's a lot of different pieces around that. And the intent is to help the entire enterprise go in the same direction, because ultimately, if everyone gets on board, there's just more and more data that we can all.
Steve Hamm: Yeah.
Marc Rind: So going back to a, a previous question that you, you raised, like, how are we connecting with the other lines of business?
We started, um, quite honestly just out of, um, in, in [00:11:00] working with our, you know, my counterpart in gbs, um, and when they started in providing data to their merchants, right? So collaboration, partnership, and open transparency and open sharing is, I think a, a key part, especially if you want to. To, um, change the culture of any organization into more of a data culture, right?
Steve Hamm: Now, when and why did Fiserv start using, um, moving its data of the cloud? . I mean, and was it the, was it the banking unit that really got it off? You know?
Marc Rind: Well, no, I, I, I think it was, you know, the realization that Fiserv had has a unique differentiator in having access to all of this. but online, you know, but like many large organizations, data has been historically been siloed. They've been, um, [00:12:00] various different folks and different products have little pieces of data.
It's not big data lakes, little puddles of data all over the place. So, um, when I came in the, you know, the ask was how can we get our data into shape so that we could. Productizing and monetizing it better, um, than we are today. Um, so, you know, I, I think it was realizing Frank and, uh, who is our CEO and Guy Chiarello, our, our Chief Operating Officer, seeing the potential of what we can do with our data, whether it be providing information, insights to the client or build a, a better fraud detect.
Capability, um, just by seeing all the activity across the board. Um, so that's where we got onto this direction to, hey, the only way to do that is we have to bring the data together. Then the other realization was, you know, cuz [00:13:00] I was, I was asked, well, to my opinion on this, the, the type of, um, of processing footprint is perfect for the cloud, right?
It's bring it in. But if you're going to model on top, Uh, or build models. It could be very batchy at points, which is it. It's just tailor made for having the data up into the cloud.
Steve Hamm: right, right. So let's talk about Snowflake for just a minute here. When did Fiserv start using Snowflake's data Cloud?
Marc Rind: So, um, it must have been roughly about three years ago when I started, that's when merchant was starting to explore it, starting to get going. And, um, really it's been about, I guess when I started, a few months after starting, I was asking where, where should we go? So we started looking into various solutions.
The thing [00:14:00] we loved about Snowflake was that there was a couple things. first. The separation of storage from compute was a big deal for us because we know we at the time had very splintered little, um, client, um, facing data warehouses all over the place, single tenant. Um, that really, you know, the, the, the care and feeding of that was extremely difficult, right?
Spread out all over the place. So we knew we had. create a better, um, client facing warehouse solution for their own reporting. Right? Um, so that out of the gate, we knew that was the first thing we had to solve for. Um, but then along the way, because of the separation of storage from compute, we realized, well, wait a second.
If we have the single set of storage, we can use it for a client specific report. Or we can go bigger, we can go industry or we can go wider, right? For very different. [00:15:00] Types of use cases for the data. Um, so that was one second. We loved the, um, fact that it is cloud agnostic. Um, so, you know, you can have it running on AWS and you can have the DR copy of that data on Azure.
Right. Um, that was a big move for us, um, because quite honestly, going back to the open and transparent, I didn't wanna have to, um, get into a. A battle over which cloud, it didn't matter to us, right? Like we just want the data available. So that was two. And then third was, you know, at such a large organization, having a single group do all of the data work is, it's a recipe for failure, I think.
Right? A, not only centralizing all that work on putting on one team that's gonna have their own priorities. , you know, and, and their, [00:16:00] um, their list of which data when, but also there's so many data sources. I don't understand the data as well as those who are working on the products that generate that data.
So, um, the capability to internally share was a big part for us. Uh, we wanted to federate the development, um, arm, the various different groups. Uh, with, Hey, why don't you, with a lot of the tools that we were building, make it easier for them to get their own data into, uh, snowflake for their own use cases, whether it be sharing directly to clients or building a reporting solution, whatever that was.
But then the hope was is get it in there. And now that it's in there, we can all interconnect it and share it. Right? So a perfect example was we're trying to come up with a list of monetizable product. , um, opportunities right now. I just said, well, [00:17:00] we have this data from merchants available. It's already flowing in.
We don't have to move it out. We can use snowflakes, um, storage separation from, uh, from compute. And they were able to build a model on, you know, to help detect, um, potential returns even before the person walked back into the door. Uh, found very interesting stats at something like 4%. Of a, of a, uh, large retailer's customer.
So 4% was responsible for almost 70% of the returns, right? So we're able to quickly build that in two weeks because we now have the data available to us, open sharing, and we're able to, to leverage that data versus what it usually takes, getting it from all different spots. It was already there. And that's, that's, I think, made a huge difference in our, in our data.
Steve Hamm: Yeah. So it sounds like each business unit has its own uses for Snowflake, but the, the real killer app or the killer capability is the ability to [00:18:00] share data amongst the business units.
Marc Rind: That's right. And that's right. And any, you know, anyone who has an idea to, um, leverage the data across the business unit should be able to Right. To a point. Meaning, and this is something you know, I've always learned, Don. Uh, build a business case. Build a product with data. If you build a regular application, it's easy, right?
You come up with requirements. This is how it should work, and this is what it should do. And that's easy, right? The problem comes when data gets involved and you want data to actually be able to tell you something you're not sure if it can, right? You're not much like this experiment. If we went into the data and we found.
You know what, uh, 50% of the clients were responsible for 70% of the returns, that's not gonna help you. But the fact that we were able to see it was only 4%. Right? So the point I'm trying to make is [00:19:00] with data you have to see if the data can do what you hope it can tell you. Do that first, right? To say, Hey, we have something here.
We think we can, we have the right to build a product around this because our data that we have, Directionally accurate enough to show us. Then it's a question of, okay, what, how do you productize it? How do you monetize it? At what level of grain? How do we keep the data safe? What data can we use? Can't we use all of that?
All that comes out. But if you don't have a a product direction and concept from the beginning, you're not gonna get very far because you're not even able to answer what it is that you're trying to build in the first place. Ho, hopefully that that makes sense.
Steve Hamm: Yeah, that makes total sense. Yeah. I, I love this idea of kind of using the data. Yeah. I mean, basically exploring, looking for opportunities, and then when you, when you spot something that's, that could be lucrative, then you build the app around it, you, and then you spend the money on that.[00:20:00]
Marc Rind: spend the money getting deeper, understand, um, a lot more of what we can and can't do with the data. So it is, but it, it all should start, uh, with an exploration right upfront to. , can we actually even do what we hope it can? And that's the other thing. It's like when you're going to build a product, sometimes you might want to have it, um, have the data predict something, but then you have to see how close it can get or how useful is it both taking the results as well as what you're allowed to do with it should shape the product, right?
So you might not want to give a specific score. You might want to give. A recommendation or a ranking, something along those lines. And I think a lot of that comes out in your initial exploration, which I think is very critical.
Steve Hamm: that makes total sense. Now you've, you've kind of flipped at this, but you know, Within data sharing, you've done a lot [00:21:00] internally, but now you've launched this major initiative data as a serviced, aimed at monetizing data across all the businesses and, and basically selling to your customers.
So what's the opportunity there and what's your strategy for making this work?
Marc Rind: it's, um, it, it's, it's very exciting. Um, a couple things. Um, primarily, first of all, I, what we are seeing, um, when and when I speak to our clients and when, um, when we speak. Our merchant clients. When I speak, I speak to a lot of banks. Uh, they're, many of them are becoming much more data literate and savvy, right?
They are, um, they are looking to build their own warehouses. They are looking to build their own models and hire their own data science teams. I'm very impressed, um, with a lot of these banks that want to get in that direction. They're just starting. Um, [00:22:00] they, um, some of them are. . They don't have data scientists if they don't have warehouses, but they know that data's important to help them run their business and attract and please their customers.
Right? And keep them coming back for more, um, data. Should the first, any data scientist or anyone you ask, what's the hardest part about any machine learning or any data? It's always, I gotta get the data and I've gotta clean the data and the data. I gotta get that data into shape, right? So what the opportunity that we see with data sharing is that we have all this Fiserv data from the cords and the various other products.
Clients will have 12, 15 of our various different products. We're pulling it together and connecting it for them. But then we are opening up [00:23:00] the ability to get at that data via snowflake's data sharing capability.
Steve Hamm: Right, right.
Marc Rind: The, our clients are getting set up with Snowflake. We are sharing them the data directly.
Um, we have history of data. We have that data that's connected. Um, so they don't have to stitch the data together themselves. We are, um, we do. Very heavy level of data encryption. Um, both on our version. Then we have a decrypted version for clients specifically. So we, we make the data extremely safe.
There's a specific key a client gets, and basically for them, they have access to the data as soon as we have it within our Snowflake environment. That's a big deal because today what happens is that data lands from the source system into a reporting. And then they're like, okay, that's great. Then they extract it out of there, they [00:24:00] drag it over to somewhere else and they're adding it into a another warehouse.
Sometimes they have to run extracts and send it to other vendors. Um, so now you have, you know, the proliferation of mult of the same data in different forms all over the place. Um, so what with Snowflake we're doing is we're removing all of. Duplication, security risk, copies of, you know, data moving around.
And instead we are pulling the data together, we're connecting it, we're enhancing it, we're cleaning it up, we're providing it directly to the client, or we're also able to, on behalf of the client, share it to some of our FinTech partners. So, uh, you know, a, um, um, a, a bank, they use us for some things. There are many beautiful, wonderful FinTech services out.
That become Fiserv Partners as part of our app Marcetplace, um, where a [00:25:00] client can say, you know what? I want that service. So instead of being on the bank to pull the data out to go send it to them, we are removing that friction by sharing it directly to them on their behalf. So we're building this capability where, you know, something which used to be a long implement.
Um, is basically a click of a button of the bank agreeing, Hey, I want this service with this FinTech. Please give them the data that is appropriate for that FinTech to have stripped out of anything that they don't need, and it's just immediately shared to them as soon as we get it within our Snowflake system.
Steve Hamm: Yeah. Yeah. You know, this sounds like it's not just a little bit of incremental business for you, it sounds like. Major new business initiative right
Marc Rind: Yes. Um, this is just a start. So just getting the data pulled in, connected, shared out to the clients, shared out to those fintechs is, is just a, as a drop in the bucket, um, where things [00:26:00] get really interest. I believe is not just the data enhancing that we can, you know, here's a list of loans and here is now the likelihood or the the risk score of those loans defaulting based on a machine learning model that we've trained by feeding it every loan that we've seen and whether it's paid off or default.
uh, for, as you know, over the last 15 years of data that we are, that we are collecting, right? So what are the signals of a loan defaulting or loans at risk? Normally risk is determined upfront by various different levers of how much is being taken out. You know, what, you know, what some of the collateral against it, but then there's a model, you know, a risk factor assigned at the start of.
well now we are seeing what's going on with the loan processing. The payment or the payments on time are, uh, you know, [00:27:00] the person who took out, let's say it's a small business loan. Um, what's going on in that community? How is activity Cliff? Don't forget, we have the merchant data also, right? So we can start to see signals and, and signs that'll help our banks understand.
who those loan officers who tho who are there in their portfolio, should they, should they reach out to, they might be struggling. Um, or they might be seeing, you know, maybe it's time to re, to rework the, the loan, um, variables, right? So here are the loans that have different levels of risk as we're seeing things process, identify features of the loans, about the economy, everything going on to. our banks be better financial institutions for their customers by giving them those recommendations. And that's just a start.
Steve Hamm: yeah. That's amazing. It's like, it's like a real time continuous risk evaluation [00:28:00] and alert system or something like that. Uh,
Marc Rind: Yes.
Steve Hamm: kind of customize per, per their end customer as well. So that's really cool. Um, so these are, these are the first steps that you're taking in data as a service.
Are there, when you look out, say over the next year, are there new technologies that are coming, you know, kind of in the data space that you think are gonna be really significant in, in helping you flesh this out, add new services, add new.
Marc Rind: Boy. Yeah, I, I mean obviously, you know, everyone is hot to trot over what? Chat? G P T. Global AI services are providing. Um, but I, I think very interesting, right, to have that played on top of the data. Um, and way too early to even tell yet right about, you know, how it's going to play. This is, it's all new Frontier, which is very exciting.
I [00:29:00] think, you know, new technologies for us, I, I'm a strong supporter of. , bringing data to the masses, democratizing it across, um, an organization you shouldn't have to, A bank shouldn't have to go to their one guy in the IT department to, Hey, can you build me a report that tells me X, Y, and Z, right? Because by the time they get that right and turned around X, Y, and Z already happened, right?
It's too late. Um, data needs to be exposed and front. in the moment, right? So if you're on a loan origination screen, it should give you all the data and information you need to know about the customer coming in for a loan and making recommendations right then and there shouldn't have to go somewhere else.
If you're coming up with, you know, strategic decisions. Um, at a, at a bank, you should have that data at your [00:30:00] disposal. It shouldn't go through three hops to go get an answer. , you know, that information needs to be exposable. So exposing everything via rest API is a big deal for us. Um, so we have a layer that we're built on top of the data to expose, so you can start asking it questions.
So based on the screens that we're building or the applications that we're pulling together, um, be able to call in to that API layer to get the important. Right then on those screens, right about whatever, wherever they're working. So I think exposing data, and that should go for everyone, right? Not just the strategy decision makers, but to the customer service person, to the teller, you know, should know, you know, what you person standing in front of you is a small business owner who has a loan that might be at risk.
You know, like take opportunities to make the human connections to help them out. But have, the only way to do that is [00:31:00] to have the. To, you know, inform them upfront. So I, I think for me, I'm a, I'm a very strong proponent in getting data everywhere, uh, when you need it, um, at all levels of a, of an organization.
So, uh, hopefully that helps.
Steve Hamm: yeah. Well, you, you've talked about kind of the ability to put data or insights at the, at the, in the hands of the person within the organization who needs them to take action. But you know, a lot of this infrastructure really goes for. The next stage, which is like more automated. I mean like, uh, data applications, intelligent applications, the kind of things where changes in the data trigger systemic changes that humans don't even have to get involved in.
Is that something that you're looking at, you know, this year or, or in the coming years?
Marc Rind: absolutely. I mean, it is, um, one of the, the biggest pieces of the puzzle are, you know, fraud detection engines, right? That, that which, [00:32:00] you know, within our data, forget even like a single person what's going on within their bank. , but understanding because of the data that we have connected across the board, if an address is being changed, uh, somewhere else on a different card than that wallet for this person, for whatever reason, address is being changed.
A, um, a new person is being added to the account, but doesn't seem to match up with everything else. I, I, I'm just, you know, using a very specific example that at, which quite honestly should not be. dictated, in my opinion, by a rule that is being set. If you see address change, B B B B, but then, you know, call somebody, right?
It really should be picking up on all past historical events across a person's wallet leading up to a fraudulent or an account takeover, for instance. Right? [00:33:00] Um, all of that data we should. We're starting to work on feeding it in so that it understands and not just the fine fraud, but also um, quite the opposite, right?
Like I always joke, I use one car at the gas station every week and I left it at home and I tried another car at the gas station and said, wait a second. Why are you using this car? And everything gets shut down my, I'm like, shouldn't it know that? Yes, they're two different companies and two different data sources, but can't it know it's me at that point?
Uh, it's all of, all of these pieces and pulling all that together at high speed, um, at decision making that can go beyond how a rule is set up to detect fraud and what to do about it, right? It should, it should go beyond it, in my
Steve Hamm: yeah. You know, you have a, a, a great view of the, the potential of technologies today and, and, and tomorrow. Let's, let's test you though. [00:34:00] I'm gonna ask you to put, I want you to put on your visionary cap for a second. Looking out five years or more. What are the major data technology advances you expect to come that would transform business, the economy, even society?
Marc Rind: Yeah. I, I think about this all the time with my kids and my, my, you know, my kids are always, I actually, I recently did a. . Hey, we, we do a, um, an afterschool program for fifth, sixth, seventh, and eighth graders here at Pfizer, where these kids come in. It's an afterschool program. They're so bright. They're super smart.
Um, and you know, I try to describe to them what life was like before the internet, right? And it, it just doesn't even hardly register, right? It's why, what do you mean barbaric? Like, how did. how did you call, like, you know, I remember an [00:35:00] answering machine was a big deal, right? So in thinking like, okay, so what's
Steve Hamm: Re remember, remember when people used to get lost, they couldn't find each other?
Marc Rind: they could, how Did anyone ever meet any, like, I'll meet you at two o'clock and if, if you're not there, you didn't know if they blew you off. If they were, if they were dead, they had no idea, right? You're just like, oh, I guess they didn't show up. And that's, that's all you could do. But, um, I. I think what's gonna be in five years.
I, I think the openness of data, I think, um, and we're starting to see it, right, like chat, G p t is learning just by scouring the, all the entirety of the internet, right? Um, I think openness of data is going to be a big deal. I think. Um, how can we share data, um, that we, as people are generating. Companies are collecting, um, how can we share data in a safe and secure [00:36:00] means to be able to connect all of the dots together, as I like to say, right?
To have a better, larger understanding of, of whether it be economics or, um, societal impact in an area. Because pulling all of that data together and having a machine learning. To call it a model at that point wouldn't be fair, but to leverage as much data across the board, right? Whether it be payroll information, employment data.
Where are people getting paid? How are they saving it? Where are they investing it? How are, you know, how are they opening businesses? What kind of loans do they need? Who should they be connected to? Like across the whole, right, of all of the potential data points to feed that in, to help feed. an intelligence that takes a look at everyone like me and what has worked and what has not worked.
It's not gonna be perfect, but to make [00:37:00] recommendations right upfront, I, I think that's where, you know, if someone asks me to go, you know, way overboard, what does that look like? Yes. Democratizing data and getting it out to clients and getting it into the applications that they use to have an informed suggestion, yes.
But I think it's ultimately, how can we share. All the data that we all produce every day and companies collect every day so that we can finally like connect the dots across the board, uh, to build something to, to help just make life easier.
Steve Hamm: Yeah, I think that's really interesting. I mean, the idea of, you know, these foundation models are everything that's on the internet, and then you're talking about your universe of, of data from all these kinds, all the ki the kinds of business you do. It's when you bridge the two that you really have this incredible, broad contextual knowledge that, uh, that could be extremely valuable.
I think that's really exciting.
Marc Rind: Yeah. I, I agree. [00:38:00] And I think it's, you know, , but I try to oversimplify it, like, you know, when you build like dashboards or metrics or K like all you know here, why, why? It should just tell me, like I shouldn't have to have to glean the insight off it. Yes, of course I need the backing information, the data about why I might be making a decision or making a recommendation based on data, but shouldn't it just be able to tell me based on everything that it knows and.
How I'm looking compared to others. This is what you should do because this is an area you're suck at, right? Like, here's something that you could definitely be better at compared to everyone else. This is a, this is a problem for you. And, and here are three things that everyone who did this really well, all seem to all do, right?
Whether that's invest in this area or, you know, go after this customer segment, whatever that might be right to, to make a suggestion on how to fix.[00:39:00]
Steve Hamm: Yeah. Yeah. No, I think that's. You know, we're coming to the end of the podcast and typically we like to end on a kind of a more personal, lighter note, not so much technology necessarily. Um, and I know that you teach part-time at a university there in New Jersey and that you have long been focused on helping people develop their careers.
Adp, Fiserv, you're, you're a mentor. and I, um, so I wanted to find out like, what drives this, what, what makes you want to do this? And what kind of, what kind of value do you think that this is, is, is bringing to the people around you.
Marc Rind: Wow. That's, um, that's a great question. Why do I do? Well, I I, I've seen it in action, right? I've seen. You know, I've run teams and when you can see a, a group be so engaged in what it is that they're building, and the, [00:40:00] that engagement comes not because someone told them this is the three things you have to do.
The engagement comes from, um, bringing them in at the experiment experimentation phase, right? The ownership phase. , here's a problem to solve. How, how would you solve it? Not like, this is what we're gonna do, go, go do that. More of here's an opportunity. Um, I've seen that work e extremely well, and it's, it, it's thrilling for me, right?
So it's something I even teach at, uh, Seaton Hall, where I, um, where I currently teach as an adjunct in helping the students build product prototypes based on.
Steve Hamm: Right,
Marc Rind: and we work backwards, right? What is the problem you're solving? Start with that. What do you wanna solve? How would you wanna solve it? Why do you think, you know, here's some data sets here, work with this and have it shape how the product ultimately works.
And that's something [00:41:00] that, um, it's a thrill to see folks be innovative with data, um, and actually start building out prototypes of that. , it's a thrill for me to see them be so engaged. And it's inspiring to, to see some of the product concepts come out of these, um, of these sessions in these groups. So, coming back to your question of, you know, why the mentoring, why do I enjoy it so much?
I, I do think, like I've seen innovative groups, um, I've seen people say they're innovative, but you can't be innovative unless you. No fear of failure or you actually should be celebrating your failure. If you find something, is it gonna work? Isn't it better for you to find that out now instead of six months from now after you like kind of spent all this money to build it, like fail early, learn from failure, great, that didn't work.
What, what are we gonna do next? Right? [00:42:00] To, to switch, to take different course and different actions. So I'm,
Steve Hamm: Yeah,
Marc Rind: that whole process of innovation I think is, is. . It really gets me going and that's what I, I always try to, um, instill in folks. I, I work with
Steve Hamm: Yeah. That's interesting. It is, like you're saying, urging people, be honest with yourself and others and be fearless in how you approach your career or your job.
Marc Rind: you. Really, it's, I think it's really important. I had a mentor myself who taught me like, you know what? You can't, there's nothing better than the truth. Nothing.
Steve Hamm: Right.
Marc Rind: no reason why, to hide from it, both for yourself or just tell folks, Hey, this isn't working. This is why, this is what we tried and that didn't work, so we're gonna try these next things.
Or, you know, alter course, whatever that is, that's okay. Right? That's, it's important. The things that should never stop a project or a concept from seeing the light of day is the headache. It might be to get the data [00:43:00] collected or available, right?
Steve Hamm: right.
Marc Rind: all of that. It's the hardest part, right? To innovation. You have to have that foundation.
Um, and that's what I'm hoping we're getting to, right? Have the foundation, have it there so that you can explore, um, and not stop exploring because it's too difficult to get the data. You should be exploring, trying, failing, trying, failing, trying, and ultimately succeeding in finding how to productize your data.
Um, and that's the point I really. Get across and drive home with my students and with the folks I work with.
Steve Hamm: No, that's a great point. That's great. This has been a fascinating conversation, just learning about Fiserv, the breadth of its services, how many , how much data it has, how many, how many touchpoints it has, and the, the notion of the, the data services that you're providing. It's just kind of mind blowing.
And you know, customers really get a much bigger picture. They get information in kind of [00:44:00] real time, they can act on it, and it seems like, It seems obvious that this will result in more efficient companies, more efficient industries, and a more efficient economy. And, uh, that's a good thing for all of us.
So I, I, I applaud what your guys are doing. It's really exciting. It's really, um, uh, I, I love to see, um, where you go with it.
Marc Rind: Well, thank you Steve. Yeah. I'm, uh,
Steve Hamm: thank Yeah.
Marc Rind: Me too. Thanks for
Steve Hamm: Thanks so much. Yeah, yeah. Yeah.
Marc Rind: Thank all.