 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. All you have to do is drive down any street in Indonesia and you see LiPo everywhere, yes. 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.