Navigating Cloud Shifts and Revitalizing Culture with John Schirripa, VP, Head of Channel Platforms, and Jean Atelsek, Senior Research Analyst, Cloud Transformation at S&P Global Market Intelligence

(Intro Narrator at 00:00:00) Today, we're talking to John and Jean from S&P Global about the remarkable recent trends in application modernization. You're listening to Joel Beasley, Modern CTO.

(Joel Beasley at 00:00:16) What are the big takeaways for 2023, Jean?

(Jean at 00:00:19) I would say, based on the macroeconomic environment, cost awareness and cost containment is becoming higher profile and more of a driver for a lot of the work that we're seeing in terms of app modernization. There's no slowing down of initiatives. People are expecting their infrastructure to change a lot in the next, say, five to ten years. And there's no slowing down, even though that does mean a cost. The benefits are proving to be—in the initial projects, the benefits have proven to be worthwhile.

(Joel Beasley at 00:01:01) I just had a conversation about this earlier this week with Puneet. He's the founder of Amberflo, and he was telling me about how we're moving towards basically the entire world being the Amazon consumption model, right, where you're just paying for what you're using. It's just usage-based pricing. Now, are people, when they're talking about cost containment, are they exploring entirely different models like usage-based pricing, or is it more about reining in the current environments and not overspending on certain things?

(Jean at 00:01:36) I would say that public cloud has really changed the conversation because now even the hardware vendors are saying, you know, we'll situate a piece of equipment in your data center, but you don't have to pay for what you don't use. So pay-per-use is a big driver of cost efficiency. Some companies have a harder time than others adapting to that model. But the subscription model, which is what I've been hearing most about—it's not necessarily, you know, because pay-per-use requires metering technology and that is not readily available on all kinds of equipment, and it adds a level of complexity that a lot of people don't want to deal with. And so what I'm seeing a lot more in terms of software services is the subscription model and annual subscription, sort of all you can eat. And then you have the effort by the provider, the vendor, to make sure that the customer is actually taking advantage of that subscription.

(Joel Beasley at 00:02:39) Yeah. I think that's what Amberflo is trying to do too. Not to talk about them a bunch, but I'm always interested when I see new companies doing things. And some of the past guests I had on, I saw them starting to use this product, and I was like, okay, what's this about? And then he told me about how metering—because I thought usage-based was per seat, and apparently it's not. It's how much you use. So he taught me a lot this week on that.

(Jean at 00:03:05) And usage-based, I mean, when you talk about app modernization, that's one of the—and cloud in particular—that's one of the biggest motivators for that as well, because cloud can provision resources as needed. So the auto-scaling can go up and down. There can be triggers. Like if you, you know, if a query hits a database, it triggers a workflow. And, you know, that's sort of really what you see in a lot of—if you go out into the cloud and you get, say, a virtual machine, you're not paying per use. You're paying per provision. You're renting a virtual machine. And if you don't manage it properly, if you don't manage what's running on top of it properly, it's going to end up costing a ton because, you know, you're basically paying a pretty high rate, relatively, for something that if you don't use it, the unit cost is going to be huge. So a lot of the modernization efforts go into better matching the demands of the application to the provisioning of the resources.

(Joel Beasley at 00:04:15) And then, John, how are you using this to help inform the products? Well, what type of products do you make? How do you use Jean's research?

(John at 00:04:22) Yeah. So at S&P, we're a traditional data vendor, and we supply a lot of fundamental ESG ratings data, index data out to the marketplace in addition to a lot of the research that's done by our in-house staff, like the team that Jean is on at 451 Research. So as a data vendor, traditionally, right, all distribution's been kind of done through, you know, file-based distribution or FTP, API delivery, et cetera. And that's kind of been the history in this space. The cloud has really helped boost the ability to get clients new data, the same data that they were always getting, more of that data, and it's helped do that in a managed and hosted way. Clients don't need to—no longer worry about FTP downloads or writing API calls to pull pieces of data. We can provide it through hosted instances in the cloud. And then kind of to the point, Joel, where you were going a minute ago, right, that's where this usage-based consumption model comes into play. We've been doing a lot with Snowflake over the past few years, and Snowflake is purely on a cloud compute consumption-based model. And so that's actually one of the biggest first questions clients will ask as they come on and start to take our data through Snowflake. "Well, how much is it going to cost?" And, you know, the answer is always, "Well, it depends on how much you're going to query and how intensive your processes are." And back to Jean's point, you buy a piece of hardware and you put it into a data center or you pay for a specific, let's say, EC2 instance, you're limited to what you're purchasing. You have to manage it effectively. But things like what we do with Snowflake, with scalability and computing, the ability to scale on the fly and everything really help our clients manage what they're doing in the cloud and hopefully monitor costs. The one thing I kind of always want to say is everybody always says cloud can be cheaper. And I'm like, yes, it can be. But you have to stay on top of what you're doing. We've seen clients scale within Snowflake from, let's say, a medium-sized warehouse instance to a four XL warehouse instance. They leave it running, and then all of a sudden they've generated, you know, a couple thousand dollars worth of compute in a few hours. It's like, no, you really got to run that process and turn it back down to the smaller instance once you're done.

(Joel Beasley at 00:07:01) That's super interesting. I hadn't heard about—well, backup. I wrote some financial software several years ago, and so I was very well educated at that time about how we would get data and the real-time and how fresh data is, the different sources, and how much it costs. It's expensive, right? But the idea that our standard reaction is to download FTPs and process them or to query APIs to pull the data over into our system—this is the first time today I've heard of the—what do you do? You clone an instance of your—how do you keep the data in sync across multiple machines? How does that work?

(John at 00:07:41) Yeah. So we're constantly loading data up into our Snowflake warehouse. And then on top of our warehouse, we're creating client-facing views of the data that will refresh continually as we keep refreshing the data. So you can come query a table or, you know, a client-facing view for information now and then come back a half an hour later, and you may see completely different data within that query result. So to help our clients manage that, right, we're providing tracking tables and change logs and everything so somebody can go in and say, "Oh, okay. You know, S&P's revenue number was just updated forty-five minutes ago. Okay. So now I understand I'm going to get a different result set than if I had done this query yesterday or four hours earlier."

(Joel Beasley at 00:08:27) Now, if I own S&P 500, the fund, the index, am I a customer of yours?

(John at 00:08:34) Indirectly, yeah.

(Joel Beasley at 00:08:36) Is it the same company, or is it not the same company?

(John at 00:08:38) Yeah. So, I mean, the index—so there is a division within S&P Global overall, which is S&P Dow Jones Indices. And they are the benchmark index division within the company and have the ETFs. I'm part of the Market Intelligence division within S&P Global, so it's not direct, but indirectly, there is a relationship. And we partner very closely with our colleagues in that division.

(Joel Beasley at 00:09:09) You must get that a lot when you're at kids' soccer games or just out living your life because you say you work for S&P and everybody's like, "Oh, that's one of the most famous ETFs there is."

(John at 00:09:18) Yeah. And well, yeah, absolutely. And that's actually the funny thing is when you're out there and you're talking to people, "What do you do?" And I'm like, "Oh, I manage a market data platform for S&P." They're like, "Who?" I'm like, "Oh, S&P 500." They're like, "Oh, okay. Now I get it." Yeah.

(Jean at 00:09:32) I will say when you were asking about some of the things to watch for in 2023, another thing that I've been hearing in particular when listening to the earnings calls of the big cloud providers is AI is going to—and I know that AI has been hyped a lot and with ChatGPT and everything, people are sort of running away with themselves. But I will say the providers themselves are really prioritizing optimizing their services and their infrastructure for building, training, and implementing AI models. And that's right from the services that they have, like the end-to-end services down to the silicon. So they're developing special CPUs and so on. And data is really the feedstock of AI. And there are more tools than ever for actually, you know, in the cloud for processing those models, applying those models, and, you know, getting insight from the data that, you know, you can test and refine. And so that's another trend that I'd say we're seeing.

(Joel Beasley at 00:10:43) Do you build products that do trading in the sense of, you know, Ray Dalio—he'll build these products where he uses them inside of his company to help trade and make decisions in these AI trading-type robots. Do you make those within, or are you just building underlying infrastructure?

(John at 00:11:02) Yeah. So, basically, to the point that Jean was making, we have the data that is the fuel behind, let's say, you know, Ray Dalio's trading engine. Right? So, basically, saying he's feeding data in. He's running proprietary analytics. He's back-testing, right, et cetera. So, you know, and to enable those analytics or to drive artificial intelligence or machine learning, you need data to base that on top of. So, you know, for us, our focus has been around how do we get our data into better shape for clients who are going to leverage it in AI use cases or in machine learning languages. I mean, for me, data delivery is as much of a value proposition as the data itself. If I can take something like an earnings call transcript or a news article or an annual report and take that from the PDF or Word document format and make that into a structured, machine-readable text format, I've now created something that is going to power somebody's sentiment engine that is using AI and machine learning to generate sentiment off of something that was typically held in the hand and read one at a time. I've now enabled analysis across that information to the masses or en masse.

(Joel Beasley at 00:12:25) That's amazing. And thank you, because when I was doing this about seven to ten years ago, we were having to build the parsers for the news articles and then tag them and figure out what stocks were mentioned in them. And it was quite the process, and now we could just subscribe to you and build on top of that. That's pretty cool.

(John at 00:12:44) Exactly. Exactly. And, you know, what's also interesting is that, you know, the AI journey is not just powering our clients. I mean, we've had to do it internally as well. I'm trying to think—maybe five or so years ago, we acquired a company called Kensho. And we've used Kensho to build out AI models, you know, first for search for our desktop or CapIQ Pro desktop platform, and then to also enable transcription. It's enabled tagging across unstructured data, and really a lot of cool things that, you know, we've used Kensho to solve internal problems. And now we've been able to say, "Okay. Hey. We built this model or we built this AI process, and now we're going to say, hey, we can sell this to you as well. And you can have this sitting on top of your calls or some of your textual documents and, you know, glean insight out of it by reading the text, driving sentiments, et cetera."

(Joel Beasley at 00:13:38) That is pretty cool. Now I'm curious from, you know, always looking to bring value to the audience. We have a unique setup here today where we're talking to somebody in product and research. How do you two interface? How do those conversations go? Does John call up Jean and say, "Hey. I need you to research this," or is Jean proactively researching? How does this work?

(John at 00:13:57) Oh, it's a very good question. So I would say, you know, not direct where I'm asking Jean to research something for me, but we're kind of looking at the themes of Jean's research.

(Joel Beasley at 00:14:10) That's why she was smiling. She's like, "This guy doesn't understand."

(John at 00:14:14) What's the piece of research? The Voice of the Customer survey. Is that—am I saying that correct?

(Jean at 00:14:20) Right. Voice of the Customer. So we do a lot of end-user survey research, Voice of the Enterprise. We have macroeconomic research that we publish, looking at trends for technology buying, what is motivating people to adopt cloud, where priorities are, organizational challenges that they're working to overcome. So we have a very broad range of survey research. I have done a lot of work on cloud pricing research. So looking at how providers are changing their prices in response to, you know, macroeconomic conditions.

(Joel Beasley at 00:15:01) Are they changing them?

(Jean at 00:15:03) So the interesting thing is actually, the big providers—it's still pretty early days in terms of cloud adoption by the enterprise—and the big providers basically have enough margin that they are finding other ways to economize than raising prices. So they're finding other ways to sort of deal with inflation and raising their own costs because they are still very much—it's very much a land-grab situation in terms of customers and workloads that they want to get onto their platform. So they're optimizing for long-term loyalty. But what they've been doing is they've been extending their depreciation schedules for their equipment. They have been working on and doing a good job of working on using less energy in their data centers so that their energy costs aren't as high. And then, of course, we've seen the more recently the layoffs and the, you know, sort of the other measures that they've had to take as the picture continues to be uncertain.

(Joel Beasley at 00:16:18) Yes. Now did you track how the rate at which they were raising prices pre-pandemic to post-pandemic? Is that how you figured out if it was increasing?

(Jean at 00:16:27) So we've been looking at cloud—this is public cloud pricing in particular, which, you know, is public information. They publish their price lists, and we've been looking at it for over five years in the division that I work in. And it has been going down and down and down. Now it has plateaued, but it has not gone up like the rest of, it seems like, every other area of the economy.

(Joel Beasley at 00:16:54) Like eggs, right? There's been no inflation.

(John at 00:16:57) You know, it's funny, Joel, you mentioned earlier, right, we were talking about the credits, right, the usage and everything. And one thing we've seen come into play is the, you know, keep the price steady, but offer some free credits and free compute to maybe cover a migration cost into one of the big providers' cloud environments and say, "Oh, you know, hey, it's going to take us a year to move all of our infrastructure out of this data center into the cloud." And the providers will maybe come in and say, "Okay, you know, we'll help you with that with some free credits to help cover the costs so you're not double paying over that period of time." But also going back to Jean's research and the voice of the customer surveys and the information they put out, it's great for helping us make product decisions and help put us in a direction, right, where our clients are going. Right? I mean, we want to make sure that if our clients are fully moving into the cloud as part of a digital transformation or AI modernization effort, that we're supporting them there. That we're not saying, "Hey, well, you know, sorry, we're just going to continue to give you CSV files off of FTP servers."

(John at 00:18:08) We want to make sure that we're following the trend that our data is going where our clients want to work.

(Joel Beasley at 00:18:15) Well, thank you on behalf of developers. I'll just take that positioning.

(Jean at 00:18:20) One of the things that I have looked at as part of the work that I do is looking at how many new services are coming online from just the top three hyperscalers: AWS, Azure, Google Cloud. And we track almost 4 million SKUs from them now. And they're adding literally hundreds of thousands. Now, granted, those aren't all completely new products. They will have a different code for a product in this region using this pricing model, and this size, and this purchase. But it's amazing, the sort of breakthroughs that they are having with this huge, seemingly endless pool of compute and engineering resources, frankly, that they have, that they're pushing into new areas, the places they're going.

(Joel Beasley at 00:19:16) I want to do a little shout out because I think, John, did you win some award? Best cloud-based application provider. Tell me about that. Oh my god. Your PR people, they're on point, man.

(John at 00:19:28) Oh, wow.

(Jean at 00:19:28) That's all

(John at 00:19:29) the good

(Joel Beasley at 00:19:29) stuff. Yeah. Wow.

(John at 00:19:31) Yeah. So I won Waters Technology. It was the best cloud-based application provider for our effort to build out Snowflake, to build out this hosted and managed instance of our data that is helping our clients power their workflows. And, yeah, I was really honored to receive it.

(Joel Beasley at 00:19:52) Now I've got a question about modernization. I have seen that word along with digital transformation so much. What, Jean, help me. What is modernization?

(Jean at 00:20:04) Well, that's a good question. I've looked at modernization a lot and I will say it is basically adapting technology infrastructure to better serve the needs of the customer. And mostly when people talk about modernization, they're talking about legacy systems. So they're talking about the state unemployment systems that got all jammed up during the pandemic because they were written in COBOL and nobody knew how to fix them or scale them. So modernization, I mean, I would describe it: it's hard. It's heavy lifting. It's tough work. And that's when you're talking about taking an application that might represent the crown jewels of what the business is built on and saying, "Okay, we can no longer—it no longer makes sense—or it makes more sense to maybe break off pieces of this monolithic, inflexible application and turn them into microservices that can be adapted, and new products can be developed with them much more easily, because you don't have this giant inflexible piece of software that you have to tinker with very carefully to avoid making breaking changes and so on." That being said, I mean, in terms of adopting cloud, modernization is being smart about not carrying over habits from your on-premises data center into the cloud because it's just not going to work.

(Jean at 00:21:43) You don't want to build the same sort of disorganized, dependency-bound mess in the cloud that you have on premises. And so it's a combination of all those things, but basically it's adapting applications to current IT technologies in a way that better serves the customer and the organization itself.

(Joel Beasley at 00:22:10) Do you have an example of any of those bad habits that people try to bring over from on-prem to the cloud?

(Jean at 00:22:16) Probably the biggest one is treating a virtual machine in the cloud like a server in your data center, which is: you turn it on, you know, in the data center, you're paying for the management, the power and so on, but you're not paying the rent for the VM in the cloud that is being monitored, upgraded and so on by the cloud provider. So if you turn it on, it's not like you're amortizing it over a period of three years, like a hardware purchase. You have to manage it carefully and turn it off when you're not using it or else you're just throwing money out the window.

(Joel Beasley at 00:22:57) And that happens a lot. I know it happens a lot because people come on with tools that help prevent that from happening all the time.

(John at 00:23:03) Yeah. We've seen it a lot with—you know, the term used to be lift and shift. I used to say, "Well, we're going to take what's in our on-prem data centers and just move it into the cloud." Exactly. But that's just not taking advantage of the technology that the cloud provides.

(John at 00:23:19) And I've worked with a number of clients who have been on that modernization path, and they're telling me that, "Hey, this has allowed me to basically shut down data centers for every division within my company, centralize everything in one main cloud-hosted data center or, yeah, a cloud-managed data center, and be able to service all my internal divisions from one centralized area." They only get much better governance over the data that they have in their environments. They're able to focus on getting the different divisions just the data they need, reducing waste. And then also, going back to that hosted managed service like a Snowflake, you know, those tech resources that were focused on the data loading to higher value work.

(Joel Beasley at 00:24:10) All right. I want to just jump in a little bit here, Jean. Okay. So at the intersection of people, culture, technology, and changing, and to touch on a little bit of John's point of the lift and shift. So I've got two questions. The first question is, is it better to lift and shift than to do nothing at all?

(Jean at 00:24:34) I would say, I mean, based on what I've heard, at some point, you're going to need to just to stay in competition with your peers in the market. At some point, you're going to have to modernize your infrastructure. And if that means shifting and then modernizing versus modernizing as you go, there will be a short-term pain, but for a long-term gain.

(Joel Beasley at 00:24:59) Yes. I 100% agree. And I did a couple of conversations, I think, about two years ago when I was trying to understand the cloud. So I found out a whole lot of information. And one of the people I had talked to was at a consultancy that helped companies move from, you know, the on-prem into the cloud. And I was surprised to find out that he said the biggest issue was the people and the culture because the people that know the on-prem tools were different from the people that knew the cloud tools. So you couldn't just take the company and change all the people and change all their habits and processes that they had in place that they've used for decades. And that was the hardest part. And they would actually, you know, one of their services as the consultancy—and I don't even remember their name—but one of the services they had was they would help you lift and shift, but they would also go take a group of people that knew new cloud technologies from an engineering standpoint, low-level standpoint, and have them go work on-site with the other people to help train them up in their knowledge. And I thought that was pretty interesting.

(Joel Beasley at 00:26:07) So when you are doing your research and it's, you know, a lot focused from what I understand, and this conversation is a lot focused on the hardware and the trends. Does people and culture and mindset changing, does that factor into your research at all?

(Jean at 00:26:23) Absolutely. I would say, you know, and when you talk about app modernization, you have to sort of, if you think about it in broader terms, the whole concept of DevOps. So development teams and operations teams sort of working together on certain capabilities of a software or service you're providing, that is a modernization tool. I mean, it's a modernization paradigm. And I think exactly what you're describing, or organizational resistance to change, is one of the big barriers and what it takes.

(Jean at 00:27:01) We found that if it's possible to, first of all, find champions within the organization, find out where the pain points are with the current processes and go to those people and give them a stake in the outcome of the transition that you're proposing. So, you know, use those early wins and sort of pick the low-hanging fruit in terms of stuff that would naturally work much better on a software as a service versus something that's managed and on infrastructure in-house or a platform service. So, you know, getting buy-in from the people who really are running the operations is a big thing. And then also getting buy-in from leadership and having, you know, sort of that bottoms-up and top-down effort to kind of move the organization forward. It's going to be disorienting, but if you give people a vision of where you're going through, you know, proofs of concept and so on, it can really sort of change hearts and minds.

(Joel Beasley at 00:28:09) Jean coming through with the leadership insight.

(John at 00:28:11) I was going to say that that was great, you know, Jean, because I was going through my mind as Jean was saying that is, you know, upgrading your tech infrastructure or modernizing is not a sexy thing. Everybody would want to be focused on creating something new and out there. But, I mean, you know, making the argument that, "Hey, this is actually needed. Right? We need to modernize. We need to move into the cloud to take advantage of the capabilities that the cloud offers." And, you know, down the road, this is what the payoff will be. Right? We'll be able to run this analytical process that took a day. We'll be able to run it in ten minutes. Right? Or, you know, have decision making. We'll be able to bring in additional data to help inform our decisions. We'll have better governance and controls. Right?

(John at 00:28:57) So those are the things that need to foster the argument to say, "Hey, you know, this is a needed path that we need to go down."

(Joel Beasley at 00:29:06) Well, since we got on the leadership topic, I've got two leadership questions. The same question, but for two different audiences. So I'll go with, I'll go with John first. Okay? For CTOs today—and maybe this is a Jean question. I don't know. I'll let you guys decide. I'll ask the question, you decide. Okay.

(Jean at 00:29:26) Okay.

(Joel Beasley at 00:29:27) With all of your experience with the market and how it's changing, all of your research and knowledge, and, John, your practitionership, if the CTOs that are listening take one thing away from this call, what should that be? Is that Jean's hand going up? All right. What is it, Jean?

(Jean at 00:29:47) I will say that getting started is the most important thing and that there will be, you know, it is not a straight line. There are going to be setbacks. There are going to be backtracking. It's a very confusing landscape, but all learning is good learning. And the sooner you get started—I mean, I'm not saying go willy-nilly into the cloud—but I'm saying the sooner you get started, the more you're going to learn and the more you're going to be able to take those, what you've learned and help it and feed it into the transition that you're going through for everyone's benefit.

(Joel Beasley at 00:30:28) Nice. Do you lead a team there, or are you an individual contributor on a team?

(Jean at 00:30:33) I'm an individual contributor.

(Joel Beasley at 00:30:35) So what do you like? John Maxwell, Mel Robbins? Where do you get all this leadership insight from?

(Jean at 00:30:40) I don't know. Experience.

(John at 00:30:41) Oh, you

(Jean at 00:30:42) Experience. Experience.

(Joel Beasley at 00:30:44) It sounds good. Yeah.

(John at 00:30:45) Yeah. So, John

(Joel Beasley at 00:30:46) Now, John, I want to

(John at 00:30:47) What I was going to say was that basically, you know, to the CTOs out there is that, you know, we may be your data vendors, but we're also your partners. Talk to us. Work with us. I mean, we definitely want to speak with you regularly. We want to understand, you know, what you're thinking, where you're going so we can help develop solutions together with you that are going to give you what you need.

(John at 00:31:10) So that's kind of the one thing that, you know, I would definitely want to emphasize out there to the CTOs in the community. I mean, I work very closely with our CTOs internally and love talking to CTOs at our client firms.

(Joel Beasley at 00:31:24) VPs of engineering. So what I was going to do is I was going to say, okay. Now what's the one takeaway for mid-level management, VPs of engineering, people that are running teams of teams, or what would their takeaway be? Would you have different insight for them, or is it the same?

(John at 00:31:38) No. I'd say it's pretty much the same. I'd say, you know, we're here for you. We both succeed together. So, you know, reach out to us. Let us know what your pain points are.

(John at 00:31:47) Basically, everything we've been building over the last, you know, X number of years has been focused on making our clients' lives easier, helping them do what they do better. And so, you know, again, partner with us. We definitely want to help you on the journey. It's a journey you don't have to go down by yourself. Work with your vendors. So, again, it all comes back to the data. Right? And, you know, here talking about hard data that delivers campaign, I'll throw the shameless plug in there. Right? Basically, it's

(John at 00:32:20) It's all about getting everybody access to the right data to help inform these models.

(Joel Beasley at 00:32:26) Yeah. Let's plug it more clearly. So tell them what to go buy and where to go do it.

(John at 00:32:32) Okay. We'll put the marketing spin on it. Right? So, basically, yeah. So, you know, the data that delivers campaign that we have going on right now is all around showing clients that the solutions that we've built at S&P Global and S&P Global Market Intelligence to help them save time and ultimately money in the data discovery process.

(John at 00:32:55) Whether that's our marketplace website, which is basically our storefront to come in and learn all about the different datasets and solutions that we have, including Jean's research and the rest of the research at 451, as well as tons of other datasets. And then even, you know, to some of the environments and solutions that we've built out to help you look at and understand that data, like our Workbench application. And again, our solutions, whether delivered through the cloud, through Snowflake, or through just good old FTP-based type of deliveries. The delivery is as much of a value proposition as the data, and getting it to the right spots to power these workflows is definitely key for our clients these days.

(Joel Beasley at 00:33:46) And what's the website? Where can they see these tools?

(John at 00:33:49) Marketplace.spglobal.com is the storefront that I encourage everybody to go take a look.

(Joel Beasley at 00:33:54) I want to be respectful of your hard stop. Jean, do you need to say anything else?

(Jean at 00:33:58) I don't think so.

(Joel Beasley at 00:33:59) Okay. Jean's awesome. So you could just say, that's a wrap.

(Jean at 00:34:03) That's a wrap.

(John at 00:34:04) Boom. She did it. We're done.

(Joel Beasley at 00:34:07) Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you'd like to hear discussed on the podcast, either add me on LinkedIn or send me an email: [email protected]. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.