>>Hey, everyone. I want to welcome you back. This is our intermission. And let me tell you what a morning we've had for those of you that don't know. I'm, Hayma Ganapati, I'm in product marketing at Docker. And I would just want to quote, actually someone who was in one of the chat rooms and this, I think encapsulates exactly how I feel today, because this is my first Docker con and the quote was from. And he said, I feel like a kid in an ice cream store where I don't know which flavor to choose. I want to go to all of the sessions and I got to tell you that's how I felt. And, you know, um, I want to just do some specific call-ups. Um, first of all, Dana way to keep it real in your interview. I love the cube interview. If you miss that, um, it was really great. >>She talks a lot about, uh, CI CD pipeline and you know, what to do with GoodHub. It was great. Um, I also want to say that I was, uh, slipping back and forth between the community rooms and way to go Brazil obrigado until all of the people who participate in the Brazil room, we had about 250 plus people in that room. And the, the chat window was just going crazy and in the French community room, Vive left hall. So if you've a uncle funny, uh, we had about 150 plus people in that room. So I just want to say that, you know, we've been seeing a lot of participation and I just want to thank everyone for attending and for participating on people have been so kind in the chat rooms, we just want to remind you to stay kind, you know, presenters put a lot of effort into their presentations, so just, you know, offer some positive and supportive critique to them. >>And the other thing I want to mention is all of the countries that we're seeing, all of the participation. So I'm just going to call out a few. We have people from the Netherlands, from Canada, from South Africa, Akron, Ohio, Belgium, Austria, yeah, Ecuador, New Zealand. And he cut up Westchester. Like, I mean, it just goes list goes on and on and on. And I think this really speaks to the power of Docker community. And it's a real testimony to how people from all over the world are in love with Docker technology and are excited to be here. And so I just wanted to thank everyone again and want to remind you that we want to leverage the power of community. And we have a fundraising campaign going on to help, uh, people who are affected by COVID. And you know, some of our big communities, especially in India and Brazil are, have been really affected by COVID. >>So we're asking you to contribute and we'd really like you to participate. Um, we have, uh, the, the link you can see here, Docker donates, you can tweet about it and would love to see the numbers go up for those donations, because, you know, I've personally been affected, had some family members pass away from COVID in India, and I'm sure other people may have stories that firsthand or secondhand. So please do that and let's show what the power of Docker community can do. And before I hand over to, to Peter, I'm just going to read out some of the tweets we've been getting, okay, this Brett and Peter, these are great. Uh, one of the, one of the tweets said dev environments is one of the most exciting features in the past few years. Super excited to try this out. Great, great, great tweet. Yeah. >>I agree to, um, another loving the content that was not your tweets. You can, you can slip me the 20 bucks later. Um, there's another tweet that says loving the content from hashtag Docker con so far fascinating use cases and interesting progress and future directions love that. And then there's another one I'm trying to find it here. Uh, I've been waiting for this so long Docker builds now work on Intel and M one. So keep those tweets coming. We love getting this kind of feedback and we love reading the chat room. So, um, Peter, you know, I attended your, your panel and I love that we were talking about a security and that moving, moving it left. So how did that go for you? >>Uh, it was, it was, uh, it was extremely fun. And for those that are, uh, I think my parents might be watching, so they probably watched it and were like, w this is the most boring thing I've ever seen, but, um, you know, you get a bunch of geeks and, uh, Brett has told me I should use geek instead of nerd, but I, I liked, uh, geek. So you get a bunch of geeks talking about security and coding and, um, what, what, what containers actually are, what vulnerabilities are. Yeah, it was, it was extremely fun. The panel was fantastic. They were very engaging the chat. I mean, I couldn't keep up with the chat. Right. It would just kept flying by, uh, luckily I had a helper to pull off questions, but, um, yeah, it's super exciting. You can, I know we're all remote, but you can just feel that energy, right. It was, it was great. It was great. Yeah. Yeah, for sure. It's super >>Connected. I felt that with your panel to Brett as well, sorry to talk over you there, but yeah. How did, how did it go for you? I, there was a lot of engagement in your session. >>Uh, ditto, like it was just, uh, there was so many questions. We only got to get a fraction of them. I tried to pick themes because, uh, when you talk about continuous testing and integration and all the things that take a part of that, um, you, you end up with lots of, well, what I like is the discussion around opinions, because so much of these pipelines from code on your machine, into production and everything in between, it's really, uh, it's a culture. It turns out to be the description of your culture and how you all perceive testing, how you, what you value in testing. And so that really started to come out as a theme, um, throughout that show. And we, we ran at a time. I was also watching Peters and it was fantastic, but like you think an hour is enough time to cover a topic, but it's just tipping tip of the iceberg kind of stuff. So I think it was super helpful. I learned some things, um, I really enjoyed watching Peters and, uh, yeah, can't wait for the next one. There's >>More than that. And likewise, great. I mean, I know, I know we're w maybe we pat chose it, but it, it was, it was super exciting to watch your panel. They were very Nikos, one of my favorite people in the world, uh, a fellow Austinite, but, um, yeah, I love that too. How you, uh, you were talking about opinions right. And playing off each other. It's, it's always interesting to hear smart people, uh, how they think, right. Yeah. I learned from how they think, right. Yeah. A hundred percent. >>So, all right. So we're, we're, um, what's next? Like, we, we gotta keep this thing going, so I've got to remember that. >>I want to, so I want to talk a bit about some of the panels that are, or the sessions that are coming up and just want to remind people that happened this afternoon. I'm all about use cases. You know, I was a developer for many decades, and it's great to hear how other developers are using the tools. But, uh, as a developer, I always wanted to know how are, what are the end user applications? And so we have two exciting sessions at 1:00 PM. We have sneak and red ventures, and they're going to be talking about how they used Docker containers. The title of the, uh, uh, session is great. An ounce of prevention, curing, insecure, container images. So check that out. And we also have another one at one 30 with Massimo, from AWS and Dexter Legaspi from Sirius XM. And they're going to be talking about a real world application using Docker containers. So I really want you to, to encourage you to attend those. >>Yeah. Um, can I say one really quick? Cause I'm Sue and a shout out to Eric Smalling. He's giving the red ventures talk with our partners. He's awesome. Go check out his, uh, but I'm really excited about Matt. Jarvis's sneak talk around. Uh, I think we might've talked about it earlier. My container image has 500 vulnerabilities vulnerabilities now what, right. I mean, I think as developers, as we're coming into this and dev ops and everybody right. You scan and then you see all these vulnerabilities just shoot by. And you're like, well, what do I do? So Matt, Matt will be addressing that. And he is fantastic. I can go on. There's a bunch of them. >>Yeah. There's a whole bunch of coming up and right up after this, I'm on a live stream with a bunch of panels on get ops. And then after that, Peter will be back. And so stay tuned and thanks for watching during the intermission. And we'll see you soon. >>I'm also leading the women in tech panel attend that. Don't forget to do that. >>Absolutely. Yep. All right. Ciao. Ciao >>For me like my first, oh, I get it about Docker was when I used a SQL server container on my neck book for the first time >>Being able to install Docker desktop, which was the first thing that I did and be able to build this without worrying about any of my software versions that I currently had on my machine. It was >>Awesome. One of the things, because I love the most about Docker is because I write books and I do video training courses to help a lot of people take their first steps with Docker and containers and to get a connection with those people and for them to come back to me and say, do you know what this is so cool, so easy, and it's going to change both my job. And, but also my organization, my team, all of that kind of stuff, change the experience that our customers have with our applications and what our business really puts a smile on my face. If >>You want to use containers, then Docker is the first toys, especially with tools like the mark Docker, compose, you can, uh, easily do your day-to-day job as a developer, or even if you're an ops person, then there are the books of the cloud and other things. So yeah, the idea is that we can go the simplicity one simple task, uh, to, uh, Daugherty mate and make that reuse as many times. Uh, that is one of the cool things I like about my >>Favorite part about Docker is using it as a developer tool. I using Docker desktop, really easy to install, really easy to run. >>Every time I come back to DACA, I love the simplicity of the way that it works, especially on things like security, which I find frustrating and hard. It's just done so seamlessly. And so my favorite thing about DACA is not just that it changed the world in the way that we develop in and ship and build applications and put that. It's just so easy that even the guy, like, I think >>It really is all about finding that aha moment, that hook where Docker really makes sense to you because once you have that moment, then all of a sudden, you, you know, you are on your way to being a Docker power user. >>We need for people to understand this technology better before they can, uh, actually dive deep into that. And Docker makes it easier to explain things, to explain the concept of containers, to explain how containers will work, how you can split your environments, how you can, uh, standardize all your pipelines and so on. It's important that we also take the time to help other people. And I think it's very important that we also give back and that's part of the motto of open sources. How do we give back to other people and how we help other people learn? And I think that's what I'm really passionate about. This whole thing is continuing, uh, giving back to the community. I just >>Hope and has fun at Docker con. And I know that there's a lot of great speakers coming and I will be watching the talks, even though they're happening at 3:00 AM and in my local time zone, um, I'm pretty excited to watch and, uh, hopefully watch more than later on streaming or YouTube or wherever they're going to be. So I hope everyone has fun and learn something and yeah, I don't see how you couldn't have fun.

>>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)

>>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 Welcome everybody to Enterprise Data Automation. Ah co created digital program on the Cube with support from my hotel. So my name is Dave Volante. And today we're using the hashtag data automated. You know, organizations. They really struggle to get more value out of their data, time to data driven insights that drive cost savings or new revenue opportunities. They simply take too long. So today we're gonna talk about how organizations can streamline their data operations through automation, machine intelligence and really simplifying data migrations to the cloud. We'll be talking to technologists, visionaries, hands on practitioners and experts that are not just talking about streamlining their data pipelines. They're actually doing it. So keep it right there. We'll be back shortly with a J ahora who's the CEO of Iot Tahoe to kick off the program. You're watching the Cube, the leader in digital global coverage. We're right back right after this short break. Innovation impact influence. Welcome to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. High tech digital coverage from around the globe. It's the Cube with digital coverage of enterprise, data, automation and event. Siri's brought to you by Iot. Tahoe. Okay, we're back. Welcome back to Data Automated. A J ahora is CEO of I O ta ho, JJ. Good to see how things in London >>Thanks doing well. Things in, well, customers that I speak to on day in, day out that we partner with, um, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring the week and serve our customers to help their customers. Um, you know, the adaptation that's happening here is, um, trying to be more agile. Got to be more flexible. Um, a lot of pressure on data, a lot of demand on data and to deliver more value to the business, too. So that customers, >>as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline, But talk about enterprise data automation. What is it to you. And how is it different from data off >>Dev Ops, you know, has been great for breaking down those silos between different roles functions and bring people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, lending itself to data with data is exciting. We look to do is build on top of that when data automation, it's the it's the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors, our r and d on bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is is the automation behind new dimension. We've come a long way in the last few years. Boy is, we started out with automating some of those simple, um, to codify, um, I have a high impact on organization across the data a cost effective way house. There's data related tasks that classify data on and a lot of our original pattern certain people value that were built up is is very much around that >>love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, >>sure. I mean right there in the middle that the heart of what we do it is, you know, the intellectual property now that we've built up over time that takes from Hacha genius data sources. Your Oracle Relational database. Short your mainframe. It's a lay and increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data. Classify that data after it's classified. Them have the ability to form relationships across those different source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts of contact and meaning around that data. So it's moving it now from bringing data driven on increasingly where we have really smile, right people in our customer organizations you want I do some of those advanced knowledge tasks data scientists and ah, yeah, quants in some of the banks that we work with, the the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality, the policies that you can apply to that data. I'm putting it in context once you've got the ability to power. Okay, a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the the tapestry that fabric across that different system could be crm air P system such as s AP and some of the newer brown databases that we work with. Snowflake is a great well, if I look back maybe five years ago, we had prevalence of daily technologies at the cutting edge. Those are converging to some of the cloud platforms that we work with Google and AWS and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenges scale quickly runs out of steam because once, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data state? It's changed, You know, you've onboard a new customer. You signed up a new partner. Um, customer has, you know, adopted a new product that you just Lawrence and there that that slew of data keeps coming. So it's keeping pace with that. The only answer really is is some form of automation >>you're working with AWS. You're working with Google, You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>That's fundamental. So, I mean, when I caimans where you tell here is the CEO of one of the, um, trends that I wanted us CIO to be part of was being open, having an open architecture allowed one thing that was close to my heart, which is as a CEO, um, a c i o where you go, a budget vision on and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with the CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using AP eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before. So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um and snowflake here is, um Is those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that. And they're leveraging the value that they've already committed to. >>Yeah, and maybe you could give us some examples of the r A y and the business impact. >>Yeah, I mean, the r a y David is is built upon on three things that I mentioned is a combination off. You're leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM, and I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have got the automation that is working right down to the level off data, a column level or the file level we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome now without hoping out which run those processes within hours of getting started And, um, Bill that picture, visualize that picture and bring it to life. You know, the PR Oh, I that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on GCB or a migration to any other clouds such as AWS or a multi cloud landscape right off the map. >>A. J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have you. >>Thank you, David. Look who is smoking in >>now. We want to bring in the customer perspective. We have a great conversation with Paul Damico, senior vice president data architecture, Webster Bank. So keep it right there. >>Utah Data automated Improve efficiency, Drive down costs and make your enterprise data work for you. Yeah, we're on a mission to enable our customers to automate the management of data to realise maximum strategic and operational benefits. We envisage a world where data users consume accurate, up to date unified data distilled from many silos to deliver transformational outcomes, activate your data and avoid manual processing. Accelerate data projects by enabling non I t resources and data experts to consolidate categorize and master data. Automate your data operations Power digital transformations by automating a significant portion of data management through human guided machine learning. Yeah, get value from the start. Increase the velocity of business outcomes with complete accurate data curated automatically for data, visualization tours and analytic insights. Improve the security and quality of your data. Data automation improves security by reducing the number of individuals who have access to sensitive data, and it can improve quality. Many companies report double digit era reduction in data entry and other repetitive tasks. Trust the way data works for you. Data automation by our Tahoe learns as it works and can ornament business user behavior. It learns from exception handling and scales up or down is needed to prevent system or application overloads or crashes. It also allows for innate knowledge to be socialized rather than individualized. No longer will your companies struggle when the employee who knows how this report is done, retires or takes another job, the work continues on without the need for detailed information transfer. Continue supporting the digital shift. Perhaps most importantly, data automation allows companies to begin making moves towards a broader, more aspirational transformation, but on a small scale but is easy to implement and manage and delivers quick wins. Digital is the buzzword of the day, but many companies recognized that it is a complex strategy requires time and investment. Once you get started with data automation, the digital transformation initiated and leaders and employees alike become more eager to invest time and effort in a broader digital transformational agenda. Yeah, >>everybody, 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. >>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, Webster Bank is regional, Boston. And that again in New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated bank 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. 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 timely, accurate, complete data on the customer and what's really a great value on off something to offer that >>at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change >>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 and multiple weights, 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. >>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 >>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. >>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. Um, exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of runnin 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 Good, um, utilize that data or we can give it out to our market. 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 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. I have eight 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 read regiment 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. This 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 and 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 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. >>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? >>Exactly. 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, um, 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 could 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, just for 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 of 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. I'm using Iot typos eight 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, um a It's an on prem. It's an oracle database. Um, and it's 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. >>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. >>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's 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. >>Next, we'll talk with Lester Waters, who's the CTO of Iot Toe cluster takes us through the key considerations of moving to the cloud. >>Yeah, right. The entire platform Automated data Discovery data Discovery is the first step to knowing your data auto discover data across any application on any infrastructure and identify all unknown data relationships across the entire siloed data landscape. smart data catalog. Know how everything is connected? Understand everything in context, regained ownership and trust in your data and maintain a single source of truth across cloud platforms, SAS applications, reference data and legacy systems and power business users to quickly discover and understand the data that matters to them with a smart data catalog continuously updated ensuring business teams always have access to the most trusted data available. Automated data mapping and linking automate the identification of unknown relationships within and across data silos throughout the organization. Build your business glossary automatically using in house common business terms, vocabulary and definitions. Discovered relationships appears connections or dependencies between data entities such as customer account, address invoice and these data entities have many discovery properties. At a granular level, data signals dashboards. Get up to date feeds on the health of your data for faster improved data management. See trends, view for history. Compare versions and get accurate and timely visual insights from across the organization. Automated data flows automatically captured every data flow to locate all the dependencies across systems. Visualize how they work together collectively and know who within your organization has access to data. Understand the source and destination for all your business data with comprehensive data lineage constructed automatically during with data discovery phase and continuously load results into the smart Data catalog. Active, geeky automated data quality assessments Powered by active geek You ensure data is fit for consumption that meets the needs of enterprise data users. Keep information about the current data quality state readily available faster Improved decision making Data policy. Governor Automate data governance End to end over the entire data lifecycle with automation, instant transparency and control Automate data policy assessments with glossaries, metadata and policies for sensitive data discovery that automatically tag link and annotate with metadata to provide enterprise wide search for all lines of business self service knowledge graph Digitize and search your enterprise knowledge. Turn multiple siloed data sources into machine Understandable knowledge from a single data canvas searching Explore data content across systems including GRP CRM billing systems, social media to fuel data pipelines >>Yeah, yeah, focusing on enterprise data automation. We're gonna talk about the journey to the cloud Remember, the hashtag is data automate and we're here with Leicester Waters. Who's the CTO of Iot Tahoe? Give us a little background CTO, You've got a deep, deep expertise in a lot of different areas. But what do we need to know? >>Well, David, I started my career basically at Microsoft, uh, where I started the information Security Cryptography group. They're the very 1st 1 that the company had, and that led to a career in information, security. And and, of course, as easy as you go along with information security data is the key element to be protected. Eso I always had my hands and data not naturally progressed into a roll out Iot talk was their CTO. >>What's the prescription for that automation journey and simplifying that migration to the cloud? >>Well, I think the first thing is understanding what you've got. So discover and cataloging your data and your applications. You know, I don't know what I have. I can't move it. I can't. I can't improve it. I can't build upon it. And I have to understand there's dependence. And so building that data catalog is the very first step What I got. Okay, >>so So we've done the audit. We know we've got what's what's next? Where do we go >>next? So the next thing is remediating that data you know, where do I have duplicate data? I may have often times in an organization. Uh, data will get duplicated. So somebody will take a snapshot of the data, you know, and then end up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer, and you can see where that will go. And trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to sort of understand where all your redundant data is? So when you go to the cloud, maybe you have an opportunity here to do you consolidate that that data, >>then what? You figure out what to get rid of our actually get rid of it. What's what's next? >>Yes, yes, that would be the next step. So figure out what you need. What, you don't need you Often times I've found that there's obsolete columns of data in your databases that you just don't need. Or maybe it's been superseded by another. You've got tables have been superseded by other tables in your database, so you got to kind of understand what's being used and what's not. And then from that, you can decide. I'm gonna leave this stuff behind or I'm gonna I'm gonna archive this stuff because I might need it for data retention where I'm just gonna delete it. You don't need it. All were >>plowing through your steps here. What's next on the >>journey? The next one is is in a nutshell. Preserve your data format. Don't. Don't, Don't. Don't boil the ocean here at music Cliche. You know, you you want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables in which they sent the columns and the way they're named. So some degree, you are gonna be doing a lift and ship, but it's an intelligent lift and ship. The >>data lives in silos. So how do you kind of deal with that? Problem? Is that is that part of the journey? >>That's that's great pointed because you're right that the data silos happen because, you know, this business unit is start chartered with this task. Another business unit has this task and that's how you get those in stance creations of the same data occurring in multiple places. So you really want to is part of your cloud migration. You really want a plan where there's an opportunity to consolidate your data because that means it will be less to manage. Would be less data to secure, and it will be. It will have a smaller footprint, which means reduce costs. >>But maybe you could address data quality. Where does that fit in on the >>journey? That's that's a very important point, you know. First of all, you don't want to bring your legacy issues with U. S. As the point I made earlier. If you've got data quality issues, this is a good time to find those and and identify and remediate them. But that could be a laborious task, and you could probably accomplish. It will take a lot of work. So the opportunity used tools you and automate that process is really will help you find those outliers that >>what's next? I think we're through. I think I've counted six. What's the What's the lucky seven >>Lucky seven involved your business users. Really, When you think about it, you're your data is in silos, part of part of this migration to cloud as an opportunity to break down the silos. These silence that naturally occurs are the business. You, uh, you've got to break these cultural barriers that sometimes exists between business and say so. For example, I always advise there's an opportunity year to consolidate your sensitive data. Your P I. I personally identifiable information and and three different business units have the same source of truth From that, there's an opportunity to consolidate that into one. >>Well, great advice, Lester. Thanks so much. I mean, it's clear that the Cap Ex investments on data centers they're generally not a good investment for most companies. Lester really appreciate Lester Water CTO of Iot Tahoe. Let's watch this short video and we'll come right back. >>Use cases. Data migration. Accelerate digitization of business by providing automated data migration work flows that save time in achieving project milestones. Eradicate operational risk and minimize labor intensive manual processes that demand costly overhead data quality. You know the data swamp and re establish trust in the data to enable data signs and Data analytics data governance. Ensure that business and technology understand critical data elements and have control over the enterprise data landscape Data Analytics ENABLEMENT Data Discovery to enable data scientists and Data Analytics teams to identify the right data set through self service for business demands or analytical reporting that advanced too complex regulatory compliance. Government mandated data privacy requirements. GDP Our CCP, A, e, p, R HIPPA and Data Lake Management. Identify late contents cleanup manage ongoing activity. Data mapping and knowledge graph Creates BKG models on business enterprise data with automated mapping to a specific ontology enabling semantic search across all sources in the data estate data ops scale as a foundation to automate data management presences. >>Are you interested in test driving the i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program? Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iot. Top Click on the link and connect with the data engineer to learn more and see Iot Tahoe in action. Everybody, we're back. We're talking about enterprise data automation. The hashtag is data automated and we're going to really dig into data migrations, data migrations. They're risky, they're time consuming and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So let's set up the problem a little bit. And then I want to get into some of the data said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>I think I mean, all migrations have to start with knowing the facts about your data. Uh, and you can try and do this manually. But when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. >>So I want to dig into this migration and let's let's pull up graphic. It will talk about We'll talk about what a typical migration project looks like. So what you see, here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this, uh and then use if I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones That Blue Bar is the time to test so you can see the second step. Data analysis. It's 24 weeks so very time consuming, and then let's not get dig into the stuff in the middle of the fine print. But there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom, and you can see hi is that sort of brown and and you could see a number of data analysis data staging data prep, the trial, the implementation post implementation fixtures, the transition to be a Blu, which I think is business as usual. >>The key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers, and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the basis of the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and probably the area that risk you could be. Stack your project team of lots and lots of people to do the next phase, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis, um, manual processes, as we all know, on the layer of trying to relate data that's in different grocery stores relating individual tables and columns, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools. Aziz said earlier the people who understand some of those systems may have left a while ago. CEO even higher risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data met on the data flow has been generated automatically, much less time and effort and much less cars stopped. >>Yeah. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the sort of Cisco said accelerated by Iot, Tom. Okay, great. And we're gonna talk about this, but look, what happens to the operational risk. A dramatic reduction in that, That that graph and then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis, data staging data prep trialling post implementation fixtures in transition to be a you all those went from high labor intensity. So we've now attacked that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its price States catalog. If you like, imagine trying to do that manually, you need to go into every individual data store. You need a DB, a business analyst, reach data store. They need to do an extract of the data. But it on the table was individually they need to cross reference that with other data school, it stores and schemers and tables you probably with the mother of all Lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. I'm not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data in the first place, get rid of duplication that sounds migrate to cloud store where things are really accessible. It's easy to build connections and to use native machine learning tools. You well, on the way up to the maturity card, you can start to use some of the more advanced applications >>massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Much appreciated. 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)