Georgia Fintech Academy

S2 - Episode 10: Fintech Data Analytics with John Wittenbrook, Global Data Analytics & Strategic Insights, FIS Global with Vinitha Ramani, Georgia State University, Masters in Big Data

April 23, 2021 Georgia Fintech Academy Season 2 Episode 10
Georgia Fintech Academy
S2 - Episode 10: Fintech Data Analytics with John Wittenbrook, Global Data Analytics & Strategic Insights, FIS Global with Vinitha Ramani, Georgia State University, Masters in Big Data
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Georgia Fintech Academy
S2 - Episode 10: Fintech Data Analytics with John Wittenbrook, Global Data Analytics & Strategic Insights, FIS Global with Vinitha Ramani, Georgia State University, Masters in Big Data
Apr 23, 2021 Season 2 Episode 10
Georgia Fintech Academy

In this episode there is a perfect pairing of a global data analytics executive from FIS Global with a student working on her masters degree in big data information systems. John Wittenbrook is the Global Data Analytics leader at FIS Global and Vinitha Ramani is our graduate student from the Georgia Fintech Academy.

Show Notes Transcript

In this episode there is a perfect pairing of a global data analytics executive from FIS Global with a student working on her masters degree in big data information systems. John Wittenbrook is the Global Data Analytics leader at FIS Global and Vinitha Ramani is our graduate student from the Georgia Fintech Academy.

Speaker 1:

Well

Speaker 2:

Come to the Georgia FinTech Academy podcast. The Georgia Vintech Academy is a collaboration between Georgia's FinTech industry and the university system of Georgia. This talent development initiative addresses a massive demand for FinTech professionals and give learners the specialized education experiences needed to enter the FinTech sector.

Speaker 1:

Bonnie , this is Tommy Marshall , the executive director of the Georgia FinTech Academy. And welcome to this week's podcast. This is season two, episode 10, and today we have John Whitten Brook of FIS. He's the global , uh , data analytics lead around strategic and handle strategic insights for FIS and the Nita Ramani who is a master's and information student , um , master information systems student at Georgia state. Uh, welcome to you both. Yeah, it's great to have you here today, John. Um, I want to start with you in terms of introductions. Um, tell us about your career journey and your passion around data analytics.

Speaker 3:

Yeah, absolutely. Um , so I actually started out in accounting, not, not any kind of , uh, or , um, I did have an MIS , uh , minor, so I guess that, that counts, I had that thought then , um, I went to work for a big four accounting firm , uh , and did that for a couple of years doing a financial statement auditing. So really learning the business there from there, I went to do private equity consulting , um , for, for mergers and acquisitions. So when private equity companies were looking to purchase other companies, we went in and analyze the financial statements and said where the , the, the risks and opportunities were. And that's really where I got into the analysis piece and the storytelling and bringing forth what is meaningful back in those days. Uh, it was all done in Excel and a lot of manual work and a lot of, a lot of late nights. Um, from there I left that and became , uh, joined the startup world and I was kind of a floating CFO for a number of growth stage companies , uh , getting into kind of strategy and analytics with them and helping them make data-driven decisions. Um, I'm , I'm thankful for that opportunity because entrepreneurs, let me realize that anything is possible and to think outside the box beside of that, that size of the company, wasn't the right place for me to play in the longterm . So I got a job , um, as a, as a , uh, in a , in a corporate finance. So I was working at a healthcare company that was doing an IPO and with my kind of quasi investment banking background was really interested in that after the IPO finalized , I got really into the data analytics and what happened was what I realized within the finance function, in the broader organization. Everyone was just moving spreadsheets around. They're taking a spreadsheet from here and they're manipulating in here and putting in another spreadsheet here, we have the 75 page reports that would take two people a month. And I said, just this data isn't timely and relevant. And there's a lot of energy and efficiency , um, kind of wasted in, in creating this. So that's when I began down the road of how do we, how do we make this more efficient, more scalable and , and build this out , uh, in SQL server at the time. Um, what I found though, was there was a gap between me as the finance person and the I'll call them the it function. They didn't understand my requirements. I knew what I wanted to get, but I had a hard time translating it to them to , to really get out of it. What I needed. It took too much bathroom back and forth. I had explained financial statements, hydrants gained the business case, right. It was too clunky. So that's when I started moving all, learn, all learn code. I hadn't even learned any code. I'll learn all this, I'll figure this out. And I'll, and I'll come kind of downstream in your world to where we're able to talk better. Right. I liked that so much got into, got into power BI and visualization was able to do some really cool stuff where it's really automated. Decision-making, that's kind of right there at your fingertips. From there. I decided that's where my passion was and that's what I wanted to do full time . So an opportunity came at a , at a company called Vantive , um , then became Worldpay that then became FIS. So what that is is the , the, the merchant processing , uh, division of FIS. So essentially we do all the work for , uh , people to take credit card transactions and processes and exchange money. So from a data analytics perspective, it's kind of a dream world, right? Because I have trillions of pieces of information to kind of sift through and figure out, figure out what's meaningful. Um, so I , I joined Vantive then Worldpay and FIS , uh, about three and a half years ago. And now I have a team of 23 people , uh , working on data analytics that sit in Cincinnati, Manchester, London, and Gateshead.

Speaker 1:

Hmm . That's really, that's awesome. I love it. Um, I relate to this too in , in , uh, I got very interested in data and analytics in my consulting career, largely because I did a ton of consulting work for American express. And that in that company has a very long history of like taking of all the payment data that they had access to. And , um , they just taught me a lot around how important data-driven decision-making is , uh, to helping grow the business , uh, and , um, and invested a lot in, in making that very efficient and capable , um, for them. It's just a , it's a really interesting area , um, beneath that , tell us, tell us about you.

Speaker 4:

Yeah. I had almost a Marshall thank you for this opportunity, which you have given me and also the fellow students. Uh, so I was into analytics with dun and Bradstreet for about seven to eight years. I think it all started back when I had my post-graduation and financial economics from Metro school of economics. So at that point in time, analytics was definitely the buzzword and I was also inclined into it. So I decided to get myself , uh , you know , a certification and got a SAS advanced certification. And then I was hired by , uh, DNV technologies and data services as a training analyst. So it's basically a subsidiary of Dannon Bradstreet, and also the dun and Bradstreet at South Asia and middle East and America. So it goes the right opportunity for me because it was FinTech and which was using SAS and it was dun and Bradstreet. And , uh, yeah, so initially I was working for the Canada market independently, and I must say the exposure was amazing, actually. So even as a level one analyst, which is like one year down the lane , I get to build predictive models, like seeing the defaulters and predicting the risk of customers and seeing the perspective clients and various other risk analysis. So over that time , uh, I was moved into the global market where the analysis in world was like much more complex and, you know, big data and diversify data was in world . So we had to, I mean, we had experience in pulling all the data and providing it , which , uh , you know , main task for any analysis. And of course, with seniority and all that, I also get to mentor some of the juniors when they started moving into , uh, you know, machine learning and various , uh , visualization tools and all that. So like, as we all know, and I would know that the globe is moved towards , uh , you know, machine learning and artificial intelligence and all that. I decided like there should be a right time for me to take a break and then upskill myself. So in fact, in DNB also, they were using a lot of machine learning models, which was initially like how , uh , Mr. John was Sterling . So my seniors used to tell me that initially they used to build a predictive model using Excel sheets. I mean, that all the tools and technologies, right? No , that sounds a humongous task to me. So , um, so yeah. Um, so as we are dealing with more solutions, I also saw that many solutions when being automized and technology was playing a huge role in it. So I thought , uh, you know, this was the time for me to , uh, take a break. And I started looking at universities and then I came across MSIs at Robinson college of business and Georgia state university. And I saw how highly ranked they are and , uh , you know, the reputation they have got. So , uh, and yeah, so we are getting exposed to all sorts of machine learning and deep learning tools and techniques and big data infrastructure as of now. So it is pretty fascinating to see how , uh, you know, big role data science and information systems are running behind the scenes for not only for Intel , but any other , uh, domain actually. Um, so yeah, this is about me.

Speaker 1:

That's great. Yeah, no, I'm , it's been great getting to know you a little bit. Uh, Vaneetha over the last , um, several months since we met , um, and I've been so excited to , um , hear more about your , uh, the pro program. And , uh, I think particularly, I guess I'm very excited for your future, just because w with all this kind of rich background you've had in the work you did with DMV , and now , um, this studies you're , you've taken on , um, with , uh, the masters in information system and focus on big data. I , uh, I I'm , I'm excited to see what's what , uh, what's next for you. I think it's , um, with the needs , did John let's kind of get into talking more about this area of , uh, of data analytics and maybe John, if you, you hit on it a little bit in your intro, but just maybe talk a bit about , um, why this capability is so critical to , um, FIS merchant processing business.

Speaker 3:

Yeah, absolutely. So, you know, it's critical from a standpoint of understanding how our , our customers operate and how we help them optimize and how we help enable them. And so an example of that without getting into kind of technical detail is the, the way that a merchant accepts a payment and the things that they have control impact their cost of the transaction. So to the extent that we can monitor and understand the way that they're doing things, we can actually proactively help them reduce their costs, right? This is, this is one example. Um, so it's, it's about that proactive monitoring to help help our merchants reduce their costs and operate efficiently. And it's about our own ability to make decisions about our business. And that comes from a high level. Um, you know, Jim Johnson, who leads our merchant services business, what are the high level metrics he has to understand to, to synthesize, to run the business down to the analyst level that may be looking at , um, collections, right? How do I understand which, which debtor to go at, which is the debt strategy to go after, how much money do they owe when's the last time we call them, right. And, and giving them that information at their fingertips to kind of automate that workflow. I think that's the thing I really like , um, working at a FinTech company is it's , it's all data everyone's job is , is interpreting data and, and , uh , you know, coming to a solution or a conclusion based on that data. Yeah .

Speaker 1:

The , um, there's one, I guess one thought that occurs to me is there's this , um, there's this one element of, of looking at analyzing understanding data to know kind of what's been happening in re in recent history or in, you know, even longer term history. Uh, and then there's this being able to look at the data and predict into the near future , um, as you look at the body of work you're involved in John , um, like how, how would, how do you think about it that way? And then, like how much would you say is kind of tilted towards the understanding recent history versus tilted towards predicting , um, into the, into the near future?

Speaker 3:

Yeah. And it's a great question, you know, certainly, certainly those two things are tied together. Um, you know, I think we spend a lot of our time looking at the recent past and understanding it. And then there are certainly for looking for looking models to try to , um, you know, whether it's, whether it's finance and you're trying to help them understand how the volume of the business and what the related revenue of that might be and helping them kind of do that, or is it, is it the call volume of our customers in the call center? Right? We've got things opening back up in COVID across the UK. What do we expect? How much of our customers have closed down? What do we expect to open up and when, and how do we kind of enable them with that information at their fingertips? So, so it's certainly a lot of, a lot of both

Speaker 1:

The , uh, I guess one thing that comes to mind for me is this like , uh, around like offers, if I'm a merchant and I'm trying to present a , uh, a product or a service to a customer and, you know, w how to choose that, that product or service to put forward to the customer, that's going to have the highest possibility of the customer buying it. Um, do you, in the work that you do is that, is that area of capability , um, uh, being something you're offering to these , uh, these merchants you're engaged with.

Speaker 3:

Yeah . So, so I'm not on, on the product side, we have things like that. Um, but it's, it's a massive organization. So, so my area of expertise is , um, is really around customer operations. So, you know, we, we get pulled in to things like that. Um, but from a, from a credit card processing transaction, we understand you processed , uh , I don't know that you bought a candy bar and a , and a soda, and therefore should sell you a water. I just know that you process a transaction.

Speaker 1:

Got it, got it. Um, you mentioned cost of acceptance. Yes . Which I know is Cost success . Is that , so I I've learned this lingo a bit cause , um, we have a couple of large merchants headquartered here in our community know notably , uh, like home Depot. Um, and , um, I've, I've gotten to know some of the folks , um, it just through our interactions in the ecosystem with home Depot, and when we talk about FinTech solutions broadly, like this is where they go immediately. They're like, we are maniacally focused on the cost. It takes us to accept a payment and how to, to, to , um, you know, really manage that effectively. Uh, and yeah, so it sounds like there's some really kind of remarkable , um, offerings capabilities that, that you're in the middle of here that are really intended to help these merchants that are your customers , um, with that, with that , uh, equation.

Speaker 3:

Yeah. I mean, there's, there's some proactive monitoring we do on their behalf. The companies of that size are often fairly sophisticated in this type of thing. You know, they have their own kind of payments team. Um, so it's, it's, it's really cool to see the work that we do in conjunction with them. And then we have, we have a products , um , that prevent fraud, help them prevent fraud , um, help them solve their chargeback issues and , and work those chargebacks issues. So , um, and, and just lower the cost of right. It's uh , you know, I think FIS has just a really great offering along that, along that path. Yeah.

Speaker 4:

Mr. John, when you say products to reduce their , they're doing doctor with these on anything, what exactly will it be? Would it be like, from my perspective , they like, and when Lisa, you guys are predictive model and then give them a solution as to how to go about things.

Speaker 3:

Yeah. It's, it's really , um, without going into details right there, when you authorize a transaction, there are certain data about that transaction, right. So it's a lot of it, is that about preventing that or allowing that, so there's, there's kind of two sides of the equation we want, we want merchants to authorize more, but we want them to authorize the right, the right transactions. Right . So the higher, the higher acceptance rate that you get, the more sales. So we're, we're motivated to , um, get you to author authorize more, but we don't want to authorize a dangerous transaction. So there's , um, I'm not, I'm not the product guy around that, but just conceptually, that's the idea. So there's, there's predictive models and kind of the cool machine learning stuff around that. Um , but , but that's, that's kind of the balancing act.

Speaker 4:

Okay.

Speaker 1:

John, we , we talked a little bit in the prep around the work you're doing from a, a process of people, a technology standpoint. Um, can you go into each of those areas, a little bit process people tack , and then beneath the, I'm curious to hear just your, you know, how your experience or what you're learning , um, um, informs , um, these, these areas that, that, that John and his team are weaving together , um, to, to provide their services to clients

Speaker 3:

Sure thing. So in terms of processes, right? I , I've got , uh , a massive organization with tons of tons of data. You know, I think we w you think about the, the transactional data. Um , but there's, there's all the kind of ancillary ancillary data that surrounds that when customers call in, what do they call in about how often are they calling in when a chargeback happens? What are the reasons , um, how is our workforce performing in those call centers? Right? So it's, it's really connecting that ecosystem. So we have , uh , a data program within merchant solutions at FIS. Um, the goal is to ingest that data from a source system and manipulate it through several layers. And each of those layers has kind of a distinct purpose with the goal of , uh , making that kind of scalable and usable to end users, right. In the first step, we kind of bring it in to two . Um, I'll go into the tech here a little bit, right. But we, and to , uh , AWS and snowflake kind of role . So that's the layer where almost nobody explores that's the advanced layer. Then we bring it into a layer. We call it domain that has a little bit of transformation and aggregation, just making it easier to search. We might change a field name. We might aggregate it instead of at a transaction value. It's at a little bit higher value , a little bit of an aggregate , um, then is the next, what we call the business intelligence layer. That's where we take these functional areas and we plug them together. So what we can do with that, with that business intelligence layer is serve a multitude of reports. Meaning we understand from the business, what are the, what are the, the facts measures, dimensions that you want to look at? And then we put it into a wide table. Then we stick that table in Tableau. So you can drag and drop and create your own report. And the beauty of it is I've created one BI layer, one Tablo extract. And that allows that kind of by functional area that allows anyone to create, kind of create their own report off of that without any incremental technical debt. So in the old world, what I've seen is right, you, you get a request in for a report. Someone would go to that kind of role layer and they'd perform all this transformation, logic. And then when someone wanted something different than I'd have to go and redo most of that logic, and there's this big backlog, right? You'd have to, you'd have to come to a team like mine we'd have to prioritize it. Um, you'd have to maintain all this stuff. It would, it would accrue this, this massive technical debt. You'll hear me say that word, technical debt , um, a lot, because every decision I make is, is cognizant of , of technical debt and the inverse of that, which is scale. So what we aim to do was, was twofold , right? Create scale through this BI layer and create access 10 users. So you don't need to know any sequel code you don't need. He didn't know the complexities of the transaction detail. You know, the example that I use is Walmart's one of our big customers , right? If you were to ask, what is the, how many transactions at Walmart process it's actually quite complicated to get to that answer , um , from kind of the role layer. So what we did is we , we created as BI layer, Tableau web interface type in Walmart , drag transactions over, and you have your answer, right? So it allows , um, groups all across the organization, technical or not to be able to use that solution, create their own reports, save that, share it, and , and really , um, democratization of analytics is what I'm really, really kind of passionate about. So it's about that, the efficiency that, that workflow workflow Clayton creates and the democratization, it's not just my team that can come up with the analytics. We're enabling this really wide group of users that if they can, if they can create a pivot table, for lack of a lack of a better comparison, they can, they can take , uh , uh, uh , a pivot table on steroids and Tableau and drag and drop and find answers. And we've had a lot of success with that, with that kind of method. So that was, that was process people. You know, I , I talked about in the intro , um, I think there is a , a spectrum of people that it takes to make this successful. And I think you have the type of people that are , um, hardcore ingestion developers on one end of the spectrum. And then you have, then you have , um, the actual dashboard users on the other end of the spectrum. You need, you need a kind of hybrid and people along the way. And I've been fortunate to been able to hire a , a fantastic team , uh, and, and work with some great partners more in the, in the it kind of data solution space , uh, to deliver that. So my team is , uh , people with business or engineering backgrounds that we've taught code, and we've taught kind of data strategy , uh, but it's, but it's kind of business first because at the end of the day, analytics don't mean anything unless they add, add business value , uh, in terms of technology, we are, we're , we're moving from an on premise , um, Hadoop Cloudera to, to an AWS snowflake , uh , and , and Tableau cloud stack . So that's kind of in process and happening. Uh, it's really exciting to see some of the performance improvements there, you know, in the, in the old days of, particularly in the UK where we had a Cloudera stuff that took 30 minutes takes three seconds. I mean, it's really incredible, just the speed of iteration at what you can look at things before you had to like, go up and get a drink of water. Now it's just like, bam, bam, bam. You can turn it around.

Speaker 1:

Um, the , the neath , I'm curious to hear your reaction , um, from your experiences or what you're learning to those dimensions that John ,

Speaker 4:

I can go a little to what John was telling. So, because this is what we have been beating in day in and day out when I was working with DNB . So it's just that when John would have probably , uh , dealt it on a transactional basis in DNB , it was just on a B2B and branch and headquarter basis. That's also, we had diversified data and we had to roll up the data at the headquarter level and then, you know, abbreviations and all that. So this is what I meant when I told that data preparation becomes a vital part when it comes to any model building or anything. So once the model is built , um, uh, we give it to , uh , uh , um , someone who is more specialized in , uh, BI, then they use , uh, uh, tools like Tableau or power BI. And then they build various dashboards for all the scorecards and the flagship score cards that , uh , DNV was building for itself and also , uh, for the customer, the specific score cards . And , um , as he also mentioned , uh, the , uh , level of expertise would also differ . So one person would be more , uh, expert in building credit to models. The other person would be more expert in building a dashboard and the other person would be more into technology where they can , uh, you know , implement what we have already built and then moving on to the next stage of , uh, implementation. Um, so yeah, as he also mentioned a VR , no getting exposed to various other newer technologies , um, in , you know, through our course, and even I heard of including DMV , places like , uh, DMV and many other places are using snowflake and , uh, uh, from you , or from mainframe to , uh, using a Hadoop or hive, it's like, you know, from three hours you are getting down to 30 minutes, which is huge. Like you will save so much of time and that all the machine learning techniques and everything, what you have been doing for days together are done in seconds and minutes as of now .

Speaker 1:

Yeah, no, thanks for that. I want to maybe just pull the thread a bit more on this time, efficiency, compression kind of thread, because I think, you know, I, you know, I know we kind of started talking about spreadsheets in the beginning of the conversation where I know, like there back , there could be easily months, many months , um, and the spreadsheet shuffling and, you know, Oh, we need to update this attribute and these 14 different model , you know , whatever it is that , um, it was the super, super long timeframe. Um, but I guess in more and more recent history, or maybe just like, w what you're working with today, John , when you go from that, I'm ingesting this RA your RA stage to that domain initial manipulation stage to the I'm dumping, I'm putting it forward into the business intelligence platform. Um, like w w what are we talking about there hours, days, minutes , uh, like what if we take the kind of , uh, uh , life cycle of a, of a piece of data flowing through that? Like how, how long is that journey?

Speaker 3:

Yeah, it's much quicker and, you know, we're, we're new into snowflake. So I don't, I don't have my, my arms around all the comparables yet, but, you know, problems we had in the past with Hadoop was we'll run it overnight because it takes nine hours. Um, and then, you know, we'll have it to you by 3:00 PM the next day. So we're eliminating some of that. Like some of our users want stuff on an hourly basis, and before I couldn't do it because the jobs would take more than an hour to run, so they would run, run into each other and collapse. So , um, you know, the , the speed of that is improving and we're , we're continuing to learn how to make it better and faster to make it , uh , more real-time even. Uh, but the challenge is with Tran it's, it's, the volume of data is massive. The other thing that we we're now having to consider with, with snowflake is the cost. So on an on-prem solution, you had the space and you used it, and you kind of went as fast as you could. Now, you kind of control your own. Do you want it faster? You can do it, but you gotta pay for it. So I think we're , we're learning how to pull those lever levers and do it in the , in the kind of the most efficient way. Uh , but that is, that is the incremental challenge to someone like me before it was, how do I get it fastest out of the on-prem now it's how do I, how do I balance speed, flexibility versus cost ? So certainly some, certainly some really exciting efficiencies gained through that.

Speaker 1:

And then go ahead. Sorry.

Speaker 4:

Yeah, I think that was also, you know, that is also a primary reason why many companies have moved from SAS to many other open source because of the cost factor.

Speaker 1:

Yeah . Um, John, what about like, is there , um, are there issues or challenges with say someone at the , uh, the kind of business users are at , at the BI level is like, I really need this data. I need this view, which is gonna take an a , which would mean back at the raw level, we need three new data sources or data attributes perhaps, like, is that , um, and then , um, and then I know historically that could be a huge, you know , problem, like it , we're going to be like, okay, if you're going to want those three things on your dashboard, that's , it's going to be a nine month project and we're going to , you know, what, you know, there'd be a , it'd be a big deal like that long. Um, what does that look like in your world today? Like if there's that kind of identified need for a new piece of information, that means going all the way back to sourcing at the raw stage.

Speaker 3:

Yeah. It's really about balancing it. So we kind of synthesize all the requests that come in and we say, well, can we add this to the BI layer and expose it in Tableau? And a lot of times what people say is I really need transaction level detail. And I say, well, really do you actually, and if they do, we have to go there to get it. Um, it'll be interesting to see with snowflake and Tableau now creating some kind of direct connection to that that allows that, that capability it's , it's something that you couldn't really do before in the, in hold Hadoop world. But we're, we're kind of testing that now. So again, it's, it's all, it's all new and try to try to kind of expand these capabilities, but those are, those are infrequent, right? We've designed that the BI layer to handle, like, what is the, what is the revenue for Kroger or, or someone that we get that question 20 times a day, right? That you can go, you can get yourself, you can get into seconds. If someone has a question and I got one yesterday, how many transactions did this group have over $10,000? Well, that's not something I can answer cause I don't have the transaction level detail. So we gotta gotta handle that one off . But as long as that is not too time consuming , we just deal with it.

Speaker 1:

Yeah. Do you also need to source data that is kind of app from, I guess I'm going to say outside the acquiring process. So it could be, I don't know, I'm kind of making stuff up now, but I'm like the weather, the weather in a certain place, like, would you bring that, would you bring data sources like that, that are outside of that, you know, payment transaction flow? Um, it , are you being asked to bring that into the BI level as for some comparative purposes or, or to, to help the business?

Speaker 3:

Yeah, I would say the only examples we've had of that in the past. Um, and you said weather, so maybe that planted a seed for me is how was our, how was our volume impacted by hurricane? Yeah. Or , or, or, or some kind of event like that. Um, there are times we we've brought in kind of COVID data and overlay that and seen what that's looked like. You know, the States are doing this, how does this state look year over year? And how do we think it's impacted by COVID? And how does that look over time? How does Florida look versus Ohio versus California? And, and when those restriction points happened and how it impacted. So, so I would say there's that, but right beyond that, there w within you say within the transaction cycle , uh, but just tertiary to the cycle, there's all the interactions with we have with our customers and how that all comes together and meet and be kind of correlated and understood it and in an ecosystem. So how do we board a customer? What is their pricing? What is their billing? What are their fees? How many times do they call in? How many chargebacks do they have and , and how do we, how do we connect all of those points and understand and enable our customers better ?

Speaker 1:

And then I'm assuming you're, you're able to expose certain views of your BI layer to your customers so that they can kind of create their own, you know, information panels or views or dashboards.

Speaker 3:

W we, we do have some of that and , and we're working on getting better at that. You know, I think you run into this security issue that especially with a company is the size of ours and the type of data that we have to have to make sure you're really, really tight on that stuff. Um, so, you know, I have a lot of stuff that I've developed internally that we use internally that we say, gosh, this would be really cool if we can get this in the hands of a merchant or a partner, and we're doing some of that. Um, but, but I think there's some really exciting frontiers , uh, of, of developing products to say, we're the payments experts, right? Here's how we look at it. The big guys, home Depot, they know their stuff pretty well, but how do I take this and give this information to maybe a , um, a smaller merchant to help them understand, help them understand what's happening a little bit better and, and, and, and where they could be doing better. It's about , but I think it's a great it's, it's, it's a great , uh ,

Speaker 1:

Great question. Yeah, that's cool. Um, well, we're close to kind of moving to our final piece of the segment, but Benita , I wanted to give you an opportunity to just , if you had any other kind of question for John around this area.

Speaker 4:

Um, so John, like generally , um, how do you see the future for all this slate that dude it's FinTech and that respect or any onto the advanced technologies and all that?

Speaker 3:

Oh, that's, that's a big question. How do I see the future for any advanced technology? Well, we don't have those driving car , those flying cars yet. So I'm hoping we get there pretty soon that was in back to the future. I think that was supposed to come in 2008, maybe. So , um , I'm , I'm hoping we get there soon. Um, you know, I, I think we'll continue to see advances in and things like snowflake and AWS and cloud computing , um, really speed up the process. I'm interested to see how AI and machine learning develops to that next, where it helps kind of with a business understanding. Um, so I I'm, I'm, I keep a close eye on that and , and, and read about that. So I'm interested to see where that takes us in the future.

Speaker 1:

John we've had , um, David Burgland joined me for one of these conversations, the global head of our intelligence for the company. And , um , we had a great discussion. I mean, he was just walking us through different, you know, ideas and thoughts. They're beginning to explore from that , um, the AI perspective couldn't, you know, company-wide sure. Um, I guess one last thing before we jumped to news stories, John was just , uh, any advice you have to the students in terms of , um, you know, coming into your field, into your area.

Speaker 3:

Yeah. Um, to be the advice is , is learn the business and ask questions. Um, because I think to me, data and analytics is a great jumping off point if you use it the right way to get anywhere in the business, right. Because you're able to understand how the business works. So don't, don't have tunnel vision, and I need to accomplish this task, understand why are we building this dashboard? What value does it add to the business? What are the action items being driven on this? What is, what is the strategy of the company that , that kind of ties into what I'm doing? Um, so, so if you can start to ask those questions and learn the business , um, I think you can really be a really valuable business partner and really branch out , um, you know, to get to , uh , to , to get to a senior kind of executive level. It's not who creates the best dashboard or writes the best code. Right. And, and , and, and the data analytics world it's it's, who can tell that story, who knows how to communicate it to the right audiences and, and deliver value. That's great advice.

Speaker 4:

And just a white horse, modern question, John. So generally, do you advice, like, you know, for example, I have a experience of about seven to eight years, which is like a mid level , uh, in Arcadia . So after this, should these students go more towards the business line that is, you know, more towards the executive line , because I believe that moving into machine learning and AI would be more on the technical side, or do you say it depends on what they are interested in?

Speaker 3:

Yeah, that's a great point, right? It depends on, it depends on what you're interested in. If you're interested in the AI and machine learning, you can, you can take that route. Um, you know, for me becoming the CTO, wasn't, wasn't the , the route that I'm, that I'm here for, I'm, I'm interested to kind of more broadly solve the business problems and , um, across , across kind of a strategic lens, but, but that's a great point. I mean, you know, I think in your first few years of your career, you really need to figure out what you're interested in and what other people are doing that are a couple years ahead of you and , and, and help. And that can help you understand where you want to land and where you want to go.

Speaker 1:

Well, let's move towards wrapping up. And as our listeners know, we always kind of do an end cap around FinTech news. That's caught our attention in the last week. Uh , so I'm going to pull you all as a group. Uh , so John, in tech news, what

Speaker 3:

I noticed FinTech news , uh, for me, it , it , it , the , the big FinTech news was the Coinbase IPO , uh, that happened last week. You know, I , I , uh, from like a personal finance standpoint, it's interesting. Um, I think just the acceleration of visibility of crypto over the last year has been incredible. I mean, you saw Tesla come on now. You've seen , um, I don't know if it was Stripe or square. Come on. Uh , so, so it's really just interesting how it's gone from this obscure kind of niche thing to , to really mainstream. You're seeing Jim Cramer talk about on CNBC. So, you know, I think it's, it's, it's really fascinating , um, to tie that into the payment space. I'm not sure where it goes. I mean, it's, it's way too volatile of a currency to be used in a mainstream transaction. Right. Um, you , you can't have customers for Walmart panic crypto because, you know, the price of something could, could change when you take it off the shelf and due to when you'd go , uh , pay for it by 50%. Uh , so really fascinating news, keeping a close eye on that space to , to see what develops, but , uh, it's interesting. Yeah.

Speaker 1:

That , um, that, that triggered a thought. I was listening to a story earlier this week about the , um, I think it was the UK there's central bankers thinking very serious talking more seriously than it ever had in the past about creating a digital currency for , um, for their nation state. Um, which of course would try to, those are the , these kinds of what they've called stable coins. Right. So they'll try to deal with that volatility issue , um, or take that away so that the store value could be more stable and more useful for commerce. Um, many of you have, what about you, anything catch your attention in the past week?

Speaker 4:

Uh , I think I read something about the anchor group and it'll become a financial holding company , um, by the Chinese central bank. And they were claiming that they will set up a license , the personal credit reporting company and restructured its online personal lending services.

Speaker 1:

Yeah, that was I that I saw that. And it, it, I guess it felt for me to me, like, you know, maybe the, all this cloud of , uh, ambiguity that's been surrounding aunt since Jack ma made some pretty strong statements right before an IPO, they were planning , it was going to be like the biggest IPO of all time. It's going to be, I think back in, I want to say it was in Q4 when it was scheduled and , uh, and Jack ma made some public statements before that, that really, I guess, angered the he's writing letters , which clearly are folks who , you know, are make any regulators mad. Uh , but certainly not bear in China. So , um, you know, there was even a moment where people were like, is Jack MA's been adopted abducted by the government ? Like we were like, where is he? Uh, but this , um, you know, it seems like obviously they've been kind of working closely with the company and this holding company status is gonna help get them , uh, out from under that cloud. Um, obviously, you know, massive number of customers that ant financial has in China. I think it's, I think it's over 600 million close to a billion customers . I mean, it's , I can't even it's mind boggling to me , uh, how many customers they have. Um, from my view, one thing that I just saw the other day, this is a bit more into the, into the card payment card , uh, issuing space, really from a retail specialty retail, private label card, if you all, if anyone follows that gap, which is, you know , obviously a huge company, a retailer , uh , multiple brands gap, old Navy, et cetera, they , um, they're moving all of their private label card business and their co-brand business away from synchrony financial over to Barclaycard . So it's a huge win for Barclay card. Um, and that's , um, you know, you don't see transactions , um, of that, of this size. Um, that's like 11 million cards that are gonna move off of a one issue , issuer being synchrony over to , uh , Barclays . You don't see events like this maybe might see one every other year or so. Right . At 22 years they had been with them. Yeah. Oh my God . Holy cow. Yeah, there are , so I guess a lot of difficult conversations happening over at synchrony today. Um, but , um, but yeah, that's a quick kind of recap. Well, good. Well , um, John, just thanks so much for being part of this today. Vinea it was wonderful to have you , um, representing , uh, our , our university system students. And , um, John, just thanks to , uh, to you thanks to FIS for this wonderful partnership. Uh, FIS is an investing sponsor , um, uh, of the Georgia FinTech Academy. And , uh , you are welcome back any time . Uh, we'd love to have you , um , and we'll, we'll keep you posted on our , uh , our efforts and activities. Um , so thanks a bunch and we'll look forward to

Speaker 5:

Catching up again soon. Yep . Thank you, Tommy . Thanks . Thank you so much.

Speaker 2:

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