Leading Financial Transformation Through Data Innovation with Jean-Christophe Lionti of Mizuho

In this episode, Jean-Christophe Lionti, Member of the Board of Trustees of RSF | Regenerative Social Finance and Managing Director and Chief Data Officer of Mizuho, discusses how the CDO role is evolving from compliance-focused to business-critical. He shares strategies for connecting silos, building business-aligned data foundations, preparing for AI and delivering measurable impact.

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Key Takeaways:

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00:00 Introduction.

02:05 The CDO balances between offense and defense, driving innovation.

04:50 A 360-degree customer view is essential for growth and scale.

10:43 Data warehouses help, but rapid tech change makes them only part of the solution.

15:48 Build a rigorous foundation with minimal data movement and progressive curation.

20:12 Legacy practices often aim to fix data issues, but cleansing remains essential.

26:12 Delivering an MVP quickly and unlocking funding and significant annual savings.

29:54 AI is everywhere, so organizations must invest strategically to maximize its value.

34:57 Stay close to the business and focus data efforts where they deliver value.

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Jean: [00:00:00] of the first role that heard when I started, evolving in the, in the data circle, which is data has to be pristine at the source. that's bs. you need to create a, a set of a pipeline and the data process to create a foundation that allows for cleansing. 

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Anthony: Welcome to the Data Masters podcast. Today we're joined by JC Leone, the Chief Data Officer for Mizuho Americas. The role of the Chief data officer in banking has evolved far beyond just [00:01:00] managing data. It's really about turning data into a strategic asset, from navigating intense regulations to driving innovation and preparing for an AI powered future.

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The CDO sits at the center of the action, and JC has been at the forefront of this evolution, and he's here to share his insights. So jc, welcome to the show.

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Jean: Very happy to be here.

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Anthony: So I thought we could start with kind of a, a big picture view of the role of the chief data officer and in particular in the context of the financial services industry. as I need not tell you, the industry is under intense regulatory pressure. and lots of growth opportunities. and that sort of presents you, I think, as a chief data officer with a challenge, a challenge of balancing the defensive game of compliance and regulation and risk management with the offensive gain of using data [00:02:00] to drive innovation and business growth.

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I'm curious from your perspective, how you think about balancing these competing, pressures.

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Jean: Balancing is the right, term. You refer to office and defense? So I'm, I'm a soccer fan, so I, I see in fact, the CDO as a midfield, or mean, try to go back to a, to a football, an, uh, uh,analogy, probably a quarterback. So we really, I will say, having to pivot between the offense and defense and. I say we have different phases of plays where some of them are much more defense intensive. And you're right, in financial services, we are heavily regulated. That's not gonna change, that's not gonna go away, and there's a reason for that. but at the same, at the same time, we have to support our businesses and I believe, Not just in financial services, but now it's becoming even more true in financial services where the speed of [00:03:00] evolution, speed to market, new product, new IDs, how to serve your client, and just a general reactivity that. The markets expect from everyone today makes it mandatory to use data to improve your, your operations, to improve your decision making processes. and so it, it's, no, in my opinion, it's no longer a or, statements in the CDO role, those two offense and defense role. Have to exist at the same time. You, it is very difficult to, be very effective. For example, the managing offense if you do cannot rely anyway on the foundation that are needed, for example, to have a sound defense practice.

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So all of that comes, meaning in my opinion, comes well together under the CDO mandate, 

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Anthony: So a good example of this in my opinion, is this idea of the single 360 degree view of the. Which I [00:04:00] know is a big goal of many banks. And, and the reason I love this example is that I think it speaks and, and you're welcome to, agree or disagree, but I think it speaks to both opportunities.

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On the one hand, a a 360 view of the customer allows you to significantly improve the service offering that you give that customer. And secondarily, it's a. Key requirements regulatorily around KYC and understanding the risk profile that that customer represents. so I guess the first question is, do you agree?

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And then the second is, what are some of the organizational and technical hurdles that get in the way of creating that 360 view of the customer?

Jean: first of all, I agree. I mean, it's, it's fundamental. In fact, it's. In my opinion, if there's one thing that a CDO should start to care for is that that precisely, to your point, it's extremely mandatory if you want to and if you want to [00:05:00] get to a business to the next level of development. Okay. think there's, every business can grow without that, in my opinion, but this is gonna come a point where meaning the size of the organization is gonna be such that meaning. Not understanding what you do with your customer, the product that you sell them, the product that you could sell them, is gonna become meaning a real hurdle too, meaning further development. So, that's key. Then you mentioned risk management, obviously, meaning. The regulators 2008, through BCBS 2 3 9 have kind of made it mandatory, so we need to understand where our risks are with whom and so on.

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So BCBS 2, 3 9 does not explicitly require a, 360 or, or, or a ma customer master. But you don't have it, the exercise becomes much more, much more complicated. Compliance or complying with different regulation is, is, is another element. [00:06:00] But one even crosses back to business development.

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So today, for example, you could sell different type of product to the same client. the level of requirements or due diligence that you need to do will be different depending on those products. Okay. for example, versus, uh, pure market versus Federal Reserve Bank. They have different requirements.

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So you have, you have a lot of different elements to be able to, Or I would say aspect of compliance that you need to be able to check and understanding what you do and what you can do for a client already, meaning as, as a, as a value if you wanna accelerate, again, delivery of additional, of additional services.

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I'm taking the fact that, you know what we have to, what you have to do before serving your client as a, as a given, but bringing it to bringing it together and accelerating, therefore optimize your compliance processes and accelerating how you can get your client ready, to be served.

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It's, it's very important. So that's a critical element being on, [00:07:00] that we've been, we started to tackle, in fact,18 months, two years ago. That was, two years ago. In fact. That was, was my, one of my first, strategic, push and change when I, when I joined Migo.

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Anthony: Yeah, which I think speaks to why it's, or how important it is. Like you, you join Mizuho and the, the first things you tackle is this single view of the customer. I was, I was asking a little bit about the technical and organizational challenges. let me throw an idea at you, get your reaction. I have this perspective that, data reflects the organization that creates it.

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So if a bank is organized geographically, then you end up with geographic silos. If you, organize a bank by. Product, you know, checking versus,investment accounts. You end up with silos by a product,silo. And of course you could do both, and there's no reason you can't have many silos. I'm curious if you would agree with that, that this idea of, the silos of data is really an inhibitor and secondarily, that it's a function of how, not just [00:08:00] banks, but any business is organized.

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Jean: I agree, and I think the silo problem is. It's usually the number one problem of of A CDO. Okay. In any organization, meaning, construct. as you mentioned, have dictated for ages, how decisions were made and therefore how data was organized to enable those decision making processes.

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So now when you want to shift them or when you want to elevate them, You have to fight those silos. and. The key for me is not to kill the silos. It's not to remove the silos. It's just to make sure that our silos are not tight and waterproof one to another. It's meaning how you connect the silos, how you make the silos interact with one another, communicate with one another, share with one another.

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That becomes, that's very important. Okay. specifically meaning financial services. [00:09:00] Think banks have been around for, long time. most, if not all of them have gone or have grown through mergers, or business combinations. So all of that creates also the weight of legacy. So, and that's something that we cannot ignore and we cannot wait to have solved it to resolve some of our business problems.

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Anthony: So, It is a challenge, but I it's, it's also ingrained in how, again, organization operates. And that's something that, in my opinion, we have to accept and understand how to deal with rather than trying to fight because it's, very, it's very difficult. this reminds me of this. I think widely discredited view that, which admittedly comes from a long time ago, that the, the future was everything was gonna be in one database, data warehouse, data mart, whatever the, the thing of the OR system, of record [00:10:00] this kind of. Beautiful vision that I think, and, and in some cases, software companies would sell, which is this idea that, well, if you just got all of your data in X, then all of your silos would be solved.

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And I think what you're pointing out is, no, actually there's a reason that we've organized the business. In this way by geography or by product, or by whatever the construct that matters. And, and that creates some benefits to the organization and the data strategy needs to accommodate that. Not, you know, not re not force a replacement of that.

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Jean: That's correct. it's also a question of, or a function rather of how quickly do you wanna deliver, deliver value to your business. There is a, there is a disconnect. There's always been, and it becomes, and then the gap becomes even, even wider and wider as we, as we go between the time it takes to change those large systems. Remember, meaning financial services or [00:11:00] lot of, a lot of legacy platforms still on cloud, some, on premises or not in cloud, even running on mainframe, to some extent. So. Changing or moving away from that takes a long time. Okay. The business cannot wait. So the, the, I think people have, we have collectively understood that, warehouses are, great. They're not the solution. They a component of a solution. they, they're mean, they're not the end. Okay. And the same way today mean. We've seen the pace at which technology evolves. the technology decision today, I remember when I, when I started my career, meaning was still in tenure, like tenure plans and we were investing, I remember discussing, uh, when I was in, when I was in the CFO offices. Why can't we amortize cost of software over 10 years because that's how long they we're gonna keep them. that's gone, that's meaning to the [00:12:00] technology moves so fast. That meaning usually will come, much ahead of, of this type of period that stays the data that you accumulated in your business 10 years ago.

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And the one that you accumulated today will stay. So how are you articulating now your solutions and how are you articulating your strategy to recognize this and get the data out, make it available, for consumption create the value that your business needs? 

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Anthony: totally agree. And let me sort of bring these two ideas together. in the context of a sort of data foundation. So I think what I hear you saying is, you need to lay a strong foundation underneath any data strategy. and, I'm curious, from your experience and perspective, what does this foundational data, data layer, if you want to call it that, what does this data foundation look like and.

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Also maybe for, listeners who [00:13:00] are earlier on the journey than you are, you know what's the first most critical step in building out this, foundational data there?

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Jean: let me answer your last question first. your foundation has to be built to serve a purpose, a business purpose. So the first step is to understand again, what. Your business, what your business needs, and what therefore you, how you want to help them achieving or fulfilling that need. So it's, it's very important to do this.

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Otherwise, you will put probably. generic solution, ahead of a very specific problem, and that you, you, you run a higher probability for the two not to, not to meet at the end. So you, maybe it's something that was not gonna be fit for purpose. So the fit for purpose is, is very critical. There are different ways to then,achieving that, that purpose. I think one of the key. [00:14:00] Concept in the data foundation or the data pipelines, is to recognize and something that goes against a, I wanna say of the first role that heard when I started, evolving in the, in the data circle, which is data has to be pristine at the source. Okay. we're online. I cannot use certain words, but that's bs. you need to create a, a set of a pipeline and the data process to create a foundation that allows for cleansing. Why? Because, you could have. Data is created for a business process initially, and by a business process, which could be slightly different from some of the purposes or the consumption of that data. you need to allow for meaning that adjustment. Second, even if you have very good pipeline. Is, can be proved difficult or impossible. Therefore, to have data corrected at the source, rerunning a certain number of processes to [00:15:00] be able to get your clean data at the end. So if just for that, that you have to, to allow a, a time constrained cleansing process so that again, your consumer can have the data it needs when he needs it. But a couple of elements when we talk about creating that foundation, it's really, again, understanding how you go from your raw data, from your, from your system of record to, I will say dataset. So how do you organize them in the logical manner, product. Meaning still like general purpose.

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And how then do you move them to your, your final consumption product? Well, where here they're much more curated. Uh, you can combine different data sets together and so on to fulfill, again, your consumer objective. So, so it's really, how do you, how do you articulate and architect those three layers?

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How do you move data [00:16:00] the least amount of time? I'm big upon that it's the time where meaning we had to copy everything and anything. yeah. I, many version as you have customer, in my opinion, it's, gone. It shouldn't exist. so that foundation has to be rigorous with a, with a progressive level of curation and an equal level of governance. So that doesn't mean that you should let this, your source system do whatever they want. Do what they wanted, but I would say you have to give them, they're here to support their own purposes, so you have to let them do that and do that well, and, and, and as well as they can. then meaning making sure that. When it comes to breaking silos, for example, that every time you create those final product ready, cons, consumable, or consumer ready, assets, that those are exposed so people know what they are and they can, see them as they, they could discover any product on, for example, [00:17:00] on an Amazon marketplace. understand what's in there, understand that, that, that the level of, Of qualification or certification so that you can, again, reuse those product as much as possible. So governance, velocity and agility, are very important. And the last we talk about governance, a, a very strong component that meaning, meaning we're pushing right now with zuo is to. Again, function along and being organized along business data domains. And make sure that, accountability, and understanding of the data is, is, is done and is, and is really the reality. A day-to-day reality from people who are using that data. We know that talk about data quality. For data to be of good quality, it needs to be used.

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If it's not used. decays very quickly and it rapidly becomes obsolete and irrelevant. So we're really pushing this and try to align the [00:18:00] technology organization gradually. We, again, to follow and fold under these type of organization.

Anthony: I think there's a, a couple or, really important ideas there. and to go back to where you used to. Started this idea of connecting the work that you're doing, the data work that you're doing to a business strategy. I can't tell you how many, data pipelines, it's like [00:19:00] a, data pipeline to know where someone's built something, it's moving data around and someone's like, well, why did we do that?

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Well, I don't know. That's, we've always moved it there. Okay, but is anyone actually using that data? So I think this is, arguably. a listener took nothing else from this conversation just to begin every data project with a conversation about business strategy and how this aligns would be key, but I wanted to, the thing I think that you, talked about that is maybe a little unexpected is, is a tendency.

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For most data practitioners to think about, the raw data, to think from the data up and not from the business problem back. And you have this wonderful story you tell, about being, surprised when you got access to data that you thought was raw data but wasn't, and maybe share that. 'cause I think it's very instructive to, to see how The good intentions of of an IT organization can often get in the way of trying to, to get a good data strategy in [00:20:00] place.

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Jean: Yeah, and, and I think that. That also come the crossroad of managing legacy practices and processes into, into a, a, I would say a newer, a newer approach. But, today the concept of cleansing exists has been existing for meaning for quite a long time. Okay. Some, some companies didn't listen to that earlier. I would say guiding principle of data governance, that your, your source data had to be, perfect and I think you have. Technology organization who've tried to solve certain problems, and for, for cons for consumers already have their form, meaning are aware of. We're aware of some of those problems and try to solve them sometimes, well, sometimes not as well, but the intention was there.

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And when you rebuild a new strategy that goes from. I really need to have the true view as to what the system of record produces. Because I mean, if I want to ask folks nonetheless to [00:21:00] correct, we need to have that uninterrupted in the very pure audit trail. and yes, we, we had a project where. Some of our IT colleagues that were helping us moving on that project thought they were very well intentioned and say, oh, know, we know the problems that our data, our colleagues are gonna be encountering. So we we gonna give them already, will say pre-washed, data. and I think the intention was fantastic.

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The, the results were very, were not as good as meaning as we started to. To un to unwind this and discover this, it became impossible then to go back to the source, the source system folks and say, guys, we need you to change this. They didn't recognize that data anymore. So, that cost, that did cost us, you know, certain number of weeks of, of delay in our project to, to bring it back the way, the way it should. That illustrate. In [00:22:00] fact, one of the principle that I try to enforce me as, as, as often as possible, which is you should not consume data, from anything that's not either gold or silver, meaning source or. One step away from the source, and that's, that's very important. You have to, we have to reduce the point of failure. We have to reduce the number of friction, you know, data pipelines. And that's, that's a great, I will say lesson to again, yes, there could be assets out there, but before using them, you, I really have to make sure that's meeting. They are what you need and not,not jumping in the end unknown. So close to the source as much as possible.

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Understand your transformation if you want to have effective pipelines.

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Anthony: No. Again, I think it's, a great lesson for people to take. Away, from this conversation, really always thinking,

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Jean: Mm-hmm.

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Anthony: think this is exactly right, like one step away from source at worst or best, but [00:23:00] source at best. shifting gears a little bit, thinking about business value, I wonder if you could share, a specific example in your experience of where getting your hands on clean trusted data has helped.

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translate into some business value, whether it was cost efficiency, better decision making, superior customers experiences. is there a practical example you can share? 

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Jean: I think I'm, I'm gonna use one here, which is more around, cost, I would say cost savings or cost cost optimization. And relates to really the, procurement and expense and expense management. So, it was, it was a simple case of having. areas of the business not be able to really understand a level of detail, meaning what they were purchasing from, where, what they meaning, how does that, was that translating in their, in their p and l? it was a fragmentation of vendor, of, of vendor landscape, [00:24:00] fragmentation of service landscape.

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And, we, I mean, we were, we've been asked to try to crack the code and help them. Connect, procurement, contract management, expense management altogether when three processes had some level of connection, but were widely independent, managed by different teams in different business units and so on. and I think. People came to us after having exhausted what was then the, I would say the traditional approach to solving a problem where, again, going to. Understand. Okay. What system are used today in those processes? oh. Yes. we do not have the link between the contract and the expense. And then when we, and then we we're missing also a link between, in the expense and the SOW that they find the services.

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And so, oh, okay. Purchasing team, how [00:25:00] long would it take to bring those data points in all systems? Years. So the procurement group came to us in those where we cannot do it. We need, we need something faster. And this is where I think again, we started to put together a, a project with them. in fact was aimed to, to develop a, a new data product for them that was bringing those data sets together, understand how the the data sets were related, how we could create those keys and connection, between the data and from there start to develop a lot of tools, decision making tools that allow them to, again, better anticipate contract renewals, understand overlap of services. And for the business to also understand meaning at, at the end with much better granularity, what they were spending and on and, and, and how and clearly. I think, what was the reason why was tangible business value is that once we [00:26:00] went through, so the POC was successful, and I think the first MVP was delivered in three months. and from there, after the first MVP. Once the procurement organization understood what they could do with it, we needed to invest more to turn the MVP into now uh, a real product which would evolve and so on. And they committed a significant. figure saving annual savings, which meaning allowed the funding of, the final product to be unlocked with still a very positive, p and l impact.

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Anthony: Yeah, and I, again, I think one of the things you see in that, in that example, Certainly not starting with thinking about a data foundation, but starting with what's the question we're trying to answer. And then I think another big idea there is starting with a solution that's not perfect, but get started and that creates the confidence to then fund the bigger idea, which is, let's go get it.

[00:27:00] one more turn of the crank on more, iteration better.

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Jean: Yeah.

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Anthony: It.

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Jean: The architecture and the, and, and the, the more I would say sustainable, longer term architecture solution behind it was built.

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Anthony: Yeah. And then it becomes obvious that we need to do that in a more systematized way, because the value's been realized. I feel like, we can't have any conversation, today without talking about ai and, I'm confident every bank is, and, and Mizuho are not accepted, is super eager to think about how to deploy AI in all kinds of different use cases.

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but I'm curious on your perspective as a chief data officer about how you help make, the bank AI ready. How do you think about. A data foundation as a driver in these AI strategies. or maybe I'm wrong and maybe AI strategies have nothing to do with data and not something you're even thinking about, but probably not.

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So give me your perspective on data and ai.

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Jean: No, I think, of course I, I would say that all banks are [00:28:00] thinking AI or started ai. I think I, I know of a few that are still, they're still lagging behind, but, data and AI goes together. Absolutely. I've heard, mean, when AI started to really become a big thing like 2, 2, 3 years ago, there was a lot of skepticism from the CDO community say, how can you do AI if you don't have good data?

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 which is not, not untrue, at all, but I think for a lot and. It became like, okay, let's wait to have good data, and then we start doing ai. It's the same thing as. The point we discussed a little bit earlier, trying to fix your, your, technology before starting to think about, I will say creating new processes and, and, and bring value to your business. cannot wait. You have to accelerate, but you have to be prudent and you have to understand in what you can and what you cannot do. So as far as we are concerned. We [00:29:00] definitely are, have invested quite a bit in ai. Okay. I know some banks, are, I would say labeling themselves as AI first. Okay. most banks are not okay.

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On, on, on our side. I would like to, I would like to believe that, or, or I would characterize this as ai Smart. We need ai. It's impossible not to use AI today. AI is, is everywhere in vendor solutions, in vendor software, in your email, in your phone that you have AI everywhere. So how can we get ready Leverage as much as we can, wherever it makes sense. And again, to fulfill value. everything. The, the way we're approaching it is, yes, there are a certain number of maybe. Core investment that needs to be made without an, an explicit ROI. And when you think about ai, r OI could be explained or, or expressed in different, [00:30:00] in different manners.

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But we're really looking at the business finality into everything we do. And in fact, we. We, we brought together AI and analytics as a, you know, like solution unit where come to us now with a problem or pain points or an objective they wanna achieve and say, help us. And the solution could be, could take different forms.

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Uh, it could take, yes, there, there are some data engineering. So plan old analytics. in most cases, yes, you have an AI component that, that, that comes in, guess what, at the end it goes back to your good old architecture, storing your output somewhere and, and making it available, to another processes and so on.

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So, that's the way we're approaching it. Okay. First, we wanna make sure that we give tools to make everyone more productive. You can think about all the Microsoft, for example, suite and how you use copilot and, and all the productivity [00:31:00] gains you can bring. You can think about, coding, assistance or solutions with, with GitHub copilot, all the type of solution you could think about, accelerating research or making research or search much more intelligent. Okay. By meaning, combining it again with, now, the interactivity of, of generative AI and large language models, but also meaning. We also make sure that we are supporting the business to deploy safely, new solutions that have AI embedded. Okay. Vendors that come and have partners that can come and help us, meaning have their own solutions. need to evaluate whether it's safe to use them, getting, making sure that we're, we're very comfortable with, with the output and the processes and so on. I think lastly, if we think about architecture, that was part of your question. what we're informing and we're trying to stay a little bit ahead of the cycle when it comes to [00:32:00] thinking about creating new, pipelines or new processes within our infrastructure and architecture further use of ai. one of the approach that we've taken is. I think AI is everywhere, so if you want good data to fuel some of your ai, I will say centric processes, we are also using AI to improve the quality of that data. I. and it's working right now, meaning pretty well we have probably close to say, two thousands of, of solutions today in production. and I think our, a of. Unsuccessful POCs have been very low, extremely low, extremely low, and the demand is growing again because we, we try to stay very close to the business needs and answering their, again, their requirements pretty quickly. So changing [00:33:00] how we deliver is also a big component of, of AI strategy.

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Anthony: So it's so relying on AI as part of the delivery mechanism, not just as an input.

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Jean: Both. And also

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Anthony: Yeah.

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Jean: do you change your approach, and your method to delivery? the standard waterfall, L-L-D-L-C, example, does not work. 

to, to show incremental value, meaning regularly you have to, you have to move to something which is much more aligned with HR principle or, or, or, or the design thinking methods be able to realize and, and execute and deploy, solutions including AI solutions, rapidly.

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Anthony: So just to wrap us up, I, I thought I would shift gears, completely, and, and take advantage of you as, a successful chief data officer and leader. I imagine many of the listeners to this podcast themselves are not yet chief data officers, but are aspiring chief data officers that would hope as, as part of their career trajectory that they would end up [00:34:00] there.

I'm curious. From your perspective, what advice you would give to a, an aspiring chief data officer of the future? what's some, what's one thing they should be doing today, to put them on the right path be the CDO of tomorrow?

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Jean: there was one, one piece of advice. Would give is to again, stay very close to the business. managing data is a output to input process, not the other way around. So, one thing that's. It's difficult to do, if not impossible to do, is to govern and manage the entire data state of a company. Very difficult. It's too big and so on. Usually when it comes to that type of scope, the only thing you're trying to do is make sure it's tight and nothing, nothing goes out. the thing you don't wanna go out, goes out. But, when it comes to extracting value, you cannot curate you the entire state. So [00:35:00] you have to put your investments where they matters. and that could take again. Financial services, a lot of us have started or have to dedicate significant amount of resources to regulatory, requirement and regulatory compliance. And that's fine, that's a value. Don't forget about it. so that's one thing. And, and the second thing is in the same vein, it's very important to be able to articulate. That value. what are you, meaning? What are you bringing? and again, even if it's yes, meaning, we have addressed a regulatory requirement that has a value. when you don't comply with a regulatory requirement, meaning you, your, your ability just to stay in business can be challenged at some point.

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So, or it can become very expensive in term of fines and, and all other, sorts of, I will say not very pleasant, consequences. So, but stay to the cross of your business and, express the value of everything you do is is quite important.

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Anthony: [00:36:00] Excellent. Well, JC I really appreciate the time today. this was really, a masterclass. On what it takes to be a successful chief data officer. Think about, balancing, the defensive moves and the offensive moves in the game. I'm happy to keep the analogy of soccer. I think that makes a ton of sense.

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looping that through this idea of the single view of the customer is a perfect example of that connecting to business value. Not at the end, but at the beginning of these processes. and I really appreciate the perspective, on AI and how that can be an important driver, in the business, especially, in financial services.

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So thank you so much for the time today and, all the best. I.

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Jean: Thank you, Anthony. Same to you.