Getting to Actionable Insights with Speed & Scale

ABOUT THIS EPISODE

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 Inguo.io , 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 Inguo.io 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 .

Listening on a desktop & can’t see the links? Just search for Application Modernization in your favorite podcast player.

You are listening to applicationmodernization, a show that spotlights the forward thinking, leaders of Hydrosoftware companies from scaling applications and accelerating time tomarket, to avoiding expensive license and costs. we discuss how you caninnovate with new technology and forward thinking processes and savesome cash in the process. Let's get into it. Thanks for listening to shadoofsapplication, modernization, podcast imre host Nick Mar Carelli the goal,the podcast is to focus on speed, scale and savings related to stories of highgross software companies. Today, I'm speaking with David Wolfe, CEO andfounder of Ingua, they help customers discover the wise and data, as alwaysthanks to red hat for being a sponsor of the application. Modernizationpodcast David thanks for joining US thanks for Hapning Nick David won, westart out, like you know, tell us a bit about your organization. You know whatyou're, what you're doing for your customers, what you're serving themwith you know how you all got started. Just you know, give us the couple ofminutes on. You know what you're doing day today, and you know why thatmatters to customers yeah. So again, thanks for having me I'm the CEO andfounder of a company called Ingo or Ingoda io, we want to be official andit's also our web address. You know it's. The the tech startup thing tohave your full weated dress, be your name yeah I mean it's, people see itand they think it's a funny name and how you pronounce it it's pronouncedIngo, which is actually a combination of Japanese and Chinese. You know we'respent out from the innovation what we're spent out from neck based out ofPaloo, California, I'm based in New York in Lois based in Brooklyn, but wedo. We are spit out from NEC, which is a subsidiary of NEC Corporation inJapan and the technology. Our current more technology is the birth of one oftheir innovation, labs and so, and the people who created the COROO which I'llget into here in a second they are Chinese and it's a Jobman Corporation,but ingua is actually the character for cause the Chinese character for causeand the Chinese character. For effect, only one is the Chinese pronunciation.One is the japes pronunciation. So it's basically a Japanese Chinesecollaboration, and so but it means cast an effect which is essentially what Igodoes and which is. We deliver causal discovery which is not to be confusedwith Caslin, which is a probably what most people when they think of theblanket term causland come to learn in this industry is a loaded term becausediscovering causality is always the challenge and what what was able to becreated was an algorithm that actually discovers and visualizes actualcausality. That is, bias free and is seeking to democratize the data. It'scloud based its machine learning driven...

...so essentially were able to producecausal discovery, graphs or directed to cyclograph in a significantly shorteramount of time than what your typical organization, whether it be market,research for public policy, insurance, etc, etc. It's able to derive thoserelationships between the variables in a very, very short amount of time, andit doesn't require running correlations and regressions running through thatbuilding a graph it actually that's what the algorithm does is. It buildsthe graph based on relationships and based on a weighted scores between eachvariable and which is extremely time consuming because it just when youstart adding variable the amount of combinations you can have is absurd,and so it's just it's a challenge got O so basically accelerates the outputthat you might want as an organization I have. You know this thought. Let meyou know pump what data have in and your platform will get them to thatinsight quicker than maybe the trition l way of looking at analytics today. Isthat fair, that's fair? I do like to take it a step further, though, becauseI think one of the assumptions with machine learning and ai is that it doesall the work and you're done in some cases. That's true, you can look at agraph. It has a great data fit that's. Rio has a great data fit model. You canlook at a correlation in causal heat map and see what those relations areand say, I'm good. But if you're impeached statistician work there,someone who wants to really get in the weeds with it you've just eliminated alarge part of your work, but you can now re focus that and really get intothe weeds with what? If and why scenario modeling, and so your work canbe done, but we know that that's that's not what we want people to think,because that's that's not entirely thet case. Sometimes you want to understandsomething better or you see something that maybe you don't entirely trust andso you're going to want to test that theory, and so what this does is thisgives you that ability to test that, but rather than taking you know, ifit's a project that would take you three to five days, we just shorted itdown to one or two. It was a project that will take you two to three weeks.We just shortened it to 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 itis that you're trying to decide, and it makes your insides much more actionable,whether that's doing market research, CPG, have the purchase brand awareness,brand, tracking or even clinical trials, some Farlo or building new materials,or how your plant operates or Ahr like you're, just able to better understandhow the relationships within the data exist. So, as you guys were puttingtogether your solution for customers,...

...there's obviously an inflection point,we 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 wecould do this quicker? We can allow time to dig deeper into what the datais telling us. You tell me a little bit about that yeah, so I think it allstarts with you know. The old saying correlation is not causation and whenthe PhDs that created the Augrim, what they were thinking of is is everyonewants causation, and it started with this first project by a man named ShokeShinio out of Shiga University in Japan, and he came up with what was known asthe lingo method, which was able to actually visualize causal discovery andonce again, causal discovery is where you're discovering and it's notcausantis or people, see our grass they're like. Oh, that's a bigonnet,and it's like well loosely speaking, yes, but a vision that was built bysomeone else based on something that they derived going through hypothesisand steps and etc, etc, etc. But the use case that that the innovation labs,what they were primarily looking at, was market research and marketingresearch and how they can get better messaging and story telling how theirclients pick it up better messaging and storytelling. And so when theyapproached me, I come from an international policy background where Iused to work with program evaluations where I would go on the ground inCambodia or India or Pakistan, or you know, x C. Developing Country and whenI looked at it I said you know when I was doing program evaluations, thiscould have saved US fifty to a hundred million dollars, because I you knowthis is amazing and they're like yeah. That's great. We built this formarketing research, and so I said okay, I see your point. I see why this isgreat for marketing. Like you know, we've done a study for Toyota in our MV.We had done a study for a large laundrette brand based on a Japanese orbased out of Japan. My sorry, my second language and culture background andeverything was Japan. So that's also where the whole ingo name helps comefrom, because my goal in graduate school was this historicalreconciliation with China and Japan. So this is me trying to fulfill that Ol,since I wasn't able to do it on the policy side, O things so anyways theirgoal was with marketing research and they said. Okay, you need to approachmarketing research, but if you want to take this public policy at now, you cango that direction if you want to do that as well. Just this has to be apart of it if you're, if we're going to tap you to do this start out, so thatis our primary focus and with the market research industry, you know whatwe're finding out is when they run their questionnaires when they runtheir studies, and things like that. We find ourselves asking a lot ofquestions like. Why is question thirteen in there when you essentiallyask someone similar and question for,...

...and why is question twenty two in there,because that kind of cancels out question fourteen in question for likeit, you know, we start to see things and we have to ask questions on youknow what is the first thing. He will ask a client if we're running servicesfor them because we're a diy platform, but we can do services, but obviouslypeople want the services first to see how it works. We can train their peopleet ce, so the first thing we ask them is: What is it that you really want tolearn from this study? And that's the big like what is the? Why that you'rereally trying to answer- and let's focus on that first and then we can gointo these other athletes, but we need to understand what your key pointswithin the Datas that are because we all know that you'll have fiftyquestions. Maybe twenty of them matter and the other thirty are filler. It'sright. It seems like when you do a market research. You get a lot ofvariations of the same questions over and over and as a as a consumer or aperson providing input. I'm like I've, already answered this question. Youknow 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 at all yeah. So it's you know for me. I would look at us. If I can take youknow a consumer survey and it can be. You knowfifteen well formed questions that you know getto the result. That would make more sense, because by the time I get toquestion twenty one, I'm distracted and I've got something you know popping upon on my laptop somewhere or someone's walked into my office. You know so Iguess being able to condense and be focused around the end goal would bereally helpful for your customers. You know being able to really streamlinethese inputs well and on the data collection side, and you know one ofthe things that we really want to work with, as well as data collectioncompanies to help with those methodologies, because the way ourplatform works is for every one attribute or every variable data pointwhatever we need ten records, and so we've seen companies that have you knowthey ask a hundred and fifty questions. And you know the survey takes twentyfive to thirty minutes, and you know we're literally asking ourselves likewho's going to do that and by the time you get to question thirty six, howmuch of their attention span. You really think you have at that pointright and then, when they bring us that they're like yeah, but we only have youknow four hundred respondents, because you can only get four hundred peoplethat would even fully participate in it. So for us that would not really workvery well because we would have at least a hundred with meaning. We wouldneed a thousand records for that now. The biggest break through one of thebiggest breakers with our platform is that we can do mix data types, meaningwe can have a continuous variable. A...

...discreet variable in ordinal variable acategorical variable and they could all be in one dataset and it can all bevisualized and they're. Actually they're identified by a symbol on agraph, so like it continuous or ordinal variable is a circle or continuous is ais a circle. A orginalite, a discreet variable is a triangle likeyou 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 leavein your racial demographics or socioeconomic demographics, etc, etc.Regional, demographics, whatever you want the other nice thing is that Piidata or personal information. You know, which is the big, why we have GDP? WHYWE HAVE CPA? All of these. You know things that are coming out for dataprotection is of no use to us like any personal indicator or marker of who anindividual is we tell clients when you're using this thing, get rid of it,because the platform is going to look at it and go. I have no idea what it isand when it looks at it, it may even try to change it into a categoricalvariable, which means you just went from a hundred to four hundred, because it'sseeing all the differences like we did a speed dating survey where they wereable to check every applicable race that they were. So when we got the dataset, you had over three hundred different types of racial categoriesbecause they were all the different combinations long together and we'relike. We were like no, no, and we don't want that. We don't want thatso we're like. Can we as though we don't like things necessarily in greatareas? We get that just like me when I'm feeling out a survey for mydaughter here for race and ethnicity they're like a pick one right thinkthis is. What do you identify ask now right occasion, my daughter's box, thekins check is acient. My wife is Japanese, my daughter's bilinguals sheself identifies as predominantly Japanese to she says: I'm American, butI'm Japanese, and so that's what we check, because that's what she likesright, O you know, yeah I mean there's just there's so many options the worldis, is so close together. Now we're not you know regionally kind of separatedby continents. Our cultures are all you know, melted together now so it's youknow. It probably brings a really interesting challenge into dataanalytics yeah on that and then, if you're, able to abstract a way thingslike that, then you probably get down to the true the true inputs that youknow your customers are looking for. Yeah. The Nice thing is is once you runthat larger Dataset and you see what the outputs are. You can run what we ohave it so the platforms automated. So...

...you can run your key driver Dalys isyou can run prediction? You can run simulations. You can also add expertknowledge. So this is where we're telling the experts at an organizationat a cantar and ASPAR episodes or etc, etc, etc, or General Motors or a cloroxor Johnson and Johnson, that there are things that you know in your data setto be true, that you know that they are so we have a feature called expertknowledge where you can actually input that it. So when it learns the graph itsays. Okay, I know that question three is a key driver to put. Has a directcausal impact of question fifteen. You can tell that to the machine learningalgorithm and it takes that into account and it strengthens it and itthat's how it comes up. The other thing is is once you have it with that povredemographics et ce, you can run whats called a binary filter, and so it willisolate what females think it will isolate what African American take. Itwill isolate and I use that term loosely what they think, because it'sjust based on an they, what their response is, but that's how theyresponded. You know how a specific group I don't like going so how aspecific group responded to that day. Sure tell us a little bit about thetechnology underlaying. You know how you guys have built your platform.Obviously you have a you know great algorithm, that's doing thingsdifferently than a lot of other places, but what are some of the architecturaldecisions you guys made around building your platform out sounds like you'vegone cloud native sounds like you: have a lot of integration points with lotsof types of Datasets, 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 itstarts with the Alberti and the output and then on the platform side. So theAlgorithm were always in rd trying to approve it. Trying to trying to makethings I mean just because you get an Auger thm. That works doesn't mean thatyou stand pat, you can nothing. You know this is where my Japanesebackground comes in, and the concept of s in Zen does not exist. It's it's anunattainable attribution. Nothing is perfect, so we're always trying toimprove or build new ways of looking at things, whether that's in regards totime series, whether that's regards to clustering, etcetera, but within how webuild around that we start with the visualization and we put ourselves onthe seat of the user. So then it's all about user experience and userinterface, and then how is this? And since we are conative and it's aboutautomation, so how do we make the platform visually pleasing? How do wemake the graph visually pleasing what our outputs on ways to extract thatdata? So you can see a graph. You can extract that data into a CSB, so youcan see what from a statistical...

...methodology in a CSB what thoseattributes are in a grit. You know how you can look at those relationships ina bar graph if you're looking at a key driver analysis, you know a data fitmetrics on another side so and it's always evolving, because we're alwaystalking with our clients who, with our users, like what could we, what is itthat you would like that, would help simplify what you're doing and to makethis easier. So we were speaking with one perspective plant. They said youknow what I really like your outputs, but I'm ingrained in correlations, andyou have this correlation graph. That looks like a balustering, because it'sjust showing all the correlation relationships and they're like if Icould get that in a heat map that would be simple. Two days later, Don, likeokay, we went to the UYUK team. They want to heat map is er. Do you know howto build this yeah? That's easy! That's no problem, though: It's on theplatform, so we're always trying to look for ways to make the userinterface in the user experience much more simplified and taking into accountwho the user is and but then also not trying to get to Cup with ourselves at saying. Oh well,we're going to build one specifically, your public policy we're going to buildone specifically your financeer. It is billed in specifically for marketingresearch and went specifically for you know for xysta. Everyone has their own,because there are times when each one of those pieces may be applicable towhat someone's doing so. Why would you want to eliminate it? So it's just amatter of cutting down confusion, but yet building trust within the withinthe user right so you're trying to make it as universal, universallyapproachable for each type of customer use cases you can, while improving thatuser experience as much as you possibly can, by giving them the options, that'scorrect or them to consume the data. That's correct and if we get a phonecall from you know a client who's in you know public policy and they'reasking about the KPI attribution feature, but yet they say you know ifyou think about it, and this is where I, my background, comes in and they'relike. Well, if you thought about the target population of what we're tryingto do with this market that we're building in Sierra Leone, if youthought about it in terms of KPI and goods distributed rather than a foodprogram, how would I structure my data or how would we be able to look at itand use that feature which is when we go to work, because it's a validquestion like okay, you know, rather than just looking at it from a straighttake I, which is you know, someone's looking at it from a business use caseright how you could look at it from a different point of view and the datascientist that I have you know they all come from finance. They all come fromthat because that's what they learn in data sciences is marketing and stufflike that, whereas I learned data...

...science on how to be efficient inspending public dollars or whatever or what's the success right. So when Icome to them and they're like I don't get it and then, when I explain it tohim, they're like Oh okay, so you do look at this like abusiness, I'm like yeah. I know that people like to think that they're justlike wastefully spending money, but there is actually a reason on howsomeone comes up with that and mistakes. Nothing. Once again, nothing is perfect.So right what happens in real time, we can all armchair quarterback somethingon the back Ed. 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 themarketing, client and they're looking at their graph, and they run their keydriver analysis. The ones that have the lesser scores will be somewhat breadout, and so what we like to tell them is you know those great out ones arereally important, because you know what that's telling you that's telling youyou don't have to spend money on those. Do not waste a dollar on those, becausethat is not what is driving what you're trying to achieve, and I think thatthat is a bigger, a bigger piece of it than just. How am I going to get peopleto buy more of my product because you're going to spend money to get themto buy more of your product? So why not be more efficient with how you're goingto spend that right, so you're helping you're helping customers to still downthe areas where they should be focused opposed to just more time in theplatform like more time more inputs? You know, I'm not sure how you guys goabout charging customers, but you know a lot of analects tools are ingestionbase. You know X, amount of data and you know equals x amount of dollars.Writing a check for right, so yeah. If you're able to kind of distil down andgo, you know these three areas, not very important for what you're tryingto accomplish that's a huge benefit to your customer yeah. I mean I liken itto it. I was running disaster yle in the state of New York and one of ourcontractors. He was the captain of the L Su football team at LS. U, when theywon the national championship with Nick Savin was their coach and then hefollowed saving to Miami and then Alabama and seretse. He said you know:Coach saving used to have a saying and I'll remove the eyes with it, but he'sjust like you can throw dirt at the wall and see what sticks. But at theend of the day you just have a dirty wall, and so what we're trying to do ismake sure that you don't have a dirty wall like it's there's more Bangard bythe way that money that you're, not spending that you're and you're moreable to be targeted and focused that's real or a lot, because now you're ableto really pole in on what you're doing right. Now. That's that's veryinteresting so, along the journey. What is a specific challenge your team hadto overcome? I think the biggest challenge we have to overcome and we'restill overcoming this it. Well I mean there's a couple considering we'recoming out of a pandemic, but t e. The first challenge that we had to overcomewas what is...

...causal discovery because we met with somany people and we met with so many people who are like no, but I'm alreadydoing cosslit running my son. So I'm doing this and I can build my vationthat I have really smart people that know how to do that, and I can tell younick in the last two weeks, I've had phone calls with very largeorganizations that have said you know. I didn't respond your emails for a yearand a half, but then I saw you had the name Judaea on your website. I was likeI got to talk to these guys because they're crazy enough to put the fatherof caslon their website and claim that this is what they're doing and when wegot done with the call they were like. I'm really sorry, I dodged you likethis is you're actually doing it and you'reactually doing it efficiently, and you know- and we always tell them, lookwe're also not trying to replace your people like your. You need people touse this, and this is the Directon things are going, and so you know we'renot trying to replace everything, but the biggest thing is getting peopleover existing technology that they've been doing for for decades and they'relike, but I have a PHD statistics. I can do this yeah, but you could do itbetter. I mean that's yeah, it's an interesting parallelbetween what we do here shot us oft. You know we're pushing formodernization around infrastructure and the way you manage applications scalethem and it's the same challenge you have people who've been doing it thesame way for a long time and it feels like a threat. Even though it's notit's just it's just a different, more efficient way of you know doing yourbusiness and we're constantly. You know coaching customers and prospects around.The idea of you can do more with what you have a most. Those organizations.Don't have unlimited budgets. Most organizations do not have a staff ofyou know all the smart people they would ever want. So one way we can getto that is through taking a modern approach and using some tools to makethings a little easier. Well, what I like to say to him is: Okay, great, Idon't discount you you're, 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 couldhave done six right. How much would your firm have earned if you'd havebeen able to do that right? If you can get more work done than that? Thatbenefits the organization that benefits that person I mean even think about thepersonal fulfilment if it takes three to four weeks to do one project, butthen you're able to apply a platform or a set of tools to help you do your jobmore efficiently and you can get six one. I mean there's, there's some poetby the way, maybe some work. Like balance, you know, because once you are,God forbid coming out of a pandemic, which this ispart of the hiring thing is people have been working remotely and that's theother thing about being abounded by the way is being remote is totally okaywith us, because you log in you log in with the Password into the cloud, youdon't need it's not a localized gowld.

So and that's you know, the whole thing isis it's like now? People are like wow spending time with my family actuallywas interesting like I know, I'm not working. You know fifty hour weeks nowand I can actually go to my kids soccer game or you know, or I can go, spendtime with my daughter, the Skate Park or you know whatever like it's. Youknow that's now topic mind for people. I mean you're reading it in the newsall the time like how dare they want work life balance. I mean they wantwork like balance. They want to week guarantee vacations that are paid. Theywant- and 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 andthough it's causal discovery and it's considered high little math andstatistics and et Cetera, it doesn't require a Ph d to use it like it doeseven require masters to revise it. To be honest with you like, I have datascientists that are, they have degrees in data science and they can learnquickly and then you can even build upon that and give them the more skills.So it's it's. It's really not that hard. If I can do it, if I can do it, thenvery approach, you yeah. No, that's that's! That's brilliant! It makes alot of sense. So we have a theme on the podcast we like to try to align thingsto speed scale or savings based on everything. We've talked about soundslike your platform is really helping customers with all of those things, butthat'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 youhad customers. Obviously you have you know you have to consider what thescale is and then you know savings we've talked about like the time andtakes you know from your customers perspective, so it sounds like you'rehitting on all those all those trends. You know that we, like that's our salesprop, is we give you a much more efficient way and scalableway in order to deliver insights, which is really going to drive your Roi,because it has a much higher accuracy and that's you know we hit on all thosepoints and it's just once. People literally sit down and take a look andrealize it's not a big scary thing. That's going to replace me for mypeople, and but this actually is really driving what it is that we want tolearn what we want to know. This is a much more efficient way to go and Ihave happier employees because I'm not screaming at them two weeks later like.Why aren't you done yeah yeah? Absolutely? Well, that's you know thatthat's the common theme we find with you know this group of companies. Wecall high growth software companies and your organization definitely fits into.This is the speed scale. N Savings is what everyone's trying to salve for,especially in that space. You know if you you're a large fortune. Fivehundred, you know you might be keeping the lights on and some ways in shapesand forms in your business, but...

...everybody's focused on the future insome way shape or form. So we like to hear about the journey and that andthat's that's very, very interesting. One thing we always like to wrap withis- 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 aroundpublic policy. That's very interesting to me. But what advice would you giveto a current or future leader of you know a high growth software companylike yours, like I've, learned a few things along the way and this seems tobe persistent across you know. If I had to do this again, I would follow thisadvice. Do you have you know any anadot like that? I do, but it's book ended bypandemic, so fair enough, we're still living in it. So yeah I mean we willsee. We went to market we launched in May two thousand and twenty. So we did that because we were like wegotta. We got to go because you know the world's coming to crushing. So Ithink if you're dealing with the technology such as ours, so it's kindof a kind of in when it's a breakthrough technology that deliverssomething new. That adoption is going to be a challenge because you're goingto 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 thesmartest person in the road and but you just have to be patient to get themthere to just test it and use it and be like okay, I get it so that that's onearea and then the other one is just patients in general, like it's it'shard for us to understand what it is, because the fact that thematter is a lot of our clients are not spending money. I mean they're, justthey're. Just not. I know that we see economy going up, but it's the stockmarkets going up. That is not a true indicator. I mean the stock market wentup the whole time through the pandemic. Right yeah people were working so thatyou know that it's kind of hard to keep setting stock market records. When youhave no, I mean I come from the policy background, so I know what drives cheatand it's like. It's like wait. A minute like how is this possible that stockmarkets going up when GDP and G N P are going to be drastically off so the theindicator? The indicators are a challenge, because now we are gettingthose people on the phone. The other thing is is don't rely on technology tosell your product, you might have been able to get away with doing zoom DaHouse and everything of the pandemic hadn't happened, but I can tell youthat you know I've had fine calls wherewe're going to do a demo and they're like I'm calling in for my phone,because I can't do it other so yeah, we've we've seen a bit of that as well.They're, the the zoom apathy or whatever your sharing platform. I don'twant to be grudges. Yeah Yeah, whether it's yeah no yeah, whether it's zoom,whether it's Google, whether it's teams like I've, had to learn all thesedifferent things to be flexible on. How do I? How do I share my screen becausehe differently and you get the the...

...natural ten minutes at the beginning ofevery meeting, because you're on a new platform where you're like? How do Ishare this? And how do I make sure you know sally can hear this and John canhear this, and we always build in like five to ten minutes, just to make surethat everybody can do what they need to do. And then you know some of y tcompanies got their fire walls up and it doesn't allow for Google meat andwell, you know, you know they always have a word of the year. Two Thousandand twenty had to have a phrase of the air, and it's you're on absolutely havehappened a few times to me over two thousand and twenty for sure guilty as chast absolutely well David.Thank you so much for your time. Those s really informative. We love to hearabout you know. Organizations like yours that are now changing the waycustomers are viewing their data or the way they're doing things or you know,whatever their material output ends up being really interesting. We appreciateyour time we know you're very busy thanks for coming on the PODCAST.Thanks for having thick, it was fine application. Modernization is sponsoredby Red Hat the world's leading provider of enterprise, open source solutions,including high performing Linux cloud container and Cubren ttes technologies. Thanks for listening to application,modernization, a podcast for high growth software companies, don't forgetto subscribe to the show on your favorite podcast player. So you nevermiss an episode and if you use apple podcast, do us a favor and leave aquick rating by tapping. The stars join us on the next episode to learn moreabout modernizing your infrastructure and applications for growth until nexttime.

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