>> Live from San Francisco, it's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Hey everyone, welcome back to the live coverage here in Moscone North in San Francisco for IBM Think. This is theCUBE's coverage. I'm here with Dave Vellante. I've got two great guests here, Carlos Guevara, chief data officer, Claro Columbia, and Carlo Appugliese- Appugliese? >> Appugliese, yeah, good. That's good. >> Engagement Manager, IBM's Data Science Elite Team, customer of IBM, conversation around data science. Welcome to theCube, thanks for joining us. >> Thanks for having us. >> Thank you. >> So we're here the streets are shut down. AI Anywhere is a big theme, Multi-Cloud, but it's all about the data everywhere. People trying to put end-to-end solutions together to solve real business problems. Data's at the heart of all this. Moving data around from cloud to cloud, using AI and technology to get insights out of that. So, take a minute to explain your situation, what you guys are trying to do. >> Okay, okay, perfect. Right now we're working a lot about the business theme, because we need to use the machine learning models or the artificial intelligence, to take best decisions for the company. We were working with Carlo and Sean Muller in order to know how can we divide the customers who leave the company. Because, for us, it's very important, to maintain our customer, to know how their behavior is from them, and their artificial intelligence is an excellent way to do it. We have a lot of challenge about that, because, you know, we have a lot of data, different systems that are running the data, but we need to put all the information together to run the models. The Elite Team that Carlo is leading right now is helping us a lot because, we know how to handle data, we know how to clean the data, we know how to do the right governance for the data and the IBM Equinix is very compromised with us in order to do that. Sofie, that is one of the engineers that is very close to us right now. She was working a lot with my team in order to run the models. Susan, she was doing a lot for our middleware, FITON, and right now we are trained to do it in over the Hadoop system, running the spark, and that is the good way that we are thinking that it's going to get the goal for us We need to maintain our customers. >> So you guys are the largest telecommunications piece, Claro in Mexico for voice and home services-- >> Yeah. >> Is that the segments you guys are targeting? >> Yeah, yeah. >> And the scope size of, how big is that? >> Claro is the largest company in Columbia for telecommunications. We have maybe 50 million customers in Columbia, more than 50% of their market share. Also, where we have many, maybe 2.5 millions of homes in Columbia, that is more than the 50% of the customers for home services. And you know that is a big challenge for us because the competitors are all the time trying to take our customers and the churn, it also adds to us, and how to avoid that and how to do the artificial intelligence to do it, machine learning is a very good way to do that. >> So, classic problem in telecommunications is churn, right, so it's a data problem, so how did it all come about? So these guys came to you and-- >> Yeah, so they came to us, and we got together, we talked about the problem, and churn was at the top, right, these guys have a ton of data. So what we did was, the team got together, we had, really the way the Data Science Elite Team works is we really help clients in three areas. It's all about the right skills, the right people, the right tools, and then the right process. So we put together a team, we put together some Agile approaches, and what we're going to do, and then we started by spinning up an environment, we took some data in, we took their, and there as a lot of data, it as a terabytes of data. We took their user data, we took their users' usage data, which is like how many texts, cellphone, and then billing data, we pulled all that together in an environment, then the data scientists, alongside with Carlos' team, really worked on the problem. And they addressed it with machine learning obviously, targeting churn, they tried a variety of models, but XGBoost ended up being one of the better approaches. And we came up with pretty good accuracy, about 90, 92% precision on the model. >> On predicting-- >> On predicting churn-- >> Yeah, churn, and also, what did you do with that data? >> That is a very good question because, the company is preparing to handle that. I have a funny history, I say to the business people, okay these customers are going to leave the company, and I forget about that, and two months later, I was asking okay, what happened, they say okay, your model is very good, all the customers goes. Oh my God, what is happening with that? They weren't working with information, that is the reason we're thinking that the good ways to think from the right to the left, because which is the purpose, the purpose is to maintain our customers, and in that case we lose 50,000 customers because we didn't do nothing. We are close in the circle, we are taking care about that, prescriptive models to have helped for us to do it. And okay, maybe that is an invoice problem, we need to correct them, to fix the problem, in order to avoid that, but the first part is to predict, to get in a score, for the churn, and to handle that with the people. Obviously, working also, at the root cause analysis, because we need the churn to fix from the root. >> Carlos, what goes through the scope of, like, just the project because, this is a concern we see in the industry, I got a lot of data, how do I attack it, what's the scope? You just come in, ingest it into a data lake, how do you get to the value of these insights quickly, because obviously they are starving for insights, take us through that quick process. >> Well, you know, every problem's a little different, we help hundreds of clients in different ways, but this particular problem, it was a big data problem, we knew we had a lot of data, they had a Hadoop environment, but some of the data wasn't there. So what we did was, is we spun up a separate environment, we pulled some of the big data in there, we also pulled some of the other data together, and we started to do our analysis on that, kind of separately in the cloud, which was a little different, but we're working now to push that down into their Hadoop data lake, because not all the data's there, but some of the data is there, and we want to use some of that computing network to-- >> So you had to almost do an audit on those, figure out what you want to pull in first, >> Absolutely. >> Tie it to the business, on the business side, what were you guys like? Waiting for the answers, or like, what was some of the, on your side of the process, how did it go down? >> Thinking about our business, we were talking a little bit about that, about the architecture to handle that, ICP for Data within that is a very good solution for that, because we need infrastructure to help us, in order to get the answers because finally, we have a question, we have questions, why the customers are leaving us. And, the answer was the data, and the data was handled in a good way, with governance, with data cleaning, with the right models to do that, and right now, our concern is business action, and business offer, because the solution for the company is that we, obviously new products are coming from the data. >> So 10 years ago, you probably didn't have a Hadoop cluster to solve this problem, the data was, maybe it was in a data warehouse, maybe it wasn't, and you probably weren't a chief data officer back then, you know, that role kind of didn't exit. So a lot has changed, in the last 10 years. My question is, do you, first of all, I'd be interested in your comment on that, but then, do you see a point in which you can now take remedial action, or maybe even automate some of that remedial action using machine intelligence and that data cloud, or however else you do it, to actually take action on behalf of the brand, before humans, or without even human involvement, did you foresee the day? >> Yeah, so, just a comment on your thought about the times you know, I've been doing technology for 20 something years, and you know, data science is something that's been around but it's kind of evolved in software development. My thought is, you know, we have these roles of data scientist, but a lot of the feature engineer and data prep does require traditional people that were DBAs and now data engineers, and a variety of skills come together, and that's what try to do in every project. Just to add to that comment. As far as predicting ahead of time, like I think you were trying to say, what data, help me understand your question. >> So you've got 93% accuracy, okay, so, I presume you take that, you give it to the business, business says okay, let's maybe, you know, reach out to them, maybe do a little incentive, or, what kind of action can the machines take action on behalf of your brand, do you foresee a day when that could happen? >> Ah. >> Ah, okay. >> Yeah, so my thought is, for Claro Columbia and Carlos, but obviously this is, to me, remain, is the predictive models we've built will obviously be deployed, and then it would interact with their digital mobile applications, so in real time it'll react for the customers. And then, obviously, you know you want to make sure that Claro and company trust that, and it's making accurate predictions, and that's where a lot more, you know we have to do some model of validation, and evaluation of that, so they can begin to trust those predictions. I think is where we're-- >> Guys. I want to get your thoughts on this because you're doing a lot of learnings here. So can you guys each take a minute and explain the key learnings from this, as you go through the process, certainly in the business side, this is a big imperative to do this. You want to have a business outcome that keeps your users there. But what did you learn, what was some of the learnings you guys got from the project? >> The most important learning from the company was cleaning the data, that sounds funny but, as we say in analysis, garbage in, garbage out. And that was very important for us, one of the things that we learned, that we need to put cleaning data or the system. Also, the governance. Many people forget about the governance, the governance of the data. And right now we're working, again with IBM, in order to put that governance soon. >> So data quality problem. >> Yeah, data quality. >> And, do you report into I guess, COO or the CIO, are you a peer of the CIO, how does that work? >> Oh, okay, that's another funny history because, because the company is, right now I'm working for planning. Yes, it's strange, we're working for planning for the company-- >> For business planning. >> Yeah, for business planning. >> I was coming for an engineer, engineering, and right now I'm working for planning, and trying to make money for the company. You know, that is an engineer thinking how to get more money for the company. I was talking about some kind of analytics that is geospatial analytics, and I went to see that engineer to know how their network's handling, how the quality of the network and right now introducing the same software, the same knowledge, to know which is the better points to do sales. It's a good combination where finally I'm working for planning, and my boss, the planning chief, is working for the CEO. And I hear about different organizations, somebody's in financial, the CDO's in financial, or the CDO for IT, it's different, it depends on the company. Right now, I'm working for planning, how to handle the things, how to make more money for the company, how to handle the churn, and it's interesting because all the knowledge that I have from engineering is perfect to do it. >> Well, I would argue that's the job of a CDO, is to figure out how to make money with data, or save money, right? >> Yeah. >> Yeah, absolutely. >> So it's number one, anyway, is start there. >> Yeah, the thing we always talk about it is, is really proving value, it starts with that use case, identify where the real value is, and then we can, you know, the technology can come and the development can work after that. So I agree 100% with that, is what we're seeing across the board. >> Carlos, thanks for coming in, largest telecommunications in Columbia, great customer reference. >> Carlo, take a minute to explain, real quick, get a plug in for your Data Science Elite Team. What do you guys do, how do you engage, what are some of the projects you work on? >> Right, yeah, so we're a team of about 100 data scientists worldwide, we work side by side with clients, and our job is to really understand the problem from end to end and help in all areas, from skills, tools, and technique. And we roll and prototype, in a three Agile sprints, we use an Agile methodology, about six to eight weeks, and we kind of develop a real, we call it a proof of value. It's not a MVP just yet, or POC, but at the end of the day we prove out that we can get a model, we can do some prediction, we get a certain accuracy, and it's going to add value to the organization. >> It's not a freebie, right? >> It actually is-- >> Sorry, I'm sorry. It's not a four page service, it's a freebie, right? >> Yeah, it's no cost. >> But you got to-- >> We don't like to use free, that's what-- >> But, you got to be saying-- >> It's a good lead. >> Good to discuss that-- >> Well, we don't charge, but >> Largely. >> But it, but it, it's something that clients can take advantage of, if they've got an interesting problem, they're potentially going to do some business with you guys. >> Absolutely. >> If you're the largest telecommunication provider in the country, you get a freebie, and then, the key is, you guys dig in. >> We dig in, it's practitioners, real practitioners, with the right skills, >> Yeah. >> Working on problems. >> Great sales model. >> By the way, Claro Columbia's team, they were amazing in Columbia, we had a really good time, six to eight weeks, you know, working on a problem, and those guys all loved it too, they were-- >> Thank you. >> Before they knew it, they were coding in Python and R, and they had already knew a lot of this stuff, but they're digging in with the team, and it came well together. >> This is the secret to modernization of digital transformation-- >> Yeah. >> Is having the sales process is getting, co-creating together-- >> Absolutely. >> You guys do a great job, and I think this is a trend we'll see more of, of course, TheCUBE is bringing you live coverage here in San Francisco, at Mascone North, that's where our set is. They're shutting down the streets for IBM Think 2019, here in San Francisco. More CUBE coverage after this short break, be right back. (energetic music)