Getting to Actionable Insights with Speed & Scale


Organizations across the world encounter the same issue with their data — speed to insight. Even with a team made up of the best and brightest statisticians, you still have to run correlations and regressions to get to that actionable insight, and that takes time.

In this episode, we talk with David Wolfe , CEO at , about the platform his team has built that can actually discover and visualize actual causality within your data and help you get to actionable insights quicker.

We discuss:

How helps companies produce actionable insights faster

The Infrastructure underlying the platform

Development challenges the team had to overcome

Want to hear more stories from high growth software companies? Subscribe to Application Modernization on Apple Podcasts , Spotify , or check out our website .

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You're listening to application modernization, a show that spotlights the forward thinking leaders of high grows software companies. From scaling applications and accelerating time to market to avoiding expensive license and costs, we discuss how you can innovate with new technology and forward thinking processes and save some cash in the process. Let's get into it. Thanks for listening to shadows offt's application modernization podcast. I'm your host, Nick Marcarelli. The goal of the PODCAST is to focus on speed, scale and savings related to stories of high growth software companies. Today I'm speaking with David Wolf, CEO and founder of inguo. They help customers discover the wise and data. As always, thanks to red hat for being a sponsor of the application modernization podcast. David, thanks for joining us. Thanks for having me, Nick. David, why don't we start out like, you know, tell us a bit about your organization. You know what you're what you're doing for your customers, what you're serving them with. You know how y'all got started. Just, you know, give us the couple of minutes on. You know what you're doing day to day and you know why that matters to your customers. Yeah, so, again, thanks for having me. I'm the CEO and founder of a company called inguo or Inguo die. Oh, we want to be official. It's also our web address. You know, it's the it's the tech startup thing to have your full web address for your name. Yeah, I mean it's people see it and they think it's a funny name. And how do you pronounce it? It's pronounced Inguo, which is actually a combination of Japanese and Chinese. You know, we're spin out from the innovation. Well, we're spin out from an ECX based out Apollo Alto California. I'm based in New York. Nguo is based in Brooklyn, but we do. We are spin out from anycx which is a subsidiary of ANYC Corporation in Japan. And the technology, our current bore technology, is the birth of one of their innovation laps. And so and the people who created the core Algorithm, which I'll get into here in the second they are Chinese and it's a Japanese corporation. But inguo is actually the character for cause, the Chinese character for cause and the Chinese character for effect. Only one is the Chinese pronunciation, one is the Japanese pronunciation. So it's basically a Japanese Chinese collaboration and so but it means cause and effect, which is essentially what Inguo does and which is we deliver causal discovery, which is not to be confused with causal in Ferns, which is a probably what most people when they think of the blanket term causal which I've come to learn in this industry is a loaded term, because discovering causality is always the challenge. And what what was able to be created was an algorithm that actually discovers and visualizes actual causality, that is bias free and is seeking to democratize the data.

It's cloud based, it's machine learning driven. So essentially we're able to produce causal discovery graphs, or directed a sickly graphs, in a significantly shorter amount of time than what your typical organization, whether it be a market research form, a public policy, insurance, etcetera, etc. It's able to derive those relationships between the variables in a very, very short amount of time and it doesn't require running correlations and regressions running through that, building a graph. It actually that's what the algorithm does, is it builds the graph based on relationships and based on a weighted scores between each variable and which is extremely time consuming because it just when you start adding variables, the amount of combinations you can have is absurd, and so it just it's a challenge. Gotcha. So basically accelerates the output that you might want as an organization. I have you know this thought. Let me you know. Pump of data have in and your platform will get them to that insight quicker than maybe the traditional way of looking at analytics today. Is that fair? That's fair. I do like to take it a step further, though, because I think one of the assumptions with machine learning and ai is that it does all the work and you're done. In some cases that that's true. You could look at a graph, it has a great data fit etsworks, it has a great date of it model. You can look at a correlation and causal heat map and see what those relations are and say I'm good. But if you're a PhD statistician or if you're someone who wants to really get in the weeds with it, you just eliminated a large part of your work. But you can now refocus that and really get into the weeds with what if and why scenario modeling, and so your work can be done. But we know that that's that's not what we want people to think, because that's that's not entirely the case. Sometimes you want to understand something better or you see something that maybe you don't entirely trust, and so you're going to want to test that theory. And so what this does is this gives you that ability to test that but rather than taking you know, if it's a project that would take you three to five days, we just shortened it down to one or two. It's a project that will take you two to three weeks, we just shortened it two, three to five days, like it's really works that way. So it's got much greater efficiency. It gives you greater accuracy with what it is that you're trying to decide and it makes your insides much more actionable, whether that's doing market research, CPG hat the purchase brand awareness, brand tracking, or even clinical trials and Pharma or building new man sterials or how your plant operates or hr like, you're just able to better understand how the relationships within the data exist. So as you guys were putting together your solution for customers.

There's obviousne inflection point decided we need to do this better than the standard or the or the norm. was there a specific use case across your industry that really drove to that? And then how did you how did you think we could do this more efficiently, or we could do this quicker, or we can allow time to dig deeper into what the data is telling us. You tell me a little bit about that? Yes, so I think it all starts with, you know, the old saying correlation is not causation, and when the PhDs that created the algorithm, what they were thinking of is is everyone wants causation. And it started with this first project by a man named show Haiti need zoo out of Shiga University in Japan, and he came up with what was known as the Lingam method, which was able to actually visualize causal discovery. And once again, causal discovery is where you're discovering and it's not causal inference. Or people see our grass, they're like, oh, that's a Bejian net and it's like well, loosely speaking, yes, but a begian that was built by someone else based on something that they derived. Going through hypothesis and steps and etcetera, etcetera, etc. But the use case that that the innovation labs, what they were primarily looking at was market research and marketing research and how they can get better messaging and storytelling, how their clients kick it out better messaging and storytelling. And so when they approached me, I come from an international policy background where I used to work with programm evaluations where I would go on the ground in Cambodia or India or Pocustan or, you know X, Y and Z, developing country. And when I looked at it, I said, you know, when I was doing programm evaluations, this could have saved this fifty to one hundred million dollars, because I you know, this is amazing. And they're like yeah, that's great. We built this for marketing research, and so I said, okay, I see your point. I see why this is great for marketing. Like you know, we've done a study for Toyota. In our MVP we had done a study for a large laundry turgent brand based out of Japanese or based out of Japan. My sorry, my second language and culture background and everything was Japan. So that's also where the whole Inguo name helps come from, because my goal and graduate school is this historical reconciliation with China and Japan. So this is me trying to fulfill that goal, since I wasn't able to do it on the policy side of things. So anyways, their goal was with marketing research and they said, okay, you need to approach marketing research, but if you want to take this public policy avenue, you can go that direction, if you want to do that as well, just this has to be a part of it if you're if we're going to tap you to do this startup. So that is our primary focus. And with the market research industry, you know, what we're finding out is when they run their questionnaires, when they run their studies and things like that, we find ourselves asking a lot of questions like, well, why is question thirteen in there when you essentially ask somewhat similar in question for it? And Why... question twenty two and there? Because that kind of cancels out question fourteen in question for like it. You know, we start to see things and we have to ask questions on you know what is the first thing will ask a client if we're running services for them, because we're we're a diy platform, but we can do services, but obviously people want the services first to see how it works, we can train their people, etc. So the first thing we ask them is what is it that you really want to learn from this study? And that's the big like what is the why that you're really trying to answer? And let's focus on that first and then we can go into these other avenues. But we need to understand what your key points within the data set are, because we all know that you'll have fifty questions. Maybe twenty of them matter and the other thirty are filler. It's right. It seems like when you do market research you get a lot of variations of the same questions over and over and as a as a consumer or a person providing input, I'm like, I've already answered this question, you know. So there's probably a reason or a method for that in the space. Oh, yeah, I mean it, but it's not not deniable. Little Yeah, so it's you know, for me I would look at us if I can take, you know, a consumer survey and it can be, you know, fifteen well formed questions that you know get to the result. That would make more sense because by the time I get to question twenty one, I'm distracted and I've got something, you know, popping up on on my laptop somewhere or someone's walked into my office, you know. So I guess being able to condense and be focused around the end goal would be really helpful for your customers, you know, being able to really streamline those inputs well, and on the data collection side, and you know, one of the things that we really want to work with as well as data collection companies to help with those methodologies, because the way our platform works is for everyone attribute or every variable, data point whatever, we need ten records. And so we've seen companies that have, you know, they ask a hundred fifty questions and you know the survey takes twenty five to thirty minutes and you know, we're literally asking ourselves, like who's going to do that? And by the time you get to question thirty six, how much of their attention span you really think you have at that point, right? And then when they bring us that, they're like yeah, but we only have, you know, four hundred respondents, because you can only get four hundred people that would even fully participate in it. So for us. That would not really work very well because we would have at least a hundred would meaning we would need a thousand records for that. Now the biggest breakthrough, one of the biggest breakthroughs with our platform, is that we can do mixed data types, meaning...

...we can have a continuous variable, a discrete variable and ordinal variable, a categorical variable, and they could all be in one data set and it can all be visualized and they're actually they're identified by a symbol on a graph. So like a continuous or ordinal variable is a circle, or continuous as a is a circle, a ordinal is a diamond, a discrete variable is a triangle, like you you know, and there's a little graph down there that tells you what what type of that was. So the thing is, though, is now you're able to leave in your demographics, you're able to leave in your racial demographics or socio economic demographics, etc. Etc. Regional Demographics, whatever you want. The other nice thing is that Pii data, or personal information, you know, which is the big why we have GDPR, why we have CCPA, all of these, you know, things that are coming out for data protection is of no use to us, like any personal indicator or marker of who would in bividual is. We tell clients, when you're using this thing, get rid of it, because the platforms going to look at it and go, I have no idea what it is, and when it looks at it, it may even try to change it into a categorical variable, which means you just went from a hundred to four hundred, because it's seeing all the differences. Like we did a speed dating survey where they were able to check every applicable race that they were. So when we got the data set you had over three hundred different types of racial categories because they were all the different combinations lung together, and we're like. We're like no, no, we don't want that. We don't want that. So we're like can we is, though, we don't like things necessarily in great areas. We get that. Just like me when I'm filling out a survey for my daughter here for race and ethnicity. They're like pick one right, this is what do you identify ass now? Right occasion, my daughter's box that gets checked is Asian. My wife is Japanese. My daughter's Bi linguals. She self identifies as predominantly Japanese. Though. She says I'm American but I'm Japanese, and so that's what we check because that's what she likes, right, you know? Yeah, I mean there's just there's so many options. The world is is so close together. Now we're not, you know, really kind of separated by continents or cultures. Are All, you know, melded together now. So it's you know, it probably brings a really interesting challenge into data analytics. Yeah, around that. And then if you're able to abstract away things like that, then you probably get down to the true the true inputs that you know your customers are looking for. Yeah, and the Nice thing is is once you run that larger data set and you see what the outputs are, you can run what we have. So the...

...platforms automated, so you can run your key driver analysis, you can run predictions, you can run simulations, you can also add expert knowledge. So this is where we're telling the experts at an organization, at a cant are and askaloner and episodes or etcetera, etcetera, etc. or a General Motors or a chlorox or Johnson Johnson, that there are things that you know in your data set to be true, that you know that they are. So we have a feature called expert knowledge where you can actually input that in. So when it learns the graph, it says, okay, I know that question three is a key driver to quick has a direct causal impact on question fifteen. You can tell that to the machine learning algorithm and it takes that into account and it strengthens it and it that's how it comes out. The other thing is is, once you have it with that pope, Pouria, demographics, etc. You can run what's called a binary filter, and so it will, i. isolate what females think. It will isolate what African Americans think. It will isolate, and I use that term loosely, what they think, because it's just based off shore. They what their response is, but that's how they responded. You know how a specific group, I don't like prodes, so how a specific group responded to that day. Sure, tell us a little bit about the technology underlaying. You know how you guys have built your platform? Obviously you have a you know, great algorithm that's doing things differently than a lot of other places, but what are some of the architectural decisions you guys made around building your platform? Out. It sounds like you've gone cloud native. Sounds like you have a lot of integration points with lots of types of data sets. I'm sure some of that was challenging at a high level. What could you share around you know, some of that journey? So essentially it's starts with the algorithm in the output and then on the platform side. So the Algorithm, were always in rd trying to improve it, trying to trying to make things I mean, just because you get an algorithm that works doesn't mean that you stand pat you can nothing. You know, this is where my Japanese background comes in and the concept of Zen Zen does not exist. It's an unattainable attribution. Nothing is perfect. So we're always trying to improve or build new ways of looking at things, whether that's it regards to time series, whether that's regards to clustering and sets are but within how we build around that, we start with the visualization and we put ourselves in the seat of the user. So then it's all about user experience and user interface. And then how is this and since we are cloud native and it's about automation, so how do we make the platform visually pleasing? How do we make the graph visually pleasing? What our outputs on ways to extract that data so you can see a graph, you can extract that data into a CSV, so you can see what from a statistical...

...methodology in a CSV file, what those attributes are in a grid. You know how you can look at those relationships in a bar graph if you're looking at a key driver analysis, you know, a data fit metrics on another side. So, and it's always evolving because we're always talking with our clients and with our users like what can we what is it that you would like that would help simplify what you're doing and to make this easier? So we were speaking with one perspective plant. They said, you know what, I really like your outputs, but I'm ingrained in correlations and you have this correlation graph that looks like a balustring because it's just showing all the correlation relationships. And they're like if I can get that in a heat map, that would be simple. Two days later done. Like okay, we went to the UIUX team. They want to heat map. Is there? Do you know how to build this? Yeah, that's easy, that's no problem. Boom, it's on the platform. So we're always trying to look for ways to make the user interface, in the user experience, much more simplified and taking into account who the user is, and but then also not trying to get to cute with ourselves as saying, oh well, we're going to build one specifically for public policy, we're going to build one specifically for finance, we're going to build one specifically for marketing research and one specifically for, you know, for x, Y and C, that everyone has their own because there are times when each one of those pieces may be applicable to what someone's doing. So why would you want to eliminate it? So it's just a matter of cutting down confusion, but yet building trust within the within the user right. So you're trying to make it as universal, universally approachable for each type of customer use cases you can, while improving that user experience as much as you possibly can by giving them the options that's correct them to consume the data that's correct. And if we get a phone call from, you know, a client who's in, you know, public policy, and they're asking about the KPI attribution feature, but yet they say, you know, if you think about it, and this is where I my background, comes in and and they're like, well, if you thought about the target population of what we're trying to do with this market that were building in Sierra Leone, if you thought about it in terms of KPI and goods distributed rather than a food program how would I structure my data or how would we be able to look at it and use that feature, which is when we go to work, because it's a valid question, like okay, you know, rather than just looking at it from a straight KPI, which is, you know, someone's looking at it from a business use case, right, how you could look at it from a different point of view? And the data scientists that I have, you know, they all come from finance, they all come from that because that's what they learned, and data sciences is marketing and stuff like...

...that, whereas I learned data science on how to be efficient in spending public dollars or whatever, or what's the successful so when I come to them and they're like, I don't get it, and then when I explain it to him, they're like, oh, okay, so you do look at this like a business, I'm like yeah, I know that. People like to think that. They're just like wastefully spending money. But there is actually a reason on how someone comes up with that and mistakes nothing. Once again, nothing is perfect. So, right, what happens in real time? We can all armchair quarterback something on the back end. So what we're doing is trying to make all of those decisions important. Last thing I'll put in there is when we're talking, like say, with a marketing client, and they're looking at their graph and they they run their key driver analysis, the ones that have the lesser scores will be somewhat great out and so what we like to tell them is, you know, those great out ones are really important because you know what that's telling you. That's telling you you don't have to spend money on those. Do not waste a dollar on those, because that is not what is driving what you're trying to achieve, and I think that that is a bigger, a bigger piece of it than just how am I going to get people to buy more of my product? Because you're going to spend money to get them to buy more of your product, so why not be more efficient with how you're going to spend them? Right? So you're helping you you're helping customers to still down the areas where they should be focused, opposed to just more time in the platform, like more time, more inputs. You know, I'm not sure how you guys go about charging customers, but you know, a lot of analecs tools are ingestion base. You know, x amount of data in, you know, equals x amount of dollars, right, and a check for right. So yeah, if you're able to kind of distill down and go, you know, these three areas not very important for what you're trying to accomplish, that's a huge benefit to your customer. Yeah, I mean I liken it to I was running disaster relief in the state of New York and one of our contractors, he was the captain of the Lsu football team at Lsu when they won the national championship with Nick Saven was their coach, and then he followed save into Miami and then Alabama, etcetera, etc. And he said, you know, coach save and used to have a saying, you know, I'll remove the x with it. But he's just like you could throw dirt at a wall and see what sticks, but at the end of the day you just have a dirty wall. And so what we're trying to do is make sure that you don't have a dirty wall like it's. There's more bang your buck. And, by the way, that money that you're not spending, that you're and you're more able to be targeted and focused. That's real are why? Because now you're able to really hone in on what you're doing right now. That's that's very interesting. So along the journey, what is a specific challenge your team had to overcome? I think the biggest challenge we have to overcome, and we're still overcoming this, it well, I mean there's a couple of considering we're coming out of a pandemic, but the the the first challenge that we had to overcome was what is causal discovery, because we met with so...

...many people and we met with so many people who are like no, but I'm already doing causal I'm running my sems, I'm doing this and I can build my Beigian net. I really smart people that know how to do that and I can tell you, nick, in the last two weeks I've had phone calls with very large organizations that have said, you know, I didn't respond her emails for a year and a half, but then I saw you had the names you, Day a Pearl on your website. I was like, I got to talk to these guys, because they're crazy enough to put the father of causal on their website and claim that this is what they're doing. And when we got done with the call, they were like, I'm really sorry, I dodge to like this is you're actually doing it and you're actually doing it efficiently, and you know, and we always sell them. Look, we're also not trying to replace your people, like your you need people to use this and this is the direction things are going, and so you know, we're not trying to replace everything. But the biggest thing is getting people over existing technology that they've been doing for four decades and they're like, but I have a PhD in statistics, I can do this. Yeah, but you could do it better. I mean that's yeah, that's an interesting parallel between what we do here at Chatisoft. You know, we're pushing from modernization around infrastructure and the way you manage applications scale them, and it's the same challenge. You have people who've been doing it the same way for a long time and it feels like a threat, even though it's not. It's just it's just a different, more efficient way of, you know, doing your business and we're constantly, you know, coaching customers and prospects around the idea of you can do more with what you have. And most most organizations don't have unlimited budgets. Most organizations do not have a staff of, you know, all the smart people they would ever want. So one way we can get to that is through taking a modern approach and using some tools to make things a little easier. Well, what I like to say to them is, okay, great, I don't discount you. Your absolutely brilliant. You can get really close. That's going to take you, what, three weeks to a month, and they're like yeah, it's like so you took three weeks in a month to do one project when you could have done six. Right, how much of your firm have earned if you'd have been able to do that? Right, if you can get more work done, then that that benefits the organization, that benefits that person. I mean even think about the personal fulfillment. If it takes three to four four weeks to do one project, but then you were able to apply a platform or a set of tools to help you do your job more efficiently and you can get six done. I mean there's there's some poet by the way, maybe some worklife balance, you know, because once you gain, God forbid, coming out of a pandemic where it's this is part of the hiring thing is people have been working remotely, and that's the other thing about being cloud nated, by the way, is being remote is totally okay with us because you log in, you log in with the password into the cloud. You don't need it's not a localized download. So and that's you know, the whole thing... is it's like now people are like, wow, spending time with my family actually was interesting, like, I know, I'm not working, you know, fifty hour weeks now, and I can actually go to my kids soccer game or you know, or I can go spend time with my daughter the Skate Park or you know whatever. Like it's, you know, that's now top of mine for people. I mean you're reading it in the news all the time, like how dare they want work life balance? I mean they want work life balance, they want to eat, guarantee vacations that are paid, they want it's just like, well, because their eyes got open that they were you know, stuck in this right, so, right, this is where I think you know, technology firms are able to do this and it's just about embracing it. And though it's causal discovery and it's consider high level math and statistics in etc. It doesn't require a PhD to use it like it does. They can require masters to re use it. To be honest with you, like I have data scientists that are they have degrees in data science and they can learn quickly and then you can even build upon that and give them even more skills. So it's it's it's really not that hard, if I can do it, if I can do it, then they very approach ate. Yeah, now, that's that's that's brilliant. It makes a lot of sense. So we have a theme on the PODCAST. We like to try to align things to speed, scale or savings. Based on everything we've talked about, sounds like your platform is really helping customers with all of those things. But that's probably what you're doing in mind when you're building the platform. You know you're looking to be able to get results to customers quicker as you had customers. Obviously you have you know, you have to consider what the scale is and then, you know, savings. We've talked about, like the time it takes, you know from your customers perspective. So it sounds like you're hitting on all those, all those trends. You know that we like. That's our sales prop is we give you a much more efficient way and scalable way in order to to deliver insights, which is really going to drive your Roy because it has a much higher accuracy. And that's you know, we hit on all those points and it's just once people literally sit down and take a look and realize it's not a big scary thing that's going to replace me or my people, and but this actually is really driving what it is that we want to learn, what we want to know. This is a much more efficient way to go and I have happier employees because I'm not screaming at them two weeks later like, why aren't you done yet? Yeah, absolutely, well, that's you know that. That's the common theme we find with, you know, this group of companies that we call high growth software companies, and your organization definitely fits into this. Is the speed, scale and savings is what everyone's trying to solve for, especially in that space. You know, if you're a you're a large fortune, five hundred, you know, you might be keeping the lights on and some ways and shapes and forms in your business, but everybody's focused on the...

...future in some way, shape or form. So we like to hear about the journey in that and that's that's very, very interesting. One thing we always like to wrap with is, and you know, sounds like you have a lot of really interesting experience. I mean I could probably go offline and ask you all kinds of questions around public policy. That's very interesting to me. But what advice would you give to a current or future leader of, you know, a high growth software company like yours? Like I've learned a few things along the way, and this seems to be persistent across you know, if I had to do this again, I would follow this advice. Do you have, you know, any antidotes like that? I do, but it's book ended by a pandemic, so fair enough. We're still living in it. So yeah, I mean we will see. We went to market. We launched in May two thousand and twenty so and we did that because we were like we gotta we gotta go because you know, the world's coming to a crushing and so I think if you're dealing with the technology such as ours, that's kind of a kind of when it's a breakthrough technology that delivers something new, that adoptions going to be a challenge because you're going to be met with a lot of skepticism and to not get frustrated with like you have to show patients and allow extremely smart people to be the smartest person in the room and but you just have to be patient to get them there that just test it and use it and be like, okay, I get it. So that that's one area and then the other one is just patients in general. Like it's it's hard for us to understand what it is because the fact of the matter is a lot of our clients are not spending money. I mean, they're just they're just not. I know that we see economy going up, but it's stock markets going up. That is not a true indicator. I mean the stock market went up the whole time through the pandemic, right, yet people weren't working. So that you know that it's kind of hard to keep setting stock market records when you have no I mean, I come from the policy background, so I know what drives cheat and it's like it's like wait a minute, like how is this possible that stock markets going up when GDP and GMP are going to be drastically off? So the the indicator, the indicators are a challenge because now we are getting those people on the phone. The other thing is is don't rely on technology to sell your product. You might have been able to get away with doing zoom demos and everything of the pandemic hadn't happen, but I can tell you that, you know, I've had client calls where we're going to do a demo and they're like, I'm calling in for my phone because I can't do another zoom. Yeah, we've we've seen a bit of that as well. They're the zoom apathy or whatever. You're sharing platform. I don't want to the grudge. Yeah, I see. Yeah, whether it's yeah, you know, yeah, but whether it's zoom, whether it's Google, whether it's teams, like I've had to learn all these different things and be flexible on how do I how do I share my screen? Because differently, and you get the...

...the natural ten minutes at the beginning of every meeting because you're on a new platform where you're like, how do I share this and how do I make sure you know, Sally can hear this and John can hear this, and we always build in like five to ten minutes just to make sure that everybody can do what they need to do. And then, you know, so I t companies got their fire walls up and it doesn't allow for Google meet and well, you know, you know they always have a word of the year. Two Thousand and twenty had to have a phrase of the year and it's you're on. Absolutely may have happened a few times to me over two thousand and twenty. For sure guilty is jest. Absolutely well, David, thank you so much for your time. Those really informative. We love to hear about, you know, organizations like yours that are changing the way customers are viewing their data or the way they're doing things or, you know, whatever their material output ends up being really interesting. We appreciate your time. We know you're very busy. Thanks for coming on the PODCAST. Thanks for having me. It goes fun. Application modernization is sponsored by Red Hat, the world's leading provider of enterprise open source solutions, including high performing Linux, cloud, container and couper Netti's technologies. Thanks for listening to application modernization, a podcast for high growth software companies. Don't forget to subscribe to the show on your favorite podcast player so you never miss an episode, and, if you use apple podcasts, do us a favor and leave a quick rating by tapping the stars. Join US on the next episode to learn more about modernizing your infrastructure and applications for growth. Until next time,.

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