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

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

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

(spirited music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are in the Palo Alto Studios for a CUBE Conversation. Part of our Western Digital Data Makes Possible Series, really looking at a lot of cool applications. At the end of the day, data's underneath everything. There's infrastructure and storage that's holding that, but it's much more exciting to talk about the applications. We're excited to have somebody who's kind of on the cutting edge of a next chapter of something you're probably familiar with. He's Steven Webster, and he is the founder and CEO of Asensei. Steven, great to see you. >> Likewise, likewise. >> So, you guys are taking, I think everyone's familiar with Fitbits, as probably one of the earliest iterations of a biometric feedback, for getting more steps. At the end of the day, get more steps. And you guys are really taking it to the next level, which is, I think you call it connected coaching, so I wondered if you could give everyone a quick overview, and then we'll dig into it a little bit. >> Yeah, I think we're all very familiar now with connected fitness in hindsight, as a category that appeared and emerged, as, like you say, first it was activity trackers. We saw those trackers primarily move into smartwatches, and the category's got life in it, life in it left. I see companies like Flywheel and Peloton, we all know Peloton now. >> [Jeff] Right. >> We're starting to make the fitness equipment itself, the treadmill, the bike, connected. So, there's plenty of growth in that category. But our view is that tracking isn't teaching, and counting and cheering isn't coaching. And so we see this opportunity for this new category that's emerging alongside connected fitness, and that's what we call connected coaching. >> Connected coaching. So the biggest word, obviously, instead of fitness tracker, to the connected coaching, is coaching. >> Yeah. >> So, you guys really think that the coaching piece of it is core. And are you targeting high-end athletes, or is this for the person that just wants to take a step up from their fitness tracker? Where in the coaching spectrum are you guys targeting? >> I saw your shoe dog, Phil Knight, founder of Nike, a book on the shelf behind you there, and his co-founder, Bill Bowerman, has a great quote that's immortalized in Nike offices and stores around the world: "If you have a body, you're an athlete." So, that's how we think about our audience. Our customer base is anyone that wants to unlock their athletic potential. I think if you look at elite sports, and elite athletes, and Olympic athletes, they've had access to this kind of technology going back to the Sydney Olympics, so we're really trying to consumerize that technology and make it available to the people that want to be those athletes, but aren't those athletes yet. You might call it the weekend warrior, or just the committed athlete, that would identify, identify themselves according to a sport that they play. >> So, there's different parts of coaching, right? One, is kind of knowing the techniques, so that you've got the best practices by which to try to practice. >> [Steven] Yep. >> And then there's actually coaching to those techniques, so people practice, right? Practice doesn't make perfect. It's perfect practice that makes perfect. >> [Steven] You stole our line, which we stole from someone else. >> So, what are you doing? How do you observe the athlete? How do you communicate with the athlete? How do you make course corrections to the athlete to move it from simply tracking to coaching? >> [Steven] I mean, it starts with, you have to see everything and miss nothing. So, you need to have eyes on the athlete, and there's really two ways we think you can do that. One is, you're using cameras and computer vision. I think most of us are familiar with technologies like Microsoft Connect, where an external camera can allow you to see the skeleton and the biomechanics of the athlete. And that's a big thing for us. We talk about the from to being from just measuring biometrics: how's your heart rate, how much exertion are you making, how much power are you laying down. We need to move from biometrics to biomechanics, and that means looking at technique, and posture, and movement, and timing. So, we're all familiar with cameras, but we think the more important innovation is the emergence of smart clothing, or smart apparel, and the ability to take sensors that would have been discrete, hard components, and infuse those sensors into smart apparel. We've actually created a reference design for a motion capture sensor, and a network of those sensors infused in your apparel allows us to recover your skeleton, but as easily as pulling on a shirt or shorts. >> [Jeff] So you've actually come up with a reference design. So, obviously, begs a question: you're not working with any one particular apparel manufacturer. You really want to come up with a standard and publish the standard by which anyone could really define, capture, and record body movements, and to convert those movements from the clothing into a model. >> No, that's exactly it. We have no desire to be in the apparel industry. We have no desire to unseat Nike, Adidas, or Under Armour. We're actually licensing our technology royalty-free. We just want to accelerate the adoption of smart apparel. And I think the thing about smart apparel is, no one's going to walk into Niketown and say, "Where's the smart apparel department? "I don't want dumb apparel anymore." There needs to be a compelling reason to buy digitally enhanced apparel, and we think one of the most compelling reasons to buy that is so that we can be coached in the sport of our choice. >> [Jeff] So, then you're starting out with rowing, I believe, is your first sport, right? >> [Steven] That's correct, yeah. >> And so the other really important piece of it, is if people don't have smart apparel, or the smart apparel's not there yet, or maybe when they have smart apparel, there's a lot of opportunities to bring in other data sources beyond just that single set. >> [Steven] And that's absolutely key. When I think about biomechanics, that's what goes in, but there's also what comes out. Good form isn't just aesthetic. Good form is in any given sport. Good form and good technique is about organizing yourself so that you perform most efficiently and perform most effectively. Yeah, so you corrected a point in that we've chosen rowing as one of the sports. Rowing is all about technique. It's all about posture. It's all about form. If you've got two rowers who, essentially, have the same strength, the same cardiovascular capability, the one with the best technique will make the boat move faster. But for the sport of rowing, we also get a tremendous amount of telemetry coming off the rowing machine itself. A force curve weakened on every single pull of that handle. We can see how you're laying down that force, and we can read those force curves. We can look at them and tell things like, are you using your legs enough? Are you opening your back too late or too early? Are you dominant on your arms, where you shouldn't be? Is your technique breaking down at higher stroke rates, but is good at lower stroke rates? So it's a good place for us to start. We can take all of that knowledge and information and coach the athlete. And then when we get down to more marginal gains, we can start to look at their posture and form through that technology like smart apparel. >> There's the understanding what they're doing, and understanding the effort relative to best practices, but there's also, within their journey. Maybe today, they're working on cardio, and tomorrow, they're working on form. The next day, they're working on sprints. So the actual best practices in coaching a sport or particular activity, how are you addressing that? How are you bringing in that expertise beyond just the biometric information? >> [Steven] So yeah, we don't think technology is replacing coaches. We just think that coaches that use technology will replace coaches that don't. It's not an algorithm that's trying to coach you. We're taking the knowledge and the expertise of world-class coaches in the sport, that athletes want to follow, and we're taking that coaching, and essentially, think of it as putting it into a learning management system. And then for any given athlete, Just think of it the way a coach coaches. If you walked into a rowing club, I don't know if you've ever rowed before or not, but a coach will look at you, they'll sit you on a rowing machine or sit you on a boat, and just look at you and decide, what's the one next thing that I'm going to teach you that's going to make you better? And really, that's the art of coaching right there. It's looking for that next improvement, that next marginal gain. It's not just about being able to look at the athlete, but then decide where's the improvement that we want to coach the athlete? And then the whole sports psychology of, how do you coach his improvements? >> Because there's the whole hammer versus carrot. That's another thing. You need to learn how the individual athlete responds, what types of things do they respond better to? Do they like to get yelled? Do they like to be encouraged? Did they like it at the beginning? Did they like it at the end? So, do you guys incorporate some of these softer coaching techniques into the application? >> Our team have all coached sport at university-level typically. We care a lot and we think a lot about the role of the coach. The coach's job is to attach technique to the athlete's body. It's to take what's in your head and what you've seen done before, and give that to the athlete, so absolutely, we're thinking about how do you establish the correct coaching cues. How do you positively reinforce, not just negatively reinforce? Is that person a kinesthetic learner, where they need to feel how to do it correctly? Are they a more visual learner, where they respond better to metaphor? Now, one of the really interesting things with a digital coach is the more people we teach, the better we can get at teaching, because we can start to use some of the techniques of enlarged datasets, and looking at what's working and what's not working. In fact, it's the same technology we would use in marketing or advertising, to segment an audience, and target content. >> Right. >> [Steven] We can take that same technology and apply it how we think about coaching sports. >> So is your initial target to help active coaches that are looking for an edge? Or are you trying to go for the weakend warrior, if you will? Where's your initial market? >> For rowing, we've actually zeroed in on three athletes, where we have a point of view that Asensei can be of help. I'll tell you who the three are. First, is the high school athlete who wants to go to college and get recruited. So, we're selling to the parent as much as we're selling to the student. >> [Jeff] That's an easy one. Just show up and be tall. >> Well, show up, be tall, but also what's your 2k time? How fast can you row 2,000 meters? That's a pretty important benchmark. So for that high school athlete, that's a very specific audience where we're bringing very specific coaches. In fact, the coach that we're launching with to that market, his story is one of, high school to college to national team, and he just came back from the Olympics in Rio. The second athlete that we're looking at is the person who never wants to go on the water, but likes that indoor rowing machine, so it's that CrossFit athlete or it's an indoor rower. And again, we have a very specific coach who coaches indoor rowing. And then the third target customer is-- >> What's that person's motivation, just to get a better time? >> Interesting, in that community, there's a lot of competitiveness, so yeah, it's about I want to get good at this, I want to get better at this. Maybe enter local competitions, either inside your gym or your box. This weekend, in Boston, we have just had one of the largest indoor world, it was the World Indoor Rowing Championships, the C.R.A.S.H B's. There's these huge indoor rowing competitions, so that's a very competitive athlete. And then finally we have, what would be the master's rower or the person for whom rowing is. There's lots of people who don't identify themselves as a rower, but they'll get on a rowing machine two or three times a week, whether it's in their gym or whether it's at home. Your focus is strength, conditioning, working out, but staying injury-free, and just fun and fitness. I think Palaton validated the existence of that market, and we see a lot of people wanting to do that with a rowing machine, and not with a bike. >> I think most of these people will or will not have access to a primary coach, and this augments it, or does this become their primary coach based on where they are in their athletic life? >> [Steven] I think it's both, and certainly, and certainly, we're able to support both. I think when you're that high school rower that wants to make college, you're probably a member of either your school rowing crew or you're a member of a club, but you spend a tremendous amount of time on an erg, the indoor rowing machine, and your practice is unsupervised. Even though you know what you should be doing, there's nobody there in that moment watching you log those 10,000 meters. One of our advisors is, actually, a two-times Olympic world medalist from team Great Britain, Helen Glover. And Helen, I have a great quote from Helen, where she calculated for the Rio Olympics, in the final of the Rio Olympics, every stroke she took in the final, she'd taken 16,000 strokes in practice, which talks to the importance of the quality of that practice, and making sure it's supervised. >> The bigger take on the old 10,000 reps, right? 16,000 per stroke. >> Right? >> Kind of looking forward, right, what were some of the biggest challenges you had to overcome? And then, as you looked forward, right, since the beginning, were ubiquitous, and there's 3D goggles, and there'll be outside-in centers for that whole world. How do you see this world evolving in the immediate short-term for you guys to have success, and then, just down the road a year or two? >> That's a really good question. I think in the short-term, I think it's incumbent on us to just stay really focused in a single community, and get that product right for them. It's more about introducing people to the idea. This is a category creation exercise, so we need to go through that adoption curve of find the early adopters, find the early majority, and before we take that technology anywhere towards our mass market, we need to nail the experience for that early majority. And we think that it's largely going to be in the sport of rowing or with rowers. The cross participation studies in rowing are pretty strong for other sports. Typically, somewhere between 60-80% of rowers weight lift, bike, run, and take part in yoga, whether yoga for mobility and flexibility. There's immediately adjacent markets available to us where the rowers are already in those markets. We're going to stick there for awhile, and really just nail the experience down. >> And is it a big reach to go from tracking to coaching? I mean, these people are all super data focused, right? The beauty of rowing, as you mentioned, it's all about your 2k period. It's one single metric. And they're running, and they're biking, and they're doing all kinds of data-based things, but you're trying to get them to think really more on terms of the coaching versus just the tracking. Has that been hard for them to accept? Do you have any kind of feel for the adoption or the other thing, I would imagine, I spent all this money for these expensive clothing. Is this a killer app that I can now justify having? >> Right, right, right. >> Maybe fancier connected clothes, rather than just simply tracking my time? >> I mean, I think, talking about pricing in the first instance. What we're finding with consumers that we've been testing with, is if you can compare the price of a shirt to the price of shirt without sensors, it's really the wrong value proposition. The question we ask is, How much money are you spending on your CrossFit box membership or your Equinox gym membership? The cost of a personal trainer is easily upwards of $75-100 for an hour. Now, we can give you 24/7 access to that personal coaching. You'll pay the same in a year as you would pay in an hour for coaching. I think for price, it's someone who's already thinking about paying for personal coaching and personal training, that's really where the pricing market is. >> That's interesting, we see that time and time again. We did an interview with Knightscope, and they have security robots, and basically, it's the same thing. They're priced comparisons was the hourly rate for a human counterpart, or we can give it to you for a much less hourly rate. And now, you don't just get it for an hour, you get it for as long as you want to use it. Well, it's exciting times. You guys in the market in terms of when you're going G80? Have a feel for-- >> Any minute now. >> Any minute now? >> We have people using the product, giving us feedback. My phone's switched off. That's the quietest it's been for awhile. But we have people using the product right now, giving us feedback on the product. We're really excited. One in three people, when we ask, the metric that matters for us is net promoter score. How likely would someone recommend asensei to someone else? One in three athletes are giving us a 10 out of 10, so we feel really good about the experience. Now, we're just focused on making sure we have enough content in place from our coaches. General availability is anytime soon. >> [Jeff] Good. Very exciting. >> Yeah, we're excited. >> Thanks for taking a few minutes of your day, and I actually know some rowers, so we'll have to look into the application. >> Right, introduce us. Good stuff. >> He's Steven Webster, I'm Jeff Frick. You're watching theCUBE. We're having a CUBE Conversation in our Palo Alto Studios. Thanks for watching. (bright music)

(stirring music) >> Hi, I'm Peter Burris, and welcome to another CUBE Conversation from theCUBE Studios in beautiful Palo Alto, California. Got a great conversation today. We're going to be talking about some of the new advances that are associated with big data analytics and improving the rate at which human beings, people who actually work with data, can get more out of their data, be more certain about their data, and improve the social system that actually is dependent upon data. To do that, we've got Aaron Kalb of Alation here with us. Aaron is the co-founder and is VP of design and strategic initiatives. Aaron, welcome back to theCUBE. >> Thanks so much for having me, Peter. >> So, then, let's start this off. The concern that a lot of folks have when they think about analytics, big data, and the promise of some of these new advanced technologies is they see how they could be generating significant business value, but they observe that it often falls short. It falls short for technological reasons, you know, setting up the infrastructure is very, very, difficult. But we've started solving that by moving a lot of these workloads to the cloud. They also are discovering that the toolchains can be very complex, but they're starting to solve that by working with companies with vision, like Alation, about how you can bring these things together more easily. There are some good things happening within the analytics space, but one of the biggest challenges is, even if you set up your pipelines and your analytics systems and applications right, you still encounter resistance inside the business, because human beings don't necessarily have a natural affinity for data. Data is not something that's easy to consume, it's not something easy to recognize. People just haven't been trained in it. We need more that makes it easy to identify data quality, data issues, et cetera. Tell us a little bit about what Alation's doing to solve that human side, the adoption side of the challenge. >> That's a great point and a great question, Peter. Fundamentally, what we see is it used to be a problem of quantity. There wasn't enough ability to generate data assets, and to distribute them, and to get to them. Now, there's just an overwhelming amount of places to gather data. The problem becomes finding development data for your need, understanding and putting it into context, and most fundamentally, trusting that it's actually telling you a true story about the world. You know, what we find now is, as there's been more self-service analytics, there's more and more dashboards and queries and content being generated, and often an executive will look at two different answers to the same question that are trending in totally different directions. They'll say, "I can't trust any of this. "On paper, I want to be data-driven, "but in actuality, I'm just going to go back to my gut, "'cause the data is not always trustworthy, "and it's hard to tell what's trustworthy and what's not." >> This is, even after they've found the data and enough people have been working on it to say, to put it in context to say, "Yes, this data is being used in marketing," or, "This data has been used in operations production." there's another layer of branding or whatnot that we can put on data that says, "This data is appropriate for use in this way." Is that what we're talking about here? >> Absolutely right. To help with finding and understanding data, you can group it and make it browsable by topic. You can enable keyword search over it in that natural language. That's stuff that Alation has done in the past. What we're excited to unveil now is this idea of trust check, which is all about saying, wherever you're at in that data value chain of taking raw data and schematizing it and eventually producing pretty dashboards and visualizations, that at every step, we can ensure that only the most trustworthy data sets are being used, because any problem upstream flows downstream. >> So, trust check. >> Trust check. >> Trust check, it's something that comes out of Alation. Is it also being used with other visualization tools or other sources or other applications? >> That's a great question. It's all of the above. Trust check starts with saying, if I'm an analyst who wants to create a dashboard or a visualization, I'm going to have to write some SQL query to do that. What we've done in that context with Alation Compose, is our home-grown SQL tool, is provided a tool, and trust check kind of gets its name from spell check. It used to be there was a dictionary, and you could look it up by hand, and you could look it up online, but that's a lot of work for every single word to check it. And then, you know, Microsoft, I think, was the first innovative saying, "Oh, let's put a little red squiggle that you can't miss "right in your workflow as you're writing, "so you don't have to go to it, it comes to you." We do the exact same thing. I'm about to query a table that is deprecated or has a data quality issue. I immediately see bright red on my screen, can't miss it, and I can fix my behavior. That's as I'm creating a data asset. We also, through our partnerships with Salesforce and with Tableau, each of whom have very popular visualization tools, to, say. if people are consuming a dashboard, not a SQL query, but looking at a Tableau dashboard or a visualization in Salesforce Einstein Analytics, what would it mean to badge right there and then, put a stamp of approval on the most trustworthy sources and a warning or caveat on things that might have an upstream data quality problem? >> So, when you say warning or caveat, you're saying literally that there are exceptions or there are other concerns associated with the data, and reviewing that as part of the analytic process. >> That's exactly right. Much like, again, spell check underlines, or looking at, if you think about if I'm driving in my car with Waze, and it says, "Oh, traffic up ahead, view route this way." What does it mean to get in the user interface where people live, whether they're a business user in Salesforce or Tableau, or a data analyst in a query tool, right there in their flow having onscreen indications of everything happening below the tip of the iceberg that affects their work and the trustworthiness of the data sets they're using. >> So that's what it is. I'll tell you a quick story about spell check. >> Please. >> Many years ago, I'm old enough that I was one of the first users of some of these tools. When you typed in IBM, Microsoft Word would often change it to DUM, which was kind of interesting, given the things that were going on between them. But it leads you to ask questions. How does this work? I mean, how does spell check work? Well, how does trust check work, because that's going to have an enormous implication. People have to trust how trust check works. Tell us a little bit about how trust check works. >> Absolutely. How do you trust trust check? The little red or yellow or bright, salient indicators we've designed are just to get your attention. Then, as a user, you can click into those indicators and see why is this appearing. The biggest reason that an indicator will appear in a trust check context is that a person, a data curator or data steward, has put a warning or a deprecation on the data set. It's not, you know, oh, IBM doesn't like Microsoft, or vice versa. You know, you can see the sourcing. It isn't just, oh, because Merriam-Webster says so. It emerges from the logic of your own organization. But now Alation has this entire catalog backing trust check where it gives a bunch of signals that can help those curators and stewards to decide what indicators to put on what objects. For example, we might observe, this table used to be refreshed frequently. It hasn't in a while. Does that mean it's ripe for getting a bit of a warning on it? Or, people aren't really using this data set. Is there a reason for that? Or, something upstream was just flagged having a data quality issue. That data quality issue might flow downstream like pollution in a creek, and that can be an indication of another reason why you might want to label data as not trustworthy. >> In Alation context with Salesforce and Tableau partners, and perhaps some others, this trust check ends up being a social moniker for what constitutes good data that is branded as a consequence of both technological as well as social activities around that data captured by Alation. I got that right? >> That's exactly right. We're taking technical signals and social signals, because what happens in our customers today before we launched trust check, what they would do is, if you had the time, you would phone a friend. You'd say, "Hey, you seem to be data-savvy. "Does this number look weird to you? "Do you know what's going on? "Is something wrong with the table that it's sourced from?" The problem is, that person's on vacation, and you're out of luck. This is saying, let's push everything we know across that entire chain, from the rawest data to the most polished asset and have all that information pushed up to where you live in the moment you're making a decision, should I trust this data, how should I use it? >> In the whole, going back to this whole world of big data and analytics, we're moving more of the workloads to the cloud to get rid of the infrastructure problems. We're utilizing more integrated toolchains to get rid of the complexity associated with a lot of the analytic pipelines. How does trust check then applied, go back to this notion of human beings not being willing to accept somebody else's data. Give us that use case of how someone's going to sit down in a boardroom or at a strategic meeting or whatever else it is, see trust check, and go, "I get it." >> Absolutely, that's a fantastic question. There's two reasons why, even though all organizations, or 80% according to Gartner, claim they're committed to being data-driven. You still have these moments, people say, "Yeah, I see the numbers, "but I'm going to ignore them, or discount them, "or be very skeptical of them." One issue is just how much of the data that gets to you in the boardroom or the exec team meeting is wrong. We had an incredibly successful data-driven customer who did an internal audit and found that 1/3 of the numbers that appeared in the PowerPoint presentations on which major business decisions were being made, a full 1/3 of them were off by an extraordinary amount, an amount so big that it would, the decision would've cut the other way had the number been accurate. The sheer volume of bad data coming in to undermine trust. The second is, even if only 5% of the data were untrustworthy, if you don't know which is which, the 95% that's trustworthy and the 5% that's not, you still might not be able to use it with confidence. We believe that having trust check be at every stage in this data value chain will solve, actually, both problems by having that spell-check-like experience in the query tool, which is where most analytics projects start. We can reduce the amount of garbage going into the meeting rooms where business choices are being made. And by putting that badge saying "This is certified," or, "Take this with a grain of salt," or, "No, this is totally wrong," that putting that badge on the visualizations that business leaders are looking at in Salesforce and Tableau, and over time, in ideally every tool that anybody would use in an enterprise, we can also help distinguish the wheat from the chaff in that context as well. We think we're attacking both parts of this problem, and that will really drive a data-driven culture truly being adoptable in an organization. >> I want to tie a couple things that you said here. You mentioned the word design a couple times. You're the VP of design at Alation. It also sounds like when you're talking about design, you're not just talking about design of the interface or the software. You're talking about design of how people are going to use the software. What is the extent to which design, what's the scope of design as you see it in this context of advanced analytics, and is trust check just a first step that you're taking? Tell us a little bit about that. >> Yeah, that's a great set of questions, Peter. Design for us means really looking at humans, and starting by listening and watching. You know, a lot of people in the cataloging space and the governance space, they list a lot of should statements. "People should adopt this process, "because otherwise, mistakes will be made." >> Because Gartner said 80% of you have! >> Right, exactly. We think the shoulds only get you so far. We want to really understand the human psychology. How do people actually behave when they're under pressure to move quickly in a rapidly changing environment, when they're afraid of being caught having made a mistake? There's all these pressures people are under. And so, it's not realistic to say, again, you could imagine saying, "Oh, every time before you go out the door, "go to MapQuest or some sort of traffic website "and look up the route and print it out, "so you make sure you plot correctly." No one has time for that, just like no one has time to look up every single word in their essay or their memo or their email and look it up in the dictionary to see if it's right. But when you have an intervention that comes into somebody's flow and is impossible to miss, and is an angel on your shoulder keeping you from making a mistake, or, you know, in-car navigation that tells you in real time, "Here's how you should route." Those sort of things fit into somebody's lifestyle and actually move impact. Our idea is, let's meet people where they are. Acknowledge the challenges that humans face and make technology that really helps them and comes to them instead of scolding them and saying, "Oh, you should change your flow in this uncomfortable way "and come to us, "and that's the only way "you'll achieve the outcome you want." >> Invest the tool into the process and into the activity, as opposed to force people to alter the activity around the limitations or capabilities of the tool. >> Exactly right. And so, while design is optimizing the exact color and size and UI/UX both in our own tools and working with our partners to optimize that, it's starting at an even bigger level of saying, "How do we design the entire workflow "so humans can do what they do best "and the computer just gives them "what they need in real time?" >> And as something as important, and this kind of takes it full circle, something as important and potentially strategic as advanced analytics, having that holistic view is really going to determine success or failure in a lot of businesses. >> That is absolutely right, Peter, and you asked earlier, "Is this just the beginning?" That's absolutely true. Our goal is to say, whatever part of the analytics process you are in, that you get these realtime interventions to help you get the information that's relevant to you, understand what it means in the context you're in, and make sure that it's trustworthy and reliable so people can be truly data-driven. >> Well, there's a lot of invention going on, but what we're really seeking here is changes in social behavior that lead to consequential improvements in business. Aaron Kalb, VP of design and strategic initiatives at Alation, thanks very much for talking about this important advance in how we think about analytics. >> Thank you so much for having me, Peter. >> This is, again, Peter Burris. This has been a CUBE Conversation. Until next time. (stirring music)