Simon Smith: Hi, Simon Smith, Head of Front Office Data and Analytics Sales at JPMorgan.

Dan Dalton: And I'm Dan Dalton, Global Head of Data Analytics Sales at JPMorgan.

Simon Smith: So, given your title and given we celebrated 25 years of your tenure here at JP last night, what led you to focus in data and analytics? How did you end up here?

Dan Dalton: I think it's a great question as a self-proclaimed lifer at JPMorgan to ask. The biggest bit of career advice I give anyone that I have sort of a mentoring relationship is actually that I really didn't know what I wanted to do. I just knew that I worked at a good company and I'd find my way. And so, actually having done a variety of roles through the firm, including being the chief operating officer for a big part of our research department, it was just very apparent to me that there was a real opportunity in the data analytics space.

So much content that we were producing, so much amazing intellectual output, but perhaps the way in which we were positioning it, serving it, the way in which clients were receiving it could do with a bit of tuning. So, I put my hand up and said, "I'd like to do something different." And as is always, people don't always want you to do something different. And so, it was inherently there was a bit of risk in that, but it's been an incredible journey. And that's why I've been doing this now for about 13 years of those 25. So, it'd be great to hear though how you came about a similar space.

Simon Smith: Yeah. So, I did a technical degree. So, I did a computer science degree and then I took an internship here at JP. I ended up in the data and analytics space within equity derivatives in that original internship. And then from there, I was actually asked to stay on and not go and finish my degree. I chose to go finish my degree anyway and came back and ended up working in the research group similar to yourself. So, I was working more on the engineering side within research.

And then for a period I, as research, were really building up a lot of the modeling capabilities they had in house, moved to build out an engineering function alongside the research desks, alongside some of the rate strategists. And then from there moved as we were seeing more focus from our clients, looking at what the output of those models were and how we were modeling a lot of the value in fixed income markets, moved to focus more on client facing analytics, more so in a product role.

And then about seven years ago, as that was becoming very adopted by our client base, we were really looking at the commercial opportunity around that. And then obviously our paths cross, you'd obviously looked at the commercial opportunity of some of the rest of the research output. And so, moved to join your group in about seven years ago now.

Dan Dalton: So, not just a life or a data analytics life of it.

Simon Smith: Yeah. Domain and company lifer, I would say.

Dan Dalton: So, how does that feel? How many years for you now?

Simon Smith: So, I'm 15 years in, so it's been great. I think whilst my domain has been consistent, my function has been very varied. Obviously starting off in more of an engineering function, moving through a product group and then ended up in a sales role.

Dan Dalton: So, I guess as both of us have gone through a bit of a transition, we weren't salespeople, we became salespeople. How would you talk about that journey a little bit and making that transition?

Simon Smith: I think people have these ideas that everyone has a ... Is that a career endpoint that they've driven towards? I think for me, a lot of it was quite organic in the sense that we were originally building content, which was then shifted to being more externally consumed. And then as that was requiring more presence and time with the clients for them to understand what it was that we'd built and how we'd built it, really that time with the clients made you realize how they were using it and then how they could maybe derive more value from it. And that really sparked my interest in being client centric and client focused. And then I think from there you end up naturally tending towards the sales side of it. I thought what about yourself?

Dan Dalton: Yeah. I think for me, it was a bit more organic, I would say. Actually, because I started the business, it was very small. You become the expert. And so, along the way, you accumulate those skills. So, going back to what I was saying really about not having a plan, it felt like the right next thing to do at the time. And through some really, really in-depth conversations with our customers and our clients about what they actually wanted from JPMorgan data analytics, you built the relationship.

And I think as we're going to talk about today, the relationship and us as a firm and our ability to aggregate all of that demand, if you like, from our clients and to build products as a result means you're really a little bit sat between product and client. So, it's a really interesting vantage point. And so, I think for us, it's not really sales in many ways. It's all about relationships. And the firm has a ton of relationships that we've been able to leverage to expand our enterprise.

Simon Smith: I guess maybe outside of the client relationship and sales side, maybe just a grounding around what is the data and analytics business at JPMorgan? What is it that we offer to our clients? What services and functions do we provide?

Dan Dalton: Yeah. Great question. I mean, really, firstly, I'd say that as a team, we are the one faced to everything data analytics that JPMorgan has to offer. What we also have very consciously decided though is that the product teams that build the data analytics don't all belong together. So, as I said at the beginning, we have a large intellectual center in our global research department. We'd spent many years, 25 plus building fixed income indices, for example. That team rightly still remains very embedded in research.

And as a result, it has access to the full power of what our research department can deliver. Our pricing direct business, given the nature of the proprietary but independent valuations that we do for client portfolio holdings, help them strike NAV and do other valuation process, it has to be independent. They have information from our customers that we wouldn't want to share with the rest of the firm. And so, that remains an independent entity.

And then if you look through then the range of content across our trading business, some of what we're doing in the data management space, it's appropriately aligned to the businesses that it supports. I guess the newest of that would be our Fusion platform, which is being built out of our security services function. Obviously, a global administrator, lots of clients challenged with using data and analytics and integrating them into their workflows. It's appropriate for to be with that business.

But what's key for me is I can amass a team of professionals that can sit across all of those different disciplines. And while we obviously align people like yourself with all of our front office solutions, we align people to those different disciplines. We can have really one conversation with the client.

Simon Smith: That's a good grounding in terms of the different product lines that we provide. And then thinking about how my team or other teams face off against our clients, how do we typically put that to a client? Because obviously there's quite a lot there. Is it just like, here's the Rolodex of items, pick and choose, or how do we make sense of it to a client?

Dan Dalton: Yeah. So, I think of it as a multidisciplinary function. We've got people aligned around our pricing and index products very much because the client at the other end really as a consumer are buying both of those things in tandem because as you'd expect, our indices are priced by our pricing direct business. And so, it makes a lot of sense for you to have one connected client discussion.

Our Neovest platform, which also, as you know, we recently acquired the LayerOne platform, has a dedicated sales team. You're having very specialists and specific conversations with our hedge fund clients around EMS, order management capabilities, et cetera.

And then really your team, and really what do you focus on? It's really the front office relationship and the solutions. And the reason we say that is it could be a bit of those things, but it's also a lot about the content that we could deliver actually from the core of JPMorgan. That's our trading desks or from our research department and other parts of the organization. And it's really like, how can we bring content to bear that's useful to our clients that we are happy to externalize and trying to find the middle ground on that.

Simon Smith: So, I guess each client conversation is bespoke to the type of client.

Dan Dalton: You would get a different version of our team and what we do for clients, depending on who you were. If you're an asset owner, you get a very specific curated services versus a hedge fund. And even at the same, even if you're thinking about a hedge fund, are you very data centric? Are you less so? And always it's about how can we actually help you?

So, it's really a coverage role and a relationship role as much as it's a sales role. It's like, what actually are you needing to find solutions for? What have we heard in aggregate from other clients battling with the same things? And then usually there's hopefully for us some commercial outcome also in terms of some of those products being relevant and you talk to the client.

Simon Smith: Yeah. And I think we skipped over some of the parts at the beginning, but that idea of solutioning with that range of products that we have, that's really what keeps me and my team excited. The fact that we go into these client sessions and you obviously get due diligence and information ahead of it, but really, it's a bit of an unknown and it's very rarely square peg square hole. It's very, very often trying to make a solution that fits the client's workflow.

There are different, even within the same client types and the same segments of clients, there's still very different workflows and practices happening. So, it's very much that that is exciting for my group to think about, okay, so how do we create something that's fit for purpose using the vast amount of technology and resources that JPMorgan has to deploy to fit the client's need?

Dan Dalton: Yeah. So, you become the master of context switching, which I know you always cite me with, but actually it's exactly what you've just said excites you about your role at JPMorgan. How do you do that?

Simon Smith: Yeah. Well, it's a requirement in terms of the role requires you to be able to go from speaking to an asset owner about post-trade reporting all the way through to the next minute talking to a startup hedge fund around how they're going to use our data in the pre-trade systematic investing space. So, it's very much a required piece and something that I picked up whilst being on the desk.

And then in terms of how you have the knowledge or the breadth of coverage to do that, it's typically around the internal connections. So, you talked about our product partners who are obviously amazing resources for us to work with. They have particular niche depth knowledge around their product area, their domain, and really leveraging that where you can to try and get enough. I think we always talk about our group being the A to F of most of our products or the majority of the products, and then the product groups behind us being like the F to Z. So, really being able to have that amount of information across a broad enough spectrum means that we can then switch between-

Dan Dalton: So, enough information to be dangerous on every product, I suppose is what you're saying, but you bring the right experts into the client discussion as required.

Simon Smith: Yeah. In terms of your tenure here, what is it that keeps you interested in turning up to JPMorgan for 25 years in a row? Yeah.

Dan Dalton: I think something really miraculous happened 13, 14 years ago when I put my hand up actually, which was I got the opportunity to work for one of the largest and best banks on the street that's obviously, as we've all seen, gone through a huge evolution and build a business. And I really feel like we've built a business and I've had the opportunity to be really entrepreneurial, which was always an itch that I wanted to scratch, but actually being able to do that in the comfort of a firm like JPMorgan where you'd built up credibility, people gave you the trust, it's really freeing actually.

And so, that was the really amazing thing that happened. I think if I put that back onto the actual business and the products, you really have to go back and think about like, why are we doing data analytics? It's actually quite unique versus some of our peers in the industry to want to be in this business and to be doing some of the things that we're doing, as we said in indices, in pricing, in the front office.

Most of the banks like us had made some investments in these spaces, but with incoming regulatory change, for example, in the provision of benchmark, there was different regulations coming in. Some of our competitor firms made decisions to divest some of those assets and they divested them to companies that more traditionally supply data analytics, the major platform providers and what have you.

As is very typical at JPMorgan, we decided that that was not for us. And actually, if you really think about why we built these in the first place, we're the number one provider, for example, in emerging market fixed income indices. Why is that? Well, we wanted to build the secondary trading market. How do you build the secondary trading market? You need to build transparency.

Clients need products to help them navigate. They need to be able to look at the investible universe that they can then select securities for in their portfolios. If we do a good job of providing that transparency as a huge market maker that has a presence in all of the relevant markets to construct that index. While it all might seem very benevolent, it's not benevolent. We built it for that purpose.

If you think about our pricing direct business that has its roots in one of our predecessor firms, we could have acquired that firm and dispensed with that. We did the opposite. We've massively expanded it, invested it to really cover all of the over-the-counter markets and derivatives. Why did we do it in the first place? To evaluate the price of assets that as a firm we were packaging and selling to our clients that they needed to strike now at the end of the day on.

In a sense, these things are extremely logical for a bank to own. It's just that the world had moved on, and I guess it's moved on where clients have a voracious appetite for data. And I think the grounding that we had because of those two businesses, well, we were already a professional data analytics company. We already knew how to serve clients. It was all done appropriately. It was all licensed appropriately. Clients had certainty of delivery of service.

And so, if we then turn our attention to the other content that we've taken from our trading desks, our structuring teams and other parts of the organization, we knew how to do it. And we've built on those foundations. And I think for me, that's been the key. We had a really big stake in investing, and it's just given us a much better foundation, I think, versus some of our competitors.

Simon Smith: Yeah. I think that story remains true for both the data and some of the system sides as well. If we think about you're supporting clients in their transaction of financial products in markets that could be through transparency in indices, valuations, in price indirect, in data and valuations and services in terms of the content and then some of our risk analytics, things of that nature.

Dan Dalton: I think that's a great point. And I think that what have we really succeeded in providing its market data, it's like a better categorization of the investible universe, pricing indices, yes, but also really root market data. We understand how a lot of the over the counter fixed income markets, for example, trade. We build a huge amount of intellectual property to support our traders. Not all of that intellectual property has a competitive dimension to it that we wouldn't want to expose it. With many people in our quant research teams, traders themselves cleansing that data state around what they do.

And so, our ability to take components of that, expose them, it means clients can save quite a lot of time. They can also do it with a very trusted partner because they know something very critical, which I think is where we're very differentiated from a lot of the data analytics platforms is we use it. It's our intellectual property, but we're using it actively. And so, I think the best stamp of approval or best alternative to an SLA or on delivery is that if there's a failure, it failed for JPMorgan, that's not acceptable. And so, you can put some trust and reliance in our ability to deliver some of this stuff.

Simon Smith: Yeah. And I think that's something that we've seen, as I say, across the content, the risk systems, everything that we provide in the front office space is very much on that basis.

Dan Dalton: I guess it's in a sense the legacy of what we built from a data perspective enabled us to go further. So, now we're exposing fully packaged risk capabilities. So, maybe you could touch on that.

Simon Smith: Yeah. So, on a similar point, thinking about our risk analytics there. So, particularly in exotic instruments where the independent instrument models are hard to come by, the market data are being of a good enough quality to value these very sensitive products is for hard to come by. Being able to provide clients with a service where they can simply upload positions and then retrieve back valuations, risk metrics, sensitivities, value at risk or P&L vectors is something that's extremely useful to clients.

It means that they can very quickly and agilely deploy it into a new asset class, a new area where they would otherwise not be able to do so, be it because of capital expenditure in terms of building systems, acquiring quants, acquiring market data, all of that time and effort and resources that would otherwise be spent essentially replicating the same thing that JPMorgan's built and does for itself is really something that we can shortcut for clients.

And so, a lot of our business as we think of it as really standing on and leveraging the spend that the bank has on technology and technology and data as a firm, and then really pivoting that to allow our clients to benefit from that same spend.

Dan Dalton: Makes sense. Makes sense.

Simon Smith: Thinking about the data and analytics business and how it's evolved, and as we've stood  now and maybe looking slightly further forward as well, what are the trends that you're seeing from our clients and the trends in our product suite as well as we respond to that client interest?

Dan Dalton: So, I'm going to put the word "AI" out there, but I'm going to park it for one second while I talk about a few others. Firstly, going back to that point on transparency, the same process that we employed 25 years ago in some of those businesses, we're having the opportunity to reemploy. And so, you're seeing the same challenges now from clients, for example, navigating alternative allocations, private assets.

And so, actually, how do you build transparency in those markets? You need pricing, you need indices, you need intelligence from experts. And so, in culmination, if you think about what we've built, there's a real opportunity to do the same again and to breathe some life into that, not just for ourselves, but for the industry at large.

I think if we flipped to data just a bit more broadly, I'd say that a couple of things are happening. One, you've had the very, let's say quantity clients that I've been in this. So, talking about AI, for us, it's not new. What's actually new is who can use it. A lot of our large clients have been using it for many years, and so we learned a lot in that process.

And I think a couple of things we learned. One, you need to be very good at systematic delivery. You can't fail because you break process. So, in a way that's given. We're a bit game ready for that. So, I feel quite prepared for that. Two though is just overwhelming amount of new demand for data that perhaps would only have been interesting to those clients historically.

And why is that? It's because a lot of these new tools that are coming out are democratizing a little use of some of the AI techniques, machine learning, et cetera, that really was the realm of clients that could invest very heavily in technology and quant resources. So, that's a big theme that we're seeing.

The second thing is what can they crunch? And so, what can they crunch? You've got much more interest in histories. And so, as we've said, we have some advantage because we've been doing this for a long time. I always say the long history is great because we've been doing it a long time. We've built up a good catalog of history, extracting it from legacy systems can be quite difficult, but we're doing a lot of work on that, as you know, but it's there. So, I think that advantage is clear.

The other point though is that what you actually define as data has changed and the structured content that we're accustomed to distributing through platforms that we can talk about isn't the total definition of data. Clients are now seeing, for example, textual content, written content from our research department, from ourselves and traders. Historically was going obviously through publishing systems or in the case of some of our sales and trading commentators through email. All of a sudden, this content can be transformed into something else. You can pull sentiment.

I think a lot of people are familiar with that, but again, the tooling that's coming out is democratizing that. More people can do that. So, there's been a spike in interest in things like data feeds of written content. And so, client's ability to process that, whether it be through a third-party vendor that's built technology or through their own internal technology, we've seen a huge spike in that.

So, what do we do? Well, the great thing is we have lots of content and we have the number one ranked research franchise, which is amazing. And so, the value of extracting that one is amazing, but also what else can we expose from that process? And so, we've been doing a lot of work in equities and really trying to bring out equity data offering up to par with our fixed income offering.

And so, actually, if you think about equities, a lot of the data that we've talked about, it's readily available, exchange listed. The index providers are very famous and well known. We don't want to compete in that space, but if you think about inputs into the alpha generating process, I think we can compete because we can not only give you a view. If you think about our research analyst process at the moment, you can read their content, you can look at the recommendations that they make, but actually what do they do inside JPMorgan to achieve that?

They consider the data that surrounds their sector, their industry. They curate it. They assess it. They might do a [inaudible 00:23:10] pervade of different providers and make decisions that these ones are the right ones for my coverage. And then they use that internally in their investment process. They build their own data sets to support their own investment thinking.

And so, actually unpacking that process a bit and starting to help clients with their own decision making allows us to do two things. You might have a competing view of how you can structure your investment thesis on a given company or industry, but you can learn from how we put that thesis together and then you can put your own inputs in. You can also save time because as we know, a lot of clients are, they have obviously constant coverage of certain companies, others they might not. They may stray in and out of it. And so, having that readily available as like additional data sources I'm seeing as a huge thing that our clients are asking for and that we need to deliver.

Simon Smith: Yeah. As we think about that knowledge map, as you say, that sits typically in an analyst's head, trying to get that into the hands of our clients has really been a lot of what we've been looking at in the equity space. So, as you think about obviously there's the written content, there's then all of the fundamental data that they generate, like estimates, all of their working models, all of their outputs from those models, the valuation metrics, et cetera.

And then increasingly there's some products that the research group generate, as you say, is that like intermediate state whereby they're often bringing together multiple external data sources or possibly some proprietary data that they've captured themselves and then commingling that to form an output which they then take a view on or they then use as a decision support tool.

So, then having that as something that they use, but externalizing that to our clients is really what we've had a lot of demand around. So, trying to extract that knowledge map from being part of the analyst's head and part of the desk content and making it a client service has really been a big ask in the equity space.

Dan Dalton: So, if you're like, this is all feedstock for like superpowered analysis. More people can do that superpowered analysis. Where do you think this AI journey is taking us?

Simon Smith: So, you mentioned, as you say, obviously a lot of our clients have been applying these same techniques maybe in slightly different iterations for a long time now. So, we have some of our quant clients who specifically see their edges being information processing and the extraction or the refinement of information from content. And so, I think as they continue to move up the spectrum, I think that's very exciting. I'm not quite sure where that ends up from their point of view, but they're continually pushing the frontier of what we provide.

So, for a long time, they've had machine-readable research feeds, as you mentioned. They're now getting their hands around ourselves and trading commentary. It's then asking for transcripts of all of our calls. It's then any analyst models in between. So, really thinking about like, they're really pushing us to provide a feed of everything that JPMorgan deems as content or generates as content to them such that they can push it through their process.

Dan Dalton: And I guess that leads back to that point on coverage and being able to aggregate the collective demand of some of those clients. And I think they have the benefit because we can create effectively some collaboration amongst those clients in terms of what we want to offer. And I guess what you're also saying is clients that are earlier in their data journey can benefit because we've done a lot of this already.

Simon Smith: Exactly. That cohort who are now taking our research feeds this year, let's say, who have using some of these new generative models are obviously starting with research because they're trying to build out a POC, they're trying to derive value, make sure that it's fit for purpose, be it systematic or discretionary. And then as we're working with those quant clients and getting pushed to provide more content in feeds that can go into these models, as these new cohort of clients who are embarking on this journey, move past research and onto the next pieces, clearly we'll have more mature products for them in that space and they'll benefit from the bleeding edge that is the quant investor who has extracted painfully in some cases information from us.

But then over time, we've then productized that and made that available. So, as they need to continue moving to make sure that they are getting an information edge and not just getting the same information in an easy to digest format that everyone else is, but those who are on the journey behind them are benefiting from what falls out behind that.

Dan Dalton: That makes sense. I guess the other thing I would say is that 12 months ago, AI was all about where is my data? How do I get it in a proper fit state, aggregate it centrally? How do I figure out the governance of that information, like who can access, who can't? And in a way readying yourself for that AI journey. I think fast forward to now, people are using it and actually some of that pre-work's been very beneficial, but we definitely see also clients struggling with having not necessarily got that bit right.

Simon Smith: Yeah. I think we obviously have a large function in house around chief data and analytics office, not purely focused around AI, but a lot of the work that actually goes on there is around making sure that the data that we want to use as feed into these models is in an appropriate, governed, labeled, described state such that it's not a garbage in, garbage out process, and you're actually deriving value from authoritative sources which have the right data and correct information that you're putting through.

Dan Dalton: And so, I guess talk me through, because I know our CDAO platform is called Fusion and we also have Fusion for our clients.

Simon Smith: Yeah. So, you can imagine that a lot of that work that we do as a bank is putting in the frameworks rather than specifically labeling the data. So, having a framework which allows you to have a data catalog, allows you to label all of your data, apply metadata, capture information around where it's come from, the lineage, et cetera. And so, a lot of that is really work that others could benefit from, going back to that same point around the text and that JPMorgan has and that being something that's a lot...

Dan Dalton: So, in a way, similar to the data journey.

Simon Smith: Exactly.

Dan Dalton: Exposing some of the capabilities we're building as a firm for ourselves, they don't necessarily have unique advantage, but we see that the industry is doing the same thing many times over.

Simon Smith: Exactly. So, it's like the common concept of a club good in the sense that if we build it once and build it at a level that is modular and customizable enough that it can be adapted to fit multiple use cases. Then obviously multiple people can benefit from the same stack essentially. So, really, we think of that as using those same investment dollars that we use inside the bank to then provide clients the support in this space because there's no economic benefit to the clients nor to society in everyone building the same thing in their own shop.

Dan Dalton: But to make that a bit more real, we're talking really about the normalization of lots of third-party information that clients use in the investment process, albeit the indices, the ESG data, their security master, their representation, their securities derivative investible universe and making connections to the industry data that you could use in that context.

Simon Smith: Yes. Exactly. So, thinking about all of that wrangling and normalization that happens when you have different sources of a common data domain, be it like reference data, ESG data, index data, all of those need to come together and be normalized and commingled in a way that you can use it uniformly. And so, in security master, that tends to be around reference data and some of that is obviously feeds from third parties. Some of that is you opining on that reference data as to which one you would choose or which source or how you would override a particular field.

Obviously in the ESG space, again, there's probably a less mature space. And so, there's less of a consensus as to the authoritative points or what are the correct points. And therefore, normalizing is quite a subjective exercise there. So, there's a lot of work that needs to be done to be able to utilize multiple ESG sources in your investment process. So, should a client really be spending all their time in that data wrangling phase or would they be better off spending their time on higher order problems?

Dan Dalton: Yeah. I like that description of higher order problems. I think what we're trying to do is get them a little bit closer to that because that's ultimately the alpha domain and where they really demonstrate to their own clients their differentiation.

Simon Smith: Yeah. We want to provide the modular building blocks of common duplicative or duplicative tasks. Anything above that is obviously the client's own expertise and own processes and choices.

Dan Dalton: I think the other thing I've definitely seen from that perspective in Fusion is when clients were earlier on in the journey, they saw a huge amount of opportunity to build some of this themselves. And actually, as more AI tools have come in, they've just realized how hard it is. And so, you're seeing it swing back a bit. And then all of a sudden, actually the ability to buy a common platform where clients are onboarding and the demands of those clients are being gathered. The tools are being built in a way that would serve multiple different types of clients, different use cases.

A lot of clients telling us that there's a real edge for us in playing in that space. I think the advantage we have is obviously we can build it for many clients. And so, there's scale in their ability to then buy a fraction of the price of developing it in house.

Simon Smith: That maybe comes back to a couple of points around like, A, why do JPMorgan think we're adapt or adept to playing in this space? Why would JPMorgan versus maybe a data vendor or an analytics provider be the kind of platform of choice? And then B, what is it in the existing client tech stacks that are deficient to allow them to benefit from?

Dan Dalton: Perfect. But maybe I split it. If I think about the Fusion platform, which as you know, we're very focused on our asset manager and asset clients and it's initial development. It's a very natural extension of our overall custody fund accounting, middle office relationship with those customers. Many of those clients had said to us, "You've taken over the administrative burden of a bunch of things that don't add alpha."

Again, same principles you can do at scale, but you've left me with something, which is this whole challenge around data. And I'm aware that I have a challenge. I'm spending quite a lot of money doing a bunch of stuff, but I can just see the challenge is going to get vastly more confidence. And so, it makes so much sense to me that you'd extend that overall administrative relationship to data. And so, really that's where Fusion can play a part. We can do it at a different scale. We have a pretty strong leverage with all of the data contributors in the industry. So, we can really represent the client and development of that.

Simon Smith: So, that's the why JPMorgan. And then thinking more to like, what are the deficiencies and what is it that a platform like Fusion helps our clients with? What are they trying to solve for? What are the limiting factors?

Dan Dalton: It's to your earlier point on this idea of if you take the security master and simplify that, all of the securities that clients investing in, they need a whole lot of information around that. So, as I said, we're a big security services provider. You want to bring in your performance. You want to bring in your fund accounting data. You want to actually provide real-time performance analytics to your front office users. You've got massive reconciliation challenges as you go through that chain, but at least with a platform like Fusion, you can surface all of those differences and discrepancies and allow a client to manage through that process effectively.

You can stitch all of their core information, that investible universe to all of that third-party data that they might want to bring in and do that seamlessly because we've already integrated it. So, rather than you going out and making contact with 50 data vendors, we've probably already done it. And so, there's just a scale in our ability to do that. As with everything, you talked about our risk platform, the same is true of Fusion. We really built up the platform with the early adopter ethos. Picking partner clients. They were having these very notable challenges that we've talked about and then working with them because the best representation of the buy side is the buy side. And so, we're very focused in the development of any of these to have anchor partner clients that really inform the development, prioritize the roadmap.

Simon Smith: Yeah. And I guess for those guys having a new platform as opposed to their legacy platforms as these new domains of data come online. So, the requirement to integrate ESG as an example.

Dan Dalton: Great point. And it's where it is. We're building a cloud native platform. I know everyone's talking about that. You have no idea the number of client discussions I have where you talk about all of these concepts I've just talked about, nothing's cloud. If you're using a third-party vendor for some components of it, they'll charge you to move to cloud. So, we've really gone a cloud first and cloud provider of your choice, but really like built this in very modern infrastructure, the architecture's hyper modern. And to your earlier point, we're using it ourselves like JPMorgan. And so, to my earlier point around we use some of this in active trading situations, we're using this in active data management situations as well.

Simon Smith: So, I guess it sounds like at one end we've got the platforms where there's the full-blown platform offering, can be a managed service, can be software as a service provided as a full functioning component, if you will. We have then the data piece, which more often than not feeds into a client's existing platform or client's own platforms. And I guess there's that piece in the middle where we have like modular building blocks where we plug and play into the tech stack. So, is there anything, I guess as we think about that platform side?

Dan Dalton: If you look at a lot of the large hedge funds, a lot of their USP is building these big platforms, they have great scale. We could still provide them a lot of input, but it tends to be the data that active trading desk information that we've scrubbed and cleaned, very valuable and useful.

Simon Smith: Yeah. The market data.

Dan Dalton: And then they build up their own systems because a USP for them is giving their portfolio, managers, differentiated and best in class technology. We could make the argument though that centrally you can build some of that. And so, again, exactly the same as we've talked about with Fusion, our Neovest platform offers electronical to management capabilities, portfolio management capabilities all out of the box.

And so, actually if you are, as you were saying, a hedge fund may be going to a new asset class, you might not want to build up that technology [inaudible 00:38:32]. You might actually want to see whether you can perform and it's something that you actually want to invest in the long term. And so, sometimes we have clients that want to use us in that experimentation process, which we're very happy to support, but usually we win those clients for the long term because actually, again, there's a scale, just a raw scale in the commerciality of being able to do it for many. But the connection back to the bank and its capability mean it's pretty good out of the box.

Simon Smith: Yes. And they have that, as you say, they have that stack all the way from execution, order management, and all the way into portfolio management. And we see some clients picking up the full stack. We see some clients taking components of any of those layers within that, if you will, and really some of them taking it just for asset classes. So, to your point, we see people moving from equities and wanting to add some credit into their books and using a system, which means that they don't have to spend six months building, hiring, developing.

Dan Dalton: It's a great point. And I think while as a firm, we want our platforms and technology to be global and complete, but one thing we really, and this isn't where a lot of the industry is we do not want to force anybody to take the whole platform. Because by doing that, you miss sales opportunities, clearly, but you also miss expert client opportunities because that piece that they want, they might be really good at. And we want that input into the further development of the platform. We also want to land and expand all of our relationships. We want to get in with something that solves a problem for you right now.

Simon Smith: Yeah. Certainly, my team, as we think about solutioning, it tends to be exactly that. You come in specifically with a client pain point or a new client expansion plan or some specific point that you're solving for, and then the idea is always to try and support that client as they have further pain points, further needs and land and expand, as you say, either that platform, that data, the overall piece.

And so, we've seen this across a lot of the hedge fund space where traditionally they were just picking up a lot of our market data, now picking up some of our valuations in their fund control functions, picking up some of our indices when we think about the historical data, especially in credit that we have around a large investable universe of investment grade debt. It's an unrivaled ecosystem if you think of it that way in the sense that they get in to know us, we get to know them, we know where they're going and can point them in the right direction.

Dan Dalton: And I guess in some cases they don't even know they wanted something. Why would a hedge fund want a benchmark index because they're clearly not benchmarking the portfolio performance?

Simon Smith: Exactly.

Dan Dalton: It's actually that enormous amount of data that we track as an index provider on those universal securities back in time because back-testing has become a big theme. And I guess on that topic, it'd be great to hear maybe about some of the developments in global research.

Simon Smith: Yeah. So, I think, as you say, in terms of the back-testing piece, that's been something that's very important for our clients, both within the market data side, and then more so as they're developing strategies on alternative data, not necessarily alternative data in the broader sense, but more non-market data. The research data that we provide being of a quality of a systematic nature that it can be back tested, it's representative of the information that was available to the market at that point in time.

And so, that's an area that the research function has really spent a lot of time and effort on over the last, I guess five years now. We have a client who we actually partner with to provide a macroeconomic information library, if you will. So, it's essentially a system which provides quantamental data for macro investing. So, whereas traditionally a lot of macro systematic strategies have been built out around purely market data, the use of macro data has been very difficult given it's often revised, it has seasonal effects, it's often rebased or that some of it is even survey based.

Dan Dalton: If I could maybe put that in a slightly simpler way, it's almost like if I was sitting at my desk in 2014, what would all the indicators in the macroeconomic space, if we take, I don't know, inflation numbers, country, payrolls, what would they have said with a predicted outcome?

Simon Smith: Yeah. If you'd snapped this state of the economic indicators across a broad range of concepts, countries, regions, and areas in the same way that you snap prices at the end of day, then you'd essentially have created what we've created and then...

Dan Dalton: If you know the market at that point would trade, their behavior would be driven by those point in time indicators, not by the ultimate release revised 15 times before it becomes official.

Simon Smith: Exactly. And then adding on some of the, or encoding some of the economic know how around that as well. So, knowing around when an inflation measure that the market looks at changes from being RPI to CPI or knowing how to compare the right measures across different countries in what are some case idiosyncratic indicators. And so, things like that is where we've made macro information investible or tradable as the group likes to say, and it's something that as a lot of our clients are amazed to have access to because it's something that you just wouldn't be able to staff and resource yourself. We have 20 people working on this project, specifically like quant economists, financial engineers, building these indicators such that a large range of our clients can benefit both from the data as well as the financial products...

Dan Dalton: So, I guess the same thread of better information, better understanding of the present and the past, better ability to navigate.

Simon Smith: And a resource spend that you just couldn't justify yourself.

Dan Dalton: So, that's all centralization.

Simon Smith: Exactly. That club good concept coming back around. And if you think about how our clients use our data, there's obviously that systematic side of back-testing and strategy creation, be it like deriving signals or be it conditioning preexisting strategies and signals. But also, on the discretionary side, we've done a lot of work applying our more quant data to discretionary managers as well. So, helping them build decision support tools.

Dan Dalton: So, it may not be programmatically put into the investment strategy, but it gives them a kind of [inaudible 00:44:55] information.

Simon Smith: It quantifies and shows them information that they wouldn't necessarily have at their fingertips. So, be that for relative value analysis, be that just for general insight, be that for the macro conditions in which their models are operating in or they're investing around. And so, that's something that's been very important on that large universe as well. If you think the discretionary manager base is still a large proportion of the investment managers that we deal with.

And then as we think about the post-trade side, obviously we then have all of the valuations, the mark to market and the risk that also sits on top of our market data, but also some of our research data as well. So, increasingly a lot of our research content feeds into that side as well. So, it's something that's really, it's put a different focus on our research groups as they think about not just the written content and the models that they've generated from the equities side, but the content that we generate from the broader strategy group there as well.

I think you mentioned that previously the origins of a lot of our products are around providing transparency or around helping our clients operate in markets. As we see this shift into private markets and our footing in that is maybe slightly different than it is in traditional public markets, what are the common problems we're hearing from clients in that space and how can we help? Where are we positioned to help with that?

Dan Dalton: I think if you ask some of the pricing direct team or the index team, it reminds them of the beginnings of much of what they did in fixed income and macro. And while they're public markets, they can be opaque and intelligence often only available end of day and what have you. And so, I think that the challenge is the same in the private market space, but it's unique because a lot of the information that you have around your investment in a private company or private security is actually unique to you in the terms that you negotiated with that client. And so, when we look to the traditional spaces where we would gather information at JPMorgan to help create some of those things, they're not necessarily available.

Simon Smith: So, it's not a one to many.

Dan Dalton: And we're obviously building up our secondary trading capabilities in the space, which means we're building up quantitative research in the space, which means we're starting to get our own ability to price on the desk, as you know with pricing direct, completely independent, but has a one-way access to some of those quantitative models. So, that's helping us think about the strategy there. And we've had some good success, as you know, in the build out of private placements, private structure credit.

When you look to things like private equity, while we would normally mine our trading desk, maybe even our research department for information to build some of these products, we can look beyond. We have a large banking business as well, and we're at the start of some of those transactions. And so, there's a level of intelligence within the firm that we know exists, but candidly, that's a journey that we've just started.

In fact, we just launched a beta private equity model. And I think what's unique is the valuation tend to be a bit more bespoke because the client has to feed us information. That information can be vast amounts of legal documentation, financial information, and actually new technology. So, if I turn the use of AI on ourselves, we can interrogate that information actually quite efficiently and we can spit out valuation for that client. But what we won't do and what we can't do is make that a common valuation if it's based on that client's specific information. And so, it's very different, but we think we can get it going.

Simon Smith: Yeah. It sounds like it's a bit of a blend between the securities valuation that pricing director and the derivatives piece where clients are required to...

Dan Dalton: A lot more information in that process. So, I think on the pricing side, we're good. And then on the index side, again, if we can build those transparency products, we think we can make it more accessible. And so, we're also in the process of evaluating where to put some investment on that side in the build out of some of those products.

If I look then to the platforms and Fusion specifically, there's many clients build their own security masters, some clients buy them from vendors. They normally do not have a single security master that goes public, private, and that's very much at the core of what we're trying to do in Fusion. And so, to give clients a common layer that allows all of that to be connected and fed into their investment processes as one thing overall, because ultimately, as you said, the allocation's going up is really quite fundamental.