So today we'll do a scene in three acts. In theater, we used to act in five acts, but we don't have enough time. So let's jump on the stage because we will talk a lot about theater. You will understand why. It's linked to the assumption that work is theater. Let's begin. What is the common characteristic between these brands? This famous luxury hotel chain. I don't know if you know Build-A-Bear. It's an American teddy bear building company which is loved by children and loved by parents. I will explain you why. And then this one, I don't present it. This one is a coffee maker. What is the common characteristic? This man. You don't understand why. Because he's the co-author of that book. Who has already read that book? You? Yeah, one. Thank you. The common characteristic is linked to the economic experience. What does it mean? It means that today we are at the fourth level of the economic vision. The first one was linked to the commodity. Commodity is very easy to copy, easy to find because it's linked with the nature. If you take back the example of Starbrook, it's the coffee beans. It's easy to find. It's not expensive. Then we jump into the product economy. This is more standardized and it becomes to be something tangible. We are able to use it, to manipulate it, to interact with it. The next level will arrive at the service economy. We use, as a UX designer, work into the services. This is customized, but this is also intangible. And we like, I don't know here, but in France I like to explain to my clients that we will help them to go through the invisible, to the tangible, through the services. This, you know, here as a UX designer, we are experienced in your jobs terms, titles. This is regarding the economic view, something that's really personal. But we also welcome the emotional impact of the experience we wanted to create. So this becomes memorable. The fifth vision of the economy, it's not totally implemented yet because it's based on the transformation. Transformation for individuals is very intimate. It's very linked to what we live, what we experience. So we're not able yet to really impact on that. But it will be the next level. The action around that, first we extract, then we make, then we deliver, then we stage and finally we guide people through the experience. Before going further, I just want to offer you some details about the conception of work as theater. Okay, when you say VJS Samantha in this movie, they are literally acting. That's their roles. Okay, but in the life, theater is not a metaphor. The author insists on that because we always are acting. When we will be during the break taking coffee with colleagues, we are acting. The waiter that are working here are also acting.  So just accept that the experience economy is based on theater but not as a metaphor but as a real view of what we have to do and how we have to act in your work as UX designer. Okay, I propose you to go further on the stage vision that Payne and Gilmo tried to give. From the theater view, we have some cycles. The first one is the link to drama. Drama in Greek means just to do. It's very simple. What we want to do? You'll see how we can switch with our work. Then we have the script or we can deliver what we want to do. Then you have the theater itself because we have to be able to have a stage to do that and we have the performance, the way you deliver the experience. Let's jump into the real world, our world, in the agencies but also in your clients. Then we can link drama with strategy. We all knew that. We can link process with script. We need to write something in order to be able to understand all the stakeholders. Then you have the walk as theater and you have finally the offering, the offering linked to performance. But you can see that one, the bigger cycle is empty now because as the experience economy needs for, we need an audience. If we come back to the three examples I showed to you at the beginning, we can just see that for the risk atom, what did they do regarding the experience economy? It's just one quick example because they have done many, many other stuff. They have decided to merge and to open all the databases linked to the customers all around the world. The name was Mystica. This vision, I just check the time because it's very short, this vision helps the customer, the guests to be at every time, at every place and in a risk atom hotel able to be welcomed with all what you wish, what you want. If you have troubles or allergies, they know this before you arrive. Before it was cut in silos, now it's totally a problem. Another example is Build-A-Bear. It's a workshop for kids, but the vision of the economic experience, they bring all the audience at the workshop so the parents are there. They are just behind and they help children to create their teddy bear with different kind of shapes. You can choose the fake fur, the foam, you can choose the color of the eyes. You can choose the clothes. And interesting fact for the kids, you can also choose hearts that you will put inside the bear before finishing it. And with the digital vision, you can also add a song. So if you want to put a song or to put some words from your mom, it's possible for the kids. And then this is very efficient to engage the audience. I won't talk a lot about that because I hope you have all experiences what you can feel and live in the Starbucks. There is a path you know that it will be really individualist because they ask for your name and you have this journey inside the bar. This is linked to the economy because who could imagine ten years ago that a coffee could be cost 300 rupees nowadays or four euros. It's crazy to think about that when you look at the community's price. So economic experience is linked also to the financial vision. So if you want to perform the act together efficiently, I propose you to go a little bit further. What do we need to promote the experience economy on the stage? First, we need a crew. The casting is very important for us as you exist. And also for the clients. Because we have big roles. You'll see that what we have in the theater or in the movie, we always have directors. We need someone that guide people, who guide people. We have the dramaturgies. It's all linked with the strategic view. If there is no strategic, you go nowhere. Then you have the script writers. You have to offer a vision more applicative to what you are thinking about. Then you have the technicians. It's not very good to hear about that, but we are kind of technicians. But it's more linked to the backstage view if we think about theater. And you have the stage crews. Stage crews that all of you who are experiencing workshops with your clients or performing tests for usability, for example. And again, the audience is also part of the recruitment. When you work on the personnel, you are working on the audience. When you recruit your testers, it's also the audience. So just to be clear about that, it's Evin Goffman, who is a famous sociologist. He said that all human activity is acted whether or not. So we can admit that everything is theater. So how I liked theater at work. There is a list, a to-do list I can offer to you. First, always imagine that you can do as if, even if you're not an actor, you can do and say, okay, today I'm here in front of you. Don't you use to speak over an Indian community? Okay, as if I do it. So it's a statement that will help you to deliver a good vision. Then the thing here is very important. The way you will act, you will present. You will enter a scene, for example, by your clients when you have a meeting. The way you will wear UPS, the famous delivery organization, they have the brown clothes, they have also the brown cars. It's easy to recognize. And it links to the economy experience. So take care also about all the details, the details with the props. For example, it's better to think about what you will need during a presentation, for example. If I was always obliged to go there to change my slides, it's not so good if your audience, for example, is there. Always think about what they will feel and what you will face during the war. Then we are quite short, near to finish. I would just like to present some takeaways around this new vision for your customers, but also for you and for us as designers. First, we have to think about your experience. I talked about UPS. UPS theme all the experience. For example, Disney also, when you go to the park, everything is theme. And people are able to pay a lot of money and wait sometimes more than half an hour an hour to practice an activity just because the theme makes sense for the people and they are able to engage really with that. So we can consider that as pain points, but people go away, go ahead with that. So forget marketing. Sorry if there is marketers here. Forget marketing. Experience will lead all the rest of that. Think global, think touch points, think context and ecosystem. So the global vision. We are not always digital. It's important to think what is outside the digital vision. Remove all your audience sacrifice. What is the audience sacrifice? You take what a user wants minus what is setters for. The global experience, horrible encounters on digital interfaces or in the real life. When you say, whoa, the services seems to be good. I paid for something and I faced horrible points that makes me say I do a sacrifice. I need to use, for example, a website. It's so unusable. I don't like that, but I'm obliged to. So if you can do that as your ex designer, it's better. Then be very careful with the commoditization of experiences. Because when we use to practice and to experience different things, it's something called in psychology, which is called habituation.  So when you work on experience, you need to think about that and to be sure that with the mass market adoption, you will be able to find another way to speak to people and to engage them through the economic experience. Just now, it's a quick presentation because the book is very dense, it's very intense. There is a lot of things inside. But I would like to share with you this view because as a UX designer, we cannot just focus on the tools, on the methodologies, and we have to be open minded about that and what we are able to create to help people to be more engaged and more effective with what we are creating, what we are designing.  Thanks a lot. In New York, in France, it's a very nice city in France. In February, we have big events organized by Ixier. It's one week events. If you are interested, you can look at the program. It's not totally open on the build now, but you have Alan Cooper as a keynote speaker who will come and a lot of people are not interested to check and I hope you will be able to see there. Thank you very much.


Live from Las Vegas. It's theCUBE. Covering Informatica World 2018. Not to you by Informatica. Okay, welcome back everyone. This is theCUBE live here in Las Vegas for Informatica World 2018 exclusive coverage of theCUBE. I'm John Furrier, the co-host of theCUBE, with Jim Kobiela as my co-host this segment. And unless that Wikibon is looking on theCUBE, our next guest is Fira Shanti, who's the Chief Data Officer at LiPo Digital Group. Welcome to theCUBE. Thank you so much, I'm very excited to be here. Thank you for coming on. But people don't know, before we came on camera, you and Jim were talking in the native tongue. Apacabar! Thanks for coming on. So I know your Chief Data Officer got a lot of questions because we love these conversations because we love data. But take a minute to explain what you guys are doing, what the company is, what the size is, and the data challenges. Okay, maybe let me introduce myself first. So my name is Fira. So my role is to see if data officers. So responsibility that actually is covered for the big data transformation for the LiPo Group data. So LiPo Group is actually part of the, one of the largest conglomerate in Indonesia. So actually we serve like a middle class for the consumer services. So we are connecting, I think more than 120 million of the customers. So what LiPo as a group doings is actually, we are like too many things. We are the largest of the hospital in Indonesia or just a supermarket. We do like department stores, coffee shops, cinema, data centers, IT business, like we own bank as well, insurance, news, cable TV. What else? You have a lot of digital assets.  Education as well, like from the kindergarten to the university. So that's why it's like a lot of diversity of the business that owned by LiPo. But recently we like endorsing a lot in the digital transformation. So we're releasing new mobile apps. It is called OVO. So actually it's like centralized loyalty imani to like providing the priority deals to all the LiPo group customers. So they're not going to maintain their own membership. Loyalty program is going to use like the OVO. So it's not only like being accepted by LiPo ecosystem but also like to the external ecosystem as well. So we start to engage with the merchant partner which is like today maybe it's already like reaching out 30,000 merchant outlets. Well I'd like to get Jim's perspective. I want you to connect the dots for me because the size and scope of data, you talk about deep learning a lot. And so let's connect the dots because we heard a lot of customers here talking about being having data all over the place. So how does deep learning, how do you, well how do you catalog everything? So if you have all these diverse assets I'm sure they're different silos. Is there a connection? How are you handling? Okay. Definitely it's not easy job to do, you know like implementing big data for this kind of a lot of the diversity of the business because like how to bring all of this data coming from the different source, coming from the different ecosystem to the single analytical platform is quite challenging. The thing is we also need to learn first about the business, what kind of the business, how they operate their business today, how they run the hospital, how they run the supermarket, how they run the cinema, how they run the coffee shop. By understanding these things, so my team is responsible to transform like not start from the calling thing, the data, cleansing the data, transform the data, then generate the insight, right? It has to be an actionable insight. Then like we also not only like doing the BI things, but also like how from their data we can like developing kind of the analytical product on top of the technology, big data that we own today. So what we deliver is actually beyond the BI. So of course we do a lot of things. For example, like we really focusing in doing the customer 360 degree profile because that's the only reason how we really can understand our customers. So today we have like more than hundreds of customer attribute attaching for individual customers. So I can understand what's your profile for the purchasing behaviors. Let's say what kind of the product that you like. If only let's say for the data coming from the supermarket, I know what's your brands, your favorite, whether your spending is declining, how you spend your point part of the loyalty program, then many things. So by understanding very deep like this, so later on that we can like engage with customers in the better way in providing the new customer experience because we not only let's say like providing them with the right deals, but also like when would be the right time, we should connect to them providing something that they might need. So that this is the way how from the data we try to connect with them with our customers. Yeah, provide a more organic experience across the entire portfolio of lipo brands throughout the ecosystem. So it doesn't feel to the customer and so it isn't simply a federation of brands. It's one unified brand of some degree from the customer's point of view, delivering value that each of the individual components of the lipo portfolio may not be able to provide. Yes, yes, so many things that actually we can do on top of that 360 degree of the customers. So our big data outcome actually in the form of the API, why it has to be in the API because when we interact with the customer, they could be unlimited customer touch point to call this API. It could be like the mobile apps as the smart customer touch point or could be the dashboard that we develop for our lipo internal business could be anything. Or even we can also like connect to the other industry from the different business, then how we can connecting each other using that, that big data API. So that's why. Is it API ecosystem, right? It's not one API, right? Or it's one API, one unified API for accessing all the backend data and services. For example like this, there are two type of the API that we develop. Number one is the API that belong to the customer 360 degree. So every attribute that attached to your profile, let's say we can like convert it to the API, right? So later on, let's say smart apps as part of customer touch point for example like Ovo, we would like to engage with our customers. So meaning that the apps can just like designing their own like the business orchestration, and then calling specific API by understanding let's say from the point of view or loyalty or like product preference that you like. So that then what kind of the offers that we need to push to the customer touch point channel using the Ovo apps. Or even let's say like our supermarket have their own apps. So the apps can also like calling our API based on their data to understand what kind of the brand or their preference product that they like. So later on in their apps when the customer connects is going to be something that really personalized. So that's why it's in order to manage the futures like agility, so it's very important for us to deliver like this big data outcome in the form of the API. It scales too, no there's not a lot of custom work. You don't have to worry about connecting people and making sure it works. Expose an API and say there it is. And then. Different countries have in terms of privacy and the use of personally identified information. Different countries and regions have their own different policies and regulations. Clearly the European Union is fairly strict European Union with GDPR coming along. The US has its own privacy mandates. In Indonesia are there equivalent privacy regulations or laws that would require for example, you ask the customers to consent to particular uses of their data that you're managing within your big data system. You know, that sits behind OVO. Is that something that you in your overall program that you reflect? Yes, actually there are some regulation in Indonesia governed like by the government. Telco having their own like regulation but we are let's say like part of the fintech yes there is a specific regulation. But regulation for the retail is not really that clear yet for now. But we put ourselves in the high restricted regulation that we put in place as part of our data protection part of our data governance compliance as well. Even though we do this kind of the monetization or like consolidating this data there is no individual data that being shared outside the entity of the organization. Because let's say when we do the monetization everything is done by system to system when we call the API. So there is no handover the customer individual data. Let's say even let's say like our partner FMCG digital agency or even like advertiser of future wise they would like to call our API what they can see about their target lead of the customers that they would like to connect is actually is not in the individual level of the data is going to be in the aggregated format. So even though many segmentation that we can deliver is not going to expose every individual customer. You have a lot of use cases that you can handle because of the control governance piece. How about, by the way that's fantastic and I know how hard it must be the challenge but you have it set up nicely. Now that it's set up with Informatica and the work you're doing how are you interfacing with developers because now you have the API. Is it just API based? Are you looking at containers, Kubernetes, cloud technologies? Are you guys looking at that down the road or is that part of the, or is it just expose the API to the developers? For today that actually who's going to consume our API actually? Definitely it's going to be like the ecosystem of the lipo internals. How the customer touch point can leverage the API. Then for the external for example like FMCG or digital agency, when they call our API usually is like they can like subscribe so they could be like some kind of the business model is divine there. But once again like I mentioned to you, let's say it's not going to reveal any individual customer information but the thing is how we deliver these API things. We develop our own API system. We develop our API gateway. In simple thing that actually about how to put the permission or grant the access of any kind of the digital channel when they consume our API and what kind of the subscription method. So what we deliver for the big data is actually is not really into we investing a lot of technology in place for us to use. The thing that makes my team so excited about this transformation because we like to create something. So that's why we create our own API gateway. We create some many like analytic product on top of the technology that we have today. So when they subscribe to the API you're setting policy for the data that they can get and you're done. Yes, something like that. So you're automated that. Yes. Cool, well we hear a lot of AI any machine learning in your future are you guys doing any automation? How are you guys thinking about some of the tools we've been seeing here at the show around automation and AI, Claire, are you tapping into any of the goodness? Yes. I believe everybody really like to talk about AI, right? I like that. You got APIs, you're good. You don't need any. Many organizations when they're really like you know implementing big data sometimes they start jumping like I need to start doing the AI things. But from our point of view, yes, AI is very important. Definitely we will go there. But for now what important for us is how we really can bring the data to a single analytical platform developing the 300 cc degree customer profile because we really need to understand our customer better than thinking about how we can connect with them how we can like bring the new experience and especially at the right time. And actually let me break down AI because I cover AI for Wikibon. It's such an amorphous topic but I break it down into specific things like for example speech recognition for voice activated access to digital assistance that might be embedded in mobile phones. Indonesia is a huge diverse country. It's an archipelago. You have many groups living in a unitary national structure but they speak different languages. They have different dialects do you use or are you considering speech recognition? How you would tailor speech recognition in a country that is so diverse as Indonesia. Is that something an application of AI you're considering using in terms of your user interface? Okay, for now we're not really into there yet because you are definitely correct. So developing that kind of the library for like Indonesia because the different dialect, different accent is kind of tough. So the AI things that we are looking for is actually is going to be like a product recommendation engine because you know let's say there are a lot of things on top of this customer 360 degree that we can do right? Because meaning is going to open unlimited opportunity how I can engage to the customers what kind of the right offer because there are a lot of brand owners like FMCG that they would like to connect like also like getting in touch reach out our customers. So by developing this kind of the like product recommendation engine let's say like using the typical machine learning. So we can understand when we introduce this thing customer like it, introduce that thing they don't like it. Let me ask a next logical question there it's such a big diverse country. Do you, in modeling the customer profile are you able to encode cultural sensitivities? Once again a very diverse country there's probably things you can recommend in terms of products to some peoples that other people might find offensive or insensitive is that something that in terms of modeling the customer you take into consideration? Yeah, can. It doesn't just apply to Indonesia it applies here too or anywhere else where you have many people. Of course can to do that kind of the modeling but what we're doing right now let's say once again speaking about the personalized offer from that point of view what we see is actually to create the definition based on customers spending power first buying power. We need to understand that these customers actually into this which level of the buying power by understanding this kind of buying power level then we really can understand that should we introduce this kind of the offers or not because this is too expensive or not because customer spending level can be also different. Let's say when our customers spend in let's say like our supermarket maybe it's going to be like medium spending level but let's say when they spend their money to purchase the coffee maybe it's regular basis so it's more spending. Could be like different spending so we also need to learn this kind of thing because sometimes that the low spending or medium spending or high spending sometimes it's not something that we put it into the average level for everything sometimes it could be different. So this is the thing that also like very exciting for us to understand this kind of the spending buying power level. You're great to have you on theCUBE thanks for coming on so I got to ask you one final question. I heard you were an honoree Informatica Innovation Award honoree. Congratulations. Thank you. What advice would you have for your peers that might want to aspire to get the award next year? Okay, the thing is our big data journey just start last year really start from the zero. So when yesterday we got an award for the advanced analytics so actually what we really focus it on to do something that actually very simple some organization when they implementing big data sometimes they would like to do everything in the phase one. So what we plan to do is actually number one is how to bring the data like very fast then understand what kind of the failure of the data that we can bring to the organization. Our favorite one is that thing developing the customer 360 degree profile because once you really understand your customer from many point of view is going to open unlimited opportunities how you can engage with your customers. It also opens another opportunity how you can bring another ecosystem to our business to engage with our customers. That one point of view is already like opening a lot of things huge. So after that thinking about what would be the next step of course that API is going to simplify your business in the future like scale, agility and so on. So that's becoming our main focus to allow us to deliver a lot of quick win low hanging fruit at the same time. So I think that that's the thing that makes us that really can within a short period of time can deliver like a lot of things. If you're a Chief Data Officer at Lipo Digital Group thanks for sharing your stories. The Cube we're here. Live in Las Vegas. They're going to be bonding here talking about all the greatness going on there. This is the Cube here in Las Vegas. Stay with us for continuing day two coverage of Informatica World 2018. We'll be right back.


The results of this study suggest that the main reasons for dropping out of the National Social Health Insurance, SHI, Program and PACRA are related to drug availability, unfriendly behavior from providers, and difficulty accessing services due to renting homes. To reduce dropouts, the government should ensure that there is sufficient stock of medicines available in all health facilities, provide training to healthcare providers to improve their attitude towards SHI scheme holders, and create awareness campaigns targeting people who live in rented houses. This article was authored by Praban Sharma, Dipendra Jadav, Narendra Shrestha, and others.


Live from Las Vegas, it's theCUBE. Covering Splunk.conf 19, brought to you by Splunk. Hey, welcome back everyone. It's theCUBE's live coverage here in Las Vegas for Splunk.conf 2019. It's the 10th year of .conf. I mean, two great guests. Rick Fitz, Senior Vice President General Manager, Groups at Splunk and Kartik Rao Vice President Area, GM of Signal FX. The big story is Signal FX, acquired by Splunk, Rick, you sponsored that. Guys, welcome to theCUBE. Great to see you guys again. Yeah, great to be here, Dan. They just broke a world record. They did. For the bike on intro there. Pretty exciting on what's going on here. A lot of records being broken. Splunk just continues to move the needle on capabilities, product, platform, brand messaging. Signal FX, company we've been reporting on since their founding, really in your wheelhouse. You guys bought them for a good number, big number. Why, what's going on? Why the interest in Signal FX? You know, for a long time we've been watching, I would say perhaps patiently, watching the market and the trends. And we were really waiting for a time where the new application architecture was really going to kind of start to take hold. Where this cloud native trend that we've been seeing where people are building applications, where people are actually delivering applications to market in quite a different way, would finally get some escape velocity. And we've been watching patiently for that to occur. And as we saw that last year start to accelerate really, we went out and surveyed the entire market. And of course, at the end of that survey resulted in the acquisition of Signal FX and also of Omniscient. And so we bought those two companies and have combined them to deliver on our vision of what we're trying to do for DevOps. Rick, you and I had a conversation in 2015 here in theCUBE at the.conf at that time. You were on the IoT, you saw this wave. Again, you've been patient. What about IT operations that's happening now that makes this so critical to Splunk? Is IT operation, we know what automation is doing, machine learning toolkit, getting a lot of rave reviews. People love to automate things, but more apps are coming. What's the motivation now? What was the critical linchpin for you to make this happen? Yeah, exactly. I think what we're seeing is in traditional IT operation is this world where developers build these monolithic applications, hand them off to operations and they operate it. And then in the same conversation, you'll get handed over to somebody running if you will, developer engineering or cloud engineering or they have various different levels for it. But you're really dealing with an engineering organization and they're being tasked with digitization of their enterprise. And very strategic investments are being made there, but they're also being asked to build things at high availability, high scalability, and highly reliable with lots of change. So it's kind of the competitive advantage of the enterprise. And as I was seeing that occur more and more, I just saw the distance between IT operations and development kind of separate. And I said, wow, that's interesting because it's being driven by this new application architecture, a cloud native architecture. And I didn't want to be left behind. I wanted to actually be able to build a bridge for IT operations into this future. And I think this future trend is something that's going to be lasting for the next 10, 15, and 20 years. So I think this is very strategic to Splunk and very important for us to get right for the long term. But I also see my role as a part of Splunk is to make sure that we take IT operations into this new world. Because these new worlds, and if you will, the existing worlds, those operating models are quite different. They operate differently, they think differently. In one, they own their code, they're on call. In another one, they're waiting for something to fix and then they try to, you know, we're waiting for something to break and then they fix it. So we're trying to actually help enterprises across that entire gambit. And certainly with security, the theme here at this event is the security event too, on top of everything, right? So this is what it's turned into. Data is driving a lot of security telemetry and data is important for security. So I mean, that's operations. That's right. And your apps have to be secured in both worlds. So I think Splunk has a role to play in helping in this transformation for all of IT as it becomes much more developer centric. And of course, as I said, that is really one of the strategic reasons why we led the acquisition of SignalFX. Well, we're looking forward to seeing how you handle the acquisition. Of course, we think we were fans of the deal. Kartik, I got to ask you, every single company in observability space is going public, you know? So why, you could have gone public. Why Splunk? Why sell to these guys? What made it a fit for you? Well, ultimately, we looked at a number of things or we looked at a number of things in making the decision. We wouldn't have done this with anyone other than Splunk. Just the strategic fit was just so great on so many levels. You know, when we started the company, our goal was to solve the monitoring and observability challenges for anyone building a cloud-native application. And we knew that was going to be a long road. There are going to be a lot of things we needed to invest in and develop. And so we started on the metric side. We layered on distributed tracing. And we took a philosophy that we wanted to build an enterprise-grade, scalable, robust, feature-rich set of technologies. We weren't in the market to build, you know, a SMB kind of very simple, you know, limited type of a product. We really focused on the larger, more sophisticated customers. And so as we looked at continuing to extend our portfolio, one of the things that we needed to invest in was in the logging space. Because, you know, when you think about the trifecta of monitoring data types that you need, you know, logging is a big part of it. And we knew that, you know, we wouldn't be able to go and build a logging system from the ground up that would be robust enough to support enterprise use cases. And so we started a partnership conversation with Rick and team. And it just became very clear through that process that there was a tremendous amount of product fit, vision fit, culture fit, values fit. Like just everything was just so aligned that we realized that we could do so much more together as one company. So, you know, we rounded out the solution portfolio quite, or the technology portfolio quite substantially overnight by becoming a part of Splunk. And then the other part of it too is, you know, we saw as we were dealing with customers, we were dealing mostly with cloud native, cloud first customers. But a lot of the customers that we were, or prospects that we were talking to were more traditional enterprises who were not 100% of the way there yet. Some of them weren't even 10% of the way there yet. And it was difficult for us to really engage in conversations early with them, to help them understand what does it mean to shift from traditional IT ops to DevOps, because we didn't have a relationship with them on the IT ops side of things. And so the other thing that we were really excited about being a part of Splunk is we can be a part of that conversation from the very beginning when the customer, you know, maybe they're just beginning to think about it. And, you know, they don't have the urgency of doing it today, but we can be there with them from the very beginning and help them get there on their timelines. Yeah, this is an interesting discussion point because when you're highlighting, and I know we've had conversations about your company about being a platform, not just a tool, right? So what you're getting at is as you guys started to get more market share, your platform needs, you needed logging, and meet the market leader right here, right? So you guys need them. So partnering's hard when you're trying to build a platform now. You can have a platform that enables partners to build on top of it, but components of a full-baked platform, it's hard to partner. Rick, what's your thoughts and reactions to that because that's my statement, but do you agree with it? It's hard to partner in the platform. It's a core competency. Look, he struggled with logging because he had to build out a boatload of new investment.  Yeah, that's right. And I think that the thing that needs to be stated here is in your large-scale enterprises, they are truly looking for the best of breed, highly-scale environments, right? That we're talking about here.  It was an obvious, you know, choice. And I think that has been the reaction that we've kind of heard universally. Like, this is a good, this is a great idea. This is a really strategic thing that you Splunk folks have actually done. And so that's really encouraging. So I would agree with you. I'm partnering, and we were talking through it, but it's like this is better not to partner in this case. Better together. One of the things that's really important is that logs, you know, that's who we're all about, we've actually spent a lot of time in trying to invest into the streaming world of dealing with things in-stream. And these guys have perfected it for metrics, which is, you know, that's the strategic aspect of this. And then combining what they had already done with tracing, with omniscient, it just doubles down on this future of this application architecture that I mentioned. So most, some M&As have a couple flavors to them. You buy a company, you throw them under a general manager or an executive. They kind of live there. Boundaries lead, you get the core tech. Some team, you know, the scenario is, full team comes in, hits the ground running, they're building out, they're going to own the build out. It seems to me, based upon the omniscient acquisition, you're giving Cartek and team kind of some reign here to go build this out. Is that how you guys see it? Yeah, that's exactly right. And so both Spiros and Cartek report to me, I'm there on boardings are, as it were. But really what we're going to focus on is customer success and achieving our business case and really capitalizing on the opportunity. These guys were running 100 miles an hour and we got to get them to go to 1,000 miles and we're only going to make adjustments to the business case in order to achieve that. And that's what we're here to do, is to shepherd this organization in its entirety to the greatness that I think we all see out there. And we're going to do that in a very careful and cautious way. Cartek, omniscient is an acquisition stealth company. Kind of a commitment, say, hey, here's some more horsepower, talk about how that happened and what's the purpose behind that acquisition. Well, I can let Rick talk to how it happened. Yeah, well, I'll tell him in the end. And I'll talk about that. Yeah, when we surveyed the market, we actually found that people have certain strengths. These guys had actually started their journey into tracing because their first release was like last December and so they'd made some strides. And we kind of found omniscient through this discussion and we weren't like, oh my gosh. And we were in the process of doing the acquisition, doing due diligence.  And that was the, this is another combination that we can't pass up. And when I told him the day we closed, I said, if you had the capital, you would have done this. And he's like, yeah, I would have. One of the things that Rick had asked me during our process was what are the top three things that you would invest in if you had Splunk resources behind you? And I said, microservices, APM, microservices, APM, microservices, APM and so on. And I got a big grin because I obviously couldn't disclose what we were doing. The omniscient team, they were still in stealth so there's not a whole lot out there on the web about them. But it's a phenomenal team. They've got people who are committers on some major open source projects, deeply technical, very, very shared philosophy to what we had at SignalFX in terms of open instrumentation, not having any proprietary locking in how you collect an instrument data. Very similar philosophies around leveraging the power of analytics and monitoring. And we just actually focused on different parts of the problem because we're both relatively early in this effort. So we effectively doubled up the team's capacity overnight and accelerated our roadmap by several quarters. So I'm really excited about what we can do together with them. Well, are they in the Bay Area? Are they from? They're Bay Area, basically, yes. Well, I want to get you guys thoughts on the keynote today. Feedback was authentic, kind of very cool keynote. As you guys bring this together, Rick, Cartigan team, the optics, the messaging, what's the core positioning? As you guys look at the holistic view now that you've invested in and are building out for customers, what's the posture? Take us through the keynote positioning. What's the marketplace customer message around the future here? Yeah, I think it's really clear that what we're trying to do for IT organizations and application development organizations is build solutions that are modern and helpful to their core mission. And by the way, as I mentioned, in the world of new development, it's different. It's a different solution set. It's a different approach, a different operating model than it is in current IT operations. And so one of the key messages we wanted to resonate is that we have the right solutions in both of these worlds for you and that we're trying to develop an operating model of reactive response or quick response or engaging the right person in the problem through our use of VictorOps, for example, and using that as a way to be very intelligent about how we educate the people that are engaging in a resolution process. So we're trying to create a bridge to both worlds so that they can both be successful. And then underpin that, of course, with automation that can be leveraged in both worlds as well. So that's what we were trying to convey. We know it's early days. By the way, these guys have been with the company for three weeks, so it's kind of like, wow. Culture shock. Throw in the deep water.  And they did phenomenal, by the way. But that was kind of the key message. And we're so excited because we feel like we're just in the first inning of perhaps a 19 or 20 inning game because I think it's going to be a lot of fun. And it's going to be close out there, but we're real excited to be able to bring this to market. I mean, it's amazing coming in now three weeks and to see the breadth of technology that's available in the Splunk platform. And what struck me today, watching the keynote, was just across, it's such a feature rich and such a broad platform from everything in with the core index and capabilities that everyone's known about a long time. All of the ML, the additional capabilities we're going to bring in on the metric side. And then the use cases just across every persona is just, there's so much that we can do. What do you think of the culture? They've run hard, they're a playful company, they like to work hard, play hard, but they also are focused on real customer value, got a great engaged community. What's your take of the culture? Yeah, absolutely. I mean, culture fit was a really important part for us, right, if we're going to be acquired by a company and be a part of a larger organization. They're kindred spirits, I feel, to the way we ran SignalFX. It's a very customer focused organization, great technology and engineering culture. And it's hard to find both, right? Where it feels like every organization is very important and very well respected. It's not like heavily skewed to, it's just all about engineering, it's all about sales. It's a very balanced culture and it's a very customer focused. Guys, congratulations. Big deal, they don't see these kind of mega deals. They come along once in a while. It's a big bet. Good luck with everything, Rick. Thanks for coming on. Final question for both of you. What's the big takeaway to take back to the office as you leave.com this week? What's going to resonate the most with you guys that you're going to take back as feedback? Yeah, for me, it's, you know, I get my energies from customer conversations. We all do here at Splunk. If you're having a bad gig, go talk to a customer and then they walk you and stop you in the hall and say, you know, we really thank you again for doing what you do. And so it just, I take back from this always that what we do matters and is important and just keep chugging along at it because we're doing some really good work out there that's really helping lives. And that's really important. That's good therapy. When you're having a bad day, talk to a customer. Go talk to a customer. I love you guys. What's your takeaway? I'm just, I'm thrilled that the number of customers are coming up to me and saying how excited they are about the acquisition and working with us. And, you know, that's, it's very reaffirming for me. And, you know, it's just, it's super exciting to see what we will have. You guys had a great tech following. A lot of tech leaders who knew you guys, knew you had good stuff. So congratulations. Great validation. Good job. Thanks guys for coming on theCUBE. Great insight. Thanks for sharing all that data. Data to everywhere here in theCUBE. I'm John Furrier, more coverage after this short break.