>> Announcer: Live from Las Vegas, it's the Cube. Covering Informatica World, 2018. Brought to you by Informatica. >> Okay welcome back everyone, this is the Cube live here in Las Vegas for Informatica World 2018 exclusive coverage of the Cube. I'm John Furrier co-host of the Cube with Jim Kobielus, my co-host this segment and with that we'll keep on continue with the Cube. Our next guest is Vira Shanti who is the chief data officer at Lippo Digital Group, welcome to the Cube. >> Thank you so much, 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. Thanks for coming on. I know your chief data officer, we've got a lot of questions 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 Vira, my role is the chief data officer. Responsibility, that actually is cover for the big data transformation for the Lippo group data. Lippo group is actually part of the one of the largest in Indonesia, we serve a middle class for the consumer services, so we are connecting I think more than 120 million of the customers. What's Lippo as a group doing is actually we do many things. We are the largest of the hospital in Indonesia or just super market, we do department stores, coffee shop, cinema, data centers. We on bang as well, news, cable TV, what else? >> You have a lot of digital assets. >> What you do is you drive to any state in Indonesia and you see Lippo everywhere. >> Yeah, education as well, from the kindergarten to the university, that's why it's a lot of diversity of the business, that owned by Lippo. But recently we're endorsing a lot in the digital transformation, so we're releasing a new mobile app, it is called OVO, O, V, O. Actually it's like centralized loyalty E money to providing the priority bills to all the Lippo group customers, so they're not going to maintain their own membership loyalty program, it's going to just like the OVO, so it's not only being accepted by Lippo ecosystem, but also to the external ecosystem as well. We start to engage with the machine partner, we just today sorted like reaching out 30000 machine outlets. >> Let's 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 let's connect the dots, cuz we've heard a lot of customers here talking about being having data all over the place. How does deep learning, why do you catalog everything? If you've always diverse assets, I'm sure there are different silos. Is there a connection, how are you handling? >> Okay, differently it's not easy job to do, implementing big data for this kind of a lot of diversity of the business, because 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, how they run the hospital, how they run the supermarket, how they run the cinema, how they run the coffee shop. By understanding this thing, my team is responsible to transform, not start from the calling the data, cleansing the data, transform the data, then generate the insight. It has to be an action inside. Then we also not only doing the BI things, but also how from their data we can developing the analytical product on top of the technology big data, that we own today. What we deliver is actually beyond the BI. Of course we do a lot of thing, for example, we really focusing in doing the customers 360 degree profile, because that's the only reason how we really can understand out customers. Today, we have more than 100s of customer attribute teaching for individual customers. I can understand what's your profile for the purchasing behaviors, what kind of the product, that you like. Let's say for the data coming from the supermarket, I know what's your brands, your favorite, whether you're spending is declining. How you spend your point, part of the loyalty program. Then many things, so by understanding very deep these, that we can engage with customers in the better way in providing the new customer experience, because we not only let's say providing them with the right deals, but also when would be the right time, we should connect to them providing something, that they might need. This is the way how from the data we try to connect with our customers. >> Yeah, provided more organic experience across the entire portfolio of Lippo brands throughout the ecosystem. It doesn't feel to the customer and so it isn't simply a federation of brands, it's one unified brand in some degree from the customer's point of view delivering value, that each of the individual components of the Lippo portfolio may not be able to provide. >> Yes, yes, so many things actually we can do on top of that 360 degree of the customers. Our big data outcome in the form of the API. Why it has to be in the API, because when we interact with the customer, there could be unlimited customer touch point to call this API. It could be like the mobile apps after smart customer touch point or could be the dashboard, that we develop for our Lippo internal business. Could be anything or even we can also connect to the other industry from the different business, then how we can connect each other using that big data API, so that's why-- >> Is it an ecosystem, isn't that one API, or it's one API, when unified API for accessing all the back end data and services? >> For something like this, there are to type of the API, that we develop, number one is the API, that belong to the customer 360 degree. Every entry would then attach to your profile and say we can convert it to the API. Let's say smart apps, as part of customer touch point, for example like OVO, we would like to engage with our customers, meaning, that the apps can just designing their online business orchestration, then calling a specific API by understanding let's say from the point of view of loyalty or product preference, that you like, so that then what kind of offers, that we need to push to the customer touch point general using the OVO apps. Or even let's say other supermarket have their on apps, so the apps can also following our API based on their data to understand what kind of the brand or the preference probably they like. Let's run in their apps, when the customer connects, it's going to be something, that really personalized. That's why it's in order to manage the future, actually it's very important for us to deliver this big data outcome in the form of the API. >> It scales too, 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, in terms of privacy in the use of personally identifiable information, different countries and regions have their own different policies and regulations, clearly the European union is fairly strict, the European union with GDPR coming along, the US has its own privacy mandates, in Indonesia, are there equivalent privacy regulations or laws, that we require for example. You ask the customers to consent to particular uses of their data, that you're managing with your big data system, that sits behind OVO. Is that something in your overall program, that you reflect? >> Yes, there are some regulation in Indonesia governed by the government, they'll call having their own regulation, but we let's say part of the thing, that, yes, there is a specific regulation. But regulation for the retail is not really that clear yet for now, but we put ourself in the higher restricted regulation, that we put in place as part of our data protection, part of our data governance compliance as well. If until we do this demonetization or consolidating this data, there is no data, that's being shared outside the entity of the organization. Because let's say, when we do that demonetization everything's done by system to system, when it's called the API, so there is no hands off for other customer in individual data. Let's say if our partner FMCG digital agency or even advertiser, future wise they would like to call our API, what they can see, but that target lead of the customers, that they would like to connect is actually not individual of the data. It's going to be in the aggregated format. 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 setup nicely. Now that the setup with Informatica and the work you're doing, how are you interfacing with developers, cuz now you have the API. Is it just API based, are you looking at containers, kubernetes, clout 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 the ecosystem of the Lippo internals, how the customer touch point can leverage the API. Then for the external, for example, like FMCG, the digital agency, when they call our API, usually it's like they can subscribe, there could be some kind of the business model 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 this API things? We develop our own API system, we develop our API gateway, in simple thing, that actually how to put the permission or grant the access of any kind of digital channel, when they consumer our API and what kind of subscription meta? What we did for the big data actually is not really into, we investing a lot of technology in place for us to use. The thing, that makes my team so exciting about this transformation, because we like to create something, that's we create our own API gateway. We create some analytic product on top of the technology, that we have today. >> When they subscribe to the API, you're setting policy for the data, that they can get and you're done. >> Something like that. >> You automated that. Cool, well we see a lot of AI, any machine learning in your future, 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, Clair, you tapping into any of the goodness? >> Yes, if everybody like to talk what AI right? >> John: You got API, you're good, you don't need anything. >> Many organization, when they're really implementing big data, sometimes they start jumping, 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's important for us is how we really can bring the data to single analytical platform, developing that 360 degree customer profile, because we really need to understand our customer better. Then thinking about how we can connect with them, how we can bring the new experience and especially at the right time. >> Actually let me break down AI, cuz I cover AI for Wiki bond, it's such an enormous topic, I break it down in specific things, like for example, speech recognition for voice activated access to digital assistance, that might be embedded in a mobile phones. Indonesia is a huge diverse country, it's an acapela, you have many groups living under the 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 not really into there yet, because you are definitely correct. Developing that kind of library for Indonesia, because different dialect, different accent, it's tough, so the AI things, that we're looking for is actually going to be product recommendation engine. Because you know, let's say, that a lot of things on top of this customer 360 degree, that we can do, right? Because meaning it's going to open unlimited opportunity how I can engage to the customers, what kind of the right offer. Because there's a lot of brand owners, like FMCG, that they would like to connect, also getting in touch, reach out our customers. By developing this kind of product recommendation engine, let's say 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 the 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 could 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? 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 the modeling, but we're doing right now, let's say once again, speaking about the personalized offer, from that point of view, what we see is to create the definition based on customer spending power first, buying power, we need to understand, that this customer's actually in 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 our supermarket, maybe it's going to 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 different spending, so we also need to learn this kind of thing, because sometimes the low spending or medium spending or high spending, sometimes it's not something, that we put in the effort level for everything, sometimes it could be different. This is the thing, that also very exciting for us to understand this kind of spending, buying power. >> Great to have you on the Cube, thanks for coming, so I got to ask you one final question. I heard you were in an honorary 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? >> The thing is, our big data journey just start last year. Really start from the zero, so when yesterday we get an award for the analytics, so actually what we really focus on to do something, that actually is very simple. Some organization, when they're implementing big data sometimes they would like to do everything in the phase one. What we're planning to do is number one, how to bring the data very fast, then understand what kind of value of the data, that we can bring to the organization. Our favorite one is developing the customer 360 degree profile, because once you really understand your customer from any point of view, it's going to open unlimited opportunities how you can engage with your customers, it also open another opportunity how you can bring another ecosystem to our business to engage with our customers, that one point of view is already opening a lot of thing, huge. Either that thinking what would be the next step. Of course, that API is going to simplify your business in the future scale so on. That's becoming our main focus to allow us to deliver a lot of quick low hanging effort at the same time. I think that's a thing, that makes us really can, within a short period of time, can deliver a lot of things. >> The chief data officer at Lippo digital group, thanks for sharing your story, it's 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.

>> Narrator: Live, from San Jose, it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners (mellow electronic music) >> Welcome back to theCUBE, on day two of our coverage of our event, Big Data SV. I'm Lisa Martin, my co-host is Peter Burris. We are the down the street from the Strata Data Conference, we've had a great day yesterday, and great morning already, really learning and peeling back the layers of big data, challenges, opportunities, next generation, we're welcoming back to theCUBE an alumni, the CMO of Kinetica, Dan Raskin. Hey Dan, welcome back to theCUBE. >> Thank you, thank you for having me. >> So, I'm a messaging girl, look at your website, the insight engine for the extreme data economy. Tell us about the extreme data economy, and what is that, what does it mean for your customers? >> Yeah, so it's a great question, and, from our perspective, we sit, we're here at Strata, and you see all the different vendors kind of talking about what's going on, and there's a little bit of word spaghetti out there that makes it really hard for customers to think about how big data is affecting them today, right? And so, what we're actually looking at is the idea of, the world's changed. That, big data from five years ago, doesn't necessarily address all the use cases today. If you think about what customers are going through, you have more users, devices, and things coming on, there's more data coming back than ever before, and it's not just about creating the data driven business, and building these massive data lakes that turn into data swamps, it's really about how do you create the data-powered business. So when we're using that term, we're really trying to call out that the world's changed, that, in order for businesses to compete in this new world, they have to think about to take data and create CoreIP that differentiates, how do I use it to affect the omnichannel, how do I use it to deal with new things in the realm of banking and Fintech, how do I use it to protect myself against disruption in telco, and so, the extreme data economy is really this idea that you have business in motion, more things coming online ever before, how do I create a data strategy, where data is infused in my business, and creates CoreIP that helps me maintain category leadership or grow. >> So as you think about that challenge, there's a number of technologies that come into play. Not least of which is the industry, while it's always to a degree been driven by what hardware can do, that's moderated a bit over time, but today, in many respects, a lot of what is possible is made possible, by what hardware can do, and what hardware's going to be able to do. We've been using similar AI algorithms for a long time. But we didn't have the power to use them! We had access to data, but we didn't have the power to acquire and bring it in. So how is the relationship between your software, and your platform, and some of the new hardware that's becoming available, starting to play out in a way of creating value for customers? >> Right, so, if you think about this in terms of this extreme data concept, and you think about it in terms of a couple of things, one, streaming data, just massive amounts of streaming data coming in. Billions of rows that people want to take and translate into value. >> And that data coming from-- >> It's coming from users, devices, things, interacting with all the different assets, more edge devices that are coming online, and the Wild West essentially. You look at the world of IoT and it's absolutely insane, with the number of protocols, and device data that's coming back to a company, and then you think about how do you actually translate this into real-time insight. Not near real-time, where it's taking seconds, but true millisecond response times where you can infuse this into your business, and one of our whole premises about Kinetica is the idea of this massive parallel compute. So the idea of not using CPUs anymore, to actually drive the powering behind your intelligence, but leveraging GPUs, and if you think about this, a CPU has 64 cores, 64 parallel things that you can do at a time, a GPU can have up to 6,000 cores, 6,000 parallel things, so it's kind of like lizard brain verse modern brain. How do you actually create this next generation brain that has all these neural networks, for processing the data, in a way that you couldn't. And then on top of that, you're using not just the technology of GPUs, you're trying to operationalize it. So how do you actually bring the data scientist, the BI folks, the business folks all together to actually create a unified operational process, and the underlying piece is the Kinetica engine and the GPU used to do this, but the power is really in the use cases of what you can do with it, and how you actually affect different industries. >> So can you elaborate a little bit more on the use cases, in this kind of game changing environment? >> Yeah, so there's a couple of common use cases that we're seeing, one that affects every enterprise is the idea of breaking down silos of business units, and creating the customer 360 view. How do I actually take all these disparate data feeds, bring them into an engine where I can visualize concepts about my customer and the environment that they're living in, and provide more insight? So if you think about things like Whole Foods and Amazon merging together, you now have this power of, how do I actually bridge the digital and physical world to create a better omnichannel experience for the user, how do I think about things in terms of what preferences they have, personalization, how to actually pair that with sensor data to affect how they actually navigate in a Whole Foods store more efficiently, and that's affecting every industry, you could take that to banking as well and think about the banking omminchannel, and ATMs, and the digital bank, and all these Fintech upstarts that are working to disrupt them. A great example for us is the United States Postal Service, where we're actually looking at all the data, the environmental data, around the US Postal Service, we're able to visualize it in real-time, we're able to affect the logistics of how they actually navigate through their routes, we're able to look things like postal workers separating out of their zones, and potentially kicking off alerts around that, so effectively making the business more efficient. But, we've moved into this world where we always used to talk about brick and mortar going to cloud, we're now in this world where the true value is how you bridge the digital and physical world, and create more transformative experiences, and that's what we want to do with data. So it could be logistics, it could be omnichannel, it could be security, you name it. It affects every single industry that we're talking about. >> So I got two questions, what is Kinetica's contribution to that, and then, very importantly, as a CMO, how are you thinking about making sure that the value that people are creating, or can create with Kinetica, gets more broadly diffused into an ecosystem. >> Yeah, so the power that we're bringing is the idea of how to operationalize this in a way where again, you're using your data to create value, so, having a single engine where you're collecting all of this data, massive volumes of data, terabytes upon terabytes of data, enabling it where you can query the data, with millisecond response times, and visualize it, with millisecond response times, run machine learning algorithms against it to augment it, you still have that human ability to look at massive sets of data, and do ad hoc discovery, but can run machining learning algorithms against that and complement it with machine learning. And then the operational piece of bringing the data scientists into the same platform that the business is using, so you don't have data recency issues, is a really powerful mix. The other piece I would just add is the whole piece around data discovery, you can't really call it big data if, in order to analyze the data, you have to downsize and downsample to look at a subset of data. It's all about looking at the entire set. So that's where we really bring value. >> So, to summarize very quickly, you are providing a platform that can run very, very fast, in a parallel system, and memories in these parallel systems, so that large amounts of data can be acted upon. >> That's right. >> Now, so, the next question is, there's not going to be a billion people that are going to use your tool to do things, how are you going to work with an ecosystem and partners to get the value that you're able to create with this data, out into the engine enterprise. >> It's a great question, and probably the biggest challenge that I have, which is, how do you get above the word spaghetti, and just get into education around this. And so I think the key is getting into examples, of how it's affecting the industry. So don't talk about the technology, and streaming from Kafka into a GPU-powered engine, talk about the impact to the business in terms of what it brings in terms of the omnichannel. You look at something like Japan in the 2020 Olympics, and you think about that in terms of telco, and how are the mobile providers going to be able to take all the data of what people are doing, and to related that to ad-tech, to relate that to customer insight, to relate that to new business models of how they could sell the data, that's the world of education we have to focus on, is talk about the transformative value it brings from the customer perspective, the outside-in as opposed to the inside-out. >> On that educational perspective, as a CMO, I'm sure you meet with a lot of customers, do you find that you might be in this role of trying to help bridge the gaps between different roles in an organization, where there's data silos, and there's probably still some territorial culture going on? What are you finding in terms of Kinetica's ability to really help educate and maybe bring more stakeholders, not just to the table, but kind of build a foundation of collaboration? >> Yeah, it's a really interesting question because I think it means, not just for Kinetica, but all vendors in the space, have to get out of their comfort zone, and just stop talking speeds and feeds and scale, and in fact, when we were looking at how to tell our story, we did an analysis of where most companies were talking, and they were focusing a lot more on the technical aspirations that developers sell, which is important, you still need to court the developer, you have community products that they can download, and kick the tires with, but we need to extend our dialogue, get out of our customer comfort zone, and start talking more to CIOs, CTOs, CDOs, and that's just reaching out to different avenues of communication, different ways of engaging. And so, I think that's kind of a core piece that I'm taking away from Strata, is we do a wonderful job of speaking to developers, we all need to get out of our comfort zone and talk to a broader set of folks, so business folks. >> Right, 'cause that opens up so many new potential products, new revenue streams, on the marketing side being able to really target your customer base audience, with relevant, timely offers, to be able to be more connected. >> Yeah, the worst scenario is talking to an enterprise around the wonders of a technology that they're super excited about, but they don't know the use case that they're trying to solve, start with the use case they're trying to solve, start with thinking about how this could affect their position in the market, and work on that, in partnership. We have to do that in collaboration with the customers. We can't just do that alone, it's about building a partnership and learning together around how you use data in a different way. >> So as you imagine, the investments that Kinetica is going to make over the next few years, with partners, with customers, what do you hope Kinetica will be in 2020? >> So, we want it to be that transformative engine for enterprises, we think we are delivering something that's quite unique in the world, and, you want to see this on a global basis, affecting our customer's value. I almost want to take us out of the story, and if I'm successful, you're going to hear wonderful enterprise companies across telco, banking, and other areas just telling their story, and we happen to be the engine behind it. >> So you're an ingredient in their success. >> Yes, a core ingredient in their success. >> So if we think about over the course of the next technology, set of technology waves, are they any particular applications that you think you're going to be stronger in? So I'll give you an example, do you envision that Kinetica can have a major play in how automation happens inside infrastructure, or how developers start seeing patterns in data, imagine how those assets get created. Where are some of the kind of practical, but not really, or rarely talked about applications that you might find yourselves becoming more of an ingredient because they themselves become ingredients to some of these other big use cases? >> There are a lot of commonalities that we're starting to see, and the interesting piece is the architecture that you implement tends to be the same, but the context of how you talk about it, and the impact it has tends to be different, so, I already mentioned the customer 360 view? First and foremost, break down silos across your organization, figure out how do you get your data into one place where you can run queries against it, you can visualize it, you can do machine learning analysis, that's a foundational element, and, I have a company in Asia called Lippo that is doing that in their space, where all of the sudden they're starting to glean things they didn't know about their customer before to create, doing that ad hoc discovery, so that's one area. The other piece is this use case of how do you actually operationalize data scientists, and machine learning, into your core business? So, that's another area that we focus on. There are simple entry points, things like Tableau Acceleration, where you put us underneath the existing BI infrastructure, and all of the sudden, you're a hundred times faster, and now your business folks can sit at the table, and make real-time business decisions, where in the past, if they clicked on certain things, they'd have to wait to get those results. Geospatial visualization's a no-brainer, the idea of taking environmental data, pairing it with your customer data, for example, and now learning about interactions. And I'd say the other piece is more innovation driven, where we would love sit down with different innovation groups in different verticals and talk with them about, how are you looking to monetize your data in the future, what are the new business models, how does things like voice interaction affect your data strategy, what are the different ways you want to engage with your data, so there's a lot of different realms we can go to. >> One of the things you said as we wrap up here, that I couldn't agree with more, is, the best value articulation I think a brand can have, period, is through the voice of their customer. And being able to be, and I think that's one of the things that Paul said yesterday is, defining Kinetica's success based on the success of your customers across industry, and I think really doesn't get more objective than a customer who has, not just from a developer perspective, maybe improved productivity, or workforce productivity, but actually moved the business forward, to a point where you're maybe bridging the gaps between the digital and physical, and actually enabling that business to be more profitable, open up new revenue streams because this foundation of collaboration has been established. >> I think that's a great way to think about it-- >> Which is good, 'cause he's your CEO. >> (laughs) Yes, that sustains my job. But the other piece is, I almost get embarrassed talking about Kinetica, I don't want to be the car salesman, or the vacuum salesman, that sprinkles dirt on the floor and then vacuums it up, I'd rather us kind of fade to the behind the scenes power where our customers are out there telling wonderful stories that have an impact on how people live in this world. To me, that's the best marketing you can do, is real stories, real value. >> Couldn't agree more. Well Dan, thanks so much for stopping by, sharing what things that Kinetica is doing, some of the things you're hearing, and how you're working to really build this foundation of collaboration and enablement within your customers across industries. We look forward to hearing the kind of cool stuff that happens with Kinetica, throughout the rest of the year, and again, thanks for stopping by and sharing your insights. >> Thank you for having me. >> I want to thank you for watching theCUBE, I'm Lisa Martin with my co-host Peter Burris, we are at Big Data SV, our second day of coverage, at a cool place called the Forager Tasting Room, in downtown San Jose, stop by, check us out, and have a chance to talk with some of our amazing analysts on all things big data. Stick around though, we'll be right back with our next guest after a short break. (mellow electronic music)