>>from >>around the globe. >>It's the cube >>covering Data Citizens >>21 brought to you by culebra. >>Well how everybody john Wallace here as we continue our coverage here on the cube of Data Citizens 21 it is a pleasure of ours to welcome in an award winner here at Data Citizens 21 were with Jacqueline Osborne who is the Managing Director and risk and Finance technology executive at Bank of America and she is also the data citizen of the Year, one of the culebra Excellence Award winners. And Jacqueline congratulations on the honor. Well deserved, I'm sure. >>Thank you so much. It is a true honor and I am so happy to be here and I'm looking forward to our conversation today. Yeah, what is it? >>It's all about just the concept of being a data citizen um in your mind um what is all that about? What are those pillars in terms of being a good data sets? And that gets to the point that you are the data citizen of the year? >>I think that's such a good question and actually is something that I don't even know if I know everything because it's constantly evolving. Being a data citizen yesterday is not what it is today and it's not what it means tomorrow because this field is evolving but with that said I think to me being a data citizen is being is having that awareness that data matters is driving to that data as an asset and really trying to lay the foundation to ensure its the right data in the right place at the right time. >>Yeah, let's talk about that high wire act because it's becoming increasingly more complex as you know, you've been in This realm if you will for what 15 years now? I believe it has evolved dramatically right in terms of capabilities but also complexity. So let's talk about that, about making finding the relevance of data and delivering it on time to the right people within your organization. >>How much >>More challenging is that now than it was maybe five or just you know, 10 years ago? >>I mean it's kind of crazy. There are some areas that make it so much easier and then for your question in some areas that make it so much harder. But if I can, let's start with the easier because I think this is something that really is important is when I started this, I've been in data my entire professional career, I've been achieved data officer since 2013. Um and when I started, I used to joke that I was a used car salesman. I was selling selling something this idea of data quality, data governance that nobody wanted. But now, so the shift of your question is the good if I am now a luxury car salesman selling a product that everybody wants, but shift to the bad nobody wants to pay for. So the complexity of it as data becomes bigger as we talk about big data and unstructured data and social media and facebook feeds. That is hard. It is complex. And the ability to truly manage and govern data to the degree of that perfection is really hard. So the more data we get, the more complexity, the more challenge, the more there is a need to really prioritize align with business strategy and ensure that you are embedding into the culture and the DNA of the corporate and not do it in the silos. >>You know, delivering that data to in the secure environment obviously, critically important for any enterprise, but even more so to put a finer point on financial services in terms of your work in that regard. So, so let's add that layer into this to not only internal, all the communication you have to do in the collaboration, you have to have but you have these external stakeholders to write, you have me, you know, a boa client if you will um that you've got to be aware of and have to communicate with. So so let's talk about that, that kind of merger if you will of not only having to work internally but also externally and making sure that with all the data you've got now that it works >>indeed. And you're kind of moving towards this new one of the newer dimensions, which is privacy, I mean G D p R was the first regulation in the UK, but now you have the C C P A and the California and it's coming and that that right to be forgotten or more importantly, as you said, as a customer of financial issues, that right to understand where your data is is very important because customers do want to know that their information is understood, trusted protected and going to be taken care of. So that ability to really transform back that you have a solid basis and that you are taking the measures and the necessary steps to ensure that that data is air quotes govern is so important. And it really again that shift from that used car salesman to a luxury car salesman. Your question is another example of how that shift is happening. It's no longer a should do or could do. Data governance is really becoming a must do and why you are seeing so many more. Chief data officers. Chief analytics officers, data management professionals. The profession is growing. I mean, incrementally every single day. >>What about the balancing act that you do? Let's just do with the internal audiences that you have to contend with. I shouldn't say content, content has that pejorative term to that you that you that you deal with, you collaborate with. Um you know, governance is also critically important because you want to make data available to the right people at the right time, but only the right people. Right. So what kind of practices or procedures are you putting in a place at B. O. A to make sure that that data is delivered to the right folks, but only to the right people and trying I guess to educate people within your organization as to the need for these strict governance processes. >>Sure. I tend to refer to them as the foundational pillars and if I was to take a step back and say what they are and how we use them. So the first one is metadata management and it is really around that. What data do you have? It's that understanding the information. So I used to refer to it or I still refer to it as when we were going to the library and you used to have to look at the card catalog That metadata manages very similar to the card catalogue for books. It tells you all the information. What's the genre? Who's the author, what the section is, where it is in the library and that is a core pieces. If you don't understand your data you can govern it. So that's kind of Pillar one. Metadata management. Pillar two is what's often referred to his data lineage. But I do think the new buzzword is that a providence? It's really that access low. It's understanding where data comes from the movement along the journey and where it's going. If you don't understand that horizontal front to back you can't govern the information as well because it can be changing hands, it can be altering and so it's that that end to end look at things. This pillar to pillar three is data quality and that's really that measurement of is it the right data and it is made up of a series of data quality dimensions, accuracy, completeness, validity, timeliness, conformity, reasonable nous etcetera. And it's really that fit freezes the data that I have the right data as I said earlier and then last but not least is issue management. At the end of the day there will be problems, there is too much data. It is in too many hands. So it's not we're not trying to remove all data issues but having a process where you can actually log prioritized and ultimately remediate is that that last and final pillar of the data management I would call circle because it has to all come back together and it's rinse and repeat. >>Yeah. And and so you you raise a point, a great point about things are going to go wrong. You know, eventually something happens. We know nothing is foolproof, nothing is bulletproof. Uh and we're certainly seeing that in terms of security now right with breaches pretty well publicized with invasions, ransom, where you name it, right, all kinds of flavors of that. Unfortunately. So from your perspective in terms of being that this data data guardian, if you will um how much of your concerns now have been amplified in terms of security and privacy and and that kind of internal uh communication you have to have or or I guess by in you know to understand the need to make this data ultra secure and ultra private, especially in this environment where the bad actors you know are are prolific, so kind of talk about that it's a struggle but maybe that challenge That you have in this environment here in 2021. >>Yeah, I think what you know the way I would do it is the struggle is again that that need or the desire to to protect everything and at the end of the day that's hard. And so the struggle right now that I have ri faces the prioritization. How do we differentiate what we call the critical few some call it cds chris critical data elements that they call it Katie key dad elements there, there's there's a term but really as that need and that demand grows whether it's for security or privacy or even data democratization, which hopefully we do talk about at some point, all these things are reliance on the right data because like statistics garbage in garbage out. So whether it's because you need the right information because of your analytics and your models or as you talked about its prevention and defensive security reasons that defensive and offensive isn't going away. So the real struggle is not around the driver, but the prioritization. How do you focus to ensure you're spending your time on the right areas and more importantly in alignment with the business priorities? Because one of the things that's critically important for me is ensuring that it's not metadata or data governance or data quality for the sake of it, it is in alignment with that business priority. >>And and and a big part of that is is strategy for the future, right strategy going forward. you know, where you're going to go in the next 18, 24 months and so from uh without, you know, revealing state secrets here. How do you how do you see this playing out in terms of this continual digital transformation? If you will from the B O a side of the fence? Um, you know, what what do you see as being important or in terms of what you would like to accomplish over the next year and a half, two years >>for me? I think it's that and I'm glad you asked that question, cause I wanted to mention that that data democratization I think. And if we if we debunk that or look into that, what do I mean by democratization? It's that real time access, but it's not real time access to the wrong information or to the wrong people as we talked about, it really is ensuring almost like an amazon model that I can simply search for the information I need, I can put it in my shopping cart and I can check out and I am able to that's that data driven, I'm able to use that information knowing it's the right data in the right hands for the right reasons and that's really my future mind where I'm getting to is how do I enable that? How do I democratize it? So data is truly and does become that enterprise asset that everybody and anybody can access, but they can do so in a way that has all of those defensive controls in place, going back to that right data, right place the right time because the shiny toys of ai machine learning all those things is if you're building models off of the wrong data from the wrong place or in the wrong hands, it's going to bite you in about whether it's today, tomorrow, the future. >>Well, exactly. I love that analogy and on that I'm going to thank you for the time. So I'm gonna call you a luxury data salesperson, not a car car salesman. But uh it certainly has paid off and we certainly congratulate you as well on the award that you wanna hear from calabria. >>Thank you so much and thank you for the time. Hopefully you've enjoyed our conversations as much as I have. >>I certainly have. Thank you very much Jacqueline Osborn, joining us on the Bank of America, the data citizen of the Year. Her data citizens 2021. I'm john walls and you've been watching the cube >>mm

[Music] welcome back to red hat summit 2021 my name is dave vellante and you're watching the cube where we go out to the events and extract the signal from the noise of course virtually in this case and i'm pleased to welcome victor carumpus who is the senior vice president of digital banking at bank mandiri coming in from jakarta welcome to the cube victor great to see you hi dave great to see you and great to be invited here thank you yeah you're very welcome i i wonder if you could just give us an overview of the bank maybe talk a little bit about your strategy your customers you know what the what the focus is of your company and what your role is there okay uh maybe i'm i'll give a short overview about bang mandir itself so bang money is a state-owned enterprise owned by the government but we also public company currently we already have a very big distribution channel in so uh you know indonesia is an island country it's very huge country so we are we are representing all over indonesia from province of aceh and i'm up to profits of papua and we have about 2600 branches all over indonesia and about uh 15 000 atms all over indonesia so bangladesh itself is focused on a lot of segment customers like indonesia from the corporate side small medium enterprise and also retail banking now uh we are we are currently focused in turning ourselves to become having to have more digital capability and currently in our uh current situations actually it is very good uh about 95 percent of our transactions is already coming from the electronic channel so it's only about five percent that coming from the branches but we know that this is still a journey uh and we are building more digital capability and features and functions on our digital channels to our customer got it um okay and so your your your digital journey kind of coincides i guess in a way with your your your container uh adoption journey uh i think that started a few years ago um and so maybe you could talk a little bit about that i i mean in thinking about modernizing your application portfolio obviously containers been around forever but they weren't packaged in a way that could actually be easily you know utilized and now you're seeing people in i.t roles like yourself really leaning in maybe you could talk about some of the technology considerations that impacted that desire to actually leverage containers i think uh first it's about the scalability because with a monolithic architecture it's kind of difficult to scale up for only specific features by doing container microservices we have options to scale up in a very fast way because one of the features is auto scaling on the container architectures uh one monitor is a very focused on the transaction banking so you might say bangladesh is supporting the economy of the country because in a in any given time in bangladesh we we're running about four thousand transactions per seconds that's a huge transactions number and have having said that uh our channel like i told you already running about 95 percent of the transactions so scalability is always important for us because especially like like now is the the in indonesia is a festive month it's a ramadan month where muslim is actually doing fasting but at the same time actually there's a lot of needs and people do a lot of transactions and on this kind of festive seasons the transition can be increased up to 40 or 50 suddenly and that's kind of things always happen in bangladesh and we must be ready and we must have a scalability on demand now containerization is enabling us to do that other thing is about flexibility because on the old days actually when we want to set up a new environment it's very difficult and takes a lot of time and that's affecting the time to market our products by doing the containerizations and putting it on a ci cd continuous integration called the development plan platform we are we call devsecops platform that kind of things becoming automatic because we set up the devsecos platform and the third one is the consistency actually so by by doing the contact investigations we can put the the apis on our back-end apis in the container itself and actually it's deliverable environment and a consistent experience to our customer because for example we promise our customer that every transaction should be finished within two seconds from their mobile banking up to our hosts and back forward to their mobile banking is only two seconds so that kind of thing is driving us to move to the current technology which we're using containerization and micro services great okay so 4 000 transactions per second you can't can't do that on erc20 ethereum for all you crypto fans out there that's that's pretty high volume uh and if i understand it correctly victor your role is really to envision this digital environment and then ultimately make it happen from a technology standpoint is that correct that's product that's got it yeah so okay so you now have a number of of product lines and teams you're using the same container platform maybe you could share with our audience some of the best practices and learnings that that you've taken away on this journey so i think first of all we can reuse a lot of components by doing this containerization platform is different when we still use the monolithic platform like the application server of java application server uh by using containerization actually uh be providing like a service banking as a service so whenever we build a new channel for example the first one we built a new service for example like a fun transfer service but when we create another channel for example a corporate banking electronic channel or we create another uh let's say wealth management channel whatever we already built before can be reusable instantly by using this technology so uh if i might say that actually there is a lot of best practices coming by using this platform and my team get a lot of benefit in terms of faster development time and also they can deliver the product and service in a high quality manner minimize the number of errors as well you know there's a lot of choices out there obviously i wonder if you could share what led you to the choice of red hat and open shift okay so first of all before we choose the platform actually we also comparing ourselves with the with the fintechs and also with the big tech in indonesia as well so we see we see that actually they already start using kubernetes and uh their platform is quite stable and even they can support about 90 to 100 million of customers without any issues at all so when we see this uh we choose a lot of we learn about a lot of platform and we finally choose opencv because we think that openshift and we we already do our research openshift is quite stable and for banks like us that have for having 4500 transaction per seconds stability is number one uh availability is also number one now uh having said that after doing our research we choose openshift and we implemented openshift in our environment because we promise our customers to provide 99.95 percent uh availability can i just i'm sorry to interrupt you victor can you just repeat that you cut out a little bit so you you said you you promised your customers to deliver and then you cut out a little bit can you just repeat what you just said there okay so we're giving a promise to our to our customer providing a 99.95 availability so this is the starting point of our channel sure in the efficiency we have efficient also to providing four nines which is 99.99 but i mean the starting point is 99.95 and because we have that the demand that requirement that's one of the reason we choose the openshift and red hat as our technology stack platform got it okay and so i have a question um what was it like in terms of just the skills and the adoption uh for your developers uh was it was it a big gap to go from where you were to you know where you are today did you have to what kind of training did you have to do did you have to do any sort of outsourcing to accelerate that maybe you could describe that how you close that skills gap so definitely in the beginning is quite challenging because although they are using modern languages like jaffa or kotlin but uh to understand the concept and to design correctly yes we we did a lot of training to them uh it takes a it takes me about three months to give them the proper training uh in terms of building the right uh microservices platform and also to building modular architecture in terms of the customer channel because this will be the fundamental when you build it correctly in the beginning and actually at the later point you will enjoy the benefit so the first three months actually is training and doing research and development and doing a lot of trial and errors but after the three months actually we already have the right technology stack have the right models and our devsecops is already working then actually after that the speed is very fast because uh it sprints uh we do agile way of working the agiles dlc it's only one month so every one month we already have new features coming in so that's what we call a huge transformation a digital transformation inside of our bank it's three months actually not bad i mean i would i would have thought on average it's going to take five or six months to get people up to speed so three months is pretty good and i'm also inferring that you weren't just paving the cow path you weren't just saying okay let's take our traditional and then you know re refactor it to digital you had to re-envision what digital looked like because the digital is different uh than the traditional uh so so that's actually pretty good uh ramp rate i wonder if you could just go ahead and comment if you could because when you say about uh revamp so actually it's not on the id side not only but also the business side we implement new way as well so actually if clearly they're implementing a new model so they're using a design thinking and also a co-creation model where now when we building a product so we're not writing the old product in a new way no we totally building it from from scratch and involving our key customer and our stakeholder when we're building this product so actually we implementing new models what we call design thinking and also uh co-creation with our customer so that's actually changing the face of the customer electronic channel a lot and and actually when we when we want to to deploy we invite our customer to test it first we call it like usability testing if they like it we continue to design if they they don't like it they give us a feedback how they would like it to be changed and and that's we appreciate our customer feedback because customers experience is everything now yeah so so the product can be accepted if the experience on that product is really making customer uh solving their problem solving the customer problem and making them enjoying uh doing transactions in our mobile banking product i think this is a really important point for people to understand so you weren't just paving the cow path i call it you're taking the old and and just trying to refactor it and make it exactly turn it into digital you had to really think about the business the business processes the dependencies the customer experience and then bring it back um what have been some of the business outcomes of this initiative and maybe you could we then after that we can get into some of the the future plans so so the outcome uh i think this journey uh since last year uh not last year actually since no october 2019 we already started the journey uh what took us by surprise is actually the pandemic uh suddenly the first three months when we have the pandemic of coffee we are being forced to close a lot of branches for temporary because we want to avoid the pandemic situations and that time actually the the demand using our digital channel is increasing a lot but because we already prepared actually we get the benefit one of the thing is uh the business benefit is relating so during the pandemic nobody can come to the branch and mostly the account opening actually happening online so uh we even got about 9000 account opening per day which is something that we are not imagining before so uh the benefit is very clear by using this this technology actually enabling us to provide digital capability for our customer and enabling us to open more accounts we see ourselves can grow even not linear but exponentially grow by using this platform uh talking about that indonesia is a is a huge country with we have about 200 250 million populations and actually there's still a lot of people is not having a bank account at all now by doing this actually we open opportunity doing financial inclusion for those people that need a banking account now they can reach us by using the digital platform as well yeah that's an awesome story and it goes back to the to the reason the real motivator for for moving to kubernetes and containers was scale uh and and you know it's you obviously started your digital journey prior to the pandemic but a lot of customers and i'm sure you as well were were forced to speed up a portion anyway of the digital component uh because of the pandemic like you said you couldn't people couldn't walk into the branches so but now you've got some more time to think about that journey you've had a lot of learnings 2020 was like a petri dish of experimentation but but in real time having to serve customers what's the future look like for the bank's technology journey okay so basically we uh we are not stopping only on the retail side yeah uh we want to redefine our customer journey also on the wholesale side and also on the small medium enterprise there is still a lot of things that need to be done uh and required by the customer actually so uh on the on the on the sme side we want to give them easier access uh for uh financing their businesses i think when we are back to the new normal uh the business need to have funding for for starting their business again so building an sme platform for them will will help a lot and will help the country as well on the retail side actually like i told you uh we are focusing on the more financial inclusion because uh i give you example right uh from the 230 million of indonesians uh populations i think by today maybe it's only about 50 million customers that already have a banking account so there is still a lot of people that need an access faster and cheaper and more efficient way for doing banking transactions so that's this also will become our focus and the last part is actually corporate what we see now a lot of the corporate require us to open uh api connectivity doing open banking with them the government actually the central bank supporting it supporting all the banks they are trying to create an api playbooks now and then they create they want to create an api standard for all the core all the use corporate also can connect it to the bank directly using api so this is also our focus because it will help the country economy when the economy costs the transaction costs getting more efficient getting more cheaper and there's a lot of transaction can be supported by our bank as well so i think i think that's the the future that we are imagining and i'm really hope that the pandemi will be finished and we come back to the to the new normal and we can support more transactions for this country yeah you're here to that i call it the new abnormal but so this is this is a great story everybody loves to talk about disruption we do as well and but people think oh it's out with out with the old in with the new and it's not like that this is a great story victor of uh of an established incumbent that is modernizing its its applications and its digital experience and of course the incumbent has the advantage of it's a real business it has customers that has a data it has experiences it and if it can modernize its infrastructure and and it's in its application portfolio it actually has an advantage because it's got way more features way more data way more customers and more resources so victor thanks so much for coming on thecube i really appreciate you sharing your story thank you dave thank you for inviting me thank you that was our pleasure and thank you for watching red hat summit 21 this is thecube you

>>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.

>> Narrator: From around the Globe, it's theCube with digital coverage of Enterprise Data Automation, and event series brought to you by Io-Tahoe. >> Everybody, we're back. And this is Dave Vellante, and we're covering the whole notion of Automated Data in the Enterprise. And I'm really excited to have Paula D'Amico here. Senior Vice President of Enterprise Data Architecture at Webster Bank. Paula, good to see you. Thanks for coming on. >> Hi, nice to see you, too. >> Let's start with Webster bank. You guys are kind of a regional I think New York, New England, believe it's headquartered out of Connecticut. But tell us a little bit about the bank. >> Webster bank is regional Boston, Connecticut, and New York. Very focused on in Westchester and Fairfield County. They are a really highly rated regional bank for this area. They hold quite a few awards for the area for being supportive for the community, and are really moving forward technology wise, they really want to be a data driven bank, and they want to move into a more robust group. >> We got a lot to talk about. So data driven is an interesting topic and your role as Data Architecture, is really Senior Vice President Data Architecture. So you got a big responsibility as it relates to kind of transitioning to this digital data driven bank but tell us a little bit about your role in your Organization. >> Currently, today, we have a small group that is just working toward moving into a more futuristic, more data driven data warehousing. 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 in to their account, to be able to give them the best offer. And the only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on offer something to offer that, or a new product, or to help them continue to grow their savings, or do and grow their investments. >> Okay, and I really want to get into that. But before we do, and I know you're, sort of partway through your journey, you got a lot to do. But I want to ask you about Covid, how you guys handling that? You had the government coming down and small business loans and PPP, and huge volume of business and sort of data was at the heart of that. How did you manage through that? >> 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 PPP Long's out to our customers within lightning speed. And part of that was is we adapted to Salesforce very for we've had Salesforce in house for over 15 years. Pretty much that was the driving vehicle to get our PPP loans in, and then developing logic quickly, but it was a 24 seven development role and get the data moving on helping our customers fill out the forms. And a lot of that was manual, but it was a large community effort. >> Think about that too. The volume was probably much higher than the volume of loans to small businesses that you're used to granting 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. >> I wasn't directly involved, but part of my data movement team was, and we had to change the logic overnight. So it was on a Friday night it was released, we pushed our first set of loans through, and then the logic changed from coming from the government, it changed and we had to redevelop our data movement pieces again, and we design them and send them back through. So it was definitely kind of scary, but we were completely successful. We hit a very high peak. Again, I don't know the exact number but it was in the thousands of loans, from little loans to very large loans and not one customer who applied did not get what they needed for, that was the right process and filled out the right amount. >> Well, that is an amazing story and really great support for the region, your Connecticut, the Boston area. So 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. 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, during this pandemic, to go to online. So that's obviously a big trend you mentioned, the data driven data warehouse, I want to understand that, but what at the top level, what are some of the key business drivers that are catalyzing your desire for change? >> The ability to give a customer, what they need at the time when they need it. And what I mean by that is that we have customer interactions in multiple ways. And I want to be able for the customer to walk into a bank or online and see the same format, and being able to have the same feel the same love, and also to be able to offer them the next best offer for them. But they're if they want looking for a new mortgage or looking to refinance, or whatever it is that they have that data, we have the data and that they feel comfortable using it. And that's an untethered banker. Attitude is, whatever my banker is holding and whatever the person is holding in their phone, that is the same and it's comfortable. So they don't feel that they've walked into the bank and they have to do fill out different paperwork compared to filling out paperwork on just doing it on their phone. >> You actually do want the experience to be better. And it is in many cases. Now you weren't able to do this with your existing I guess mainframe based Enterprise Data Warehouses. 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. And you need a timely process to be able to get the customers to understand what's happening. 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. The traditional EDW. It's sort of a science project. There's a few experts that know how to get it. You can so line up, the demand is tremendous. And then oftentimes by the time you get the answer, it's outdated. So you're trying to address that problem. So part of it is really the cycle time the end to end cycle time that you're progressing. And then there's, if I understand it residual benefits that are pretty substantial from a revenue opportunity, other offers that you can make to the right customer, that you maybe know, through your data, is that right? >> Exactly. It's drive new customers to new opportunities. It's enhanced the risk, and it's to optimize the banking process, and then obviously, to create new business. And the only way we're going to be able to do that is if 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 by doing creating more to New York times near real time data, or the data warehouse team that's giving the lines of business the ability to work on the next best offer for that customer as well. >> But Paula, we're inundated with data sources these days. Are there other data sources that maybe had access to before, but perhaps the backlog of ingesting and cleaning in cataloging and analyzing maybe the backlog was so great that you couldn't perhaps tap some of those data sources. Do you see the potential to increase the data sources and hence the quality of the data or is that sort of premature? >> Oh, no. Exactly. Right. So right now, we ingest a lot of flat files and from our mainframe type of front end system, that we've had for quite a few years. But now that we're moving to the cloud and off-prem and on-prem, moving off-prem, into like an S3 Bucket, where that data 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. Right now we're about we do work in batch mode still. So we're doing 24 hours. >> Okay. So when I think about the data pipeline, and the people involved, maybe you could talk a little bit about the organization. You've got, I don't know, if you have data scientists or statisticians, I'm sure you do. You got data architects, data engineers, quality engineers, developers, etc. And oftentimes, practitioners like yourself, will stress about, hey, the data is in silos. 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 sort of a play on DevOps applied to the data pipeline. Can you just sort of describe your situation in that context? >> Yeah, so we have a very large data ops team. And everyone that who is working on the data part of Webster's Bank, has been there 13 to 14 years. So they get the data, they understand it, they understand the lines of business. So it's right now. We could the we have data quality issues, just like everybody else does. But we have places in them where that gets cleansed. And we're moving toward and there was very much siloed data. The data scientists are out in the lines of business right now, which is great, because I think that's where data science belongs, we should give them and that's what we're working towards now is giving them more self service, giving them the ability to access the data 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. 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 data. Is that help? >> 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 by having the data scientists in the line of business, the 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 Paula's problem. >> Well, I have my problem is I have data engineers, data architects, database administrators, traditional data reporting people. And because some customers that I have that are 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. And we still have to provide that. So we still want to provide them some kind of regiment that they wake up in the morning, and they open up their email, and there's the report that they subscribe to, which is great, and it works out really well. And one of the things is why we purchased Io-Tahoe was, I would have the ability to give the lines of business, the ability to do search within the data. And we'll read the data flows and data redundancy and things like that, and help me clean up the data. And also, 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, four weeks we're going to go and we're going to look at the data and then we'll come back and tell you what we can do. But now with Io-Tahoe, they're able to look at the data, and then in one or two days, they'll be able to go back and say, Yes, we have the data, this is where it is. This is where we found it. This is the data flows that we found also, which is what I call it, is the break of a column. It's where the column was created, and where it went to live as a teenager. (laughs) And then it went to die, where we archive it. And, yeah, it's this cycle of life for a column. And Io-Tahoe helps us do that. And we do data lineage is done all the time. And it's just takes a very long time and that's why we're using something that has AI in it and machine running. It's accurate, it does it the same way over and over again. If an analyst leaves, you're able to utilize something like Io-Tahoe to be able to do that work for you. Is that help? >> Yeah, so got it. So a couple things there, in researching Io-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. And that speeds things up and gives everybody additional confidence. And then the other piece is essentially infusing AI or machine intelligence into the data pipeline, is really how you're attacking automation. And you're saying it repeatable, and then that helps the data quality and you have this virtual cycle. Maybe you could sort of affirm that and add some color, perhaps. >> Exactly. So you're able to let's say that I have seven cars, lines of business that are asking me questions, and one of the questions they'll ask me is, we want to know, if this customer is okay to contact, and there's different avenues so you can go online, do not contact me, you can go to the bank and you can say, I don't want email, but I'll take texts. And I want no phone calls. All that information. So, seven different lines of business asked me that question in different ways. One said, "No okay to contact" the other one says, "Customer 123." All these. In each project before I got there used to be siloed. So one customer would be 100 hours for them to do that analytical work, and then another analyst would do another 100 hours on the other project. Well, now I can do that all at once. And I can do those types 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, "No, 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 go back to the cloud a little bit. So you mentioned S3 Buckets. So you're moving to the Amazon cloud, at least, I'm sure you're going to get a hybrid situation there. You mentioned snowflake. 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 I've been in the data IT information field for the last 35 years. I started in the US Air Force, and have moved on from since then. And my experience with Bob Graham, was with snowflake with working with GE Capital. And that's where I met up with the team from Io-Tahoe as well. And so it's a proven so there's a couple of things one is Informatica, is worldwide known to move data. They have two products, they have the on-prem and the off-prem. I've used the on-prem and off-prem, they're both great. And it's very stable, and I'm comfortable with it. Other people are very comfortable with it. So we picked that as our batch data movement. We're moving toward probably HVR. It's not a total decision yet. But we're moving to HVR for real time data, which is changed capture data, moves it into the cloud. And then, so you're envisioning this right now. In which is you're in the S3, and you have all the data that you could possibly want. And that's JSON, all that everything is sitting in the S3 to be able to move it through into snowflake. And snowflake has proven to have a stability. You only need to learn and train your team with one thing. AWS as is completely stable at this point too. So all these avenues if you think about it, is going through from, this is your data lake, which is I would consider your S3. And even though it's not a traditional data lake like, you can touch it like a Progressive or Hadoop. And then into snowflake and then from snowflake into sandbox and so your lines of business and your data scientists just dive right in. That makes a big win. And then using Io-Tahoe with the data automation, and also their search engine. I have the ability to give the data scientists and data analysts the way of they don't need to talk to IT to get accurate information or completely accurate information from the structure. And we'll be right back. >> Yeah, so talking about snowflake and getting up to speed quickly. I know from talking to customers you can get from zero to snowflake very fast and then it sounds like the Io-Tahoe is sort of the automation cloud for your data pipeline within the cloud. Is that the right way to think about it? >> I think so. Right now I have Io-Tahoe attached to my on-prem. And I want to attach it to my off-prem eventually. So I'm using Io-Tahoe data automation right now, to bring in the data, and to start analyzing the data flows to make sure that I'm not missing anything, and that I'm not bringing over redundant data. The data warehouse that I'm working of, it's an on-prem. It's an Oracle Database, and it's 15 years old. So it has extra data in it. It has things that we don't need anymore, and Io-Tahoe's helping me shake out that extra data that does not need to be moved into my S3. So it's saving me money, when I'm moving from off-prem to 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. 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. So there could be possibly three tables that have the same data in it, but it's used for different lines of business. We have identified using Io-Tahoe identified over seven terabytes in the last two months on data that has just been repetitive. It's the same exact data just sitting in a different schema. And that's not easy to find, if you only understand one schema, that's reporting for that line of business. >> More bad news for the storage companies out there. (both laughs) So far. >> It's cheap. That's what we were telling people. >> And it's true, but you still would rather not waste it, you'd like to apply it to drive more revenue. And so, I guess, let's close on where you see this thing going. Again, I know you're sort of partway through the journey, maybe you could sort of describe, where you see the phase is going and really what you want to get out of this thing, down the road, mid-term, longer term, what's your vision or your data driven organization. >> I want for the bankers to be able to walk around with an iPad in their hand, 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 they were 12 years old there and now our multi whatever. I want them to be able to have the best experience with our bankers. >> That's awesome. That's really what I want as 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 follow. Great story. Love your experience, your background and your knowledge. I can't thank you enough for coming on theCube. >> Now, thank you very much. And you guys have a great day. >> All right, take care. And thank you for watching everybody. Keep right there. We'll take a short break and be right back. (gentle music)

>> Narrator: From around the Globe, it's theCube with digital coverage of Enterprise Data Automation, and event series brought to you by Io-Tahoe. >> Everybody, we're back. And this is Dave Vellante, and we're covering the whole notion of Automated Data in the Enterprise. And I'm really excited to have Paula D'Amico here. Senior Vice President of Enterprise Data Architecture at Webster Bank. Paula, good to see you. Thanks for coming on. >> Hi, nice to see you, too. >> Let's start with Webster bank. You guys are kind of a regional I think New York, New England, believe it's headquartered out of Connecticut. But tell us a little bit about the bank. >> Webster bank is regional Boston, Connecticut, and New York. Very focused on in Westchester and Fairfield County. They are a really highly rated regional bank for this area. They hold quite a few awards for the area for being supportive for the community, and are really moving forward technology wise, they really want to be a data driven bank, and they want to move into a more robust group. >> We got a lot to talk about. So data driven is an interesting topic and your role as Data Architecture, is really Senior Vice President Data Architecture. So you got a big responsibility as it relates to kind of transitioning to this digital data driven bank but tell us a little bit about your role in your Organization. >> Currently, today, we have a small group that is just working toward moving into a more futuristic, more data driven data warehousing. 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 in to their account, to be able to give them the best offer. And the only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on offer something to offer that, or a new product, or to help them continue to grow their savings, or do and grow their investments. >> Okay, and I really want to get into that. But before we do, and I know you're, sort of partway through your journey, you got a lot to do. But I want to ask you about Covid, how you guys handling that? You had the government coming down and small business loans and PPP, and huge volume of business and sort of data was at the heart of that. How did you manage through that? >> 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 PPP Long's out to our customers within lightning speed. And part of that was is we adapted to Salesforce very for we've had Salesforce in house for over 15 years. Pretty much that was the driving vehicle to get our PPP loans in, and then developing logic quickly, but it was a 24 seven development role and get the data moving on helping our customers fill out the forms. And a lot of that was manual, but it was a large community effort. >> Think about that too. The volume was probably much higher than the volume of loans to small businesses that you're used to granting 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. >> I wasn't directly involved, but part of my data movement team was, and we had to change the logic overnight. So it was on a Friday night it was released, we pushed our first set of loans through, and then the logic changed from coming from the government, it changed and we had to redevelop our data movement pieces again, and we design them and send them back through. So it was definitely kind of scary, but we were completely successful. We hit a very high peak. Again, I don't know the exact number but it was in the thousands of loans, from little loans to very large loans and not one customer who applied did not get what they needed for, that was the right process and filled out the right amount. >> Well, that is an amazing story and really great support for the region, your Connecticut, the Boston area. So 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. 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, during this pandemic, to go to online. So that's obviously a big trend you mentioned, the data driven data warehouse, I want to understand that, but what at the top level, what are some of the key business drivers that are catalyzing your desire for change? >> The ability to give a customer, what they need at the time when they need it. And what I mean by that is that we have customer interactions in multiple ways. And I want to be able for the customer to walk into a bank or online and see the same format, and being able to have the same feel the same love, and also to be able to offer them the next best offer for them. But they're if they want looking for a new mortgage or looking to refinance, or whatever it is that they have that data, we have the data and that they feel comfortable using it. And that's an untethered banker. Attitude is, whatever my banker is holding and whatever the person is holding in their phone, that is the same and it's comfortable. So they don't feel that they've walked into the bank and they have to do fill out different paperwork compared to filling out paperwork on just doing it on their phone. >> You actually do want the experience to be better. And it is in many cases. Now you weren't able to do this with your existing I guess mainframe based Enterprise Data Warehouses. 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. And you need a timely process to be able to get the customers to understand what's happening. 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. The traditional EDW. It's sort of a science project. There's a few experts that know how to get it. You can so line up, the demand is tremendous. And then oftentimes by the time you get the answer, it's outdated. So you're trying to address that problem. So part of it is really the cycle time the end to end cycle time that you're progressing. And then there's, if I understand it residual benefits that are pretty substantial from a revenue opportunity, other offers that you can make to the right customer, that you maybe know, through your data, is that right? >> Exactly. It's drive new customers to new opportunities. It's enhanced the risk, and it's to optimize the banking process, and then obviously, to create new business. And the only way we're going to be able to do that is if 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 by doing creating more to New York times near real time data, or the data warehouse team that's giving the lines of business the ability to work on the next best offer for that customer as well. >> But Paula, we're inundated with data sources these days. Are there other data sources that maybe had access to before, but perhaps the backlog of ingesting and cleaning in cataloging and analyzing maybe the backlog was so great that you couldn't perhaps tap some of those data sources. Do you see the potential to increase the data sources and hence the quality of the data or is that sort of premature? >> Oh, no. Exactly. Right. So right now, we ingest a lot of flat files and from our mainframe type of front end system, that we've had for quite a few years. But now that we're moving to the cloud and off-prem and on-prem, moving off-prem, into like an S3 Bucket, where that data 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. Right now we're about we do work in batch mode still. So we're doing 24 hours. >> Okay. So when I think about the data pipeline, and the people involved, maybe you could talk a little bit about the organization. You've got, I don't know, if you have data scientists or statisticians, I'm sure you do. You got data architects, data engineers, quality engineers, developers, etc. And oftentimes, practitioners like yourself, will stress about, hey, the data is in silos. 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 sort of a play on DevOps applied to the data pipeline. Can you just sort of describe your situation in that context? >> Yeah, so we have a very large data ops team. And everyone that who is working on the data part of Webster's Bank, has been there 13 to 14 years. So they get the data, they understand it, they understand the lines of business. So it's right now. We could the we have data quality issues, just like everybody else does. But we have places in them where that gets cleansed. And we're moving toward and there was very much siloed data. The data scientists are out in the lines of business right now, which is great, because I think that's where data science belongs, we should give them and that's what we're working towards now is giving them more self service, giving them the ability to access the data 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. 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 data. Is that help? >> 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 by having the data scientists in the line of business, the 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 Paula's problem. >> Well, I have my problem is I have data engineers, data architects, database administrators, traditional data reporting people. And because some customers that I have that are 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. And we still have to provide that. So we still want to provide them some kind of regiment that they wake up in the morning, and they open up their email, and there's the report that they subscribe to, which is great, and it works out really well. And one of the things is why we purchased Io-Tahoe was, I would have the ability to give the lines of business, the ability to do search within the data. And we'll read the data flows and data redundancy and things like that, and help me clean up the data. And also, 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, four weeks we're going to go and we're going to look at the data and then we'll come back and tell you what we can do. But now with Io-Tahoe, they're able to look at the data, and then in one or two days, they'll be able to go back and say, Yes, we have the data, this is where it is. This is where we found it. This is the data flows that we found also, which is what I call it, is the break of a column. It's where the column was created, and where it went to live as a teenager. (laughs) And then it went to die, where we archive it. And, yeah, it's this cycle of life for a column. And Io-Tahoe helps us do that. And we do data lineage is done all the time. And it's just takes a very long time and that's why we're using something that has AI in it and machine running. It's accurate, it does it the same way over and over again. If an analyst leaves, you're able to utilize something like Io-Tahoe to be able to do that work for you. Is that help? >> Yeah, so got it. So a couple things there, in researching Io-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. And that speeds things up and gives everybody additional confidence. And then the other piece is essentially infusing AI or machine intelligence into the data pipeline, is really how you're attacking automation. And you're saying it repeatable, and then that helps the data quality and you have this virtual cycle. Maybe you could sort of affirm that and add some color, perhaps. >> Exactly. So you're able to let's say that I have seven cars, lines of business that are asking me questions, and one of the questions they'll ask me is, we want to know, if this customer is okay to contact, and there's different avenues so you can go online, do not contact me, you can go to the bank and you can say, I don't want email, but I'll take texts. And I want no phone calls. All that information. So, seven different lines of business asked me that question in different ways. One said, "No okay to contact" the other one says, "Customer 123." All these. In each project before I got there used to be siloed. So one customer would be 100 hours for them to do that analytical work, and then another analyst would do another 100 hours on the other project. Well, now I can do that all at once. And I can do those types 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, "No, 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 go back to the cloud a little bit. So you mentioned S3 Buckets. So you're moving to the Amazon cloud, at least, I'm sure you're going to get a hybrid situation there. You mentioned snowflake. 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 I've been in the data IT information field for the last 35 years. I started in the US Air Force, and have moved on from since then. And my experience with Bob Graham, was with snowflake with working with GE Capital. And that's where I met up with the team from Io-Tahoe as well. And so it's a proven so there's a couple of things one is Informatica, is worldwide known to move data. They have two products, they have the on-prem and the off-prem. I've used the on-prem and off-prem, they're both great. And it's very stable, and I'm comfortable with it. Other people are very comfortable with it. So we picked that as our batch data movement. We're moving toward probably HVR. It's not a total decision yet. But we're moving to HVR for real time data, which is changed capture data, moves it into the cloud. And then, so you're envisioning this right now. In which is you're in the S3, and you have all the data that you could possibly want. And that's JSON, all that everything is sitting in the S3 to be able to move it through into snowflake. And snowflake has proven to have a stability. You only need to learn and train your team with one thing. AWS as is completely stable at this point too. So all these avenues if you think about it, is going through from, this is your data lake, which is I would consider your S3. And even though it's not a traditional data lake like, you can touch it like a Progressive or Hadoop. And then into snowflake and then from snowflake into sandbox and so your lines of business and your data scientists just dive right in. That makes a big win. And then using Io-Tahoe with the data automation, and also their search engine. I have the ability to give the data scientists and data analysts the way of they don't need to talk to IT to get accurate information or completely accurate information from the structure. And we'll be right back. >> Yeah, so talking about snowflake and getting up to speed quickly. I know from talking to customers you can get from zero to snowflake very fast and then it sounds like the Io-Tahoe is sort of the automation cloud for your data pipeline within the cloud. Is that the right way to think about it? >> I think so. Right now I have Io-Tahoe attached to my on-prem. And I want to attach it to my off-prem eventually. So I'm using Io-Tahoe data automation right now, to bring in the data, and to start analyzing the data flows to make sure that I'm not missing anything, and that I'm not bringing over redundant data. The data warehouse that I'm working of, it's an on-prem. It's an Oracle Database, and it's 15 years old. So it has extra data in it. It has things that we don't need anymore, and Io-Tahoe's helping me shake out that extra data that does not need to be moved into my S3. So it's saving me money, when I'm moving from off-prem to 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. 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. So there could be possibly three tables that have the same data in it, but it's used for different lines of business. We have identified using Io-Tahoe identified over seven terabytes in the last two months on data that has just been repetitive. It's the same exact data just sitting in a different schema. And that's not easy to find, if you only understand one schema, that's reporting for that line of business. >> More bad news for the storage companies out there. (both laughs) So far. >> It's cheap. That's what we were telling people. >> And it's true, but you still would rather not waste it, you'd like to apply it to drive more revenue. And so, I guess, let's close on where you see this thing going. Again, I know you're sort of partway through the journey, maybe you could sort of describe, where you see the phase is going and really what you want to get out of this thing, down the road, mid-term, longer term, what's your vision or your data driven organization. >> I want for the bankers to be able to walk around with an iPad in their hand, 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 they were 12 years old there and now our multi whatever. I want them to be able to have the best experience with our bankers. >> That's awesome. That's really what I want as 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 follow. Great story. Love your experience, your background and your knowledge. I can't thank you enough for coming on theCube. >> Now, thank you very much. And you guys have a great day. >> All right, take care. And thank you for watching everybody. Keep right there. We'll take a short break and be right back. (gentle music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a Himalayan Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tomb of length thanks so much for coming in the queue thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to find the curve and we're grateful for being able to be protected in this way so all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get into it but here with us you know how you spend your time okay well I head up a date operations function and a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and liberate it as as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago of the Big Data move data became an asset you had a lot of shadow I people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that the banker in 1989 had to mainly just know debits credits and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all it's the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the face if data is not the main photo really um it's always been an asset I think organizations just never consciously knew that data was that okay so so what sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like self as challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data Ops what was that like what were some of the challenges that you faced and how'd you get through them okay first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like chatter IP but when you really acknowledge that data is an essay just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not abdicate its management to a separate function that's not cold to the business and oftentimes IT are seen as a support or an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability the business function it presides in business makes to product management makes to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing offense and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about they just kind of leave us to to it was was also a journey but that was kind of the first step into it in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could take us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Dave is that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our date of college and from that perspective it then brought the case for data quality management so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they then buy into into the concept so sometimes you need to just wait for a good Christ and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity all the other capabilities at the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that's when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think the the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a P&L any department worth its salt has a FDA or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities in managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that they data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely um in the sense that unless the whole business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific you say so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners will the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of of 2019 into the beginning of this year so what we did was they I'm worried about the sunlight I mean through the window you look creative to me like sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in mates 2019 coming in too early this year we we had long kind of achieved the the compliance and regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity um the one that comes to mind is we're a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to get go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a database fee from a reporting history and we were in a place where in some cases based on how our Ledger's roll up and the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements were reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can learn at any time I see the sales they've made which is very important right now at the time of covert nineteen from a form of productivity and executional competitiveness those are two great use cases of women lying so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data bills profanity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barret data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm going to ask you then so when gdpr hit were you able to very quickly leverage this capability and and apply and then maybe other of compliance edik as well well actually you know what we just now was post TDP our us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes they use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time it but we're able to do real-time reporting from a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because Dana monetization is actually the penis' t the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know it kind of happened with enterprise data warehouse but it was too slow it was complicated and it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you I mean like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're out who we the the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of ovid 19 well I mean because of of the quality of kind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government put us on lockdown relatively early in in the curve or in the cycle of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue or potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student and students with loans on our books a instant three month payment holiday assuming they were in good standing and we did that upfront though it was actually an opt-out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we had not been able to provide productivity dashboard and and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are now that people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various methods being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric but business lines care about you know into these dashboards you usually the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and in an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important stupid like I can go on for an hour talking about this topic but unfortunately we're we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to be applied in terms of the use cases that we can find the second one is really providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are really utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa to be like thanks again great having you thank you very much Dave appreciate the opportunity thank you for watching everybody that we go we are digging in the data ops we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music] you

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi Bri welcome back this is Dave Volante and welcome to this special presentation made possible by IBM we're talking about data op data ops in Acton Steve Lucas here he's the senior vice president and director of data management at Associated Bank be great to see how are things going and in Wisconsin all safe we're doing well we're staying safe staying healthy thanks for having me Dave yeah you're very welcome so Associated Bank and regional bank Midwest to cover a lot of the territories not just Wisconsin but another number of other states around there retail commercial lending real estate offices stuff I think the largest bank in in Wisconsin but tell us a little bit about your business in your specific role sure yeah no it's a good intro we're definitely largest bank at Corvis concen and then we have branches in the in the Upper Midwest area so Minnesota Illinois Wisconsin our primary locations my role at associated I'm director data management so been with the bank a couple of years now and really just focused on defining our data strategy as an overall everything from data ingestion through consumption of data and analytics all the way through and then I'm also the data governance components and keeping the controls and the rails in place around all of our data in its usage so financial services obviously one of the more cutting-edge industries in terms of their use of technology not only are you good negotiators but you you often are early adopters you guys were on the Big Data bandwagon early a lot of financial services firms we're kind of early on in Hadoop but I wonder if you could tell us a little bit about sort of the business drivers and and where's the poor the pressure point that are informing your digital strategy your your data and data op strategy sure yeah I think that one of the key areas for us is that we're trying to shift from more of a reactive mode into more of a predictive prescriptive mode from a data and analytics perspective and using our data to infuse and drive more business decisions but also to infuse it in actual applications and customer experience etc so we have a wealth of data at our fingertips we're really focused on starting to build out that data link style strategy make sure that we're kind of ahead of the curve as far as trying to predict what our end users are going to need and some of the advanced use cases we're going to have before we even know that they actually exist right so it's really trying to prepare us for the future and what's next and and then abling and empowering the business to be able to pivot when we need to without having everything perfect that they prescribed and and ready for what if we could talk about a little bit about the data journey I know it's kind of a buzzword but in my career as a independent observer and analyst I've kind of watched the promise of whether it was decision support systems or enterprise data warehouse you know give that 360 degree view of the business the the real-time nature the the customer intimacy all that in and up until sort of the recent digital you know meme I feel as though the industry hasn't lived up to that promise so I wonder if you could take us through the journey and tell us sort of where you came from and where you are today and I really want to sort of understand some of the successes they've had sure no that's a that's a great point nice I feel like as an industry I think we're at a point now where the the people process technology have sort of all caught up to each other right I feel that that real-time streaming analytics the data service mentality just leveraging web services and API is more throughout our organization in our industry as a whole I feel like that's really starting to take shape right now and and all the pieces of that puzzle have come together so kind of where we started from a journey perspective it was it was very much if your your legacy reporting data warehouse mindset of tell me tell me the data elements that you think you're going to need we'll figure out how do we map those in and form them we'll figure out how to get those prepared for you and that whole lifecycle that waterfall mentality of how do we get this through the funnel and get it to users quality was usually there the the enablement was still there but it was missing that that rapid turnaround it was also missing the the what's next right than what you haven't thought of and almost to a point of just discouraging people from asking for too many things because it got too expensive it got too hard to maintain there was some difficulty in that space so some of the things that we're trying to do now is build that that enablement mentality of encouraging people to ask for everything so when we bring out new systems - the bank is no longer an option as far as how much data they're going to send to us right we're getting all of the data we're going to we're going to bring that all together for people and then really starting to figure out how can this data now be used and and we almost have to push that out and infuse it within our organization as opposed to waiting for it to be asked for so I think that all of the the concepts so that bringing that people process and then now the tools and capabilities together has really started to make a move for us and in the industry I mean it's really not an uncommon story right you had a traditional data warehouse system you had you know some experts that you had to go through to get the data the business kind of felt like it didn't own the data you know it felt like it was imposing every time it made a request or maybe it was frustrated because it took so long and then by the time they got the data perhaps you know the market had shifted so it create a lot of frustration and then to your point but but it became very useful as a reporting tool and that was kind of this the sweet spot so so how did you overcome that and you know get to where you are today and you know kind of where are you today I was gonna say I think we're still overcoming that we'll see it'll see how this all goes right I think there's there's a couple of things that you know we've started to enable first off is just having that a concept of scale and enablement mentality and everything that we do so when we bring systems on we bring on everything we're starting to have those those components and pieces in place and we're starting to build more framework base reusable processes and procedures so that every ask is not brand new it's not this reinvent the wheel and resolve for for all that work so I think that's helped if expedite our time to market and really get some of the buy-in and support from around the organization and it's really just finding the right use cases and finding the different business partners to work with and partner with so that you help them through their journey as well is there I'm there on a similar roadmap and journey for for their own life cycles as well in their product element or whatever business line there so from a process standpoint that you kind of have to jettison the you mentioned waterfall before and move to a more being an agile approach did it require different different skill sets talk about the process and the people side of yeah it's been a it's been a shift we've tried to shift more towards I wouldn't call us more formal agile I would say we're a little bit more lean from a an iterative backlog type of approach right so what are you putting that work together in queues and having the queue of B reprioritized working with the business owners to help through those things has been a key success criteria for us and how we start to manage that work as opposed to opening formal project requests and and having all that work have to funnel through some of the old channels that like you mentioned earlier kind of distracted a little bit from from the way things had been done in the past and added some layers that people felt potentially wouldn't be necessary if they thought it was a small ask in their eyes you know I think it also led to a lot of some of the data silos and and components that we have in place today in the industry and I don't think our company is alone and having data silos and components of data in different locations but those are there for a reason though those were there because they're they're filling a need that has been missing or a gap in the solution so what we're trying to do is really take that to heart and evaluate what can we do to enable those mindsets and those mentalities and find out what was the gap and why did they have to go get a siloed solution or work around operations and technology and the channels that had been in place what would you say well your biggest challenges in getting from point A to point B point B being where you are today there were challenges on each of the components of the pillar right so people process technology people are hard to change right men behavioral type changes has been difficult that there's components of that that definitely has been in place same with the process side right so so changing it into that backlog style mentality and working with the users and having more that be sort of that maintenance type support work is is a different call culture for our organization and traditional project management and then the tool sets right the the tools and capabilities we had to look in and evaluate what tools do we need to Mabel this behavior in this mentality how do we enable more self-service the exploration how do we get people the data that they need when they need it and empower them to use so maybe you could share with us some of the outcomes and I know it's yeah we're never done in this business but but thinking about you know the investments that you've made in intact people in reprocessing you know the time it takes to get leadership involved what has been so far anyway the business outcome and you share any any metrics or it is sort of subjective a guidance I yeah I think from a subjective perspective the some of the biggest things for us has just been our ability to to truly start to have that very 60 degree view of the customer which we're probably never going to get they're officially right there's there everyone's striving for that but the ability to have you know all of that data available kind of at our fingertips and have that all consolidated now into one one location one platform and start to be that hub that starts to redistribute that data to our applications and infusing that out has been a key component for us I think some of the other big kind of components are differentiators for us and value that we can show from an organizational perspective we're in an M&A mode right so we're always looking from a merger and acquisition perspective our the model that we've built out from a data strategy perspective has proven itself useful over and over now in that M&A mentality of how do you rapidly ingest new data sets it had understood get it distributed to the right consumers it's fit our model exactly and and it hasn't been an exception it's been just part of our overall framework for how we get that data and it wasn't anything new that we had to do different because it was M&A just timelines were probably a little bit more expedited the other thing that's been interesting in some of the world that were in now right from a a Kovach perspective and having a pivot and start to change some of the way we do business and some of the PPP loans and and our business models sort of had to change overnight and our ability to work with our different lines of business and get them the data they need to help drive those decisions was another scenario where had we not had the foundational components there in the platform there to do some of this if we would have spun a little bit longer so your data ops approach I'm gonna use that term helped you in this in this kovat situation I mean you had the PPE you had you know of slew of businesses looking to get access to that money you had uncertainty with regard to kind of what the rules of the game were what you was the bank you had a Judah cape but you it was really kind of opaque in terms of what you had to do the volume of loans had to go through the roof in the time frame it was like within days or weeks that you had to provide these so I wonder if we could talk about that a little bit and how you're sort of approach the data helped you be prepared for that yeah no it was a race I mean the bottom line was it felt like a race right from from industry perspective as far as how how could we get this out there soon enough fast enough provide the most value to our customers our applications teams did a phenomenal job on enabling the applications to help streamline some of the application process for the loans themselves but from a data and reporting perspective behind the scenes we were there and we had some tools and capabilities and readiness to say we have the data now in our in our lake we can start to do some business driven decisions around all all of the different components of what's being processed on a daily basis from an application perspective versus what's been funded and how do those start to funnel all the way through doing some data quality checks and operational reporting checks to make sure that that data move properly and got booked in in the proper ways because of the rapid nature of how that was was all being done other covent type use cases as well we had some some different scenarios around different feed reporting and and other capabilities that the business wasn't necessarily prepared for we wouldn't have planned to have some of these types of things and reporting in place that we were able to give it because we had access to all the data because of these frameworks that we had put into place that we could pretty rapidly start to turn around some of those data some of those data points and analytics for us to make some some better decisions so given the propensity in the pace of M&A there has to be a challenge fundamentally in just in terms of data quality consistency governance give us the before and after you know before kind of before being the before the data ops mindset and after being kind of where you are today I think that's still a journey we're always trying to get better on that as well but the data ops mindset for us really has has shifted us to start to think about automation right pipelines that enablement a constant improvement and and how do we deploy faster deploy more consistently and and have the right capabilities in place when we need it so you know where some of that has come into place from an M&A perspective is it's really been around the building scale into everything that we do dezq real-time nature this scalability the rapid deployment models that we have in place is really where that starts to join forces and really become become powerful having having the ability to rapidly ingesting new data sources whether we know about it or not and then exposing that and having the tools and platforms be able to expose that to our users and enable our business lines whether it's covent whether it's M&A the use cases keep coming up right they we keep running into the same same concept which is how rapidly get people the data they need when they need it but still provide the rails and controls and make sure that it's governed and controllable on the way as well [Music] about the tech though wonder if we could spend some time on that I mean can you paint a picture of us so I thought what what what we're looking at here you've got you know some traditional IDI w's involved I'm sure you've got lots of data sources you you may be one of the zookeepers from the the Hadoop days with a lot of you know experimentation there may be some machine intelligence and they are painting a pic before us but sure no so we're kind of evolving some of the tool sets and capabilities as well we have some some generic kind of custom in-house build ingestion frameworks that we've started to build out for how to rapidly ingest and kind of script out the nature of of how we bring those data sources into play what we're what we've now started as well as is a journey down IBM compact product which is really gonna it's providing us that ability to govern and control all of our data sources and then start to enable some of that real-time ad hoc analytics and data preparation data shaping so some of the components that we're doing in there is just around that data discovery pointing that data sources rapidly running data profiles exposing that data to our users obviously very handy in the emanating space and and anytime you get new data sources in but then the concept of publishing that and leveraging some of the AI capabilities of assigning business terms in the data glossary and those components is another key component for us on the on the consumption side of the house for for data we have a couple of tools in place where Cognos shop we do a tableau from a data visualization perspective as well that what that were we're leveraging but that's where cloud pack is now starting to come into play as well from a data refinement perspective and giving the ability for users to actually go start to shape and prep their data sets all within that governed concept and then we've actually now started down the enablement path from an AI perspective with Python and R and we're using compact to be our orchestration tool to keep all that governed and controlled as well enable some some new AI models and some new technologies in that space we're actually starting to convert all of our custom-built frameworks into python now as well so we start to have some of that embedded within cloud pack and we can start to use some of the rails of those frameworks with it within them okay so you've got the ingest and ingestion side you've done a lot of automation it sounds like called the data profiling that's maybe what classification and automating that piece and then you've got the data quality piece the governance you got visualization with with tableau and and this kind of all fits together in a in an open quote unquote open framework is that right yeah I exactly I mean the the framework itself from our perspective where we're trying to keep the tools as as consistent as we can we really want to enable our users to have the tools that they need in the toolbox and and keep all that open what we're trying to focus on is making sure that they get the same data the same experience through whatever tool and mechanism that they're consuming from so that's where that platform mentality comes into place having compact in the middle to help govern all that and and reprovision some of those data sources out for us has it has been a key component for us well see if it sounds like you're you know making a lot of progress or you know so the days of the data temple or the high priest of data or the sort of keepers of that data really to more of a data culture where the businesses kind of feel ownership for their own data you believe self-service I think you've got confidence much more confident than the in the compliance and governance piece but bring us home just in terms of that notion of data culture and where you are and where you're headed no definitely I think that's that's been a key for us too as as part of our strategy is really helping we put in a strategy that helps define and dictate some of those structures and ownership and make that more clear some of the of the failures of the past if you will from an overall my monster data warehouse was around nobody ever owned it there was there wasn't you always ran that that risk of either the loudest consumer actually owned it or no one actually owned it what we've started to do with this is that Lake mentality and and having all that data ingested into our our frameworks the data owners are clear-cut it's who sends that data in what is the book record system for that source data we don't want a ability we don't touch it we don't transform it as we load it it sits there and available you own it we're doing the same mentality on the consumer side so we have we have a series of structures from a consumption perspective that all of our users are consuming our data if it's represented exactly how they want to consume it so again that ownership we're trying to take out a lot of that gray area and I'm enabling them to say yeah I own this I understand what I'm what I'm going after and and I can put the the ownership and the rule and rules and the stewardship around that as opposed to having that gray model in the middle that that that we never we never get but I guess to kind of close it out really the the concept for us is enabling people and end-users right giving them the data that they need when they need it and it's it's really about providing the framework and then the rails around around doing that and it's not about building out a formal bill warehouse model or a formal lessor like you mentioned before some of the you know the ivory tower type concepts right it's really about purpose-built data sets getting the giving our users empowered with the data they need when they need it all the way through and fusing that into our applications so that the applications and provide the best user experiences and and use the data to our advantage all about enabling the business I got a shove all I have you how's that IBM doing you know as a as a partner what do you like what could they be doing better to make your life easier sure I think I think they've been a great partner for us as far as that that enablement mentality the cloud pack platform has been a key for us we wouldn't be where we are without that tool said I our journey originally when we started looking at tools and modernization of our staff was around data quality data governance type components and tools we now because of the platform have released our first Python I models into the environment we have our studio capabilities natively because of the way that that's all container is now within cloud back so we've been able to enable new use cases and really advance us where we would have a time or a lot a lot more technologies and capabilities and then integrate those ourselves so the ability to have that all done has or and be able to leverage that platform has been a key to helping us get some of these roles out of this as quickly as we have as far as a partnership perspective they've been great as far as listening to what what the next steps are for us where we're headed what can we what do we need more of what can they do to help us get there so it's it's really been an encouraging encouraging environment I think they as far as what can they do better I think it's just keep keep delivering write it delivery is ping so keep keep releasing the new functionality and features and keeping the quality of the product intact well see it was great having you on the cube we always love to get the practitioner angle sounds like you've made a lot of progress and as I said when we're never finished in this industry so best of luck to you stay safe then and thanks so much for for sharing appreciate it thank you all right and thank you for watching everybody this is Dave Volante for the cube data ops in action we got the crowd chat a little bit later get right there but right back right of this short break [Music] [Music]

>>live from Atlanta, Georgia. It's the Q covering answerable best 2019. Brought to you by Red hat. >>Hey, welcome back to the Cuban Live coverage in simple fest. Two days of coverage. Day one, wrapping up. I'm John forwards. Accused Too many men. My guest co host today, our next two guests at his van. Van Velzen. Okay, welcome to the Cube. You're an engineer at I n G Bank and Robert de Bock, product owner, engineer I n g. Bank. Hey, guys, Thanks for coming on. Thank you. Have the practitioner on. Well, first of all, we have a lot of great feedback from the practitioners here. And also people in deploying answerable and other other cool Dev ops Tools on automation is at the top of the list. Yes, More efficient. Getting things done. Focus. You got satisfaction in job because things go awaiting time savings. I'm saving security drives a conversation and re skilling opportunities. Love. These are cutting edge. Things you got to do is take a minute to explain what you guys do. What a night. What a night. Angie bank. >>Yeah. I work in a team that provides redhead images for other teams. in 90 to consume to use two insane she ate way. Also live from playbooks, amendable code and rolls to manage those things. And he's very scattered, which sort of decentralized, which is a good thing. In my opinion, it's ready for scaling. In that case, I used to work with Dennis are lots in the tower team, so take it away. >>Okay, so I still work at the answer, built our squad What we do, it's ah, We make sure that the instable tower service keeps running 24 7 and we also ensure that we, uh, provide updates next to all this. We also have unanswerable community where we basically support our end users, which are their love. So, uh, from some numbers, I heard we have 1200 applications teams that are using our service. Um, and they all have, like, answerable playbook, sensible rolls, questions, difficulties with, uh, with anything. And we're basically there to support them as well. >>So 1200 teams are using answerable, Yes, inside the bank. Yes. Yeah, like >>it's set up very decentralized. And I think what I hear from instable fest that is not very common. I still think it's very good thing to do. We try to basically give these teams all the tools they need to do their stuff on. What I hear hear mostly is that there's essential team off administrators pushing the buttons for them. Towers. Great answer was great in that case, I think, for our case is really it's a perfect fit. >>E guess help Explain. Is this do you provide? You know, he said it's not centralized, but is this you know, here's best practices here. Some play boat out. How do you end? You support them? Because they're a little bit those relationships. >>Okay. Okay. Um so what we do is we basically all the rules and get ah ah, good lap. So it's an own premise. Get environment. You can search in this. Get for rules. Uh, not like all rules are easily to be found when searching for them. So that's why there are these communities to share what you have made. Um, >>plus these teams, they can themselves pick and choose. Some will try to rewrite everything That's fine. Others can can benefit from existing coat, so it's just a good trick. Thio enable these team to participate on it really different. Some people make it all themselves another >>next to this. So we basically have these 12 on the teams do their own thing. But next to this, we also have a self service portal where they can choose, like from, uh, generic finks like us. But your machine at new disc. So New capacity Cp use memory. That's all being done through a portal s so they don't need to do anything on their own for this they can, but most of them choose the easy way off using this portal. This portal basically doesn't a vehicle to instable tower, which executes a sensible playbook and some other stuffs. Maybe some AP eyes. And this is one of the things you guys create A manage these books. So, um >>and if you go back in time so the alternative way, which we happily got rid off, is to do it ourselves. I think it was before we we work together. Way had batch weekends, for example, and it >>was no very different. No life. Oh, that's working on weekends, >>weekends and, for example, he used to patch machine some 10,000 or so, and we were not aware what was important. What? Not so you you'd stop the whole pitch. Oh, this machine has a problem. Let's stop everything in focus and that's >>not important. Was like a complete order. >>And the other way around Also this machine. I guess it's not that important. Let's just >>continue this >>Sunday morning. Oh, my God. Everything's broken. >>Can you give us a little flavor of kind of the spectrum of solutions that you leverage answerable on >>tap? Yeah. We, uh I think what we see Moses for Lennox machines, eso fetching is a big one. We got a second operation, so there's a few of them. The deployment also depends on and small. So if you order a new machine, answer was involved somewhere to do to make it happen on network on board and the Windows teams are very interested. I'm not sure if we notice on board yet. To >>be honest, I know we did some book in the boss so a couple of months ago, using wind around when you needed set on policies there, But you can see that the networking teams were getting more momentum. Uh, five. There's some suffer suffer to find switches Bob. I don't know. The, uh Never mind the name, but ah, you can see some momentum in the in the networking. Uh, it's not Morgan departments >>configuration network networking with the activists. So that's where the action is in the >>network. Um, there were some cool talks also here on five workshops. So you can see there is, um, that there is some attention on these modules and integrations as well. >>What's your guy's goal here for the show? What brought you here? I'll see Big user. >>Yeah. So what do you think was like sharing our own thing? We did. They talk this morning. Ah, regarding and programming A really cool we wanted to share. It is this behavioral thing, and and >>we'll talk about take a minute and programming. >>So, um, basically, it's, ah programming with the whole team and making sure that you get something done with all the knowledge in the team. So you don't have to align off the words or if some other if you're Kulik says from basically session, you can do better using this staying. It's all, um it's It's all done during the decision >>as basically a good way to get a team up to speed. So in a team that's probably a few few people that are very quick and understand the concept and few starters or so So >>you guys decentralized, which makes sense for scale. I get that. So this sounds like you can operate decentralized, but where danceable. You can still have that common a book Switch >>teams, for example. So it used to be very specific. H team would have their own type of coat. Now that more answers used people can switch a little easier to to another product of surface because the languages have lied, shared, steal it, steal. It's quite >>well happy with this, right? I am. I really, really have to work on the weekend. That's good. I think >>the good thing is that you have one generic way of working. So his playbook is readable by all engineers. And if you want to learn this thing, you just do the inevitable course. So you know what this thing is? A mosque and roll, and it's all like >>way. We do see horrible >>koto. Come on, don't throw your college under the bus. But here's the international tough question can see is what we have been here. I want you guys to test this. We hear that there's a lot of time savings involved. Yes, with answer. True or false. That's true order of magnitude. What? What kind of saving way talking about? I >>think it depends on the thing because we saw a huge I don't know, except numbers. But this this os patching that Really? Really Uh, >>yes. Now, especially waas. Two people working a full time basically collecting, who needs to do what? The win. And then for a weekend, 10 15 people or so. So, uh, that's reduced now to sort of nothing. Yes, some maintenance to that playbook and roll. But I mean, yeah, it's difficult to express what message? So >>no one's getting phone call? Hey, come in on the weekend. So 15 people on the weekend jam and then to Fulton will just managing it all Go away. >>Yeah, not needed, but not needed. But they basically they can do something else, so those people are still there. But now they're not doing Os patching and doing all the excel sheets and keeping order off. The systems are important, and this shall be the first, and then they because way are basically doing the thing they know better. This application team knows their dependency, so they know they. But first I need to patch the database machine and then there during the front end or Andi. It's difficult to do this so they do it themselves. >>That's Dev Ops. That's that's the way it's supposed to be, right? >>So you've matured this thes deployments over time. As you look back, What key learnings do you have that maybe you'd recommend to your peers toe? You know how things could run a little bit smoother >>next time, a good amount of time. So they're stools. That's not the problem, So answer is great, but there's others to their great Give it time to sink in with the people. So you start something and you have to have a pretty strong team to do the long the long stretch with it and give it some time, maybe a year or so before everyone's on board it. In our case, in the beginning, we spend lots of time on this community model where we basically organized small meet ups or get together, too, show things or to hear problems and try to express them. That really helped a lot. And by now it's starting to get normal, more normal. So all the teams do sensible, basically. And problem starts slowly disappearing. Also. So So >>one of the things, um, that will be better. Probably in our scenario. Housekeeping metrics. So what are the improvements over time? I don't know how to measure this. No, no, no aspect. But it will be better if you had, like, better numbers like we did hair Very good. Or this is something like, what did the community thing bring way indirectly what the results are Because the engineers are doing things really, really things. They're really patching the replication. And they're really, um, restarting their own machines, for example, when there is something wrong. Whatever. Um, but our days related to our community thing or all that's really related to Sensible Tower >>last. I think we we are very technical focus. So So we like it as a nerd, so to say, to do things but what the business value is, for example, I'm not so interested or less interested so way typically, like the technology, so it could be good to have some someone onboard and your team that says, Yeah, but this is the problem. It's crossed. This amount of money and that solved now are improved. >>Well, they assume the applications are doing a good job. So you guys helped those guys out. They get to do their own thing. They do the heavy lifting. They're doing the coding anyway for those guys that were coming in managing full time on the 15 or so on the weekend. What are they doing now? >>Most are spread across. All the application teams go back. But the other side there is now it's our team that was not there s. So that's the price you have to pay. And that's a serious team. I mean, it's far six people now 86 people and 100 machines or so. So it is a serious amount of time, but it makes it at least much more constant. So people are not surprised by machines being patched, and Monday they come back into the half broken or so. So it's a lot more control now, so I don't know if you can express it in price, but at least it's more stable >>more consistent. >>Well, one of the things that we hear here and I want to get your thoughts as we wrap up is as you go forward, you got answerable 1200 teams using it. You got a lot of collaboration. The work cultures change. Sounds like a shower. Team steps service everything else. So some scale building out what's next? Because as it becomes a platform. Okay, you have to enable something. There has value there. Okay, technical nerd value and then business value >>scaling, uh, because we continuously see this thing growing like more application teams are adapting answerable, invincible tower. So, um, right now we have, like, a cluster. We have different clusters running. Go into much detail, but we can see that the load is getting higher and higher, so we need to skill. Um, and this is sort of difficult, but red. That is really supporting in this because they're going to change some things at the application level two to allow scaling even better. Um, >>plus, also, for most teams, they're starting their configuration. Everything is coat process. They're not there yet. As soon as they discover the power of it, I'm sure that's being used a lot. A lot more. And plus, there's other countries that are going to be connected. So you have a lot of work >>because your engineering doing some getting down and dirty with the code, automating everything. >>Yeah. Yeah. So, um, what else do we >>Oh, what's the coolest thing you've done that you've automated? >>Uh >>uh, Pick your favorite. >>So but the child during Encircle Tower and with answerable, um, let me think about this. >>I I really like the patching that saved us so much work. And, uh, I think also one of the next goes to make much more simpler. So we as a company, we're complex and the people also like complexity. That's wrong. We should change >>that. Patching up our >>offense, Melissa Simplicity. So we should really use that. >>You don't want any open holes in the network housely and assistance >>about your previous question. Like I have sort of a finger and all these small things. So it's sort of what I did. It's more like an A team thing. We created the OS patch playbooks, the configure stuff, the second day offs. So we did this as a team >>like sports but the playbooks together run the play. Some defense on security >>and programming. So you're doing >>this as a team, which is very cool. Has a scoreboard look good? Winning? >>Yeah, Yeah, yeah, yeah. We're looking at the graphite. Uh, it's girl. >>Final question. How you enjoying the show here? Having a good time? What's the vibe here? What's it like here? Share for the people who aren't here. What's going on? What's the vibe with >>a conversation? It's great. We went to some sessions yesterday really technical stuff with developers. And this was really amazing because you heard details that that are not in the India in the talks today and tomorrow. Um, yeah, it's great. It's great community. It's just I really I really enjoy it because you can. It's You can have, like one on one conversations go into depth. I was showing something I created, and this guy's we'll hold. This is really great in the It's cool. It's just if you it's really great. It's really >>cool. Really? Yeah, for me also, it feels like coming home, So I know these people and I think the first day, the collaboration day, what's it called and I'm not sure you community, that's it's great because it's been a bit rough and unpolished in today's more polished and more presented and prepared to, uh, both are great. >>Good. Give the hard feedback. >>Yeah, you meet all the people. So, for example, I used instable a lot, and then I'm getting up. I see all these names. Like, who would that be there walking here and shake hands like, Oh, that's >>why guys like your code looking good. Yeah. Looks good. A contributor. Summit contributed. Okay. Sorry. After it for >>anyone that goes to visit that day, too. That's just great. >>It's great to see people face to face that, you know, online for their digital identity or the code >>you can You can't complain about stuff out on. Do you know that you don't hurt them or something with just commenting on get like after this issue and this issue and this issue. Then you can see them in person. And then you >>him a high five assault, you know? Hey, >>it's really very cool. >>Guys. Great conversations were coming on cue. Thanks, Dennis. Appreciate Robert. Thanks for coming on. Skew coverage here Day one of two days of live coverage here inside the Cube here in Atlanta, Georgia for Ansel Fest is the cute I'm John 1st 2 minute. Thanks for watching.

>> from Bahrain. It's the Q covering AWS Public sector Bahrain brought to you by Amazon Web service is >> over and welcome back to the Cube. Special coverage here in by rain in the Middle East for Amazon Web service is eight of US Summit. I'm John for the Cube our second year, where cloud computing has changed in the landscape. It's changing the entrepreneur equation. It's changing the money equation, and Fintech is very popular. Next guest. Special guests. Ideal worrying. Who's the Deputy Group? CEO Bank, ABC. A legend in the industry. Also the founder. The Fintech Forum Coming up on your fourth big event, But you're keynoting here at Teresa Carlson and A W s Summit car on digital only bank. Welcome to the Cube. Thank you, John. Thank you. Pleasure to speak to you today, so I got a lot to talk about, but digital only bank. This is a really special time in history. Now we're living in a digital error and the digital is driving business change and digital business is on the on the plate of every major executive in the world. How are you enabling digital business, >> John? The way, The way I look at things that two years ago I have went to my board. I said, I wanna disrupt the bank. I want to develop a business digital strategy. And to do that, we have three pictures. We have to run the band first and transform the back. To do that, we have to continue investing on modernizing the bank. Then you evolve the bank investment by creating new product. And finally you just hop the back and how you disrupt the bank. And she's using the digital highway and introducing the cloud computing on bringing a new tools wishes changed the framework and changed the mechanics of the bank. So we created way are not a digital only bank which will be going live in two months time. We launch a wallet on all that sitting on our eco systems it in the cloud the very brave and bold move. But I can tell you, without that you will lose the transformation. Banking today is different than they're gonna be banking of tomorrow. Yeah, we have to start getting into the journey of transformation. >> Bold moves require bold leadership obscenity. You are that this is hard. It sounds easy on paper. You got people to convince. I agree. Changes hard people. Cultural change. How did you do it? What >> was the great question? John are started that in December 2016 I went to my board when the frantic start moving heating our partnership. Look, I want to be on the transformation. Jr and I worked for about 6 to 9 months in greeting our niche with the board unit. Explain to them frantic abroad. Them actually a special fintech speakers guidelines. So I invested heavily. I would always take holder board level people, groups Vo and tell him about the impact of digitization. New cannot afford distant idea. Feel distant. I didn't do nothing. He parked the chain was moved. We'll go without you. So what you're saying is that investment is awareness. You have to explain to him why you cannot treat fintech with the threat. You have to embrace it. >> I love that. I love that leadership. It takes time to nurture and unset the table. Absolutely. And then understand the cloud only strategy. And then you got to sell it and then you gotta implement. These are the new dynamic >> fears fears. John is that most of the stakeholder. They think of cloud as secure and the spending of times They're not security. Instead, he was spending the time questioning spend their time to improve the security with Amazon. We are partners Now we sit with them and doing a demon years. And I tell you, I'm putting all my critical banking system on the ground. >> When you talk to Amazon, you're a bank. Thanks. Have money. Thanks can be act. There were people who worried about all this. Trust is important opportunity. What made you decide to go a W s? >> There's a very first of all. When I decided to individuals with my decision, I looked at you guys, the investment you made it in security, the investment you make or not. Indeed, I looked you committed to the region. I was someone my neighbor you guys have invested that made a bold move to be in Bahrain That did a lot, picked a lot of boxes for me and that was an important move from AWS. And I tell you many regulators today they were going to cloud eventually. So it's very important that anybody is Bridget himself in the >> region. But we love a W s because we covered them. And in depth is part of our media business. And we look at disruptions to and I want to get your thoughts on fintech disruption. We were talking before we came on camera about some key times in history where disruption happened in trade. That is a tale sign for what's happening in that. Tell the story. >> All right, let me tell you the story. In 1953 the first word cargo ship sent from New York to Houston. That chip change the word made history. Why? Because it has the container. So you could supplier trade, sending through the container to different destinations. You know, before how you used to be put them in the trade through It was a nightmare. That disruption have actually improved the globalization of increased trade business. Today all this equipment here comes Chicago comes through container and each container is labelled through computer. So we re vision today Fintech made the same making same disruption in banking on my notions, everything >> and wears a similarity. Scale >> matter, united disruption. This came a volume betrayed should become disrupted. Everybody start using your container. The business the globalization fintech, globalization, skin awareness. So what? The way I see similar to that we had a disruption. Trade become more familiar. Lifestyle has improved. You can now put your house. Your TV is coming From where? From? Your Maybe >> all the cargo containers have changed. Shipping fintech is changing banking, banking >> and lifestyle. You got it. You got a job. >> And so now, well, ironically, two containers or changing the cloud Because containers and kubernetes and frustration, This is about software. That's right. Software money has been a term kicked around. When you hear the word software defined currency software to find money, what do you think of? >> Look, at the end of the day, who writes software people? Human. So basically, the thinking has become from you and I for the people that sit and think about the codes. Yeah, programmer the corner. But there's somebody behind it thinking So you have a thinker on this thing? Got Arjun you the word not the coda. >> Big moves and Finn ticker happening. I want to get your thoughts on leadership in this world where cloud has pretty much instant benefits if you execute properly. Absolutely. There's also architectural thinking. So the word business architecture is not something that they teach in business school or sometimes has to be learned over time to operate a business and go have a growth strategy in changing technological landscapes requires a business. Architecture has to have a ballistic systems thinking this is hard. You've done that for multiple years. What if you learned what were some of the challenges that you came? >> You have to look at when you're running a big enterprise. You have a lot of investment around the world, and a lot of duplication are out of inefficiency at the leader. Money to show my steak stakeholder efficiency. I want our own better shop. Now, if technology can help me to do that, why not? You'll have to jump onto the wagon? Yeah, yeah, I cannot sit idle, and I see a better efficient shop but bank running more efficiently. So I looked with technology addition to disrupt the way I work, but positive disruption. Yeah. The main thing is that the disruption should be a catalyst for a positive change. So what I learned I learned efficiency would the digital disruption with the cloud today and instead of putting 25,000 servants to serve my 18 countries. I put in the cloud. You done it. You know the economy is Ken Jordan and the shaving >> the flywheels. Amazing. The operational efficiency are amazing. As an executive, you managed to results at the end of the day. Business is business. Results matter. How are your results? You gotta make money >> at the bank. It I have a good show. Uh, results. >> Got to make some money. How do you see that? Evolving digital only business. What's your strategy? What's your roadmap? For? How you see the money making kicking >> in, John, that our clients becoming sophisticated. My corporate client today if I don't deliver in a digital solution through his statement, give the payment, Foster. He's gonna go for a fintech company because today, front companies are competing with me eating my lunch. So I have to prove, if my client becoming digital Chevy more sophisticated, I cannot sit and watch. So I have to invest heavily. You make sure my client is satisfied giving the right cash management to transaction banking. All this the book payments or this have to be alternated. So I have to be continue looking after my client. That's where the money >> are you happy where you are, Where you're at right now? Are you happy with work? >> There's always room for improvement. I will continue. Invest on Are we innovate because you cannot stop. I mean, look, I'm a zone today. Would they stop? >> No, no, they're not stopping >> way. We need to continue. >> What's your areas that you think about for the next five years? Fintech. What are an important area? >> You know what? I think Joe's gonna affect our lifestyle. A I artificial intel. I feel way only seen the beginning. We have seen nothing out of the way. A bank, ABC. We just lost our first digital employees. Uh, her name is Fatima, and we will be coming live in November. I tell you, this is the beginning. I feel artificial intelligence is gonna affect our work force in the world. >> Tell me more about this digital employees concept. >> Well, that until passionate about what we worked with a company called So machine in you Zeeland on there are the same people who actually invented you've seen the movie of it all. Yeah. So the same people actually design movie and avatar work. We work with them to create a footman. Fatma is already being trained. Do help a new Crying Toe Bank, ABC. The Tissue Bank Open account. Two shoes. A correct credit cards for you Help you and also to be able to have a chat with her to be able to ask you a question. Jerry. A question about the Bahrain about the population but banking. It's a journey on. The journey's so far being successful, and I continue. If you ask me the question in a year's time, I would tell you probably thought it would be somebody else. >> This is helping augment the experience for your customers. That's the goal, >> absolutely. And from experience with my consumer. >> Okay, I want to change the subject and talk about the fact you're the founder of the Fintech Forum. You've had your third edition for the coming up. Talk about the event. What's the purpose? >> Uh, in in about 2016 December, I went to them sentimental by rain Governor and I said, We want to sponsor a frantic conference so self like impressed that immediately and British idea. And then I went back to my board my good CEO, I said, We want to sponsor the fish interconference on my papers at the time, the warning Because at that time in nearly 2017 we hear different around the region. Nobody actually sat there and say, Was the interactive printed on banking? You see, other victim. So I launched the first winter conference Was its success 2nd 1 also a sex. And the 3rd 1 was a kook because we put the top speaker on the world. And now people are judging me for the 4th 1 event business Now. No way about Responsive Bank. NBC's was the sole sponsor. >> That's exciting. And I think these events are changing, too. The fact that you're getting into events, you're contributing your knowledge, your also sponsoring providing some working capital >> contribution as a bank. International bank. At least I can do to support my reign infrastructure in the vision in a fintech. Sorry, >> no problem. Think out of the water. I want to say I want to get your thoughts on something. I feels important. I said this last year and the year before, when Amazon launches a new region, yeah, it creates a revitalisation. It has computing power has all those things. It's a center point of innovation. How do you see that same thing? And what's, um, things that people might not know about the Amazon relationship to the area? Because all this innovation and enablement fintech societal change the government ministries are coming online here by rain. Entrepreneurs are creating value. They're getting funded this liquidity banks, air going fintech A modernization wave is happening with a new generation of young people and existing businesses. This is a digital complete modernisation. Your thoughts on on all this digital transformation Societal at a societal level with Amazon >> Amazon already contributed to the digital economy was in it in the world. And I've seen already the impact this part of the world. The fact that this conference is summer today I can't answer your question. Look at the contribution. You have about 2000 people here look the excitement of what to bring into the region. I have seen people today from Chaudhary from Kuwait, from Morocco, from Amman, from Egypt here. So you are actually building the knowledge excitement and you also have been people to understand the ecosystem and what was missing. So what you doing any bill Us now actually investing heavily on educating awareness of the digital destruction and digital economy. You are participating in the digital economy. I mean, also today I heard from Malaysia. Very good. Exactly. Sponsoring a program a degree with University of Bahrain doesn't pass just free. So this is basically you Continue doing that. And you find AWS is already effective. Life like the clock is gonna be so I think it's body is already contributing the digital economy worldwide. >> You know, I'm fascinated. I'd love to have more conversations with you on this, Maybe at your forum. But one thing I want to get your thoughts on is with digital collaboration is not just face to face. You meet people here from different countries, but then we go back to our place is but we're still together digitally. So the scale of cloud computing and digital is impacting not just money collaboration. What's your vision on how collaboration and the role of people are going to play in this new dynamic? John, >> if you have asked me three years ago our video I was a threat with winter company with your banker. They're gonna eat my lunch. But today you realize with time the only way you can move on trust through collaborating with the company. That's why today I'm sitting with AWS sitting with other victim basically breaking people. I know my banks, but I don't know how to build a clock ticking. So I caught a break. I don't know how to move on a new Sophia or so I go with. I want to use it to get that. You see what I mean? >> Yeah, I think you have another good point that we reported on many times. And that is that when you collaborate with these technologies, it makes the domain expertise and the data that you >> have >> more intellectual, more emotional property because, you know, banking intimately. You have data, you have customers. That's your intellectual property. You could use that faster with the resource. This is a new competitive advantage >> with analytics would get the science. The data is the new order, if you should, but you need the tours. You need another unique data scientist. And when you have the distant scientist dental become than yours. >> So he'll thank you for sharing your awesome insights here and let you hear about rain. Really appreciated. Congratulations. Tino speaking. Bank, ABC. Thank you. Going all digital. Bold moves. Bold leadership. Thank you. Thank you very much. We're here in the Cube. We're live broadcasting here and by rain. 80. This summit. I'm John Ferrier. Thanks for watching.

>> From Cambridge Massachusetts, it's the CUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by silicon angle media. >> Welcome back to Cambridge Massachusetts everybody you're watching the cube. The leader in live tech coverage. The cubes two day coverage of MIT's CDOIQ. The chief data officer information quality event. Thirteenth year we started here in 2013. I'm Dave Vallante with my co-host Paul Gillin. Veda Bawo. Bowo. Bawo. Sorry Veda Bawo is here. Did I get that right? >> That's close enough. >> The director of data governance at Raymond James and Althea Davis the former chief data officer of ING bank challengers and growth markets. Ladies welcome to the cube thanks so much for coming on. >> Thank you. >> Thank you. >> Hi Vita, talk about your role at Raymond James. Relatively new role for you? >> It is a relatively new role. So I recently left fifth third bank as their managing director of data governance and I've moved on to Raymond James in sunny Florida. And I am now the director of data governance for Raymond James. So it's a global financial services company they do asset wealth management, investment banking, retail banking. So I'm excited, I'm very excited about it. >> So we've been talking all day and actually several years about how the chief data officer role kind of emerged from the back office of the data governance. >> Mmm >> And the information quality and now its come you know front and center. And actually we've seen a full circle because now it's all about data quality again. So Althea as the former CDO right is that a fair assessment that it sort of came out of the ashes of the back room. >> Yeah, I mean its definitely a fair assessment. That's where we got started. That's how we got our budgets that's how we got our teams. However, now we have to serve many masters. We have to deal with all of the privacy, we have to deal with the multiple compliancies. We have to deal with the data operations and we have to deal with all of the new, sexy emerging technologies. So to do AI and data science you need a lot of data. You need data rich. You need it to be knowledge management, you need it to be information management. And it needs to be intelligent. So we need to actually raise the bar on what we do and at the same time get the credibility from our sea sweet peers. >> Well I think we no longer have the. We don't have the luxury of being just a cost center anymore . >> No. >> Right, we have to generate revenue. So it's about data monetization. It's about partnering with our businesses to make sure that we're helping to drive strategy and deliver results for the broader organization. >> So you got to hit the bottom line. >> Yeah. >> Either raise revenue or cut costs >> Yeah absolutely >> You know directly that can be tangibly monetized. >> Exactly keep them out of jail. Right. Save money >> That too. >> Save money, make money. (inaudible laughter) keep them out of jail. >> Like both CDO's you do not study for this career path because it didn't exist a few years ago. So talk about your backgrounds and how you came to come into this role Veda. >> Yeah absolutely so you know you talked about you know data kind of starting in the bowels of the back office. So I am that person right. So I am an accountant by training. So I am the person who is non legally entity controllership by book journal entries I've closed the books. I've done regulatory reporting so I know what it feels like to have to deal with dirty data every single month end, every single quarter end right. And I know the pain of having to cleanse it and having to deal with our business partners and having experienced that gave me the passion to want to do better. Right so I want to influence my partners upstream to do better as well as to take away some of the pain points that my teams experiencing over and over again it really was groundhog day. So that really made me feel passionate about going into the data discipline. Right and so you know the benefit is great it's not an easy journey but yeah out of accounting finance and that kind of back office operational support was boring right. A data evangelist and some passionate were about it. >> Which made sense because you have to have quality. >> Absolutely. >> Consistency. You have to have so called single version of the truth. >> Absolutely because you look regularly there's light for the financial reports to be accurate. All the time. (laughter) >> Exactly >> How about you? >> I came at it from a totally different angle. I was a marketeer so I was a business manager, a marketeer I was working with the big retail brands you know the Nikes and the Levi's strauss's of the world. So I came to it from a value chain perspective from marketing you know from rolling out retail chains across Europe. And I went from there as a line management position and all the pains of the different types of data we needed and then did quite a bit of consulting with some of the big consultancies accenture. And then rolled more into the data migration so dealing with those huge change projects and having teams from all of the world. And knowing the pains what all of the guys didn't want to work on. I got it all on my plate. But it put me in position to be a really solid chief data officer. >> Somebody it was called like data chicks or something like that (laughter) and I snuck in I was like the lone >> Data chicks >> I was like the lone data dude >> You can be a data chick. It's okay no judgement here. >> And so one of the things that one of the CDO's said there. She was a woman obviously. And she said you know I think that and the stat was there was a higher proportion of women as CDO's than there were across tech which is like I don't know fifty seventeen percent. And she's positive that the reason was because it's like a thankless job that nobody wants and so I just wonder as woman CDO your thoughts on that is that true. >> Well first of all we're the newest to the table right so you're the new kid on the block it doesn't matter if you're man or woman you're the new kid on the block so you know the CFO's got the four thousand year history behind him or her. The CIO or CTO they've got the fifty, sixty year up on us. So we're new. So you have to calve out your space and I do think that a lot of women by nature like to take on things big. To do things that other people don't want to do. So I can see how women kind of fell into that. But, at the same time you know data it's an asset and it is the newest asset. And it's definitely misunderstood. So I do think that you know women you know we kind of fell into it but it was actually something that happened good for women because there's a big future in data. >> Well let's just be realistic right. Woman have unique skillset. I may be a little bias but we have a unique skillset. We're able to solve problems creatively. Right there's no one size fits all solution for data. There's no accounting pronouncement that tells me how to handle and manage my data. Right I have to kind of figure it out as I go along and pivot when something doesn't work. I think that's something that is very natural to women. >> Yeah. >> I think that contributes to us kind of taking on these roles. >> Can I just do a little survey here (laughter) We hear that the chief data officer of function is defined differently at different organizations. Now you both are in financial services. You both have a chief data function. Are you doing the same thing? (laughter) >> Absolutely not! (laughter) >> You know this is data by design. I mean I'm getting lucky I've had teams that go the whole gammon right so. From the compliancy side through to the data operations through to all of the like I said the exotics, sexy you know emerging technologies stuff with the data scientists. So I've had the whole thing. I've also had my last position at ING bank I had to you know lead a team of chief data officers across three different continents Australia, Asia and also Eastern and Western Europe. So it's totally different than you know maybe another company that they've only got to chief data officer working on data quality and data governance. >> So again another challenge of being the new kid on the block right. Defining roles and responsibilities. There's no one globally, universally accepted definition of what a chief data officer should do. >> Right >> Right is data science in or out are analytics in or out. Right. >> Security sometimes. >> Security right sometimes privacy is it or out. Do you have operational responsibilities or are you truly just a second line governance function right? There's a mixed bag out there in the industry. I don't know that we have one answer that we know for sure is true. But I do know for sure is that data is not an IT function. >> Well okay. That's really important. >> It's not an IT asset. >> Yeah. >> I want to say that it's not an IT asset. It is an information asset or a data asset which is a different asset than an IT asset or a financial asset or a human asset. >> But and that's the other big change is that fifteen. Ten to fifteen years ago data was assumed to be a liability right. >> Mmm. >> Federal rules set up a civil procedure we got to get rid of the data or you know we're going to get sued. Number one and number two is that data because it's digital you know people say data is the new oil. I always say it's not. It's more important than oil. >> It's like blood. >> Oil you can only use in one use case. Data you can reuse over and over again. >> Reuse, reuse perpetual. It goes on and on and on. And every time you reuse it the value increases. So I would agree with you it is not the new oil. It is much bigger than that and it needs to I mean I know from some of my colleagues in the profession. We talk about borrowing from other more mature disciplines to make data management, information management and knowledge management much more robust and be much more professional. We also need to be more professional about it as the data leaders. >> So when you're a little panel today. One of the things that you guys addressed is what keeps the CDO up at night. >> Yes >> I presume it's data. (laughter) >> No, no, no. >> It's our payers that don't get it. (laughter) >> That's what keeps us up at night. >> Its the sponsors that keep us up at night. (laughter) So what was that discussion like? >> So yeah I mean it was a lively discussion. Um, great attendance at the panel so we appreciate everyone who came out and supported. >> Full house. >> Definitely a full house. Great reviews so far. >> Yep. >> Okay, so the thing that definitely keeps folks up at night and I'm going to start with my standard one which is quality. Right you can have all of the fancy tools, right you can have a million data scientists but if the quality is not good or sufficient. Then you're no where. So quality is fundamentally the thing that the CDO has to always pay attention to. And there's no magic you know pill or magic right potion that's going to make the quality right. It's something that the entire organization has a rally around. And it's not a one thing done right it has to be a sustainable approach to making sure the quality is good enough so that you can actually reap the benefits or derive the value right from your data. >> Absolutely and I would say you know following on from the quality and I consider that trustworthiness of the data. I would say as a chief data officer you're coming to the table. You're coming to the executive table you need to bring it all so you need to be impactful. You need to be absolutely relevant to your peers. You also need to be able to make their teams in a position to act. So it needs to be actionable. And if you don't have all of that combination with the trustworthiness you're dead in the water. So it is a hard act and that's why there is a high attrition for chief data officers. You know it's a hard job. But I think it's very much worthwhile because this particular asset this new asset we haven't been able to even scratch the surface of what it could mean for us a society and for commercial organizations or government organizations. >> To your point it's not a technology problem when Mark Ramsay who was surveying the audience this morning. He said you know why have we had so many failures and the first hand that went up said. It's because of relations with the database. >> And I wanted to say it's not a technology problem. >> It's a hearts, minds and haves >> Absolutely. Absolutely. You couldn't make an impact to your data landscape without changing your technology. >> You said at the outset how important it is for you to show a bottom line impact. >> Right >> What's one project you've worked on or that you've led in your tenure that did that. >> If we're talking about for example I can't say specifics but if we're looking at one of institutions I worked at in an insurance firm and we looked at the customer journey. So we worked with some of the different departments that traditionally did not get access to data for them to be able to be effective at their jobs. But they wanted to do in marketing was create actually new products to make you know increase the wallet from the existing customers other things they wanted to do was for example, when there were problems with the customers instead of customer you know leaving you know the journey they were able to bring them back in by getting access to the data. So we either gave them insight like you know looking back to make sure that things didn't happen wrong the next time or we helped them giving them information so they could develop new products so this is all about going to market. So that's absolutely bottom line. It's not just all cost efficiency and products to begin . >> Yeah pipeline. (laughter) >> And that's really valid but you know. >> Absolutely so I'll give you one example where the data organization partnered with our data scientists. To try to figure out the best location for various branches. For that particular institution. And it was taking right trillions of data points right about current footprint as well as other information about geographic information that was out there publicly available. Taking that and using the analytics to figure out okay where should we have our branches, our ATM's etc... and then conslidating the footprint or expanding where appropriate. So that is bottom line impact for sure. >> I remember in the early part of the two thousands I remember reading a Harvard business review article about gut feel trumps data every time. But that's an example where no way. >> Nope. >> You could never do better with the gut than that example that you just gave. >> Absolutely. >> Veda. I want to ask you a question. I don't know if you've heard Mark Ramsays talk this morning but he sort of. He sort of declared that data governance was over. >> Mmm. >> And as the director of data governance >> Never! >> I wondered if you would disagree with that. >> Never! >> Look. >> Were you surprised? >> It's just like saying that I should stop brushing my teeth. Right I always will have to maintain a certain level of data hygiene. And I don't think that employees and executives and organizations have reached a level of maturity where I can trust them to maintain that level of hygiene independently. And therefore I need a governance function. I need to check to make sure you brush your teeth in the morning and in the evening. Right and I need you to go for your annual exam to make sure you don't have any cavities that weren't detected. Right so I think that there's still a role for governance to play. It will evolve over time for sure. Right as you know the landscape changes but I think there's still a role right for like governance. >> And that wasn't my takeaway part. I think he said that basically enterprise data warehouse fail massive data management fail. The single data model failed so we punted to governance and that's not going to solve the enterprise data problem. >> I think it's a one leg in the stool. It's one leg in the stool. ` >> Yeah I think I would really sum it up as a monolithic data storage approach failed. Like that. And then our attention went to data governance but that's not going to solve it either. Look, data management is about twelve different data capabilties it's a discipline so we give the title data governance but it means multiple things. And I think that if we're more educated and we have more confidence on what we're doing on those different areas. Plus information and knowledge management then we're way ahead of the game. I mean knowledge graphs and semantics. That puts companies you know at the top of that you know corporate inequality gap that we're looking at right now. Where you know companies are you know five and thousand times more valuable then their competition and the gap is just going to get bigger considering if some of those companies at the bottom of the gap are you know just keep on doing the same thing. >> I agree I was just trying to get you worked up. (laughter) >> Well you did. >> It's going to be a different kind of show. >> But that point you're making. Microsoft, Apple, Amazon and Google, Facebook. Top five companies in terms of market cap. And they're all data companies. They surpass all the financial services, all the energy companies, all the manufacturers. >> And Alibaba same thing. >> Oh yeah. >> They're doing the same thing. >> They're coming right up there. With four or five hundred billion. >> They're all doing the knowledge approach. They're doing all of this stuff and that's a much more comprehensive approach to looking at it as a full spectrum and if we keep on in the financial industry or any industry keep on just kind of looking at little bits and pieces. It's not going to work. It's a lot of talk but there's no action. >> We are losing right. I know that Fintechs are right fringing upon are territory. Right if Amazon can provide a credit card or lend you money or extend you credit. They're now functioning as a traditional bank would. If we're not paying attention to them as real competitors. We've lost the battle. >> That's a really important point you're making because it's all digital now. >> Absolutely. >> You used to be you'd never see companies traverse industries and now you see it Apple pay and Amazon and healthcare. >> Yeah. >> And government organizations teaming up with corporations and individuals. Everything is free flowing so that means the knowledge and the data and the information also needs to flow freely but it needs to be managed. >> Now you're into a whole realm of privacy and security. >> And regulations right. Regulations for the non right traditional banks. So we're doing banking transactions. >> Do you think traditional banks will lose control over the payment systems? >> If they don't move with the time they will. If they don't. I mean it's not something that's going to happen tomorrow but you know there is a category of bank called Challenger banks so there's a reason. You know even within their own niche there's a group of banks. >> I mean not even just payments right. Think about cash transactions like if I do money transfer am I going to my traditional bank to do it or am I going to cashapp. >> I think it's interesting particularly in the retail banking business where you know one banking app looks pretty much like other and people don't go to branches anymore and so that brand affinity that used to exist is harder and harder to maintain and I wonder what role does data play in reestablishing that connection. >> Well for me right I get really excited and sometimes annoyed when I can open up my app for my bank and I can see the pie chart of my spending. They're using my data to inform me about my behaviors sometimes a good story, sometimes a bad story. But they're using it to inform me. That's making me more loyal to that particular institution right so I can also link all of my financial accounts in that one institutions app and I can see a full list of all of my credit cards, all of my loans, all of my investments in one stop shopping. That's making me go to their app more often versus the other options that are out there. So I think we can use the data in order to endear the customer source but we have to be smart about it. >> That's the accountant in you. I just refuse to not look. (laughter) >> You can afford to not look. I can't. >> Thank you. >> Thanks for riling us up. >> Alright thank you for watching everybody we'll be right back with our next guest right after this short break. You're watching the cube from MIT in Boston, Cambridge. Right back. (atmospheric music)

(upbeat music) >> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering your Red Hat Summit 2019. Brought to you by Red Hat. >> And welcome back to Boston, we continue our coverage here in theCUBE of the Red Hat Summit and welcoming now to theCUBE stage for the first time, I believe, David Gledhill. Who is the group chief information officer and head of group technology and operations at DBS Bank. David, good morning to you. >> Morning, hi, it's great to be here. >> All the way from Singapore, and he was early for the segment this morning. So you get extra points for that, congratulations. >> Put that up to jet lag. (men laughing) >> Thanks for being with us. David, anymore we talk about companies in general, everybody says everybody's a tech company now, right? Not the way it used to be. How is that playing out in your world in financial services as far as how deeply ingrained you have to be with the technology? >> Yeah, so very much, we are. Tech transformation started about 10 years ago. Been CIO of the company about 10 years. And frankly, the first five years were just fixing the basics. So getting in place what we'd call world-class systems. Doing a bunch of stuff on resilience and security and all of that kind of stuff. And the other thing, and this is the dramatic change, you know 10 years ago when I joined the company, we were 85% outsourced to managed service vendors. So I had technology people that basically were signing contractors and managing service agreements. We didn't have technology DNA. Over those five years and a full 10 years actually. We've been doing a lot about just insourcing and rebuilding our technical muscle if you like. So now we're, we've gone from 85% outsourced to 90% insourced. So we run, build and manage our own. We're now a technology company. >> And five years ago, we had a real big shift and we were closest to what was going on in China and so probably saw this before many, many of the other banks saw this around the world. Of what Alibaba was doing with Ant Financial and Tencent and this whole, just complete disruption of how customers interact with the banking industry. So we got an early lead on this digital transformation and really for the last five, six years have been doubling down on building a pure digital offering and we see ourselves as a technology company providing banking services, not as a bank with some technology department in the backend. >> Yeah, I'd love if you can, a little bit, to help us dig into that because, I think back it was okay, what does digitization mean 10 years ago, it was like oh, okay, I need to make sure I have a good website and maybe a good mobile app. Which is a fine starting piece, and also the piece you talked about is when it was outsourced, I'm managing pieces but I sign up for what I need today and when the business needs something, those outsources aren't necessarily tied to them, so you're playing telephone with them. Most the companies I've talked to that have brought skills back inside, it's because the needs of the business are constantly asking for more and it can't be well, it's not part of our contract with what we have. We'll get to that in a year or two. >> Yeah, yeah, so that's a very interesting shift. It plays out in a number of different dimensions. First of all, let me go into that question of business and tech and that separation. When we were managed service, it was literally writing contracts and the specs and handing to vendors. It was just a horrible process. And how can you be a modern technology firm like that? So insourcing, for us, was one big thing to owe those people, but when you insource, then you end up with a business unit and a technology unit. And you still have got silos, so how do you break that? Because that really is a problem. If you look at the way technology companies work, they don't work like that. Five years ago when we said, okay, how do you make that flip to being the digital company? We went very deep into how some of the great technology companies operate. We wanted to understand, what is it? How does that DNA work? How does that culture work? How do they organize themselves? How do they build technology? How do they become agile, speed to market? And so we looked at, we studied Google, Amazon, Netflix Apple, LinkedIn, Facebook and we call them the Gandalf companies. And we said, how can we be more like them? Well, a Gandalf is missing a D. And fortunately at DBS, we happen to have a D. (laughing) So our goal became how do we become the D in Gandalf? And that was just like a lightening rod through the organization because all of the sudden it said to our people, forget about biz and tech and things. You need to think about how these technology comp operates and be more like them. Which means there's no separation, there are no silos we are together building great technical products for our customers so we need to re-think the organization to make sure that happens. >> There's magic. (laughing) You've got a saying, making banking joyful. >> Yep. >> All right, which is not exactly the emotion that I associate with having to deal with my bank. It's fine, but joyful? A very unusual adjective there. What's that all about? And again, at the end of the day, how does technology enhance that? How does that compliment that and really boosted that? >> Yeah, so it was quite a radical moment for us. That came up, we were at a leadership meeting and talking about what is our purpose and how do we. Lots of people talk about customer journey, thinking and stuff like that but how do you bring that to life and one of the execs there said, well what about making banking joyful? And the rest of us just looked at him like he was from a different planet. Saying, are you kidding, what do you mean by that? But as we thought about it more, it has a great meaning and a great purpose to it, that we're not there just to do transactions but we're there to enrich lives, create new businesses, to make customers feel great in their financial stability, in the way they deal with us. And it applies on so many levels. So if you're a bank teller, you understand how to make banking joyful by just that, going the extra mile, in terms of service. If you're in infrastructure, and you're dealing with data centers and servers, you understand that making banking joyful, you must be there all the time. You must have sub-second responses, you must feel great in the customers hands, so it's something that you can apply to all aspects of banking and everybody plays a part in making banking joyful for our customers. >> All right, so David, bring us inside a little bit your organization, you said transforming to a technology company. What's that mean, what technology are you using? We're here at the Red Hat Show. Open-source, not the first thing that people think about when they they think about banking. So how does that fit into the culture that you're building? >> Yeah, yeah, okay, so, this Gandalf thing that I talked about, that's great as a logo and a mindset shift, but it doesn't get you very far. And so what we came up with is five key elements that have to change. And we had to work on to become more like a technology company. And one was a shift from projects to running technology like a series of platforms. That enables you to do agile at scale, but for that you need to organize very differently, which is the third thing. And then the fourth thing is that you need to build for modern systems. The legacy way of building technology just wasn't going to get us to where we needed to be as a technology company. And then the last bit is alternate everything. So, if you want speed to market and agility, you have to alternate. That modern technology stat, it was very obvious to us that the legacy, corporate technologies that we used to build systems, were just not going to win it for us. And so that's our move to open-source. Red Hat was a fabulous partner in that and we used Red Hat extensively throughout our entire infrastructure. And so we went through this rapid modernization of moving to open-source, moving to open-source database we used Merare DD quite extensively, but also, picking up pieces of the open-source from the Gandalf companies. We've seen the way they use open-source to scale. Plus also, to provide just amazing services. So for example, Netflix. We run a bunch of banking platforms on Netflix, believe it or not. It's kind of cool, banking on Netflix is a kinda crazy concept, but we brought that do life. What is is that Netflix we loved? We loved their engineering discipline around chaos engineering and the use of chaos to really build resilient platforms. So in our whole test and deployment framework, we have a lot of Netflix chaos elements built into that to make sure that when we actually are testing, we're testing for chaos and random failures which we inject into those platforms. We don't do it in production like Netflix do quite yet. This whole concept of site reliability and chaos and excellence of service is again something we learned from the Gandalf companies. So Gandalf was not just a, oh yeah, let's pester in the heart of the business article. It was really, let's use their engineering disciplines and design principles to build our own systems. Our network, Facebook, which is, you think of it as a network company. We think of network and the infrastructure layer. And our infrastructure and our networking is designed on a bunch of concepts that Facebook have about how they build their network within their data centers. >> Can you help connect the dots, you talk about a phenomenal technologies, chaos engineering, networking like these global companies. How does that lead to the outcome that you talked about? You know, joy to your end users? >> So if you want to make banking joyful, you have to be super-reliable. You have to be on the edge of the innovation curve all the time. Which means you need to be test and learn, which means you'll be very agile. You need to be able to scale very well and the open-source technologies enable us to scale superbly. You need to be able to to perform as well, superbly well. When I joined the company, our measure of how well our applications were performing is are they up or down? And then we advanced that to, well, are they up and maybe 80% of the time they responded within four seconds. Those are terrible measures because they're not joyful. That means 20% of the time we're awful. That doesn't bring joy to a customer. So what brings joy is we've now scrapped all those things and we're starting to look at performance, for example at the 99th percentile, so anything below that is just noise and the signal is what is our worst performing 99%, because if you wanna be joyful every single time that a customer opens your application you wanna be there and respond well, et cetera, et cetera. Same thing goes for customer science. Where do you get customer drop offs and how do you fix problems? So customer science and the engineering disciplines around observing and instrumenting a platform all the time become very , very important. So it goes very, very deep. You know it's a simple concept. But totally changes the way we engineer. >> Your line of work, or your industry obviously is very security oriented, right? >> Yeah. >> I think of healthcare being another with health information what have you. But certainly financial services, so in the open-source community, how do you address this, I would say it's not a clash by any means, but it's a concern, I would think, still that you have to be micro-observant of security practices and yet this is an open-community and exchange of ideas and could be an exchange of vulnerabilities or problems, too. >> So, sure, and we absolutely do. There are certain things that we, certain places that we won't go, or we will go but only for experimentation reasons because of that question. But you know arguably, we think that open-source company can more secure over time than non-open source, because you're also getting a bunch of people fixing it the whole time. And we've seen some of the issues with some of the open-source and the heart bleed and those other bits and pieces. But they were shut down pretty quickly and found pretty quickly. So we move with caution, we're very cognoscente. We move with our eyes open and it's really the zero day vulnerabilities that we are exposed to. But equally, if you're in a proprietary state. The whole thing that came up with the X86 platform in terms of the vulnerabilities there that apply open-source or not. So yeah, security issues exist everywhere. >> Right, all right, so, David, bring us in. You talked about some of the open-sourced technologies. Where does Red Hat fit into this? What's it like, how have they advanced that journey that DBS has been on? >> So for the heavy lifting, for the big applications that we want to run, and the majority of our workload going with open-source is fine, but you want to have open-source that also you think isn't going to break or have big security vulnerabilities to your question. And that's really where a partner like Red Hat comes in because it gives us access to all the wonderful benefits of open-source with a trusted partner that's putting industrial strength quality releases out that we can really rely on and bank on. So, in awhile we used open-source, at the periphery and true open-source that we just plug it off the internet. The really very, very high demanding workloads and very secure workloads we will always work with a partner that can wrap that into an enterprise-quality offering for us. So Red Hat has gone from zero to running way over 50%, 60% of our workload. And we'll continue to put it even more on that because it's a platform we can trust. >> That's great, so, when you look at your journey overall, how far are you along that journey? Anything when you look out, what are some of the things that are exciting you looking forward? >> So while we believe we're ahead of most banks. There are some that are in the mix, Goldmans are pretty far advance, Duetsche, a couple of the others, Capital One, a few. But it's a sort of rare breed. We're about 80%, 90% done on our transformation journey to get stuff to what we'd call cloud ready or optimized. But we think we're just scratching the surface because if you think about plugging ourselves into customers lives, making banking joyful, our external brand promise for that is live more, bank less. And nobody wakes up in the morning and says, oh, I can't wait to go to my local bank branch and go and do some banking. >> (laughing) I heard Steve talking about it yesterday. >> Actually there is one place. My wife loves going there because we do some great free cookies at DBS. >> John: Oh, excellent. >> But other that my wife, the rest of the planet doesn't really do that. >> John: Fair enough. >> If you want to live more, bank less, it's how do we get the toil of banking away from customers yet embed ourselves in their journeys. And for that we believe that this whole play on ecosystems is very important. So being a creator of an ecosystem or participating so that we take the banking toil out, and yet we inject ourselves, be that leisure or travel or insurance or whatever. And you don't see the bank, the bank is invisible. Then you're live more, bank less. To do that, you need great ecosystems. And we think three things help us to plug into ecosystems. Number one is you have to be able to scale very easily. And all the work we've done on the Gandalf stack means that were no longer afraid of scale, just bring it on. The second thing you need a lot of connectivity, and DBS two years ago, we launched the world's largest banking API platform. We went live with 150 different APIs and 60 live partners at the time. That's now grown to over 350 APIs and 100s of corporates and SME partners that wanna partner with us to pug us into their offering. So the more we do that, the more we disappear and let people live more, bank less. >> Well, next time, if you wanna bring some cookies with you, by all means, okay. (David laughs) We're always up for that, David, thanks for the time. >> Sure. >> We appreciate that and good luck on the mission and the journey there at DBS. >> Sure, thanks very much and great to be here, thank you. >> David Gledhill joining us this morning here, as we continue our coverage of the Red Hat Summit. We're in Boston. You're watching theCUBE. (upbeat music)

>> Live from Los Vegas. It's theCUBE covering Dell Technologies World 2019. Brought to you by Dell Technologies and it's ecosystem partners. >> Welcome back to Los Vegas everybody. You're watching theCUBE, the leader in live tech coverage. This is our wall-to-wall coverage. We're wrapping up day one. I'm Dave Alonte, my cohost here at this segment is Stu Miniman. Jon Siegal is here as the vice president of product marketing, cubulum from Dehli MC. Good to see you again Jon. >> Great to be back as always guys! >> And I love that you brought a customer, Suzan Pickett is here. >> That's what I do, by the way. You realize that, that's my new thing. >> Suzan is the VP and director of Converged Infrastructure at US Bank. >> Thank you for having me. >> Welcome, one of my banks. I got a lease with US Bank. You guys are great. >> Thank you. >> Great to have you guys. >> Let's start with a customer, if that's okay? >> Absolutely. >> Tell us about your role, you got CI in your title that's interesting. >> I do. >> That's a relatively new trend, explain that. >> Yeah absolutely, so I've been at the bank a couple years now and my teams focus on Converged and Hyperconverged Infrastructure, delivering solutions and infrastructure as a service for our business. >> You guys have been working together for a while if I understand it Jon, right? Talk a little bit about what's happening here at the show maybe give us a quick overview of what's happening in CI and HCI in your world. >> Absolutely, so a lot going on as you saw today in Dell Tech cloud announcement. HCI was a key pillar there. Really VxRail, in particular, was featured as the simple and fast foundation for the Dell Tech cloud both as the on-prem manage version as well as, as you heard, the Data Center as a service. So really exciting to see how HCI continues to evolve and it's use cases around cloud and infrastructure as a service, as well as platform as a service as well. So a lot of exciting announcements there. In addition to that, just this past week, by the way, we also, since you mentioned CI, Converged Infrastructure, we just announced that we re-upped our agreement with Cisco, a new multiyear agreement extension to continue to innovate with Cisco on the VxBlock, which, as you know, was the pioneer in this, Converged Infrastructure space and with all the recent integrations we've done now with VMware, VxBlock as well as HCI, is really built to be a on-prem foundation for the cloud. >> Yeah so, this goes back to 2009, when Cisco and VMware and EMC got together and created this concept of Converged Infrastructure. There were other competitors in the market, but you guys kind of lead that trend, and so when you go back to that years ago, that's our storage and networking and compute, they were different parts of the organization. I presume you guys went through a similar journey. You had to put all that together. Herd some calves. And what did that do for your business? What was your journey like to CI? >> I think we're still on that journey, but I think it's also evolving as we go more Agile and more DevOps, more software-defined, we're seeing a lot more blending of the teams as well so we're creating a lot of virtual teams that encompass not just infrastructure but security, developers, networking as well and really being able to deliver that infrastructure's service, platform as a service, end-to-end provisioning for our business lines. >> Suzan, I love that story because I remember talking to, when this started, you talk to the storage group and they'd say, "Oh my gosh, you're going to take away my job." I'm like, "You know that security thing that they've been yelling at you to fix for a while? You talk about the new business apps that we need to do. These are the types of jobs that we want you to do." I heard you talk about Agile and DevOps and all these things. Talk a little bit about, what are the pressures you're facing from the business and the relationship between your group that help you to meet those now. >> Sure, well the first thing we did was we created an infrastructure automation services team and people looked at us like we're a little crazy to do that and we pull those highly, highly motivated potentials from within the organization that we already had to focus on automation and get the foundation for infrastructure as a service and get that part right. Something as basic as provisioning a virtual machine would take 12 weeks or longer and through our journey with Kubernetes today, containers, vRealize Automation Suite, on both Converge and Hyperconverge, VxFlex. We're now reducing that down to about three days and we anticipate, with a lot of our sprints and iterations, that we're going to be getting that down to less than a day within the next quarter. >> So John Furry says that automation is the killer app for infrastructure, so are you guys, are you building essentially out on infrastructure as a service platform, where people used to call it private cloud. I don't know if you use that term still. I think it's still valid. >> We do, yeah. >> How's that going? What's been the business impact of that so-called private cloud? >> We had a Business Critical Application that would often take year release cycles, more than 12 weeks to get a server, primarily focusing on physical servers, and now what we're doing is we're partnering with them with not only the business, the application folks, the developers, the middleware teams, networks, security, but also all of the infrastructure teams to deliver that faster speed to market, and so now they're down to days now to provision. They actually gave us a stat the other day that said, "By using our automation with Kubernetes on Hyperconverge VxFlex, that they were able to have cost avoidance of hiring a bunch of people to build physical servers. So that in and of itself was a huge win, but the fact that we can repurpose and releverage that automation, those workflows, the orchestration models, means that we can continue this conversation with the next business line and the next business line and keep telling that story and it's a good one. >> Jon I'd live to hear from what Suzan was saying and there's so many of the modern things that they're doing. When you look at your customer base, how are they doing on that journey? We used to always ask, in the earlier days, it was like, alright how much was I just eliminating sub-silos but pretty much doing the same apps and same processes before or have I really gone through some transformation? >> I tell you what, we've seen quite a bit of transformation in our customer base because they had to. You look at now, as you see with US Banks, they're now transforming their organization to support DevOps, right? That's an entirely new realm for them to focus on. That means they need to make infrastructure easier and simpler so we're finding that is really, I think, that's the catalyst and that they're realizing that the way to do this is let's make infrastructure as simple as possible. Infrastructure service. Make that platform as a service available so our customers can spend less, wait, our IT department can spend less time on the speeds and fees, if you will, of maintaining infrastructure, more time innovating up the stack versus down the stack, right? >> Alright Suzan, I got to ask a question Jon probably doesn't want me to ask you. You're trying to simplify, 'cause you're doing all this stuff that's not really adding value to your business, you want to do stuff that's going to make you more competitive. Well why don't you just throw all this stuff in the cloud? >> Good question and I think that eventually we will have a multicloud strategy, but it is a bank and we don't want to be in the news for a data breach and that's the real answer but also because we want to, again, lay that foundation for an on-premise, solid infrastructure as a service with service catalogs at the business. We can then drive that product taxonomy and they know that they get a good, solid product from IT and then we extend that into the cloud so as much as we can do that, and maybe there will be some cloud native apps down the road that go 100% in the public cloud. I don't have a crystal ball. I suspect there will be, but again we want to do it right and we think this is the right foundation to lay for that. >> You want to have total control over, certainly, your mission critical apps, I'm presuming, right? Maybe put some stuff up. I'm sure you have plenty of stuff in the cloud. Well why Dell EMC? >> I think it goes back to our strategic partnership. It's always been that strong partnership, that enablement, and that continuous feedback loop. We need something, we go talk to our product teams. We get that back, we get it back from our product teams, so it's not always perfect, and there are competitors out there, but at the end of the day, when we look at the Dell Technologies family and that ecosystem and our ability to integrate, iterate, automate within that family, it just helps us stream like that and standardize. >> We've heard this morning from a lot of folks. Michael Dell talked about it. Jeff Clark talked about it. Companies want to consolidate the number of suppliers, certainly infrastructure suppliers, throwing sass forget it, so many apps now. Are you seeing that? Is there pressure to consolidate the number of suppliers, or do you still have, in certain cases, where you really want to go best of breed, so-called best of breed, for some niche app, or do you want to consolidate suppliers? >> So I always want to standardize because that's going to help our automation story, but I still want best of breed, and so that's one of the primary reasons that we're standardized on Dell Technologies today. VxFlex being one of them and Converged Infrastructure being another. There are use cases for multi-vendor strategy, but again, you would look at the right solution for the right job at the right time. >> Okay Jon, that was a totally loaded question, so can you be both a portfolio company like yours and still be best of breed and if so, how so? >> Well I think what we are, we certainly are a portfolio company in the way that, but I think we have leading infrastructure, leading solutions in each case. You take things like Hyperconverge and Converge, great example of that, and I think what we see at the US Bank is that that porfolio of solutions is what's actually enabling US Bank to essentially address all other challenges, right? Whether it's the IS, whether it's the crown jewel applications that Suzan's trying to support, whether it's the DevOps that they're trying to actually build out right now. We've got best of breed solutions for each of those as well within our portfolio. And also, I would say that we're really focused on, ultimately, a portfolio with a purpose meaning that we're taking our networking, for example, portfolio, you just talk to Drew Shulkin. Together with out HCI portfolio, and we're ensuring that they work really seamlessly together so that in the case of, for example, working with, say VxRail or VxRack, we're able to automate all the networking for a HCI environment or at least 98% of it. That's really, again, taking but that's because we're best of breed and porfolio at the same time. >> Yeah so, I'm throwing all kinds of loaded questions out here, and I want to understand this because as independent observers you get Company A says this, Company B says that, but the customer's ultimately the arbiter. How do you, maybe not define, but how do you look at best of breed, what is best of breed to you? >> I look at the technology that's going to make me look good and that's going to make my teams look good and that's not just day one, that's day two and I think that's where the differentiator is as well. We've always found that Dell Technologies is there to support us. Stuff breaks, right? Your car needs oil, your tires need rotating, and it's the same with equipment in the Data Center. How those companies react and they support and they have your back when that happens, I think is the key differentiator and we always found Dell Technologies to be there for us. >> So I'm hearing the breadth, the porfolio. We haven't talked about services but I know that's a key part of it. >> Well, Suzan I hear you talking about day two. CI helped simplify that day one and then, as it matured, it worked more on the day two, and HCI even more. When you talk about the cloud solutions from Dell EMC, that cloud operating model. When you think about public cloud, I don't think about what version I'm on, it takes care of that. When I hear some of the solutions from Dell, it's getting to that model. How are they doing along that that spectrum, I guess, from the, "Okay I need to do the RCM and manage when I do the updates" to "I don't even think about it anymore." >> Sure, I think it is still something that we all care about as much as we're told we shouldn't care about it, I care. I want to make sure that we're doing the right things at the right time. I think it's a journey. I think we've come a long way in the last few years and I think that every year it gets better and as we start extending to that multicloud, obviously that's going to drive some of that solutioning as well. I think we'll continue to see improvement in that area. >> What is something that you'd like to see Jon do to make your life better? (laughs) Besides cut prices, you can't say cut prices. >> Alright, cut prices. >> Every year you cut prices. >> Let's talk about that deal. I think just continuing to be there, continuing to represent, bringing forth the products, the products team, helping us be strategic and also be very tactical. While I have this one last opportunity 'cause I don't know where we are timewise. I just want to shout out to my team. Right, so it's not just the Dell Technologies team that's bringing all this to the table, it's my team and the organization and my peer teams as well. We just keep sharing, we keep collaborating, and we keep iterating. >> Yeah Jon, one of the things, talk about collaboration, my understanding is Suzan's part of one of the user groups here. You know, big community. >> Yes. >> We always talk about at these shows. Maybe you can share that. >> Yeah so Suzan is actually a new board member for our Converge user group which has been around for several years now and she just joined a few months ago. >> I did. >> And I think that we talk about collaboration and feedback. Suzan is representing not just her own team, she's representing teams around IT around the world. And I think she's a great example of providing feedback, not just at Dell EMC directly, but to other users as well, and best practices and tips and tricks. We have a user group tomorrow at three o'clock. I think couple big executives might be there as well, so it's going to be a lot of fun. So tomorrow at three o'clock. I think it's, at least, our sixth annual that we've had here. But the user group itself, I think exemplifies as much as you've been talking about 'cause that's evolved from being what used to just be about a user group just about blocks, VxBlocks, now it's about CI, it's about HCI, it's about VxBlock, it's about Dell Tech cloud. We have VMware on the panel as well as Dell EMC so I think you see the user group has evolved with our customers and with our portfolio. >> It's a community, it's a mechanism for people to say, "How did you do that" or "How should I do this" or "How do I get my team motivated" or "How do I collaborate with security?" These are tough questions and so I think just having that network of people that can come together and ask those questions and be transparent and be authentic, that's what it's about. >> Appearance, problem-solving, sharing ideas. >> Yeah. >> You've been a Converged Infrastructure client, customer for a number of years. >> I have. >> So you've seen pre-acquisition, how has the Dell EMC merger affected your perception of the company and your relationship with them? >> I think in the last year, or the previous year, we were all waiting to see where things fell and what was going to happen, and I think now it's found it's feet, right? We're starting to see some announcements in both the Converged and the VxFlex space, and it's really starting to come together and I think that story, the Dell Technologies family story is really starting to come together where maybe in the last 12, 18 months, there was a little bit of unknown there and so, we just kind of sitting back and waiting and curious but keep doing what we're doing using that best of breed, the best practices that we have on the floor. >> Alright awesome. Suzan, Jon, thanks so much for coming on theCUBE. It was a great segment. >> Thank you. >> I appreciate it. Alright, that's a wrap for day one. Dave Alonte, Stu Miniman, John Furry's over there. Lisa Martin, Rebecca Knight is here. This is day one, we got wall-to-wall coverage. Tomorrow, day two and day three. Check out siliconangle.com for all the news. Michael Dell's coming on tomorrow. We got Pat Kelsey going to be on tomorrow. Tom Sweet's coming on later on in the week. Awesome coverage, check out thecube.net. This is Dave Alonte, Stu Miniman. We'll see you tomorrow, thanks for watching.

>> Live, from Las Vegas, it's theCUBE, covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back, everyone, to theCUBE's live coverage of the AWS Executive Summit here in Las Vegas at the Venetian. I'm your host, Rebecca Knight. We are joined by folks from MONETA Money Bank. We have Martin Berdych, who is the senior manager IT infrastructure, and Martin Trcka, Cloud Technical Architect Manager at Accenture. Thank you so much for coming on the show. >> Thank you. >> Thank you for having us. >> So, we're talking today about MONETA Money Bank's journey to the cloud, but first I want to start with you, Martin, talk a little bit, tell our viewers a little bit about MONETA Money Bank. >> So, MONETA Money Bank, it's the fourth largest bank in Czech Republic, which is not big, because basically the country's small, but the bank is far big. We are serving something around one million customers, and we're providing all the services that you can imagine, so, bank accounts, loans, mortgages, credit cards, whatever you find out, and there's one special thing I'd like to point out, we have a brilliant mobile application, which is consistently getting the awards every year for the best mobile banking app on the market, so this is MONETA. >> So, you're already a pioneer in technology, really on the vanguard, and recently made the decision to move twelve of your, or two dozen of your existing apps, to the public cloud. What was the impetus for that decision? >> I think there's a wider strategy, which is around being digital, being agile, all those buzz words you hear around everywhere, you know. >> No, actually go into that a little bit. >> It's an automation, all the other stuff, I think one of the big ones, as well, was the legacy infrastructure, because the banks got a huge legacy staff, which is causing a lot of issues, if you want to go fast on the market, you want to be quick, you want to respond to your customers, this is slowing us down. So, I think apart from all the other strategy, or at least in my area, the infrastructure part, definitely the big one was the legacy asset we are actually trying to remove by moving to cloud, which is, that's the thing. >> So, they needed a partner to help them move to the public cloud, in this case, AWS, of course, and, so, when Accenture comes into this, first of all, is this a standard client sort of someone who is a company that is already technologically minded, and trying to do this, would you say that this is the kind of organization that gravitates? >> So, MONETA, as a pioneer, in terms of federal public cloud on the checking market, for them, it was a huge step to adopt public cloud, so we are very happy that they ask us for help, the first thing that we did, is we helped them design their IT strategy, what the steps should be in terms of adopting public cloud, actually, we helped them also to define that cloud production would be one of the pillars of their future growth along with other initiatives. So, we helped them with the IT strategy, and then we basically went through that whole journey together with one of the infrastructure teams, one of the security teams, and with our team who helped the client to migrate, eventually, those two dozens workloads into the cloud. >> So, is it a co-creative process, in the sense of are you together, figuring out, the steps on the journey, or is it Accenture in the background, and- >> I think, one of my goal was to make my team part of that as much as possible, so, obviously, Accenture help is appreciated, and they were needed, because the knowledge of public cloud, not only the company, I think, even on the market itself, is very limited, the experience with that is very limited, so Accenture played a strong role in that, but what I make sure from the beginning, or I was trying to make sure from the beginning, is that the team will be part of that from, really, end to end. So they, whatever Accenture was helping, the team was contributing, and they were able to actually do it together, so the knowledge has been increased in the wider theme, so now, we are definitely much more capable than we have been before, and when we started. >> So, how did you help? As you said, figuring out the business challenges, and then actually finding solutions. >> So, it all started with what we call preparation for forging into cloud. This means that we helped the client to assess their risk, because we are speaking about their banks, there's a regulation, which needs to be met, those requirements are regulatory. So, we help the client to assess the risk associated with going to cloud, we help them design their exit strategy when they need to actually exit the cloud, and after we complete those, let's say, preparation tasks, we focus on what we call blueprints. This is basically designing concept of how the target environment will look like in terms of the architecture, in terms of security, in terms of government, so we have, jointly, you know, with Mark Markstein, designed those blueprints, and after that, it was basically ready to take the journey to cloud, actually, itself. >> You mentioned governments, you mentioned security, privacy, GDPR was recently enforced in Europe, did you come up on any challenges with- >> There have been many, obviously, the regulation itself, if it's GDPR, if it's the banking regulation, all the other elements have to be considered, and I think this is a constant task, it's not over, because obviously as we are opening on the market we are learning, and we are showing the other competitors that this is actually possible, and what needs to be done to make it possible, so, obviously, the regulator Czech National Bank had a lot of steps, they gave us a lot of next steps we have to fulfill, so we can actually proceed, and this is an ongoing journey and we have to, kind of, work on this, still work on it, it's not over, there have been a significant risk analysis done, obviously, so do I think it's more than hundred risk has been identified, around the cloud. Now, this has been much reduced, and, obviously, there are still next steps we need to fulfill to get this done. >> So, you have fully migrated to the public cloud? >> Those twenty applications. >> The twenty applications, yes, exactly. What have you seen so far, both from your clients and both from your colleagues? Have you seen changes? >> I think a couple of things. One of things is that the team, not only IT team, but internal people in the bank see that it's actually working, there have been some skepticism in the beginning, obviously, people are looking for reasons why we shouldn't be doing it, because of this and that, I think this is now a bit clearer, and people are kind of getting the feeling that this is actually working. So, this is one of the outcome of the thing. Obviously, the other walls that we've quickly find out and essentially help them that we need to optimize. So, we move it as it is, we lift and shift, and then we find out we're actually wasting resources, therefore we're wasting a lot of money, so we had to start looking, so by moving it, it didn't stop, it's not over, we have to now work on that and we need to find out how do we actually optimize the whole workload and what we can do to actually make it better, apart from the fact we are looking at the other phases of the project and we want to move more, we have to work on the older beta as well, so we need to make sure we get the most from the cloud. >> And what other learnings throughout this process did you come up with, sort of best practices that have emerged, as you said, you are showing your competitors that it is possible, the other top three banks in the Czech Republic, are, sort of, learning from MONETA Money Bank's experience, what would you say are the best practices? >> So, it really depends, you know, from which perspective you look on those lessons learned, from the regulation perspective, the answer is yes, it's possible to edit public cloud, even with, in higher FS Market, however, you need to meet money requirements. From the technology perspective, I believe that MONETA was really surprised how easy it was to adopt the technology itself. The migration happened, basically, in just four and a half months, so this is something you normally are not able to accomplish in traditional, like I said, data center and data center environment. So, this is from a technology perspective. As you said, journey to cloud is how you migrate to cloud, but then journey in cloud begins. So, another lesson learned is once you are in the cloud, you need to change your operating model, you need to start optimizing not only your span, but also, I would say, optical performance, so basically, the job is not done when you migrate to cloud, it just begins. >> So, you're now at the beginning of this journey, now that you're there, what is the future work? What does the future look like? >> Obviously, we have big plans. I think our aim is to migrate 50% of the workload until the end of 2019. It's a challenging task, because, I mean, we obviously created the base line because we have the environment, we have some obligations, so now we just build on top of that and we still have to work on it, but it's a challenging task, and this is what we're looking at in the future. >> And, do clients feel it, would a banking customer sense any difference, this is the thing, you win awards for your mobile apps, so you're- >> Absolutely, absolutely. I think, the plans for the 2019 will be really, that's going to be the shift for the clients, we have clients to move that are really, like, a strong production workloads, which are affecting the clients, on the end of the day, and I think that's going to be the visible element for them, when we do that. >> And finally, what's your word of advice for other banks that are considering, pondering, this move to the public cloud, what would you say, what is, sort of, the strategy, the strategic advice. >> So, when we are speaking to clients about public cloud adoption, they usually think about public cloud adoption, in terms of technology, like, that you are basically pricing data center technologies, on premise technologies, with some other technology in the cloud. This is not the case. That part of the whole journey, is just a small part, it's about changing how the organization works, it changes the operating model, it touches almost every function in the organization, you know, the business, HR, finance, security, risk things, all those things and functions are affected by cloud adoption. So, my recommendation would be think of cloud adoption from that perspective, it's not just a technology change, you're not just changing a platform for another platform. >> I would have one recommendation, and that is, don't be afraid. >> Don't be afraid, I like it, it's a good word of advice to end on. Martin and Martin, thank you so much for coming on theCUBE, it was a really great conversation. >> Thank you. >> Thank you. >> I'm Rebecca Knight, that wraps up day one of theCUBE's live coverage of the AWS Executive Summit, we will be back here tomorrow with more. Signing off, thank you so much for joining us. (funky outro music)

(upbeat techno music) >> From San Francisco, it's theCUBE. Covering Sumo Logic Illuminate 2018. Now, here's Jeff Frick. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at Sumo Logic Illuminate, at the Hyatt, at the airport in Burlingame. We're excited to have, from Virgin Money Digital Bank, two great guests, we love to get customers on, Andy Makings, he is the head of Cloud operations, Andy, good to see you and Julian Howe, head of Cloud Business Office. >> Hi >> So welcome gentlemen. >> Thank you. >> Great to be here. >> How're you liking the weather? >> It's great. >> Improvement already. (laughing) >> Improvement already, alright. So let's jump into it Virgin Money Digital Bank, what is that exactly? >> Yep. >> Do you want to take that? >> Oh okay, I've been there longer, yeah. 2017, start of 2017, they decided to build a completely new digital bank, for Virgin Money, as an offshoot to the core bank, Virgin Money Brand, using the same banking license with our partner, TenX Future Technologies in London. So building a bank from scratch, whole new business model, data driven, data analytics, Big Data DevOps Agile, whole new business model completely. >> So just starting in 2017 so, you're still pretty early on the journey? >> Yeah, so still in the build phase, pilot phase, and then go live next year. >> So A, what are the key drivers to that decision? That's a pretty innovative decision, which doesn't surprise us, right? Virgin always seems to be kind of out on the leading edge but, when the conversation happened, what is a hundred percent digital bank? How is that different than a traditional bank, besides obvious things, like branches, but, what are some of the motivations, some of the attributes? >> I think they wanted to leverage the brand, of course, Virgin, 'cause there's a lot of new digital bank start ups, which they're competing against. So they thought , let's do it from scratch, let's do it how we want it, make it truly focus on data, driving customer value through the data. And they thought, we can compete because we've got this big Virgin Digital Brand, that we can really use to get customer base. >> Right. >> Yeah, so I think that was the big driver, compared to what they're currently doing, with the bank, the core bank, and what they want to do with the brand new bank. >> Wow. But it's not co-mingled so you're not leveraging existing data, existing clients, or all those things, or are you seeing kind of a transfer over? >> Eventually we may, but that's the future. Yeah, the first thing is to launch the digital bank and then we'll see where the Big Data platform, that we're putting in, drives. Yeah, it makes sense to economies of scale to obviously migrate the rest of the customers. >> So when does it launch, what's the timeframe? >> 2019. >> 2019? >> Yeah, absolutely. >> Okay, so you're here at Sumo Logic, what role is Sumo playing in this big project? >> Well, so from my perspective, so I'm looking at, so Andy's been involved in, as he said, for the last 12 months, in terms of building the new platform, really making sure that we're bringing on the bleeding edge technologies, and tech partners and, certainly from my perspective, it's around making sure that I understand who we're going with, what technologies we're using, and how we can utilize those technologies, going forward, to really make sure that our customers are getting the best service from the new digital proposition. >> Right. >> And Sumo Logic is absolutely part of that. >> And are you building your own cloud eco system, in the back, or are you using one of the public clouds? >> Yeah, I'm using Amazon, Amazon public cloud. >> Using Amazon public cloud. >> Yeah, so my team's responsible for building the Big Data platform, TenX Future Technologies are responsible for building the API based banking platform, and then we take streams of data into the data and analytics platform that we're building. So Sumo, obviously, is our logging platform, and we'll then use more and more features of Sumo as they release, so, logging initially, everything goes into Sumo, for the whole of the Amazon platform that we're building, and the data lake, and then what we'll do later on is we just started beta work to do the SIM implementation for security and then we're revolutionizing the SOCs, security operation center, as well to be cloud based, sort of driven because, obviously traditionally, we've been hosted in data centers. >> Right, so you're using it now as part of your build-out process, but then you'll be using it again obviously in your operations as well. >> Absolutely. And yeah and some of the messaging out from this morning with the keynote around just the business intelligence and customer metrics and data that Sumo Logic can almost sort of draw in and present back. >> Right. >> I think that's really powerful. >> Right, are there certain kind of customer features that you look forward to offering that you just can't do in the traditional bank or is it more a lot of kind of marginal improvements because you've got the data? >> It's more the agility, I think. >> Yeah. >> Agility of build. Agility of delivering new business features so it's business driven. As I say we're doing proper DevOps, proper agile across the business in the new digital bank. >> Right. >> Whereas before it's more traditional in the core bank, as we call it. >> Right. >> So it's silos of teams, sand storage, yeah, systems administrators, legacy, so. >> And it is, yeah, that transition into a digital business, as well so how we're set up and how we're aligned, not just the technologies that we're looking to use and the companies we're looking to partner with. >> Right, so on the data driven, you know, being a data driven company in this new bank, I'm fascinated by some of the financing options that are there now, I mean these are some of the pure digital plays that you've been talking about where they're making loan decisions based on some really strange factors that you would think, no way could you make this loan based on a traditional kind of analysis, you would never do it. >> Yep. >> And yet they're pulling some data somewhere that's telling them that this is actually a good loan, so I assume those are the types of things you're looking forward to? >> Yeah, of course. So when we take the feeds from obviously TenX, the platform that TenX is providing with the new customers but you also take feeds from the existing data warehouses, yeah and then we build business models on top of that in the data lake with the data science team and they then get pushed back in to feed scoring models and things like that across the digital platform. And that will just grow and grow. There'll be more and more models as the business gets more mature. >> Right, any super big hurdles that you didn't anticipate that you got to get over to make this happen? >> Technically no, I think more about business transformation. Yeah, we're still part of a bigger bank that holds a bank licensing so a lot of it's around education of cloud, public cloud so that's been key, we've done quite a lot of presentations to the core bank. Especially around the security teams and managing expectations and what they need to look at and how dynamic. We're using LAMDA a lot, so they've got to get their head around how all that works and yeah, what they're doing with that and how dynamic it is. We can spin out thousands of servers in minutes. That's been a bit of a hurdle. >> Right. >> But I think we're getting there and I think the next few months as we build more of the platform we'll definitely get there better. >> Yeah. >> And I think you hit the nail on the head around agility. It's being agile enough and being able to keep pace with, this, the innovation you see with companies like Sumo Logic. >> Right so it's like the parent Virgin Bank kind of looking over the shoulder, going, hey, hey, hey, what're you got? How do I get some of that? >> No, they're fully involved, obviously. They're excited, same as we are, by the prospect of what we're doing because it should drive more customers. >> Well I was going to say, is there going to be some spillover, I would imagine, in terms of innovation and features and those types of things as well? >> Yeah. >> I know already some of the tools we're putting in, we've gone through the pain of going past the security validation and put in, they're now looking and go, well actually that's really useful for hybrid cloud if you want to move some of the existing workloads into public cloud. If we want to, say, leverage marketing or leverage log platforms or leverage monitoring platforms? >> Right. >> As well as the automation we're putting in, we can easily, all the designs have been built to bring in other business units and business areas within the current business. >> Yeah, I'm curious was there push back on using a public cloud for this all 100% digital bank? How did that decision finally get sorted out? I mean, I think generally we're past it for a lot of people obviously in our business but I would imagine, there's still some stodgy guys that are, you know, wearing very expensive suits in mahogany row that are probably like, are you kidding me, you know? >> Yeah, there's still a lot of compliance to sort out. Obviously we've done some, there's more to do as we go nearer to production. >> Yeah. >> There's been some hurdles, we'd be lying if we said there wasn't. >> Right. >> But there's definitely been some hurdles but I think we're getting there and of course, other additional banks have done it in Amazon as well. >> Exactly. >> You're following that model and you need to get through the regulatory compliance. >> And it's about having, making decisions based on facts and there's increasing numbers of facts around how secure and how successful and the benefits that cloud platforms give you. >> Right, it took a while for the facts to kind of out weigh the hype, right? Not so much the hype but the scare. >> The scare is thing, yeah, once you can show, you know we did a BAC late last year to show that we could do it and it was secure and it went through more pen testing than most of the current products would go through, purely because of that scare. >> Right, right. >> They were scared of going to public cloud. >> Interesting. >> Yeah. >> So when again is the anticipated launch date? I won't hold you to it, I'm not-- >> Yeah, 2019. >> Yeah so next year. >> 2019, yeah. >> Yeah, 2019. >> Sometime between January 1 and December 30th? >> Yeah, yeah. (laughing) >> I think it's Q1, I think officially it's Q1. >> Alright. >> Early rather than late. >> Early rather than later, yes. >> It's a great story, I mean an old bank coming out with 100% digital bank. >> Yeah. >> It'd be an interesting story to watch unfold, we'll look forward to it. >> Absolutely. >> Yeah, thank you. >> Alright Andy, Julian, thanks for taking a few minutes of your day and I hope you enjoy the rest of your time almost in San Francisco, you got to get up there, at least one, right? >> Yeah, we're going to try to go there, yeah. >> Alright he's Andy, he's Julian, I'm Jeff. You're watching theCUBE. We're at Sumo Logic Illuminate 2018. Thanks for watching. (upbeat techno music)