>>from >>around the globe. It's the cube with digital >>Coverage of IBM think 2021 brought to you by IBM. >>Welcome back to IBM Think 2021 The virtual edition. My name is Dave Volonte and you're watching the cubes continuous coverage of think 21. And right now we're gonna talk about banking in the post isolation economy. I'm very pleased to welcome our next guest. Look at wag lee is the general manager, Global banking financial markets at IBM and john Degnan is the global ceo and vice president and distinguished engineer for banking and financial services. Gentlemen, welcome to the cube. >>Thank you. Yeah >>that's my pleasure. Look at this current economic upheaval. It's quite a bit different from the last one, isn't it? I mean liquidity doesn't seem to be a problem for most pecs these days. I mean if anything they're releasing loan loss reserves that they didn't need. What's from your perspective, what's the state of banking today and hopefully as we exit this pandemic soon. >>So so dave, I think, like you say, it's, you know, it's a it's a state and a picture that in a significantly different from what people were expecting. And I think some way, in some ways you're seeing the benefits of a number of the regulations that were put into into place after the, you know, the financial crisis last time around, right? And therefore this time, you know, a health crisis did not become a financial crisis, because I think the banks were in better shape. And also, you know, governments clearly have put worldwide a lot of liquidity into the, into the system. I think if you look at it though, maybe two or three things ready to call out firstly, there's a there's a massive regional variation. So if you look at the U. S. Banking industry, it's extremely buoyant and I'll come back to that in a minute in the way in which is performing, you know, the banks that are starting to report their first quarter results are going to show profitability. That's you know significantly ahead of where they were last year and probably some of the some of their best performance for quite a long time. If you go into europe, it's a completely different picture. I think the banks are extremely challenged out there and I think you're going to see a much bleaker outlook in terms of what those banks report and as far as Asia pacific is concerned again, you know because they they have come out of the pandemic much faster than consumer businesses back into growth. Again, I think they're showing some pretty buoyant performance as far as as far as banking performance is concerned. I think the piece that's particularly interesting and I think him as a bit of a surprise to most is what we've seen in the U. S. Right. And in the US what's actually happened is uh the investment banking side of banking businesses has been doing better than they've ever done before. There's been the most unbelievable amount of acquisition activity. You've seen a lot of what's going on with this facts that's driving deal raised, you know, deal based fee income for the banks. The volatility in the marketplace is meaning that trading income is much much higher than it's ever been. And therefore the banks are very much seeing a profitability on that investment banking side. That was way ahead of what I think they were. They were expecting consumer businesses definitely down. If you look at the credit card business, it's down. If you look at, you know, lending activity that's going down going out is substantially less than where it was before. There's hardly any lending growth because the economy clearly is flat at this moment in time. But again, the good news that, and I think this is a worldwide which are not just in us, the good news here is that because of the liquidity and and some of the special measures the government put out there, there has not been the level of bankruptcies that people were expecting, right. And therefore most of the provisioning that the banks did um in expectation of non performing loans has been, I think, a much more, much greater than what they're going to need, which is why you're starting to see provisions being released as well, which are kind of flattering, flattering the income, flattering the engine. I think going forward that you're going to see a different picture >>is the re thank you for the clarification on the regional divergence, is that and you're right on, I mean, european central banks are not the same, the same position uh to to affect liquidity. But is that nuances that variation across the globe? Is that a is that a blind spot? Is that a is that a concern or the other other greater concerns? You know, inflation and and and the the pace of the return to the economy? What are your thoughts on that? >>So, I think, I think the concern, um, you know, as far as the european marketplace is concerned is um you know, whether whether the performance that and particularly, I don't think the level of provisions in there was quite a generous, as we saw in other parts of the world, and therefore, you know, is the issue around non performing loans in in europe, going to hold the european uh european banks back? And are they going to, you know, therefore, constrain the amount of lending that they put into the economy and that then, um, you know, reduces the level of economic growth that we see in europe. Right? I think, I think that is certainly that is certainly a concern. Um I would be surprised and I've been looking at, you know, forecasts that have been put forward by various people around the world around inflation. I would be surprised if inflation starts to become a genuine problem in the, in the kind of short to medium term, I think in the industry that are going to be two or three other things that are probably going to be more, you know, going to be more issues. Right. I think the first one which is becoming top of mind for chief executives, is this whole area around operational resiliency. So, you know, regulators universally are making very very sure that banks do not have a technical debt or a complexity of legacy systems issue. They are and you know, the U. K. Has taken the lead on this and they are going so far as even requiring non executive directors to be liable if banks are found to not have the right policies in place. This is now being followed by other regulators around the world. Right. So so that is very much drop in mind at this moment in time. So I think discretionary investment is going to be put you know, towards solving that particular problem. I think that's that's one issue. I think the other issue is what the pandemic has shown is that and and and this was very evident to me and I mean I spent the last three years out in Singapore where you know, banks have become very digital businesses. Right? When I came into the U. S. In my current role, it was somewhat surprising to me as to where the U. S. Market place was in terms of digitization of banking. But if you look in the last 12 months, you know, I think more has been achieved in terms of banks becoming digital businesses and they've probably done in the last two or three years. Right. And that the real acceleration of that digitization which is going to continue to happen. But the downside of that has been that the threat to the banking industry from essentially fintech and big tex has exactly, it's really accelerated. Right, Right. Just to give you an example, Babel is the second largest financial services institutions in the US. Right. So that's become a real problem I think with the banking industry is going to have to deal with >>and I want to come back to that. But now let's bring john into the conversation. Let's talk about the tech stack. Look, it was talking about whether it was resiliency going digital, We certainly saw over the pandemic, remote work, huge, huge volumes of things like TPP and and and and and mortgages and with dropping rates, etcetera. So john, how is the tech stack Been altered in the past 14 months? >>Great question. Dave. And it's top of mind for almost every single financial services firm, regardless of the sector within the overall industry, every single business has been taking stock of how they handled the pandemic and the economic conditions thereafter and all of the business needs that were driven by the pandemic. In so many situations, firms were unable to service their clients or we're not competitive in serving their clients. And as a result they've had to do very deep uh architectural transformation and digital transformation around their core platforms. Their systems of analytics and their systems different end systems of engagement In terms of the core processing systems that many of these institutions, some in many cases there are 50 years old And with any 50 year old application platform there are inherent limitations. There's an in flex itty inflexibility. There's an inability to innovate for the future. There's a speed of delivery issue. In other words, it can be very hard to accelerate the delivery of new capabilities onto an aging platform. And so in every single case um institutions are looking to hybrid cloud and public cloud technology and pre packaged a ai and prepackaged solutions from an I. S. V. Ecosystem of software vendor ecosystem to say. As long as we can crack open many of these old monolithic cause and surround them with new digitalization, new user experience that spans every channel and automation from the front to back of every interaction. That's where most institutions are prioritizing. >>Banks aren't going to migrate, they're gonna they're gonna build an abstraction layer. I want to come back to the disruption is so interesting. The coin base I. P. O. Last month see Tesla and microstrategy. They're putting Bitcoin on their balance sheets. Jamie diamonds. Traditional banks are playing a smaller role in the financial system because of the new fin text. Look at, you mentioned Paypal, the striped as Robin Hood, you get the Silicon Valley giants have this dual disrupt disruption agenda. Apple amazon even walmart facebook. The question is, are traditional banks going to lose control of the payment systems? >>Yeah. I mean I think to a large extent that is that has already happened, right? Because I think if you look at, you know, if you look at the experience in ASia, right? And you look at particularly organizations like and financial, you know, in India, you look at organizations like A T. M. You know, very substantial chance, particularly on the consumer payments side has actually moved away from the banks. And I think you're starting to see that in the west as well, right? With organizations like, you know, cloud, No, that's coming out with this, you know, you know, buying out a later type of schemes. You've got great. Um, and then so you've got paper and as you said, strike, uh and and others as well, but it's not just, you know, in the payment side. Right. I think, I think what's starting to happen is that there are very core part of the banking business. You know, especially things like lending for instance, where again, you are getting a number of these Frontex and big, big tech companies entering the marketplace. And and I think the threat for the banks is this is not going to be small chunks of market share that you're going to actually lose. Right? It's it's actually, it could actually be a Kodak moment. Let me give you an example. Uh, you know, you will have just seen that grab is going to be acquired by one of these facts for about $40 billion. I mean, this organization started like the Uber in Singapore. It very rapidly got into both the payment site. Right? So it actually went to all of these moment pop shops and then offered q are based um, 12 code based payment capabilities to these very small retailers, they were charging about half or a third or world Mastercard or Visa were charging to run those payment rails. They took market share overnight. You look at the Remittance business, right? They went into the Remittance business. They set up these wallets in 28 countries around the Asean region. They took huge chunks of business completely away from DBS, which is the local bank out there from Western Union and all of these, all of these others. So, so I think it's a real threat. I think Jamie Dimon is saying what the banking industry has said always right, which is the reason we're losing is because the playing field is not even, this is not about playing fields. Been even write, all of these businesses have been subject to exactly the same regulation that the banks are subject to. Regulations in Singapore and India are more onerous than maybe in other parts of the world. This is about the banking business, recognizing that this is a threat and exactly as john was saying, you've got to get to delivering the customer experience that consumers are wanting at the level of cost that they're prepared to pay. And you're not going to do that by purely sorting out the channels and having a cool app on somebody's smartphone, Right? If that's not funny reported by arcade processes and legacy systems when I, you know, like, like today, you know, you make a payment, your payment does not clear for five days, right? Whereas in Singapore, I make a payment. The payment is instantaneously clear, right? That's where the banking system is going to have to get to. In order to get to that. You need to water the whole stack. And the really good news is that many examples where this has been done very successfully by incumbent banks. You don't have to set up a digital bank on the site to do it. And incumbent bank can do it and it can do it in a sensible period of time at a sensible level of investment. A lot of IBM s business across our consulting as well as our technology stack is very much trying to do that with our clients. So I am personally very bullish about what the industry >>yeah, taking friction out of the system, sometimes with a case of crypto taking the middle person out of the system. But I think you guys are savvy, you understand that, you know, you yeah, Jamie Diamond a couple years ago said he'd fire anybody doing crypto Janet Yellen and says, I don't really get a Warren Buffett, but I think it's technology people we look at and say, okay, wait a minute. This is an interesting Petri dish. There's, there's a fundamental technology here that has massive funding that is going to inform, you know, the future. And I think, you know, big bags are gonna lean in some of them and others, others won't john give you the last word here >>for sure, they're leaning in. Uh so to just to to think about uh something that lick it said a moment ago, the reason these startups were able to innovate fast was because they didn't have the legacy, They didn't have the spaghetti lying around. They were able to be relentlessly laser focused on building new, using the app ecosystem going straight to public and hybrid cloud and not worrying about everything that had been built for the last 50 years or so. The benefit for existing institutions, the incumbents is that they can use all of the same techniques and tools and hybrid cloud accelerators in terms And we're not just thinking about uh retail banking here. Your question around the industry that disruption from Bitcoin Blockchain technologies, new ways of processing securities. It is playing out in every single securities processing and capital markets organization right now. I'm working with several organizations right now exactly on how to build custody systems to take advantage of these non fungible digital assets. It's a hard, hard topic around which there's an incredible appetite to invest. An incredible appetite to innovate. And we know that the center of all these technologies are going to be cloud forward cloud ready. Ai infused data infused technologies >>Guys, I want to have you back. I wish I had more time. I want to talk about SPAC. So I want to talk about N. F. T. S. I want to talk about technology behind all this. You really great conversation. I really appreciate your time. I'm sorry. We got to go. >>Thank you. Thanks very much indeed for having us. It was a real pleasure. >>Really. Pleasure was mine. Thank you for watching everybody's day. Volonte for IBM think 2021. You're watching the Cube. Mhm.

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe, >>my buddy, We're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Hi. Nice to see you, too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the bank. >>Yeah, Um, Webster Bank >>is regional Boston And that again, and New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated saying regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community and, um, are really moving forward. Technology lives. They really want to be a data driven bank, and they want to move into a more robust Bruce. >>Well, we got a lot to talk about. So data driven that is an interesting topic. And your role as data architect. The architecture is really senior vice president data architecture. So you got a big responsibility as it relates to It's kind of transitioning to this digital data driven bank. But tell us a little bit about your role in your organization, >>right? Um, currently, >>today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you >>have uh huh. >>Timely, accurate, complete data on the customer and what's really a great value on off something to offer that or a new product or to help them continue to grow their savings or do and grow their investment. >>Okay. And I really want to get into that. But before we do and I know you're sort of part way through your journey, you got a lot of what they do. But I want to ask you about Cove. It how you guys you're handling that? I mean, you had the government coming down and small business loans and P p p. And huge volume of business and sort of data was at the heart of that. How did you manage through that? >>But we were extremely successful because we have a big, dedicated team that understands where their data is and was able to switch much faster than a larger bank to be able to offer. The TPP longs at to our customers within lightning speeds. And part of that was is we adapted to Salesforce very, for we've had salesforce in house for over 15 years. Um, you know, pretty much, uh, that was the driving vehicle to get our CPP is loans in on and then developing logic quickly. But it was a 24 7 development role in get the data moving, helping our customers fill out the forms. And a lot of that was manual. But it was a It was a large community effort. >>Well, think about that. Think about that too. Is the volume was probably much, much higher the volume of loans to small businesses that you're used to granting. But and then also, the initial guidelines were very opaque. You really didn't know what the rules were, but you were expected to enforce them. And then finally, you got more clarity. So you had to essentially code that logic into the system in real time, right? >>I wasn't >>directly involved, but part of my data movement Team Waas, and we had to change the logic overnight. So it was on a Friday night was released. We've pushed our first set of loans through and then the logic change, Um, from, you know, coming from the government and changed. And we had to re develop our our data movement piece is again and we design them and send them back. So it was It was definitely kind of scary, but we were completely successful. We hit a very high peak and I don't know the exact number, but it was in the thousands of loans from, you know, little loans to very large loans, and not one customer who buy it's not yet what they needed for. Um, you know, that was the right process and filled out the rate and pace. >>That's an amazing story and really great support for the region. New York, Connecticut, the Boston area. So that's that's fantastic. I want to get into the rest of your story. Now let's start with some of the business drivers in banking. I mean, obviously online. I mean, a lot of people have sort of joked that many of the older people who kind of shunned online banking would love to go into the branch and see their friendly teller had no choice, You know, during this pandemic to go to online. So that's obviously a big trend you mentioned. So you know the data driven data warehouse? I wanna understand that. But well, at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change? >>Um, the ability to give the customer what they need at the time when they need it. And what I mean by that is that we have, um, customer interactions in multiple ways, right? >>And I want >>to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look, and also to be able to offer them the next best offer for them. But they're you know, if they want looking for a new a mortgage or looking to refinance or look, you know, whatever it iss, um, that they have that data, we have the data and that they feel comfortable using it. And that's a untethered banker. Um, attitude is, you know, whatever my banker is holding and whatever the person is holding in their phone, that that is the same. And it's comfortable, so they don't feel that they've, you know, walked into the bank and they have to do a lot of different paperwork comparative filling out paperwork on, you know, just doing it on their phone. >>So you actually want the experience to be better. I mean, and it is in many cases now, you weren't able to do this with your existing against mainframe based Enterprise data warehouse. Is is that right? Maybe talk about that a little bit. >>Yeah, we were >>definitely able to do it with what we have today. The technology we're using, but one of the issues is that it's not timely, Um, and and you need a timely process to be able to get the customers to understand what's happening. Um, you want you need a timely process so we can enhance our risk management. We can apply for fraud issues and things like that. >>Yeah, so you're trying to get more real time in the traditional e g W. It's it's sort of a science project. There's a few experts that know how to get it. You consider line up. The demand is tremendous, and often times by the time you get the answer, you know it's outdated. So you're trying to address that problem. So So part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity. Other other offers that you can you can make to the right customer, Um, that that you, you maybe know through your data. Is that right? >>Exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. And, um, by doing, creating more to New York time near real time data for the data warehouse team that's giving the lines of business the ability to to work on the next best offer for that customer. >>Paulo, we're inundated with data sources these days. Are there their data sources that you maybe maybe had access to before? But perhaps the backlog of ingesting and cleaning and cataloging and you know of analyzing. Maybe the backlog was so great that you couldn't perhaps tap some of those data sources. You see the potential to increase the data sources and hence the quality of the data, Or is that sort of premature? >>Oh, no. Um, >>exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of Brennan system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into like an s three bucket. Where That data king, We can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake could utilize that data or we can give it out to our market. >>Okay, so we're >>about the way we do. We're in batch mode. Still, so we're doing 24 hours. >>Okay, So when I think about the data pipeline and the people involved, I mean, maybe you could talk a little bit about the organization. I mean, you've got I know you have data. Scientists or statisticians? I'm sure you do. Ah, you got data architects, data engineers, quality engineers, you know, developers, etcetera, etcetera. And oftentimes, practitioners like yourself will will stress about pay. The data's in silos of the data quality is not where we want it to be. We have to manually categorize the data. These are all sort of common data pipeline problems, if you will. Sometimes we use the term data ops, which is kind of a play on Dev Ops applied to the data pipeline. I did. You just sort of described your situation in that context. >>Yeah. Yes. So we have a very large data ops team and everyone that who is working on the data part of Webster's Bay has been there 13 14 years. So they get the data, they understand that they understand the lines of business. Um, so it's right now, um, we could we have data quality issues, just like everybody else does. We have. We have places in him where that gets clans, Um, and we're moving toward. And there was very much silo data. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on. And that's what we're working towards now is giving them more self service, giving them the ability to access the data, um, in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. Um, so they're going to more not, I don't want to say a central repository, but a more of a robust repository that's controlled across multiple avenues where multiple lines of business can access. That said, how >>got it? Yes, and I think that one of the key things that I'm taking away from your last comment is the cultural aspects of this bite having the data. Scientists in the line of business, the line of lines of business, will feel ownership of that data as opposed to pointing fingers, criticizing the data quality they really own that that problem, as opposed to saying, Well, it's it's It's Paulus problem, >>right? Well, I have. My problem >>is, I have a date. Engineers, data architects, they database administrators, right, Um, and then data traditional data forwarding people. Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of regimen that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data. And we read the data flows and data redundancy and things like that help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report, and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is that what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. Yeah, it's this, you know, cycle of life for a column. And Iot Tahoe helps us do that, and we do. Data lineage has done all the time. Um, and it's just takes a very long time. And that's why we're using something that has AI and machine learning. Um, it's it's accurate. It does it the same way over and over again. If an analyst leads, you're able to utilize talked something like, Oh, to be able to do that work for you. I get that. >>Yes. Oh, got it. So So a couple things there is in in, In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing AI or machine intelligence into the data pipeline is really how you're attacking automation, right? And you're saying it's repeatable and and then that helps the data quality, and you have this virtuous cycle. Is there a firm that and add some color? Perhaps >>Exactly. Um, so you're able to let's say that I have I have seven cause lines of business that are asking me questions and one of the questions I'll ask me is. We want to know if this customer is okay to contact, right? And you know, there's different avenues, so you can go online to go. Do not contact me. You can go to the bank and you can say I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said okay to contact the other one says, you know, customer one to pray All these, You know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say, Yes, we already have that documentation. Here it is. And this is where you can find where the customer has said, you know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. >>Got it. Okay? Yeah. Okay. And then I want to come back to the cloud a little bit. So you you mentioned those three buckets? So you're moving to the Amazon cloud. At least I'm sure you're gonna get a hybrid situation there. You mentioned Snowflake. Um, you know what was sort of the decision to move to the cloud? Obviously, snowflake is cloud only. There's not an on Prem version there. So what precipitated that? >>Alright, So, from, um, I've been in >>the data I t Information field for the last 35 years. I started in the US Air Force and have moved on from since then. And, um, my experience with off brand waas with Snowflake was working with G McGee capital. And that's where I met up with the team from Iot to house as well. And so it's a proven. So there's a couple of things one is symptomatic of is worldwide. Now to move there, right, Two products, they have the on frame in the offering. Um, I've used the on Prem and off Prem. They're both great and it's very stable and I'm comfortable with other people are very comfortable with this. So we picked. That is our batch data movement. Um, we're moving to her, probably HBR. It's not a decision yet, but we're moving to HP are for real time data which has changed capture data, you know, moves it into the cloud. And then So you're envisioning this right now in Petrit, you're in the S three and you have all the data that you could possibly want. And that's Jason. All that everything is sitting in the S three to be able to move it through into snowflake and snowflake has proven cto have a stability. Um, you only need to learn in train your team with one thing. Um, aws has is completely stable at this 10.2. So all these avenues, if you think about it going through from, um, you know, this is your your data lake, which is I would consider your s three. And even though it's not a traditional data leg like you can touch it like a like a progressive or a dupe and into snowflake and then from snowflake into sandboxes. So your lines of business and your data scientists and just dive right in, Um, that makes a big, big win. and then using Iot. Ta ho! With the data automation and also their search engine, um, I have the ability to give the data scientists and eight analysts the the way of they don't need to talk to i t to get, um, accurate information or completely accurate information from the structure. And we'll be right there. >>Yes, so talking about, you know, snowflake and getting up to speed quickly. I know from talking to customers you get from zero to snowflake, you know, very fast. And then it sounds like the i o Ta ho is sort of the automation cloud for your data pipeline within the cloud. This is is that the right way to think about it? >>I think so. Um, right now I have I o ta >>ho attached to my >>on Prem. And, um, I >>want to attach it to my offering and eventually. So I'm using Iot Tahoe's data automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not a It's an on Prem. It's an Oracle database and its 15 years old. So it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>And so that was a challenge prior because you couldn't get the lines of business to agree what to delete or what was the issue there. >>Oh, it was more than that. Um, each line of business had their own structure within the warehouse, and then they were copying data between each other and duplicating the data and using that, uh so there might be that could be possibly three tables that have the same data in it. But it's used for different lines of business. And so I had we have identified using Iot Tahoe. I've identified over seven terabytes in the last, um, two months on data that is just been repetitive. Um, it just it's the same exact data just sitting in a different scheme. >>And and that's not >>easy to find. If you only understand one schema that's reporting for that line of business so that >>yeah, more bad news for the storage companies out there. Okay to follow. >>It's HCI. That's what that's what we were telling people you >>don't know and it's true, but you still would rather not waste it. You apply it to, you know, drive more revenue. And and so I guess Let's close on where you see this thing going again. I know you're sort of part way through the journey. May be you could sort of describe, you know, where you see the phase is going and really what you want to get out of this thing, You know, down the road Midterm. Longer term. What's your vision or your your data driven organization? >>Um, I want >>for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers, and >>that's awesome. I mean, that's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that is a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Alright, Take care. And thank you for watching everybody keep it right there. We'll take a short break and be right back. >>Yeah, yeah, yeah, yeah.

(electronica) >> Live, from San Francisco, California it's theCube. Covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at Fisherman's Wharf covering the IBM Chief Data Officer event, the 10th anniversary. You're watching theCube, the leader in live tech coverage. Just off the keynotes, Martin Schroeter is here as the Senior Vice President of IBM Global Markets responsible for revenue, profit, IBM's brand, just a few important things. Martin, welcome to theCube. >> They're important, they're important. >> Inderpal Bhandari, Cube alum, Global Chief Data Officer at IBM. Good to see you again. >> Good to see you Dave, >> So you guys, just off the keynotes, Martin, you talked a lot about disruption, things like digital trade that we're going to get into, digital transformation. What are you hearing when you talk to clients? You spent a lot of time as the CFO. >> I did. >> Now you're spending a lot of time with clients. What are they telling you about disruption and digital transformation? >> Yeah, you know the interesting thing Dave, is the first thing every CEO starts with now is that "I run a technology company." And it doesn't matter if they're writing code or manufacturing corrugated cardboard boxes, every CEO believes they are running a technology company. Now interestingly, maybe we could've predicted this already five or six years ago because we run a CEO survey, we run a CFO, we run surveys of the C-suite. And already about five years ago, technology was number one on the CEO's list of what's going to change their company in the next 3-5 years. It led. The CFO lagged, the CMO lagged, everyone else. Like, CEO saw it first. So CEOs now believe they are running technology businesses, and when you run a technology business, that means you have to fundamentally change the way you work, how you work, who does the work, and how you're finding and reaching and engaging with your clients. So when we talk, we shorthand of digitizing the enterprise. Or, what does it mean to become a digitally enable enterprise? It really is about how to use today's technology embedded into your workflows to make sure you don't get disintermediated from your clients? And you're bringing them value at every step, every touchpoint of their journey. >> So that brings up a point. Every CEO I talk to is trying to get "digital right." And that comes back to the data. Now you're of course, biased on that. But what are your thoughts on a digital business? Is digital businesses all about how they use data and leverage data? What does it mean to get "digital right" in your view? >> So data has to be the starting point. You actually do see examples of companies that'll start out on a digital transformation, or a technology transformation, and then eventually back into the data transformation. So in a sense, you've got to have the digital piece of it, which is really the experience that users have of the products of the company, as well as the technology, which is kind of the backend engines that are running. But also the workflow, and being able to infuse AI into workflows. And then data, because everything really rides on the data being in good enough shape to be able to pull all this off. So eventually people realize that really it's not just a digital transformation or technology transformation, but it is a data transformation to begin with. >> And you guys have talked a lot at this event, at least this pre-event, I've talked to people about operationalizing AI, that's a big part of your responsibilities. How do you feel about where you're at? I mean, it's a journey I know. You're never done. But feel like you're making some good progress there? Internally at IBM specifically. >> Yes, internally at IBM. Very good progress. Because our whole goal is to infuse AI into every major business process, and touch every IBM. So that's the whole goal of what we've been doing for the last few years. And we're already at the stage where our central AI and data platform for this year, over 100,000 active users will be making use of it on a regular basis. So we think we're pretty far along in terms of our transformation. And the whole goal behind this summit and the previous summits as you know, Dave, has been to use that as a showcase for our clients and customers so that they can replicate that journey as well. >> So we heard Ginni Rometty two IBM thinks ago talk about incumbent disruptors, which resonates, 'cause IBM's an incumbent disruptor. You talked about Chapter One being random acts of digital. and then Chapter Two is sort of how to take that mainstream. So what do you see as the next wave, Martin? >> Well as Inderpal said, and if I use us as an example. Now, we are using AI heavily. We have an advantage, right? We have this thing called IBM Research, one of the most prolific Inventors of Things still leads the world. You know we still lead the world in patents so have the benefit. For our our clients, however, we have to help them down that journey. And the clients today are on a journey of finding the right hybrid cloud solution that gives them bridges sort of "I have this data. "The incumbency advantage of having data," along with "Where are the tools and "where is the compute power that I need to take advantage of the data." So they're on that journey at the same time they're on the journey as Inderpal said, of embedding it into their workflows. So for IBM, the company that's always lived sort of at the intersection of technology and business, that's what we're helping our clients to do today. Helping them take their incumbent advantage of data, having data, helping them co-create. We're working with them to co-create solutions that they can deploy and then helping them to put that into work, into production, if you will, in their environments and in their workflows. >> So one of the things you stressed today, two of the things. You've talked about transparency, and open digital trade. I want to get into the latter, but talk about what's important in Chapter Two. Just, what are those ingredients of success? You've talked about things like free flow of data, prevent data localization, mandates, and protect algorithms and source codes. You also made another statement which is very powerful "IBM is never giving up its source code to our government, and we'd leave the country first." >> We wouldn't give up our source code. >> So what are some of those success factors that we need to be thinking about in that context? >> If we look at IBM. IBM today runs, you know 87% of the world's credit card transactions, right? IBM today runs the world's banking systems, we run the airline reservation systems, we run the supply chains of the world. Hearts and lungs, right? If I just shorthand all of that, hearts and lungs. The reason our clients allow us to do that is because they trust us at the very core. If they didn't trust us with our data they wouldn't give it to us. If they didn't trust us to run the process correctly, they wouldn't give it to us. So when we say trust, it happens at a very base level of "who do you really trust to run you're data?" And importantly, who is someone else going to trust with your data, with your systems? Any bank can maybe figure out, you know, how to run a little bit of a process. But you need scale, that's where we come in. So big banks need us. And secondly, you need someone you can trust that can get into the global banking system, because the system has to trust you as well. So they trust us at a very base level. That's why we still run the hearts and lungs of the enterprise world. >> Yeah, and you also made the point, you're not talking about necessarily personal data, that's not your business. But when you talked about the free flow of data, there are governments of many, western governments who are sort of putting in this mandate of not being able to persist data out of the country. But then you gave an example of "If you're trying to track a bag at baggage claim, you actually want that free flow of data." So what are those conversations like? >> So first I do think we have to distinguish between the kinds of data that should frow freely and the kinds of data that should absolutely, personal information is not what we're talking about, right? But the supply chains of the world work on data, the banking system works on data, right? So when we talk about the data that has to flow freely, it's all the data that doesn't have a good reason for it to stay local. Citizen's data, healthcare data, might have to stay, because they're protecting their citizen's privacy. That's the issue I think, that most governments are on. So we have disaggregate the data discussion, the free flow of data from the privacy issues, which are very important. >> Is there a gray area there between the personal information and the type of data that Martin's talking about? Or is it pretty clear cut in your view? >> No, I think this is obviously got to play itself out. But I'll give you one example. So, the whole use of a blockchain potentially helps you address and find the right balance between privacy of sensitive data, versus actually the free flow of data. >> Right. >> Right? So for instance, you could have an encryption or a hashtag. Or hash, sorry. Not a hashtag. A hash, say, off the person's name whose luggage is lost. And you could pass that information through, and then on the other side, it's decrypted, and then you're able to make sure that, you know, essentially you're able to satisfy the client, the customer. And so there's flow of data, there's no issue with regard to exposure. Because only the rightful parties are able to use it. So these things are, in a sense, the technologies that we're talking about, that Martin talked about with the blockchain, and so forth. They are in place to be able to really revolutionize and transform digital trade. But there are other factors as well. Martin touched on a bunch of those in the keynote with regard to, you know, the imbalances, some of the protectionism that comes in, and so on and so forth. Which all that stuff has to be played through. >> So much to talk about, so little time. So digital trade, let's get into that a little bit. What is that and why is it so important? >> So if you look at the economic throughput in the digital economy, the size of the GDP if you will, of what travels around the world in the way data flows, it's greater than the traded goods flow. So this is a very important discussion. Over the last 10 years, you know, out of the 100% of jobs that were created, 80% or so had a digital component to it. Which means that the next set of jobs that we're creating, they require digital skills. So we need a set of skills that will enable a workforce. And we need a regulatory environment that's cooperative, that's supportive. So in the regulatory environment, as we said before, we think data should flow freely unless there's a reason for it not to flow. And I think there will be some really good reasons why certain data should not flow.. But data should flow freely, except for certain reasons that are important. We need to make sure we don't create a series of mandates that force someone to store data here. If you want to be in business in a country, the country shouldn't say "Well if you want to business here "you have to store all your data here." It tends to be done on the auspice of a security concern, but we know enough about security that doesn't help. It's a false sense of security. So data has to flow freely. Don't make someone store it there just because it may be moving through or it's being processed in your country. And then thirdly, we have to protect the source code that companies are using. We cannot force, no country should force, a company to give up their source code. People will leave, they just won't do business there. >> That's just not about intellectual property issue there, right? >> It's huge intellectual property issue, that's exactly right. >> So the public policy framework then, is really free flow of data where it makes sense. No mandates unless it makes sense, and- >> And protection of IP. >> Protection of IP. >> That's right. >> Okay, good. >> It's a pretty simple structure. And based on my discussions I think most sort of aligned with that. And we're encouraged. I'm encouraged by what I see in TPP, it has that. What I see in Europe, it has that. What I see in USMCA it has that. So all three of those very good, but they're three separate things. We need to bring it all together to have one. >> So it was a good example. GDDPR maybe as a framework that seems to be seeping its way into other areas. >> So GDPR is an important discussion, but that's the privacy discussion wrapped around a broader trade issue. But privacy is important. GDPR does a good job on it, but we have a broader trade issue of data. >> Inderpal give me the final word, it's kind of your show. >> Well, you know. So I was just going to say Dave, I think one way to think about it is you have to have the free flow of data. And maybe the way to think about it is certain data you do need controls on. And it's more of the form in which the data flows that you restrict. As opposed to letting the data flow at all. >> What do you mean? >> So the hash example that I gave you. It's okay for the hash to go across, that way you're not exposing the data itself. So those technologies are all there. It's much more the regulatory frameworks that Martin's talking about, that they've got to be there in place so that we are not impeding the progress. That's going to be inevitable when you do have the free flow of data. >> So in that instance, the hash example that you gave. It's the parties that are adjudicating, the machines are adjudicating. Unless the parties want to expose that data it won't be exposed. >> It won't happen, they won't be exposed. >> All right. Inderpal, Martin, I know you got to run. Thanks so much for coming out. >> Thank you. Thanks for the talk. >> Thank you >> You're welcome. All right. Keep it right there everybody, we'll be back with our next guest from IBMCDO Summit in San Francisco. You're watching theCube. (electronica)