MSCI Inc. (MSCI) Earnings Call Transcript & Summary

Hi, I'm Ashish Sabadra, and I cover info-services companies here at RBC Capital Markets. I'm excited to have Jigar here with us. Jigar is the CTO of MSCI. Jigar, maybe we'll start off with your background. So [indiscernible] Microsoft and now 4 years at MSCI. Can you just help us, provide your perspective, what are the differences between a software company and an investment tech company?

Great. Thanks for having me here, Ashish. Great to speak with all of you. Yes, I spent nearly 20 years at Microsoft. My last gig at Microsoft was launching Microsoft Teams, which has close to maybe 400 million users now. And since then, I have spent over 4 years at MSCI, and you all know what MSCI does. We're in the world of quantitative investments. And we have the intersection of data technology and project investments. And some of the big differences I see are, in the technology world, you start with the vision, where do you want to go, what can the technology do, what can it empower and let's figure out how we can make money. So -- and in the investment world, it sometimes starts the opposite direction. So they're both -- the commonality is they're both trying -- started making sure that what are your client needs? Where are the investment opportunities? Where is your fiscal discipline to drive these things and then invest in the right way and work your way back into what technology solutions you want to do. The other big difference, I would say, is one is more engineering-centric, and you built up and you figured some finance -- figure out what to do with it; sales figures, and what to do with it. There is more where technology is seen as an enabler, but not the main product. And my role here at MSCI to make that bridge, like I say, between the Silicon Valley and Wall Street and where we get the best of both. We can innovate at a fast pace, like, in technology, a leader, like, a technology company does, at the same time have the financial discipline and the smart ROI that we have -- that you see in the investments in finance companies.

That's very helpful color. And we'll talk a lot more about the Investment as a Service at MSCI. But before we do that, I do want to talk about the environment that we are in. Earlier in the year, obviously, you had a huge war for talent, and it seems to be easing now. But just, if you can talk about what you're seeing right now, but also how have you approached sourcing talent and developers.

It's never easy. And we don't think of it as a war for talent. But using your words, if you do, we are winning the war. Right now, if you look at -- we have over a dozen MDs, senior leaders from Microsoft, Google, Amazon and so on and so forth, and a lot of engineers in the company as well. So I hear a lot more about the recruits that we've made from technology companies and very less about the attrition we have to technology companies. So we don't have that challenge and the reason is very simple. We have a very keen story to tell, that if you spend 5, 10, 20 years on the technology industry, you'll know what's next with your career. You want to apply to a different space. If you think about quantitative investments, it's actually a bit of engineering and technology-centric space. It's all about mathematics, data, models, finance and technology. So it's more easy for us to show that you can come to an industry like ours and make a big impact. You learn a lot of stuff about the cloud, great user experience and driving a -- creating a platform, network effect, all of those things in the technology industry, come to that in the quantitative investment world. And there are people who want to drive change and they will do something new. For them it's an easy sell, come to MSCI. And for those who want to stay in their comfort zone and they're enjoying what they're doing, then just stay where -- in the technology company and move from Facebook to Oracle to Microsoft and Google and in that circle. And largely, they do similar things. So for us, it's not been a challenge. At the same time, I wouldn't say that getting great talent is easy. But I think we are doing really well right now.

That's great. And maybe with what's happening with the recent development in tech, it might make it easier for you to also hire.

Exactly. I think, I mean, when we launched Investment Solutions as a Service last year at the Investor Day in February of 2020, '21, before that, in mid-2020, we did the partnership with Microsoft. So if you look up MSCI in news on technology, you will see we launched maybe a dozen services since our platform -- announcement of Investment Solutions as a service. And the cloud platform is there now. We're building -- we have a great platform. So the speed of innovation and the [ scale ] velocity is there now. So anybody joining today will be able to launch services at a much faster pace than I did when I joined 4 years ago. So that's been an exciting journey as well.

No, that's great. Actually, that's a good segue into my next question around your Microsoft partnership, particularly for Azure. Can you just talk about the progress that you've made on the cloud migration and how is, yes, the success so far?

Yes. So we -- so when I came in 2018, we definitely decided that we want to be more cloud-centric. And the agenda was to say, look, we want to innovate at a faster pace. We want to meet our clients' needs at a faster pace. They need data resiliency and data presence in multiple geographies in the world. We are now going to create data centers in all those parts of world. So there are many reasons we wanted to be in the cloud. And a lot of our innovation we sold, the services were around for the last 18 months are running in the cloud at a much faster pace of building products. It's not just whether you're running in the cloud or data centers. But the kind of developers you need, you get when you are recruiting for cloud is also the modern thinkers with DevOps and better user experiences, more open standards or open source technologies, blending it all together, people who want to move at a fast pace. And that's what we are seeing. So that journey and the partnership with Microsoft has been fantastic. We meet frequently on various topics. We've used the Power BI platform for enhancing our user experience and client experience quite a bit, our data processing. A lot of our computer is running on Azure, and it's been a great partnership.

That's great. That's fantastic. As we think about the cloud, right? A lot of companies, when they were migrating to the cloud, they were doing more lift-and-shift, and then optimizing it for the cloud over a period of time. So I was just wondering how did you approach it and how much of it was lift or shift and how -- where are you in your journey for optimizing those applications, which are on the cloud to really leverage the true functionality of the cloud?

Yes. So you have to know the motivations behind your infrastructure strategy. Cloud is a big part of it. But cloud is not the be all, end all, right? So you are optimizing for speed, innovation, cost, resiliency, all of those things, client-centricity, what do the clients need out of these things. So when you do that, you have to be really smart about not having a fixed mindset of my strategy is to get everything in the cloud or 50% in the cloud or nothing in the cloud. You have to see that the change in the business where you want fast innovation, new services, APIs, new UI, you want to be able to do it at a fast pace and not make a request to a data center, which you get, like, a machine 3 months later or something like that. On the other hand, you want to be extremely fiscally responsible and make sure that, 1, that you don't really -- that you have the right kind of footprint in your own data centers for services which large-scale, running billions of operations a day, like, there are several, for example, for us. There is -- and if it's fine-tuned for the data center, there is no need to rush that into the cloud. So you have to pick the right workload to go to the cloud. Regarding the lift-and-shift versus cloud-native, I mean, we just give a credit card limit to our engineers. This is what you can spend on running the service in the cloud, right? So then it looks like -- suddenly it forces all these optimizations. Because the way you engineer for the cloud, cost has to be part of your mindset as a developer. When you have your own data center, it's not because utilization is so low, you bought lots of machines for the rainy day. We are worried about that. But as soon as the meter starts going on the cloud, that's when you think about performance, memory leak, security functionality, costs. It's a big element of what you do. So I think that's really hard to be very careful, and we decided to not move something to the cloud unless it's performing at a rate at which you can sustain.

That's very helpful color because you do hear from a lot of companies with -- which have issues with cloud sprawl. Like, it doesn't seem -- like, you've always had some pretty good controls upfront to prevent those.

Exactly. So we break it down and say, look, each product line -- under each product line, every product, what is the meter? What's your price tag of running in Azure? Or what's your price tag on the data center? And there is elements of CapEx versus OpEx involved. But still, the transparency has to be provided to each engineering team so they get to see, "Oh, wow." This is -- when you're innovating something, you toss it out there and say, oh, we got this thing out there in 2 months. And then the bill is shooting up as the usage goes up. So you have to throttle that back and make sure that you're scaling correctly. So once you provide the transparency, the developers are smart enough to find ways to make this thing optimized.

That's great. That's great. I was just wondering if you can share any metrics on where you are in terms of cloud migration in terms of, like, number of servers or applications that have been migrated to the cloud? Any kind of metrics that you could share?

Yes. So I mean, the interesting thing to note is like all of the issues running in the cloud, for example, right? And the areas where you have high entropy, a lot of growth, lot of new demand, lots of new services, ESG and climate, those things are running in the cloud. What we have seen is higher performance. We have seen -- it's also self-selective, though, because if we had seen poor performance, we would have pulled it back, right? So if we don't see that fits us, we don't -- you repatriate it back in the data center. So performance has been better. The client experience, when you start baking in, let's say, for example, you want to use Power BI for Climate Lab, it is fast, it's better. Another example would be data distribution. You want to take our data from cloud, share it over Snowflake, the right data model, the factor models. So our clients can easily adopt that data from Snowflakes. So that has become much easier for them to do. So there are dozens of benefits around speed of execution, performance and there are some clients in various parts of the world who demand that the data needs to be in this country. So those are the kinds -- you just can't do that unless you create a new data center there. So that's -- we've gotten benefits out of the partnership with Microsoft.

That's great. And then maybe just drilling down further on the cloud distribution, right? You talked about how some of the data, you're leveraging Snowflake, for example. So I was just wondering, like meeting the customers where they are, can you just talk about, like, how the distribution has evolved as well compared to how it was historically?

Yes. So that's a really good question, Ashish. See, we always were -- we had, like, close to 50 distribution partners, right? Whether it's Bloomberg, FactSet, whether it's [ Trust ], we are always open to what the clients' needs are. If a client wants to see our data in Bloomberg, by all means. Go do that. FactSet, whatever you want, we will be there. At the same time, you don't want a complete cacophony and completely not -- lack control over where the retail is flowing, and then you don't have any clue how clients are using it. If you don't have any client intelligence. It is sitting somewhere else. You cannot control the freshness of your content. Our index is going through -- if they go through a lot of layers, it won't be as fast as getting it directly from us. So the strategic change I made is to make sure that you have a great data production and data distribution strategy. So you create a clear firehose where the data goes out. Now how clients pick it up, from where, we don't really mandate that. We meet the client where they are. So you can have multiple last-mile providers, but we aim to have the best end-to-end experience when clients meet us on our platform. It is not to penalize other distribution channels, but it's to provide the best experience. Because when you have these extra layers involved, there are latencies, there's cost to the clients. Sometimes they're paying a lot of money to the distributor for really, the intelligence that is the content that is coming from MSCI researchers. So eliminating that cost, our clients are looking for how far in reducing -- running more efficiencies in their system. But when they see a stronger distribution pipe from us, they will want to use that instead of trying to go through multiple channels.

That's great. And then as we think about distribution, does that -- as you simplify distribution, does it also expand your addressable market, like in terms of ability for some customers who historically may not have been a customer of MSCI? Or ability to do it because you only have to pay as you go rather than trying to get the full buffet?

Yes. I mean, think about your sales process, right? If you're a high-touch, high-expense sales process because of poor technology versus superior technology where you can be low-touch and no-touch in fact, then your target client, you can let it be a lot more wider, right? If it took you a [ much ] people and technology in order to get something deployed over a year, you can't really scale unless you're going up a very high revenue generating plans, right? So in this case, when you have a more democratized content sitting on open architecture, and this -- we should talk quite a bit about that, because MSCI stands for that open architecture, and it helps you get our data where you want. It definitely helps you go to different places.

Yes. No, I was wondering if you could -- first of all, we do want to talk about the Investment as a Service, you briefly touched on it, but I was just wondering if you could talk more about those for Investment as a Service. The index as a service analytics.

Yes. So we announced iTraxx last year, and Investment Solutions as a Service has evolved from what people thought was a technology strategy to MSCI strategy. See, there is no sustained technology strategy these days. It's not just an enabler. This is how any modern firm should be operating in any industry. So that's the first thing I want to put out there. The second is the idea was to liberate our clients from doing the plumbing work. We modeled it out of the cloud, right? The cloud evolution over the last 10, 15 plus years has been -- first came Infrastructure as a Service then Platform as a Service, then Software as a Service somewhat is overlapping. So when you think about the investment world, how have we evolved, right? So our value proposition to the clients is you want performance attribution, risk insights, you want index indexes, you want to customize them, you have a hypothesis, you want to create a portfolio, you want to look at the factor models. All of these things should be basic building blocks on top of the cloud. My topic is speaking not necessarily on the public cloud, it could be in a private cloud. But you can basically access and tap into it just as easily as they can do other SaaS -- do with other SaaS companies. So the other idea was to liberate the internal engines, more than 2 decades of engines of MSCI. The data -- transference of data underneath those engines. To give you an example, our largest business being Index. 1969 was the first time we launched our Index from capital investment company, which is part of MSCI now. This is the year when we put our foot on the moon. So think about that. It's been that long, the indexes have been around. But it was the first time in the last few months, we actually provide an index builder, where you can take our own indexes, our methodology, customize it, run a test on it, come up with your own hypothesis of what your investment thesis is. And if you like the performance of your own idea, then you come to MSCI and ask us to put into production for you. [indiscernible] patient of how it used to be in the past, right? Where you come and tell us what you need, we spend months talking about it, sending e-mails back-and-forth, fill out these documents, we've exchanged notes, our researchers get involved. All of that process was to access the brains of MSCI in terms of how an index gets computed and what are the metrologies underneath that. So that we modified and completely opened up. Other example, ESG and Climate. Now in ESG rating, we have thousands of instruments where we rate public assets and fixed income instruments. And those ratings have thousands of data points that make that rating. Our clients want to know, for transfers, they then say, "How do you come up with a rating?" Well, now we have that information. You can go to Climate Lab. You can go to our ESG data explorer and get all these data. Then they want to know, how can I improve my rating? Okay, you can go look at that. You want to compare them -- yourselves with your competitors. You can do all of these things on your own. So that is another example of how, for ESG and Climate, we are providing a lot more transparency to the clients. And to the extent, the clients are using their climate data to for portfolio construction processes as well. So now they're looking at indexes, starting with an index, [ flies ] and that is through the climate data and they put it through the analytics system. So then it becomes much more easier when you open up all of our services through APIs, to data to do all of these things. The 2 other services announced last year, and since then, we announced 6 more services. So the 2 of the services, data is lower, right? Back in the day, clients used to call us, do you have this data? Do you have that data? And we would go through e-mail exchange and met inside the company, and a few days later, you get an answer, yes or no. It's like you call Netflix, and then you have the latest James Bond movie or somebody says, okay, let me look into that. And then he say, okay. Then you call again, if you have this movie, do you have Titanic? And so, wait a minute. Why don't I just provide you an application where you just search for it yourself and you find out what it is. You'll see 100 data sets. Each of these 100 data levels for each of these data sets, you don't have to even pay for anything for that. But when you like it, you end up buying the whole data set from us. So at least we cut down the process of discovery. So obviously, the content is something paid for. It's not complimentary. But that -- so that eliminates the time it takes for you to find out if we have some data. The other thing that it helpful for us is when somebody is searching, we look at that telemetry as to so what are the biggest searches, what's missing. We can fulfill that gap. And the last example is the developer committee we created. We had hundreds of APIs across product lines. You have to log into various websites to get access to those things. We've put it all out in the open to give developers access to all the APIs out there. So we made these APIs more interoperable. More -- less for -- more easy for them to access. Because we want to provide the -- the open architecture philosophy in MSCI to us means, if you want to use our applications, you'll see them all in 1 platform. Here you go. You go to platform.msci.com, you see everything in 1 place. But if you want API because you're a complex ecosystem, you don't want to use this whole framework. By all means, get all the APIs, plugins to your framework. But if you don't even want API, you want just the data feed, sure, how do you want it? You want it through Snowflake? You want to search for it in our data explorer? So basically, what I'm saying is everything we do, all the rich content we have across performance, risk, public markets, private markets, indexes, climate, ESG, we are completely making it transparent so you can easily get access to all of these things and plug it into your systems the way you prefer. So we don't force you into a locked system. It's an open system.

That's great. Yes. For me, the key takeaway was obviously because of the open architecture and everything that you talked about, it's making it much easier for customers to discover. And essentially, what you're doing is lowering the barriers to adoption, like -- and making it easier for them to adopt the data. One of the other things that we talk a lot about is how cloud is making it easier, lowering the friction to scale. And so I was wondering, in that context, if you can talk about your data lake, not only have you put the explorer on top, but how you put all the data together and how it resided before versus now.

Yes. That's a great question because you see, this data is the lifeblood of this industry, right? And the company is all of our data. We say a lot about technology, but it's in the context of helping get this data to the clients here. So if you think about the data, pretty simply said, there is data ingestion process, data acquisition process. The data processing internally and then data distribution, 3 large levels, right? Across all of these areas, we have multiple work streams driving a complete transformation of how does MSCI do data and how do clients get access to this data. So on the acquisition side, we are building the best-in-class AI and OP and machine learning-based data acquisition platform. Especially if you look at ESG and Climate, the need for alternate data, the number of data providers who are approaching us every week to see value through our data, we have to develop muscle to get all this data in, process it, look at the quality, look at the price points, see if it's worth it and put it into production, put it into the engine room to create new ratings on those things. Now internally, how do we produce all of these things? 270,000 indexes per day we produce and 800 billion pricing calculations we do for risk and performance and all of those things. Now on the 1 hand, around technology and data, you have researchers. They need access to data so that then create new models and methodologies. On the other hand, there are the data operations folks who are looking at the quality of data that's coming in, quality checking. They're also collecting more data that can be infused back into the system. So we are building tools to make these things go at a much faster pace. And this is where your point about data lake comes in. So internally, we have the data lake, where we bring everything together, where it's easy for you to apply the right set of tools. So what took researchers a couple of months to do with -- once they tell us, "Can you acquire this data for us," can get cut down. You want no-code solutions. We don't even need them to talk to developers. You just point the data source, and we'll tell you when it's ready. You can start putting on that on your own. We will tag it as if it's quality-checked or not, so you can decide what you -- that you should not be putting into production. So that's an example. On the data operations side, we are building tools to make it very, very straightforward and making a lot more efficient, driving those data operations folks a lot more efficient. Because if you think about it, in the next 3 to 5 years, as we go from a few million data sets -- data points, rather, to a few billion data points across spatial, across all kind of financial data we collect, you're not going to scale by having 1,000x more number of humans. It has to come through technology. So that's the blend of having the best -- finding the best technologies to -- with data technology experience and helping with -- in the last part is distribution. Like, we already talked about that once this data is produced and it is converted into insights. Another example of the Insights product that we launched on top of that as a framework, where it used to be you have to go into a risk manager or BarraOne to look at the millions of data set -- data points we would have created or on a [ time-seize ] of your results data. Instead, now you go to a Power BI solution for various kinds of risks on your portfolios, and it runs on top of Snowflake. So you can export it to any dashboard you want. And it gives a CRO or a CIO a much cleaner, easier way to digest all of this information through insights rather than massive amounts of data dumped onto their lap and then their technology people have to figure out what to do with it.

Yes. That's very helpful. I want to go back to a reference custom index buildout that you talked about and how that's taking the whole ability to build custom index indices much more efficient. I was just wondering if you could drill down further on the index builder, where are we in the process? Has it been rolled out? And how does that really change the game in terms of custom indices or direct indexing going forward?

That's a great question. So it helps in 2 ways. One is internal efficiency. So when clients just call us and say we need this, we just use that as an internal tool, and we do it at a much faster pace. What used to be weeks can be done in days. What used to be days can be done in a day, right? On the other hand, the clients who have started using directly, they are really excited about it, like, now I don't have to talk to anybody. Some clients can do a lot more experimentation by themselves. So I think that cuts down the communication cost and time it takes for them to tell us what metrologies they want and put it all together. The other element, which is still -- there's more potential to do here is the number of hypothesis you have for a portfolio. I mean, think about the search business. Google doesn't have, like, 1 or 2 big ideas of how to improve search. They have thousands of experiments to improve the relevance of ranking of search and ads. That's how they make money, right? In investment process, when you are creating a portfolio in a very complex world with so much information, you don't need to have 1 or 2 hypothesis. We want you to have dozens of them, and don't worry about which one is the best one, because if you can experiment at a fast pace and see the results of that test in moments and then decide which strategy is the right one, then you pick and choose the right strategy based on throwing a lot to the system as opposed to waiting for that perfect idea that -- to come to you, right? So that is an element where -- that area, we see a lot more scale coming in terms of going from thousands of indexes today, hundreds of thousands, to millions of indexes at some point. Every portfolio could have an index.

That's great. And again, similar to the point that you made with the data lake, your ability to move from millions of points to 1 billion of points without adding people, the same could be applicable with -- as you build from -- go from thousands of indices to millions of indices, ability to scale without necessarily adding more researchers. Is that the right way to think about it?

Yes. Yes.

That's great. Yes. Just moving on to the Analytics as a Service. Again, there was an initiative beyond, which was then canceled and switched over to Analytics as a Service. Can you talk about how that helps position -- obviously, you've always had a very strong position with BarraOne and the risk metrics, but how does Analytics as a Service significantly improve your competitive positioning?

Yes. I'm very proud of the work Andy and his team has done this year. They have fully embraced and moved on to the ISaaS framework. So if you think about all these amazing systems, essentially the engines underneath this server and BarraOne VPN, these engines provide a ton of value around performance attribution, value with technology and calculations to the clients. The things about that, how do you share these things, right? How does the client digest so much information? That's where we have announced faction models in Snowflake, for example. We announced risk insights, where you can see all these risk insights in 1 place in Power BI. We have -- we are liberating all of these engines and pulling out the APS that the clients can use directly and plug it into the process, whether you're doing portfolio construction, you have your own strategies, you want to create using our technologies and APIs. So analytics is becoming a lot more open. Other thing that the team is doing is fundamentally think about the first principles, what is our value-added investment industry? What are the key things we do? And if you had to start over the company, where you don't have 12 different applications and lots of different APIs, how would you do things differently? That is the North Star we have created, and we are migrating to that at a pace faster than most people thought we can. And the great example is the platform that I'm talking about where you can go and see without having to log into a risk manager or our BarraOne, you can see a lot of these insights on platform.msci.com.

That's great. Again, it goes back to our ability to access these engines using -- the way the customers want them to use, right? It's through APIs, through applications.

That's right.

Also, can you maybe talk about modernization of these applications? Like, where are you in terms of -- consumerization of IT was a term which was used a lot, but UI, UX and making it more modern?

Yes, it's a great thing that a lot of people think we're a content company. Who cares about is apps? But actually, the clients do care a lot about these apps, right? So we have completely modernized the front end. It's all web-based, Power BI, open source, open standards, easily -- so our pace of creating a new application like Climate Lab company, for example, is much faster today than what it took beyond, which was a multiyear journey, right? So we are able to -- that's why we are on a path where you can create and launch new services at a much faster pace.

And also maybe how does it help you combine the expertise, like, we always, from outside, sell like index, ESG, analytics and private assets or real estate. These were really strong franchises, but operating independently. And Climate Lab is obviously an example of where you're able to combine ESG with analytics. Are there other examples where you've been able to combine these strong franchises and the data sets together to create new offerings?

These examples actually are all over the place. So we are going from product-centricity to client-centricity. What that means is all the technology you create in the front end, the APIs and the data we acquire. It's focused on the first principles of what are we doing for the clients in the investment industry. What are we doing for their portfolios. Then it comes into, okay, this data comes from Climate, this comes from ESG, this comes from real estate and so on and so forth. I think that mindset of putting investments, quantitative investments and clients first, technology first, in fact, and then thinking about the product lines is very essential. So we completely changed the culture of the organization that you are not focused on individual product lines. You're focused on what are the clients doing. The last thing I'll point out is the cultural change in the company to start using a lot of these products and make it very easy to use. So when everybody uses each of those products through platform.msci.com, it becomes very apparent that, okay, these are just -- there are fictitious boundaries. Our clients don't see the boundaries that the way we report the earnings. That's not how the way -- that is not the way the clients see our company.

This is very helpful. So maybe, Jigar, I'll just throw an open-ended question, do you have any closing comments, anything that you wanted to add?

Yes, thanks. So we -- it's been an exciting journey. We are driving a major revolution in technology in data at MSCI. The clients are excited. They're seeing innovation in a fast pace. And when they talk about the vision, I don't show them PowerPoints, I log in and show them the platform and show them the demos. I'm saying, you don't have to trust me. You just have to look at what we are doing and start playing with it, right? So my message to everyone has been expect even a higher quality of data, technology and experiences from MSCI because we are continuously improving the client experience.

That's great. Thank you. Thanks, everyone. Thanks, Jigar.