>>From around the globe, but it's the cube presenting active DQ, intelligent automation for data quality brought to you by IO Tahoe. >>Now we're going to look at the role automation plays in mobilizing your data on snowflake. Let's welcome. And Duncan Turnbull who's partner sales engineer at snowflake and AIG Vihara is back CEO of IO. Tahoe is going to share his insight. Gentlemen. Welcome. >>Thank you, David. Good to have you back. Yeah, it's great to have you back >>A J uh, and it's really good to CIO Tao expanding the ecosystem so important. Um, now of course bringing snowflake and it looks like you're really starting to build momentum. I mean, there's progress that we've seen every month, month by month, over the past 12, 14 months, your seed investors, they gotta be happy. >>They are all that happy. And then I can see that we run into a nice phase of expansion here and new customers signing up. And now you're ready to go out and raise that next round of funding. I think, um, maybe think of a slight snowflake five years ago. So we're definitely on track with that. A lot of interest from investors and, um, we're right now trying to focus in on those investors that can partner with us, understand AI data and, and automation. >>So personally, I mean, you've managed a number of early stage VC funds. I think four of them, uh, you've taken several comp, uh, software companies through many funding rounds and growth and all the way to exit. So, you know how it works, you have to get product market fit, you know, you gotta make sure you get your KPIs, right. And you gotta hire the right salespeople, but, but what's different this time around, >>Uh, well, you know, the fundamentals that you mentioned though, those are never change. And, um, what we can say, what I can say that's different, that's shifted, uh, this time around is three things. One in that they used to be this kind of choice of, do we go open source or do we go proprietary? Um, now that has turned into, um, a nice hybrid model where we've really keyed into, um, you know, red hat doing something similar with Santos. And the idea here is that there is a core capability of technology that independence a platform, but it's the ability to then build an ecosystem around that made a pervade community. And that community may include customers, uh, technology partners, other tech vendors, and enabling the platform adoption so that all of those folks in that community can build and contribute, um, while still maintaining the core architecture and platform integrity, uh, at the core of it. >>And that's one thing that's changed was fitting a lot of that type of software company, um, emerge into that model, which is different from five years ago. Um, and then leveraging the cloud, um, every cloud snowflake cloud being one of them here in order to make use of what customers, uh, and customers and enterprise software are moving towards. Uh, every CIO is now in some configuration of a hybrid. Um, it is state whether those cloud multi-cloud on prem. That's just the reality. The other piece is in dealing with the CIO is legacy. So the past 15, 20 years they've purchased many different platforms, technologies, and some of those are still established and still, how do you, um, enable that CIO to make purchase while still preserving and in some cases building on and extending the, the legacy, um, material technology. So they've invested their people's time and training and financial investment into solving a problem, customer pain point, uh, with technology, but, uh, never goes out of fashion >>That never changes. You have to focus like a laser on that. And of course, uh, speaking of companies who are focused on solving problems, don't can turn bill from snowflake. You guys have really done a great job and really brilliantly addressing pain points, particularly around data warehousing, simplified that you're providing this new capability around data sharing, uh, really quite amazing. Um, Dunkin AAJ talks about data quality and customer pain points, uh, in, in enterprise. It, why is data quality been such a problem historically? >>Oh, sorry. One of the biggest challenges that's really affected by it in the past is that because to address everyone's need for using data, they've evolved all these kinds of different places to store all these different silos or data marts or all this kind of clarification of places where data lives and all of those end up with slightly different schedules to bringing data in and out. They end up with slightly different rules for transforming that data and formatting it and getting it ready and slightly different quality checks for making use of it. And this then becomes like a big problem in that these different teams are then going to have slightly different or even radically different ounces to the same kinds of questions, which makes it very hard for teams to work together, uh, on their different data problems that exist inside the business, depending on which of these silos they end up looking at and what you can do. If you have a single kind of scalable system for putting all of your data into it, you can kind of sidestep along to this complexity and you can address the data quality issues in a, in a single and a single way. >>Now, of course, we're seeing this huge trend in the market towards robotic process automation, RPA, that adoption is accelerating. Uh, you see, in UI paths, I IPO, you know, 35 plus billion dollars, uh, valuation, you know, snowflake like numbers, nice cops there for sure. Uh, agent you've coined the phrase data RPA, what is that in simple terms? >>Yeah, I mean, it was born out of, uh, seeing how in our ecosystem concern community developers and customers, uh, general business users for wanting to adopt and deploy a tar hose technology. And we could see that, um, I mean, there's not monkeying out PA we're not trying to automate that piece, but wherever there is a process that was tied into some form of a manual overhead with handovers and so on. Um, that process is something that we were able to automate with, with our ties technology and, and the deployment of AI and machine learning technologies specifically to those data processes almost as a precursor to getting into financial automation that, um, that's really where we're seeing the momentum pick up, especially in the last six months. And we've kept it really simple with snowflake. We've kind of stepped back and said, well, you know, the resource that a snowflake can leverage here is, is the metadata. So how could we turn snowflake into that repository of being the data catalog? And by the way, if you're a CIO looking to purchase a data catalog tool stop, there's no need to, um, working with snowflake, we've enable that intelligence to be gathered automatically and to be put, to use within snowflake. So reducing that manual effort, and I'm putting that data to work. And, um, and that's where we've packaged this with, uh, AI machine learning specific to those data tasks. Um, and it made sense that's, what's resonated with, with our customers. >>You know, what's interesting here, just a quick aside, as you know, I've been watching snowflake now for awhile and, and you know, of course the, the competitors come out and maybe criticize why they don't have this feature. They don't have that feature. And it's snowflake seems to have an answer. And the answer oftentimes is, well, its ecosystem ecosystem is going to bring that because we have a platform that's so easy to work with though. So I'm interested Duncan in what kind of collaborations you are enabling with high quality data. And of course, you know, your data sharing capability. >>Yeah. So I think, uh, you know, the ability to work on, on datasets, isn't just limited to inside the business itself or even between different business units. And we were kind of discussing maybe with their silos. Therefore, when looking at this idea of collaboration, we have these where we want to be >>Able to exploit data to the greatest degree possible, but we need to maintain the security, the safety, the privacy, and governance of that data. It could be quite valuable. It could be quite personal depending on the application involved. One of these novel applications that we see between organizations of data sharing is this idea of data clean rooms. And these data clean rooms are safe, collaborative spaces, which allow multiple companies or even divisions inside a company where they have particular, uh, privacy requirements to bring two or more data sets together for analysis. But without having to actually share the whole unprotected data set with each other, and this lets you to, you know, when you do this inside of snowflake, you can collaborate using standard tool sets. You can use all of our SQL ecosystem. You can use all of the data science ecosystem that works with snowflake. >>You can use all of the BI ecosystem that works with snowflake, but you can do that in a way that keeps the confidentiality that needs to be presented inside the data intact. And you can only really do these kinds of, uh, collaborations, especially across organization, but even inside large enterprises, when you have good reliable data to work with, otherwise your analysis just isn't going to really work properly. A good example of this is one of our large gaming customers. Who's an advertiser. They were able to build targeting ads to acquire customers and measure the campaign impact in revenue, but they were able to keep their data safe and secure while doing that while working with advertising partners, uh, the business impact of that was they're able to get a lifted 20 to 25% in campaign effectiveness through better targeting and actually, uh, pull through into that of a reduction in customer acquisition costs because they just didn't have to spend as much on the forms of media that weren't working for them. >>So, ha I wonder, I mean, you know, with, with the way public policy shaping out, you know, obviously GDPR started it in the States, you know, California, consumer privacy act, and people are sort of taking the best of those. And, and, and there's a lot of differentiation, but what are you seeing just in terms of, you know, the government's really driving this, this move to privacy, >>Um, government public sector, we're seeing a huge wake up an activity and, uh, across the whole piece that, um, part of it has been data privacy. Um, the other part of it is being more joined up and more digital rather than paper or form based. Um, we've all got stories of waiting in line, holding a form, taking that form to the front of the line and handing it over a desk. Now government and public sector is really looking to transform their services into being online, to show self service. Um, and that whole shift is then driving the need to, um, emulate a lot of what the commercial sector is doing, um, to automate their processes and to unlock the data from silos to put through into those, uh, those processes. Um, and another thing I can say about this is they, the need for data quality is as a Dunkin mentions underpins all of these processes, government pharmaceuticals, utilities, banking, insurance, the ability for a chief marketing officer to drive a, a loyalty campaign. >>They, the ability for a CFO to reconcile accounts at the end of the month. So do a, a, uh, a quick, accurate financial close. Um, also the, the ability of a customer operations to make sure that the customer has the right details about themselves in the right, uh, application that they can sell. So from all of that is underpinned by data and is effective or not based on the quality of that data. So whilst we're mobilizing data to snowflake cloud, the ability to then drive analytics, prediction, business processes off that cloud, um, succeeds or fails on the quality of that data. >>I mean it, and, you know, I would say, I mean, it really is table stakes. If you don't trust the data, you're not gonna use the data. The problem is it always takes so long to get to the data quality. There's all these endless debates about it. So we've been doing a fair amount of work and thinking around this idea of decentralized data, data by its very nature is decentralized, but the fault domains of traditional big data is that everything is just monolithic and the organizations monolithic technology's monolithic, the roles are very, you know, hyper specialized. And so you're hearing a lot more these days about this notion of a data fabric or what calls a data mesh. Uh, and we've kind of been leaning in to that and the ability to, to connect various data capabilities, whether it's a data warehouse or a data hub or a data Lake that those assets are discoverable, they're shareable through API APIs and they're governed on a federated basis. And you're using now bringing in a machine intelligence to improve data quality. You know, I wonder Duncan, if you could talk a little bit about Snowflake's approach to this topic. >>Sure. So I'd say that, you know, making use of all of your data, is there a key kind of driver behind these ideas that they can mesh into the data fabrics? And the idea is that you want to bring together not just your kind of strategic data, but also your legacy data and everything that you have inside the enterprise. I think I'd also like to kind of expand upon what a lot of people view as all of the data. And I think that a lot of people kind of miss that there's this whole other world of data they could be having access to, which is things like data from their business partners, their customers, their suppliers, and even stuff that's more in the public domain, whether that's, you know, demographic data or geographic or all these kinds of other types of data sources. And what I'd say to some extent is that the data cloud really facilitates the ability to share and gain access to this both kind of between organizations inside organizations. >>And you don't have to, you know, make lots of copies of the data and kind of worry about the storage and this federated, um, you know, idea of governance and all these things that it's quite complex to kind of manage this. Uh, you know, the snowflake approach really enables you to share data with your ecosystem all the world, without any latency with full control over what's shared without having to introduce new complexities or having complex attractions with APIs or software integration. The simple approach that we provide allows a relentless focus on creating the right data product to meet the challenges facing your business today. >>So, Andrea, the key here is to don't get to talking about it in my mind. Anyway, my cake takeaway is to simplicity. If you can take the complexity out of the equation, we're going to get more adoption. It really is that simple. >>Yeah, absolutely. Do you think that that whole journey, maybe five, six years ago, the adoption of data lakes was, was a stepping stone. Uh, however, the Achilles heel there was, you know, the complexity that it shifted towards consuming that data from a data Lake where there were many, many sets of data, um, to, to be able to cure rate and to, um, to consume, uh, whereas actually, you know, the simplicity of being able to go to the data that you need to do your role, whether you're in tax compliance or in customer services is, is key. And, you know, listen for snowflake by auto. One thing we know for sure is that our customers are super small and they're very capable. They're they're data savvy and know, want to use whichever tool and embrace whichever, um, cloud platform that is gonna reduce the barriers to solving. What's complex about that data, simplifying that and using, um, good old fashioned SQL, um, to access data and to build products from it to exploit that data. So, um, simplicity is, is key to it to allow people to, to, to make use of that data. And CIO is recognize that >>So Duncan, the cloud obviously brought in this notion of dev ops, um, and new methodologies and things like agile that brought that's brought in the notion of data ops, which is a very hot topic right now. Um, basically dev ops applies to data about how D how does snowflake think about this? How do you facilitate that methodology? >>Yeah, sorry. I agree with you absolutely. That they drops takes these ideas of agile development of >>Agile delivery and of the kind of dev ops world that we've seen just rise and rise, and it applies them to the data pipeline, which is somewhere where it kind of traditionally hasn't happened. And it's the same kinds of messages as we see in the development world, it's about delivering faster development, having better repeatability and really getting towards that dream of the data-driven enterprise, you know, where you can answer people's data questions, they can make better business decisions. And we have some really great architectural advantages that allow us to do things like allow cloning of data sets without having to copy them, allows us to do things like time travel so we can see what data looked like at some point in the past. And this lets you kind of set up both your own kind of little data playpen as a clone without really having to copy all of that data. >>So it's quick and easy, and you can also, again, with our separation of storage and compute, you can provision your own virtual warehouse for dev usage. So you're not interfering with anything to do with people's production usage of this data. So the, these ideas, the scalability, it just makes it easy to make changes, test them, see what the effect of those changes are. And we've actually seen this. You were talking a lot about partner ecosystems earlier. Uh, the partner ecosystem has taken these ideas that are inside snowflake and they've extended them. They've integrated them with, uh, dev ops and data ops tooling. So things like version control and get an infrastructure automation and things like Terraform. And they've kind of built that out into more of a data ops products that, that you can, you can make yourself so we can see there's a huge impact of, of these ideas coming into the data world. >>We think we're really well-placed to take advantage to them. The partner ecosystem is doing a great job with doing that. And it really allows us to kind of change that operating model for data so that we don't have as much emphasis on like hierarchy and change windows and all these kinds of things that are maybe use as a lot of fashioned. And we kind of taking the shift from this batch data integration into, you know, streaming continuous data pipelines in the cloud. And this kind of gets you away from like a once a week or once a month change window, if you're really unlucky to, you know, pushing changes, uh, in a much more rapid fashion as the needs of the business change. >>I mean, those hierarchical organizational structures, uh, w when we apply those to begin to that, what it actually creates the silos. So if you're going to be a silo Buster, which aji look at you guys in silo busters, you've got to put data in the hands of the domain experts, the business people, they know what data they want, if they have to go through and beg and borrow for a new data sets, et cetera. And so that's where automation becomes so key. And frankly, the technology should be an implementation detail, not the dictating factor. I wonder if you could comment on this. >>Yeah, absolutely. I think, um, making the, the technologies more accessible to the general business users >>Or those specialists business teams that, um, that's the key to unlocking is it is interesting to see is as people move from organization to organization where they've had those experiences operating in a hierarchical sense, I want to break free from that and, um, or have been exposed to, um, automation, continuous workflows, um, change is continuous in it. It's continuous in business, the market's continuously changing. So having that flow across the organization of work, using key components, such as get hub, similar to what you drive process Terraform to build in, um, code into the process, um, and automation and with a high Tahoe leveraging all the metadata from across those fragmented sources is, is, is good to say how those things are coming together. And watching people move from organization to organization say, Hey, okay, I've got a new start. I've got my first hundred days to impress my, my new manager. >>Uh, what kind of an impact can I, um, bring to this? And quite often we're seeing that as, you know, let me take away the good learnings from how to do it, or how not to do it from my previous role. And this is an opportunity for me to, to bring in automation. And I'll give you an example, David, you know, recently started working with a, a client in financial services. Who's an asset manager, uh, managing financial assets. They've grown over the course of the last 10 years through M and a, and each of those acquisitions have bought with it tactical data. It's saying instead of data of multiple CRM systems now multiple databases, multiple bespoke in-house created applications. And when the new CIO came in and had a look at those well, you know, yes, I want to mobilize my data. Yes, I need to modernize my data state because my CEO is now looking at these crypto assets that are on the horizon and the new funds that are emerging that around digital assets and crypto assets. >>But in order to get to that where absolutely data underpins and is the core asset, um, cleaning up that, that legacy situation mobilizing the relevant data into the Safelite cloud platform, um, is where we're giving time back, you know, that is now taking a few weeks, whereas that transitioned to mobilize that data, start with that, that new clean slate to build upon a new business as a, a digital crypto asset manager, as well as the legacy, traditional financial assets, bonds stocks, and fixed income assets, you name it, uh, is where we're starting to see a lot of innovation. >>Yeah. Tons of innovation. I love the crypto examples and FTS are exploding and, you know, let's face it, traditional banks are getting disrupted. Uh, and so I also love this notion of data RPA. I, especially because I've done a lot of work in the RPA space. And, and I want to, what I would observe is that the, the early days of RPA, I call it paving the cow path, taking existing processes and applying scripts, get letting software robots, you know, do its thing. And that was good because it reduced, you know, mundane tasks, but really where it's evolved is a much broader automation agenda. People are discovering new, new ways to completely transform their processes. And I see a similar, uh, analogy for data, the data operating model. So I'm wonder whenever you think about that, how a customer really gets started bringing this to their ecosystem, their data life cycles. >>Sure. Yeah. So step one is always the same is figuring out for the CIO, the chief data officer, what data do I have, um, and that's increasingly something that they want towards a mate, so we can help them there and, and do that automated data discovery, whether that is documents in the file, share backup archive in a relational data store, in a mainframe really quickly hydrating that and bringing that intelligence, the forefront of, of what do I have, and then it's the next step of, well, okay. Now I want to continually monitor and curate that intelligence with the platform that I've chosen. Let's say snowflake, um, in order such that I can then build applications on top of that platform to serve my, my internal, external customer needs and the automation around classifying data reconciliation across different fragmented data silos, building that in those insights into snowflake. >>Um, as you say, a little later on where we're talking about data quality, active DQ, allowing us to reconcile data from different sources, as well as look at the integrity of that data. Um, so they can go on to remediation, you know, I, I wanna, um, harness and leverage, um, techniques around traditional RPA. Um, but to get to that stage, I need to fix the data. So remediating publishing the data in snowflake, uh, allowing analysis to be formed performance snowflake. Th those are the key steps that we see and just shrinking that timeline into weeks, giving the organization that time back means they're spending more time on their customer and solving their customer's problem, which is where we want them to be. >>This is the brilliance of snowflake actually, you know, Duncan is, I've talked to him, then what does your view about this and your other co-founders and it's really that focus on simplicity. So, I mean, that's, you, you picked a good company to join my opinion. So, um, I wonder if you could, you know, talk about some of the industry sectors that are, again, going to gain the most from, from data RPA, I mean, traditional RPA, if I can use that term, you know, a lot of it was back office, a lot of, you know, financial w what are the practical applications where data RPA is going to impact, you know, businesses and, and the outcomes that we can expect. >>Yes, sir. So our drive is, is really to, to make that, um, business general user's experience of RPA simpler and, and using no code to do that, uh, where they've also chosen snowflake to build that their cloud platform. They've got the combination then of using a relatively simple script scripting techniques, such as SQL, uh, without no code approach. And the, the answer to your question is whichever sector is looking to mobilize their data. Uh, it seems like a cop-out, but to give you some specific examples, David, um, in banking where, uh, customers are looking to modernize their banking systems and enable better customer experience through, through applications and digital apps. That's where we're, we're seeing a lot of traction, uh, and this approach to, to pay RPA to data, um, health care, where there's a huge amount of work to do to standardize data sets across providers, payers, patients, uh, and it's an ongoing, um, process there for, for retail, um, helping to, to build that immersive customer experience. >>So recommending next best actions, um, providing an experience that is going to drive loyalty and retention, that's, that's dependent on understanding what that customer's needs intent, uh, being out to provide them with the content or the outfit at that point in time, or all data dependent utilities is another one great overlap there with, with snowflake where, you know, helping utilities, telecoms energy, water providers to build services on that data. And this is where the ecosystem just continues to, to expand. If we, if we're helping our customers turn their data into services for, for their ecosystem, that's, that's exciting. And they were more so exciting than insurance, which we always used to, um, think back to, uh, when insurance used to be very dull and mundane, actually, that's where we're seeing a huge amounts of innovation to create new flexible products that are priced to the day to the situation and, and risk models being adaptive when the data changes, uh, on, on events or circumstances. So across all those sectors that they're all mobilizing that data, they're all moving in some way, shape or form to a, a multi-cloud, um, set up with their it. And I think with, with snowflake and without Tahoe, being able to accelerate that and make that journey simple and as complex is, uh, is why we found such a good partner here. >>All right. Thanks for that. And then thank you guys. Both. We gotta leave it there. Uh, really appreciate Duncan you coming on and Aja best of luck with the fundraising. >>We'll keep you posted. Thanks, David. All right. Great. >>Okay. Now let's take a look at a short video. That's going to help you understand how to reduce the steps around your data ops. Let's watch.

>>The data Lake concept was intriguing when first introduced in 2010, but people quickly realized that shoving data into a data Lake may data Lake stagnant, repositories that were essentially storage bins that were less expensive than traditional data warehouses. This is Dave Vellante joined me for IO. Tahoe's latest installment of the data automation series, active DQ, intelligent automation for data quality management. We'll talk to experts from snowflake about their data assessment utility from within the snowflake platform and how it scales to the demands of business. While also controlling costs. I have Tahoe CEO, AIG Hora will explain how IO Tahoe and snowflake together are bringing active DQ to market. And what the customers are saying about it. Save the date Thursday, April 29th for IO Tahoes data automation series active DQ, intelligent automation for data quality show streams promptly at 11:00 AM Eastern on the cube, the >>In high tech coverage.

>>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.

>>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. >>Okay, we're back. Welcome back to data Automated. A J ahora is CEO of I o Ta ho, JJ. Good to see you. How have things in London? >>Big thing. Well, thinking well, where we're making progress, I could see you hope you're doing well and pleasure being back here on the Cube. >>Yeah, it's always great to talk to. You were talking enterprise data automation. As you know, with within our community, we've been pounding the whole data ops conversation. Little different, though. We're gonna We're gonna dig into that a little bit. But let's start with a J how you've seen the response to Covert and I'm especially interested in the role that data has played in this pandemic. >>Yeah, absolutely. I think everyone's adapting both essentially, um, and and in business, the customers that I speak to on day in, day out that we partner with, um they're busy adapting their businesses to serve their customers. It's very much a game of and showing the week and serve our customers to help their customers um, you know, the adaptation that's happening here is, um, trying to be more agile, kind of the most flexible. Um, a lot of pressure on data. A lot of demand on data and to deliver more value to the business, too. Serve that customer. >>Yeah. I mean, data machine intelligence and cloud, or really three huge factors that have helped organizations in this pandemic. And, you know, the machine intelligence or AI piece? That's what automation is all about. How do you see automation helping organizations evolve maybe faster than they thought they might have to >>Sure. I think the necessity of these times, um, there's there's a says a lot of demand doing something with data data. Uh huh. A lot of a lot of businesses talk about being data driven. Um, so interesting. I sort of look behind that when we work with our customers, and it's all about the customer. You know, the mic is cios invested shareholders. The common theme here is the customer. That customer experience starts and ends with data being able to move from a point that is reacting. So what the customer is expecting and taking it to that step forward where you can be proactive to serve what that customer's expectation to and that's definitely come alive now with they, um, the current time. >>Yes. So, as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline. But talk about enterprise data automation. What is it to you and how is it different from data off? >>Yeah, Great question. Thank you. I am. I think we're all familiar with felt more more awareness around. So as it's applied, Teoh, uh, processes methodologies that have become more mature of the past five years around devil that managing change, managing an application, life cycles, managing software development data about, you know, has been great. But breaking down those silos between different roles functions and bringing people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, landing itself to data with data is exciting. We're excited about that, Andi shifting the focus from being I t versus business users to you know who are the data producers. And here the data consumers in a lot of cases, it concert in many different lines of business. So in data role, those methods those tools and processes well we look to do is build on top of that with data automation. It's the is the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors our R and D and bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is, Is the automation behind the automation we can take? I'll give you an example. Okay, a bank where we did a lot of work to do make move them into accelerating that digital transformation. And what we're finding is that as we're able to automate the jobs related to data a managing that data and serving that data that's going into them as a business automating their processes for their customer. Um, so it's it's definitely having a compound effect. >>Yeah, I mean I think that you did. Data ops for a lot of people is somewhat new to the whole Dev Ops. The data ops thing is is good and it's a nice framework. Good methodology. There is obviously a level of automation in there and collaboration across different roles. But it sounds like you're talking about so supercharging it, if you will, the automation behind the automation. You know, I think organizations talk about being data driven. You hear that? They have thrown around a lot of times. People sit back and say, We don't make decisions without data. Okay? But really, being data driven is there's a lot of aspects there. There's cultural, but it's also putting data at the core of your organization, understanding how it effects monetization. And, as you know, well, silos have been built up, whether it's through M and a, you know, data sprawl outside data sources. So I'm interested in your thoughts on what data driven means and specifically Hi, how Iot Tahoe plays >>there. Yeah, I'm sure we'll be happy. That look that three David, we've We've come a long way in the last four years. We started out with automating some of those simple, um, to codify. Um, I have a high impact on organization across the data, a data warehouse. There's data related tasks that classify data on and a lot of our original pattern. Senai people value that were built up is is very much around. They're automating, classifying data across different sources and then going out to so that for some purpose originally, you know, some of those simpler I'm challenges that we have. Ah, custom itself, um, around data privacy. You know, I've got a huge data lake here. I'm a telecoms business. I've got millions of six subscribers. Um, quite often the chief data office challenges. How do I cover the operational risk? Where, um, I got so much data I need to simplify my approach to automating, classifying that data. Recent is you can't do that manually. We can for people at it. And the the scale of that is is prohibitive, right? Often, if you had to do it manually by the time you got a good picture of it, it's already out of date. Then, starting with those those simple challenges that we've been able to address, we're then going on and build on that to say, What else do we serve? What else do we serve? The chief data officer, Chief marketing officer on the CFO. Within these times, um, where those decision makers are looking for having a lot of choices in the platform options that they say that the tooling they're very much looking for We're that Swiss army. Not being able to do one thing really well is is great, but more more. Where that cost pressure challenge is coming in is about how do we, um, offer more across the organization, bring in those business lines of business activities that depend on data to not just with a T. Okay, >>so we like the cube. Sometimes we like to talk about Okay, what is it? And then how does it work? And what's the business impact? We kind of covered what it is but love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, I wonder if you could tell us and what is the secret sauce behind Iot Tahoe? And if you could take us through this slot. >>Sure. I mean, right there in the middle that the heart of what we do It is the intellectual property. Yeah, that was built up over time. That takes from Petra genius data sources Your Oracle relational database, your your mainframe. If they lay in increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data, classify that data after it's classified them have the ability to form relationships across those different, uh, source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts a contact and meaning around that data. So it's moving it now from bringing data driven on increasingly well. We have really smile, right people in our customer organizations you want do some of those advanced knowledge tasks, data scientists and, uh, quants in some of the banks that we work with. The the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality policies that you apply to that data. I'm putting it in context once you've got the ability to power. A a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the tapestry that fabric across that different systems could be crm air P system such as s AP on some of the newer cloud databases that we work with. Snowflake is a great Well, >>yes. So this is you're describing sort of one of the one of the reasons why there's so many stove pipes and organizations because data is gonna locked in the silos of applications. I also want to point out, you know, previously to do discovery to do that classification that you talked about form those relationship to glean context from data. A lot of that, if not most of that in some cases all that would have been manual. And of course, it's out of date so quickly. Nobody wants to do it because it's so hard. So this again is where automation comes into the the the to the idea of really becoming data driven. >>Sure. I mean the the efforts. If we if I look back, maybe five years ago, we had a prevalence of daily technologies at the cutting edge. Those have said converging me to some of these cloud platforms. So we work with Google and AWS, and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenge at scale. I quickly runs out of steam because once, um, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data estate? It's changed, you know, you've onboard a new customer. You signed up a new partner, Um, customer has no adopted a new product that you just Lawrence and there that that slew of data it's keeps coming. So it's keeping pace with that. The only answer really is is some form of automation. And what we found is if we can tie automation with what I said before the expertise the, um, the subject matter expertise that sometimes goes back many years within an organization's people that augmentation between machine learning ai on and on that knowledge that sits within inside the organization really tends to involve a lot of value in data? >>Yes, So you know Well, a J you can't be is a smaller company, all things to all people. So your ecosystem is critical. You working with AWS? You're working with Google. You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>Yeah, that's that's fundamental. So I mean, when I caimans, we tell her here is the CEO of one of the, um, trends that I wanted us to to be part of was being open, having an open architecture that allowed one thing that was nice to my heart, which is as a CEO, um, a C I O where you've got a budget vision and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using ap eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um, and snowflake here is, um it's those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that, and they're leveraging the value that they've already committed to. >>Okay, so we've talked about kind of what it is and how it works, and I want to get into the business impact. I would say what I would be looking for from from this would be Can you help me lower my operational risk? I've got I've got tasks that I do many year sequential, some who are in parallel. But can you reduce my time to task? And can you help me reduce the labor intensity and ultimately, my labor costs? And I put those resources elsewhere, and ultimately, I want to reduce the end and cycle time because that is going to drive Telephone number R. A. Y So, um, I missing anything? Can you do those things? And maybe you could give us some examples of the tiara y and the business impact. >>Yeah. I mean, the r a y David is is built upon on three things that I mentioned is a combination off leveraging the existing investment with the existing state, whether that's home, Microsoft, Azure or AWS or Google IBM. And I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have you got the automation that is working right down to the level off data, a column level or the file level so we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs, that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome. It could be a customer who wants that experience on a mobile device. A tablet oh, face to face within, within the store. I mean game. Would you provision the right data and enable our customers do that? But their customers, with the right data that they can trust at the right time, just in that real time moment where decision or an action is being expected? That's, um, that's driving the r a y two b in some cases, 20 x but and that's that's really satisfying to see that that kind of impact it is taking years down to months and in many cases, months of work down to days. In some cases, our is the time to value. I'm I'm impressed with how quickly out of the box with very little training a customer and think about, too. And you speak just such a search. They discovery knowledge graph on DM. I don't find duplicates. Onda Redundant data right off the bat within hours. >>Well, it's why investors are interested in this space. I mean, they're looking for a big, total available market. They're looking for a significant return. 10 X is you gotta have 10 x 20 x is better. So so that's exciting and obviously strong management and a strong team. I want to ask you about people and culture. So you got people process technology we've seen with this pandemic that processes you know are really unpredictable. And the technology has to be able to adapt to any process, not the reverse. You can't force your process into some static software, so that's very, very important. But the end of the day you got to get people on board. So I wonder if you could talk about this notion of culture and a data driven culture. >>Yeah, that's that's so important. I mean, current times is forcing the necessity of the moment to adapt. But as we start to work their way through these changes on adapt ah, what with our customers, But that is changing economic times. What? What we're saying here is the ability >>to I >>have, um, the technology Cartman, in a really smart way, what those business uses an I T knowledge workers are looking to achieve together. So I'll give you an example. We have quite often with the data operations teams in the companies that we, um, partnering with, um, I have a lot of inbound enquiries on the day to day level. I really need this set of data they think it can help my data scientists run a particular model? Or that what would happen if we combine these two different silence of data and gets the Richmond going now, those requests you can, sometimes weeks to to realize what we've been able to do with the power is to get those answers being addressed by the business users themselves. And now, without without customers, they're coming to the data. And I t folks saying, Hey, I've now built something in the development environment. Why don't we see how that can scale up with these sets of data? I don't need terabytes of it. I know exactly the columns and the feet in the data that I'm going to use on that gets seller wasted in time, um, angle to innovate. >>Well, that's huge. I mean, the whole notion of self service and the lines of business actually feeling like they have ownership of the data as opposed to, you know, I t or some technology group owning the data because then you've got data quality issues or if it doesn't line up there their agenda, you're gonna get a lot of finger pointing. So so that is a really important. You know a piece of it. I'll give you last word A J. Your final thoughts, if you would. >>Yeah, we're excited to be the only path. And I think we've built great customer examples here where we're having a real impact in in a really fast pace, whether it helping them migrate to the cloud, helping the bean up their legacy, Data lake on and write off there. Now the conversation is around data quality as more of the applications that we enable to a more efficiently could be data are be a very robotic process automation along the AP, eyes that are now available in the cloud platforms. A lot of those they're dependent on data quality on and being able to automate. So business users, um, to take accountability off being able to so look at the trend of their data quality over time and get the signals is is really driving trust. And that trust in data is helping in time. Um, the I T teams, the data operations team, with do more and more quickly that comes back to culture being out, supply this technology in such a way that it's visual insensitive. Andi. How being? Just like Dev Ops tests with with a tty Dave drops putting intelligence in at the data level to drive that collaboration. We're excited, >>you know? You remind me of something. I lied. I don't want to go yet. It's OK, so I know we're tight on time, but you mentioned migration to the cloud. And I'm thinking about conversation with Paula from Webster Webster. Bank migrations. Migrations are, you know, they're they're a nasty word for for organizations. So our and we saw this with Webster. How are you able to help minimize the migration pain and and why is that something that you guys are good at? >>Yeah. I mean, there were many large, successful companies that we've worked with. What's There's a great example where, you know, I'd like to give you the analogy where, um, you've got a lot of people in your teams if you're running a business as a CEO on this bit like a living living grade. But imagine if those different parts of your brain we're not connected, that with, um, so diminish how you're able to perform. So what we're seeing, particularly with migration, is where banks retailers. Manufacturers have grown over the last 10 years through acquisition on through different initiatives, too. Um, drive customer value that sprawl in their data estate hasn't been fully dealt with. It sometimes been a good thing, too. Leave whatever you're fired off the agent incent you a side by side with that legacy mainframe on your oracle, happy and what we're able to do very quickly with that migration challenges shine a light on all the different parts. Oh, data application at the column level or higher level if it's a day late and show an enterprise architect a CDO how everything's connected, where they may not be any documentation. The bright people that created some of those systems long since moved on or retired or been promoted into so in the rose on within days, being out to automatically generate Anke refreshed the states of that data across that man's game on and put it into context, then allows you to look at a migration from a confidence that you did it with the back rather than what we've often seen in the past is teams of consultant and business analysts. Data around this spend months getting an approximation and and a good idea of what it could be in the current state and try their very best to map that to the future Target state. Now, without all hoping out, run those processes within hours of getting started on, um well, that picture visualize that picture and bring it to life. You know, the Yarra. Why, that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on gcb or migration to any other clouds such as AWS or a multi cloud landscape right now with yeah, >>that visibility is key. Teoh sort of reducing operational risks, giving people confidence that they can move forward and being able to do that and update that on an ongoing basis, that means you can scale a J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have >>you. Thank you, David. Look towards smoking in. >>Alright, keep it right there, everybody. We're here with data automated on the Cube. This is Dave Volante and we'll be right back. Short break. >>Yeah, yeah, yeah, yeah

(upbeat music) >> Reporter: From around the globe, it's The Cube with digital coverage of enterprise data automation and event series brought to you by Io-Tahoe. >> Okay, we're back. Focusing on enterprise data automation, we're going to talk about the journey to the cloud. Remember, the hashtag is data automated. We're here with Lester Waters who's the CTO of Io-Tahoe, Lester, good to see you from across the pond on video, wish we were face to face, but it's great to have you on The Cube. >> Also I do, thank you for having me. >> Oh, you're very welcome. Hey, give us a little background on CTO, you got a deep expertise in a lot of different areas, but what do we need to know? >> Well, David, I started my career basically at Microsoft, where I started the Information Security Cryptography Group. They're the very first one that the company had and that led to a career in information security and of course, as you go along with the information security, data is the key element to be protected. So I always had my hands in data and that naturally progressed into a role with Io-Tahoe as their CTO. >> Guys, I have to invite you back, we'll talk crypto all day we'd love to do that but we're here talking about yeah, awesome, right? But we're here talking about the cloud and here we'll talk about the journey to the cloud and accelerate. Everybody's really interested obviously in cloud, even more interested now with the pandemic, but what's that all about? >> Well, moving to the cloud is quite an undertaking for most organizations. First of all, we've got as probably if you're a large enterprise, you probably have thousands of applications, you have hundreds and hundreds of database instances, and trying to shed some light on that, just to plan your move to the cloud is a real challenge. And some organizations try to tackle that manually. Really what Io-Tahoe is bringing is trying to tackle that in an automated version to help you with your journey to the cloud. >> Well, look at migrations are sometimes just an evil word to a lot of organizations, but at the same time, building up technical debt veneer after veneer and year, and year, and year is something that many companies are saying, "Okay, it's got to stop." So what's the prescription for that automation journey and simplifying that migration to the cloud? >> Well, I think the very first thing that's all about is data hygiene. You don't want to pick up your bad habits and take them to the cloud. You've got an opportunity here, so I see the journey to the cloud is an opportunity to really clean house, reorganize things, like moving out. You might move all your boxes, but you're kind of probably cherry pick what you're going to take with you and then you're going to organize it as you end up at your new destination. So from that, I get there's seven key principles that I like to operate by when I advise on the cloud migration. >> Okay. So, where do you start? >> Well, I think the first thing is understanding what you got, so discover and cataloging your data and your applications. If I don't know what I have, I can't move it, I can't improve it, I can't build up on it. And I have to understand there is dependency, so building that data catalog is the very first step. What do I got? >> Now, is that a metadata exercise? Sometimes there's more metadata than there is data. Is metadata part of that first step or? >> In deed, metadata is the first step so the metadata really describes the data you have. So, the metadata is going to tell me I have 2000 tables and maybe of those tables, there's an average of 25 columns each, and so that gives me a sketch if you will, of what I need to move. How big are the boxes I need to pack for my move to the cloud? >> Okay, and you're saying you can automate that data classification, categorization, discovery, correct using math machine intelligence, is that correct? >> Yeah, that's correct. So basically we go, and we will discover all of the schema, if you will, that's the metadata description of your tables and columns in your database in the data types. So we take, we will ingest that in, and we will build some insights around that. And we do that across a variety of platforms because everybody's organization has you've got a one yeah, an Oracle Database here, and you've got a Microsoft SQL Database here, you might have something else there that you need to bring site onto. And part of this journey is going to be about breaking down your data silos and understanding what you've got. >> Okay. So, we've done the audit, we know what we've got, what's next? Where do we go next? >> So the next thing is remediating that data. Where do I have duplicate data? Often times in an organization, data will get duplicated. So, somebody will take a snapshot of a data, and then ended up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer. And you can see where that will go when trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to understand where all your redundant data is. So when you go to the cloud, maybe you have an opportunity here to consolidate that data. >> Yeah, because you like to borrow in an Einstein or apply an Einstein Bromide right. Keep as much data as you can, but no more. >> Correct. >> Okay. So you get to the point to the second step you're kind of a one to reduce costs, then what? You figure out what to get rid of, or actually get rid of it, what's next? >> Yes, that would be the next step. So figuring out what you need and what you don't need often times I've found that there's obsolete columns of data in your databases that you just don't need, or maybe it's been superseded by another, you've got tables that have been superseded by other tables in your database. So you got to understand what's being used and what's not and then from that, you can decide, "I'm going to leave this stuff behind, "or I'm going to archive this stuff "cause I might need it for data retention "or I'm just going to delete it, "I don't need it at all." >> Well, Lester, most organizations, if they've been around a while, and the so-called incumbents, they've got data all over the place, their data marts, data warehouses, there are all kinds of different systems and the data lives in silos. So, how do you kind of deal with that problem? Is that part of the journey? >> That's a great point Dave, because you're right that the data silos happen because this business unit is chartered with this task another business unit has this task and that's how you get those instantiations of the same data occurring in multiple places. So as part of your cloud migration journey, you really want to plan where there's an opportunity to consolidate your data, because that means there'll be less to manage, there'll be less data to secure, and it'll have a smaller footprint, which means reduced costs. >> So, people always talk about a single version of the truth, data quality is a huge issue. I've talked to data practitioners and they've indicated that the quality metrics are in the single digits and they're trying to get to 90% plus, but maybe you could address data quality. Where does that fit in on the journey? >> That's, a very important point. First of all, you don't want to bring your legacy issues with you. As the point I made earlier, if you've got data quality issues, this is a good time to find those and identify and remediate them. But that can be a laborious task. We've had customers that have tried to do this by hand and it's very, very time consuming, cause you imagine if you've got 200 tables, 50,000 columns, imagine, the manual labor involved in doing that. And you could probably accomplish it, but it'll take a lot of work. So the opportunity to use tools here and automate that process is really will help you find those outliers there's that bad data and correct it before you move to the cloud. >> And you're just talking about that automation it's the same thing with data catalog and that one of the earlier steps. Organizations would do this manually or they try to do it manually and that's a lot of reason for the failure. They just, it's like cleaning out your data like you just don't want to do it (laughs). Okay, so then what's next? I think we're plowing through your steps here. What what's next on the journey? >> The next one is, in a nutshell, preserve your data format. Don't boil the ocean here to use a cliche. You want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables on which they sit, the columns and the way they're named. So, some degree you are going to be doing a lift and shift, but it's an intelligent lift and shift using all the insights you've gathered by cataloging the data, looking for data quality issues, looking for duplicate columns, doing planning consolidation. You don't want to also rewrite your application. So, in that aspect, I think it's important to do a bit of lift and shift and preserve those data formats as they sit. >> Okay, so let me follow up on that. That sounds really important to me, because if you're doing a conversion and you're rewriting applications, that means that you're going to have to freeze the existing application, and then you going to be refueling the plane as you're in midair and a lot of times, especially with mission critical systems, you're never going to bring those together and that's a recipe for disaster, isn't it? >> Great analogy unless you're with the air force, you'll (mumbles) (laughs). Now, that's correct. It's you want to have bite-sized steps and that's why it's important to plan your journey, take these steps. You're using automation where you can to make that journey to the cloud much easier and more straightforward. >> All right, I like that. So we're taking a kind of a systems view and end to end view of the data pipeline, if you will. What's next? I think we're through. I think I've counted six. What's the lucky seven? >> Lucky seven, involve your business users. Really, when you think about it, your data is in silos. Part of this migration to the cloud is an opportunity to break down these silos, these silos that naturally occur as part of the business unit. You've got to break these cultural barriers that sometimes exist between business and say, so for example, I always advise, there's an opportunity here to consolidate your sensitive data, your PII, your personally identifiable information, and if three different business units have the same source of truth for that, there's was an opportunity to consolidate that into one as you migrate. That might be a little bit of tweaking to some of the apps that you have that are dependent on it, but in the long run, that's what you really want to do. You want to have a single source of truth, you want to ring fence that sensitive data, and you want all your business users talking together so that you're not reinventing the wheel. >> Well, the reason I think too that's so important is that you're now I would say you're creating a data driven culture. I know that's sort of a buzz word, but what it's true and what that means to me is that your users, your lines of business feel like they actually own the data rather than pointing fingers at the data group, the IT group, the data quality people, data engineers, saying, "Oh, I don't believe it." If the lines of business own the data, they're going to lean in, they're going to maybe bring their own data science resources to the table, and it's going to be a much more collaborative effort as opposed to a non-productive argument. >> Yeah. And that's where we want to get to. DataOps is key, and maybe that's a term that's still evolving. But really, you want the data to drive the business because that's where your insights are, that's where your value is. You want to break down the silos between not only the business units, as I mentioned, but also as you pointed out, the roles of the people that are working with it. A self service data culture is the right way to go with the right security controls, putting on my security hat of course in place so that if I'm a developer and I'm building a new application, I'd love to be able to go to the data catalog, "Oh, there's already a database that has the customer "what the customers have clicked on when shopping." I could use that. I don't have to rebuild that, I'll just use that as for my application. That's the kind of problems you want to be able to solve and that's where your cost reductions come in across the board. >> Yeah. I want to talk a little bit about the business context here. We always talk about data, it's the new source of competitive advantage, I think there's not a lot of debate about that, but it's hard. A lot of companies are struggling to get value out of their data because it's so difficult. All the things we've talked about, the silos, the data quality, et cetera. So, you mentioned the term data apps, data apps is all about streamlining, that data, pipelining, infusing automation and machine intelligence into that pipeline and then ultimately taking a systems view and compressing that time to insights so that you can drive monetization, whether it's cut costs, maybe it's new revenue, drive productivity, but it's that end to end cycle time reduction that successful practitioners talk about as having the biggest business impact. Are you seeing that? >> Absolutely, but it is a journey and it's a huge cultural change for some companies that are. I've worked in many companies that are ticket based IT-driven and just do even the marginalist of change or get insight, raise a ticket, wait a week and then out the other end will pop maybe a change that I needed and it'll take a while for us to get to a culture that truly has a self service data-driven nature where I'm the business owner, and I want to bring in a data scientist because we're losing. For example, a business might be losing to a competitor and they want to find what insights, why is the customer churn, for example, happening every Tuesday? What is it about Tuesday? This is where your data scientist comes in. The last thing you want is to raise a ticket, wait for the snapshot of the data, you want to enable that data scientist to come in, securely connect into the data, and do his analysis, and come back and give you those insights, which will give you that competitive advantage. >> Well, I love your point about churn, maybe it talks about the Andreessen quote that "Software's eating the world," and all companies are our software companies, and SaaS companies, and churn is the killer of SaaS companies. So very, very important point you're making. My last question for you before we summarize is the tech behind all of these. What makes Io-Tahoe unique in its ability to help automate that data pipeline? >> Well, we've done a lot of research, we have I think now maybe 11 pending patent applications, I think one has been approved to be issued (mumbles), but really, it's really about sitting down and doing the right kind of analysis and figuring out how we can optimize this journey. Some of these stuff isn't rocket science. You can read a schema and into an open source solution, but you can't necessarily find the hidden insights. So if I want to find my foreign key dependencies, which aren't always declared in the database, or I want to identify columns by their content, which because the columns might be labeled attribute one, attribute two, attribute three, or I want to find out how my data flows between the various tables in my database. That's the point at which you need to bring in automation, you need to bring in data science solutions, and there's even a degree of machine learning because for example, we might deduce that data is flowing from this table to this table and upon when you present that to the user with a 87% confidence, for example, and the user can go, or the administrator can go. Now, it really goes the other way, it was an invalid collusion and that's the machine learning cycle. So the next time we see that pattern again, in that environment we will be able to make a better recommendation because some things aren't black and white, they need that human intervention loop. >> All right, I just want to summarize with Lester Waters' playbook to moving to the cloud and I'll go through them. Hopefully, I took some notes, hopefully, I got them right. So step one, you want to do that data discovery audit, you want to be fact-based. Two is you want to remediate that data redundancy, and then three identify what you can get rid of. Oftentimes you don't get rid of stuff in IT, or maybe archive it to cheaper media. Four is consolidate those data silos, which is critical, breaking down those data barriers. And then, five is attack the quality issues before you do the migration. Six, which I thought was really intriguing was preserve that data format, you don't want to do the rewrite applications and do that conversion. It's okay to do a little bit of lifting and shifting >> This comes in after the task. >> Yeah, and then finally, and probably the most important is you got to have that relationship with the lines of business, your users, get them involved, begin that cultural shift. So I think great recipe Lester for safe cloud migration. I really appreciate your time. I'll give you the final word if you will bring us home. >> All right. Well, I think the journey to the cloud it's a tough one. You will save money, I have heard people say, you got to the cloud, it's too expensive, it's too this, too that, but really, there is an opportunity for savings. I'll tell you when I run data services as a PaaS service in the cloud, it's wonderful because I can scale up and scale down almost by virtually turning a knob. And so I'll have complete control and visibility of my costs. And so for me, that's very important. Io also, it gives me the opportunity to really ring fence my sensitive data, because let's face it, most organizations like being in a cheese grater when you talk about security, because there's so many ways in and out. So I find that by consolidating and bringing together the crown jewels, if you will. As a security practitioner, it's much more easy to control. But it's very important. You can't get there without some automation and automating this discovery and analysis process. >> Well, great advice. Lester, thanks so much. It's clear that the capex investments on data centers are generally not a good investment for most companies. Lester, really appreciate, Lester waters CTO of Io-Tahoe. Let's watch this short video and we'll come right back. You're watching The Cube, thank you. (upbeat music)

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

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe, everybody, We're back. We're talking about enterprise data automation. The hashtag is data automated, and we're going to really dig into data migrations, data, migrations. They're risky. They're time consuming, and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So your role is is interesting. We're talking about data migrations. You're gonna head of partnerships. What is your role specifically? And how is it relevant to what we're gonna talk about today? >>Uh, I work with the various businesses such as cloud companies, systems integrators, companies that sell operating systems, middleware, all of whom are often quite well embedded within a company. I t infrastructures and have existing relationships. Because what we do fundamentally makes migrating to the cloud easier on data migration easier. A lot of businesses that are interested in partnering with us. Um, we're interested in parting with, So >>let's set up the problem a little bit. And then I want to get into some of the data. You know, I said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>Uh, I think I mean, all migrations have to start with knowing the facts about your data, and you can try and do this manually. But when that you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. Um, now they're understanding of what they have. Ai's often quite limited because you can try and draw a manual maps, but they're outdated very quickly. Every time that data changes the manual that's out of date on people obviously leave organizations over time, so that kind of tribal knowledge gets built up is limited as well. So you can try a Mackel that manually you might need a db. Hey, thanks. Based analyst or ah, business analyst, and they won't go in and explore the data for you. But doing that manually is very, very time consuming this contract teams of people, months and months. Or you can use automation just like what's the bank with Iot? And they managed to do this with a relatively small team. Are in a timeframe of days. >>Yeah, we talked to Paul from Webster Bank. Awesome discussion. So I want to dig into this migration and let's let's pull up graphic it will talk about. We'll talk about what a typical migration project looks like. So what you see here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this Ah, and then use. If I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones. That blue bar is the time to test so you can see the second step data analysis talking 24 weeks so, you know, very time consuming. And then Let's not get dig into the stuff in the middle of the fine print, but there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom and you can see high is that sort of brown and and you could see a number of data analysis, data staging data prep, the trial, the implementation post implementation fixtures, the transition toe B A B a year, which I think is business as usual. Those are all very labor intensive. So what do you take aways from this typical migration project? What do we need to know yourself? >>I mean, I think the key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the data. So the direction in which the data is going between different data stores and tables, so you start from a position where you have pretty high risk and alleviate that risk. You could be stacking project team of lots and lots of people to do the next base, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis manage. That, in a sense, is, as we all know, on the idea of trying to relate data that's in different those stores relating individual tables and columns. Very, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools, Aziz said earlier. The people who understand some of those systems may have left a while ago. See you even high risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with it, Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data map on the data flow has been generated automatically, much less time and effort and much less cars. Doctor Marley. >>Okay, so I want to bring back that that first chart, and I want to call your attention to the again that area graph the blue bars and then down below that labor intensity. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the So let's go Said Accelerated by Iot, Tom. Okay, great. And we're going to talk about this. But look, what happens to the operational risk. A dramatic reduction in that. That graph. And then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis data staging data prep. Try a lot post implementation fixtures in transition to be a you. All of those went from high labor intensity. So we've now attack that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its Price States catalog, if you like, um, imagine trying to do that manually. You need to go into every individual data store. You need a DB a business analyst, rich data store they need to do in extracted the data table was individually they need to cross reference that with other data school, it stores and schemers and tables. You probably were the mother of all lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems um, take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. Uh, no migrating away from them because they're not able to actually do the work of understanding the data g duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. Go back to the data catalog example. Um, whatever you discover invades, discovery has to persist in a tool like a data catalog. And so we automate data catalog books, including Out Way Cannot be others, but we have our own. The only alternative to this kind of automation is to build out this very large project team or business analysts off db A's project managers processed analysts together with data to understand that the process of gathering data is correct. To put it in the repository to validate it except etcetera, we've got into organizations and we've seen them ramp up teams off 2030 people costs off £234 million a year on a time frame, 15 20 years just to try and get a data catalog done. And that's something that we can typically do in a timeframe of months, if not weeks. And the difference is using automation. And if you do what? I've just described it. In this manual situation, you make migrations to the cloud prohibitively expensive. Whatever saving you might make from shutting down your legacy data stores, we'll get eaten up by the cost of doing it. Unless you go with the more automated approach. >>Okay, so the automated approach reduces risk because you're not gonna, you know you're going to stay on project plan. Ideally, it's all these out of scope expectations that come up with the manual processes that kill you in the rework andan that data data catalog. People are afraid that their their family jewels data is not going to make it through to the other side. So So that's something that you're you're addressing and then you're also not boiling the ocean. You're really taking the pieces that are critical and stuff you don't need. You don't have to pay for >>process. It's a very good point. I mean, one of the other things that we do and we have specific features to do is to automatically and noise data for a duplication at a rover or record level and redundancy on a column level. So, as you say before you go into a migration process. You can then understand. Actually, this stuff it was replicated. We don't need it quite often. If you put data in the cloud you're paying, obviously, the storage based offer compute time. The more data you have in there that's duplicated, that is pure cost. You should take out before you migrate again if you're trying to do that process of understanding what's duplicated manually off tens or hundreds of bases stores. It was 20 months, if not years. Use machine learning to do that in an automatic way on it's much, much quicker. I mean, there's nothing I say. Well, then, that costs and benefits of guitar. Every organization we work with has a lot of money existing, sunk cost in their I t. So have your piece systems like Oracle or Data Lakes, which they've spent a good time and money investing in. But what we do by enabling them to transition everything to the strategic future repositories, is accelerate the value of that investment and the time to value that investment. So we're trying to help people get value out of their existing investments on data estate, close down the things that they don't need to enable them to go to a kind of brighter, more future well, >>and I think as well, you know, once you're able to and this is a journey, we know that. But once you're able to go live on, you're infusing sort of a data mindset, a data oriented culture. I know it's somewhat buzzword, but when you when you see it in organizations, you know it's really and what happens is you dramatically reduce that and cycle time of going from data to actually insights. Data's plentiful, but insights aren't, and that is what's going to drive competitive advantage over the next decade and beyond. >>Yeah, definitely. And you could only really do that if you get your data estate cleaned up in the first place. Um, I worked with the managed teams of data scientists, data engineers, business analysts, people who are pushing out dashboards and trying to build machine learning applications. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data stays in the first place, get rid of duplication. If that pans migrate to cloud store, where things are really accessible on its easy to build connections and to use native machine learning tools, you're well on the way up to date the maturity curve on you can start to use some of those more advanced applications. >>You said. What are some of the pre requisites? Maybe the top few that are two or three that I need to understand as a customer to really be successful here? Is it skill sets? Is it is it mindset leadership by in what I absolutely need to have to make this successful? >>Well, I think leadership is obviously key just to set the vision of people with spiky. One of the great things about Ayatollah, though, is you can use your existing staff to do this work. If you've used on automation, platform is no need to hire expensive people. Alright, I was a no code solution. It works out of the box. You just connect to force on your existing stuff can use. It's very intuitive that has these issues. User interface? >>Um, it >>was only to invest vast amounts with large consultants who may well charging the earth. Um, and you already had a bit of an advantage. If you've got existing staff who are close to the data subject matter experts or use it because they can very easily learn how to use a tool on, then they can go in and they can write their own data quality rules on. They can really make a contribution from day one, when we are go into organizations on way. Can I? It's one of the great things about the whole experience. Veritas is. We can get tangible results back within the day. Um, usually within an hour or two great ones to say Okay, we started to map relationships. Here's the data map of the data that we've analyzed. Harrison thoughts on where the sensitive data is because it's automated because it's running algorithms stater on. That's what they were really to expect. >>Um, >>and and you know this because you're dealing with the ecosystem. We're entering a new era of data and many organizations to your point, they just don't have the resources to do what Google and Amazon and Facebook and Microsoft did over the past decade To become data dominant trillion dollar market cap companies. Incumbents need to rely on technology companies to bring that automation that machine intelligence to them so they can apply it. They don't want to be AI inventors. They want to apply it to their businesses. So and that's what really was so difficult in the early days of so called big data. You have this just too much complexity out there, and now companies like Iot Tahoe or bringing your tooling and platforms that are allowing companies to really become data driven your your final thoughts. Please use it. >>That's a great point, Dave. In a way, it brings us back to where it began. In terms of partnerships and alliances. I completely agree with a really exciting point where we can take applications like Iot. Uh, we can go into enterprises and help them really leverage the value of these type of machine learning algorithms. And and I I we work with all the major cloud providers AWS, Microsoft Azure or Google Cloud Platform, IBM and Red Hat on others, and we we really I think for us. The key thing is that we want to be the best in the world of enterprise data automation. We don't aspire to be a cloud provider or even a workflow provider. But what we want to do is really help customers with their data without automated data functionality in partnership with some of those other businesses so we can leverage the great work they've done in the cloud. The great work they've done on work flows on virtual assistants in other areas. And we help customers leverage those investments as well. But our heart, we really targeted it just being the best, uh, enterprised data automation business in the world. >>Massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Appreciate. All right. And thank you for watching everybody. We'll be right back right after this short break. >>Yeah, yeah, yeah, yeah.

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe Welcome everybody to Enterprise Data Automation. Ah co created digital program on the Cube with support from my hotel. So my name is Dave Volante. And today we're using the hashtag data automated. You know, organizations. They really struggle to get more value out of their data, time to data driven insights that drive cost savings or new revenue opportunities. They simply take too long. So today we're gonna talk about how organizations can streamline their data operations through automation, machine intelligence and really simplifying data migrations to the cloud. We'll be talking to technologists, visionaries, hands on practitioners and experts that are not just talking about streamlining their data pipelines. They're actually doing it. So keep it right there. We'll be back shortly with a J ahora who's the CEO of Iot Tahoe to kick off the program. You're watching the Cube, the leader in digital global coverage. We're right back right after this short break. Innovation impact influence. Welcome to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. High tech digital coverage from around the globe. It's the Cube with digital coverage of enterprise, data, automation and event. Siri's brought to you by Iot. Tahoe. Okay, we're back. Welcome back to Data Automated. A J ahora is CEO of I O ta ho, JJ. Good to see how things in London >>Thanks doing well. Things in, well, customers that I speak to on day in, day out that we partner with, um, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring the week and serve our customers to help their customers. Um, you know, the adaptation that's happening here is, um, trying to be more agile. Got to be more flexible. Um, a lot of pressure on data, a lot of demand on data and to deliver more value to the business, too. So that customers, >>as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline, But talk about enterprise data automation. What is it to you. And how is it different from data off >>Dev Ops, you know, has been great for breaking down those silos between different roles functions and bring people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, lending itself to data with data is exciting. We look to do is build on top of that when data automation, it's the it's the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors, our r and d on bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is is the automation behind new dimension. We've come a long way in the last few years. Boy is, we started out with automating some of those simple, um, to codify, um, I have a high impact on organization across the data a cost effective way house. There's data related tasks that classify data on and a lot of our original pattern certain people value that were built up is is very much around that >>love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, >>sure. I mean right there in the middle that the heart of what we do it is, you know, the intellectual property now that we've built up over time that takes from Hacha genius data sources. Your Oracle Relational database. Short your mainframe. It's a lay and increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data. Classify that data after it's classified. Them have the ability to form relationships across those different source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts of contact and meaning around that data. So it's moving it now from bringing data driven on increasingly where we have really smile, right people in our customer organizations you want I do some of those advanced knowledge tasks data scientists and ah, yeah, quants in some of the banks that we work with, the the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality, the policies that you can apply to that data. I'm putting it in context once you've got the ability to power. Okay, a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the the tapestry that fabric across that different system could be crm air P system such as s AP and some of the newer brown databases that we work with. Snowflake is a great well, if I look back maybe five years ago, we had prevalence of daily technologies at the cutting edge. Those are converging to some of the cloud platforms that we work with Google and AWS and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenges scale quickly runs out of steam because once, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data state? It's changed, You know, you've onboard a new customer. You signed up a new partner. Um, customer has, you know, adopted a new product that you just Lawrence and there that that slew of data keeps coming. So it's keeping pace with that. The only answer really is is some form of automation >>you're working with AWS. You're working with Google, You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>That's fundamental. So, I mean, when I caimans where you tell here is the CEO of one of the, um, trends that I wanted us CIO to be part of was being open, having an open architecture allowed one thing that was close to my heart, which is as a CEO, um, a c i o where you go, a budget vision on and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with the CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using AP eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before. So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um and snowflake here is, um Is those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that. And they're leveraging the value that they've already committed to. >>Yeah, and maybe you could give us some examples of the r A y and the business impact. >>Yeah, I mean, the r a y David is is built upon on three things that I mentioned is a combination off. You're leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM, and I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have got the automation that is working right down to the level off data, a column level or the file level we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome now without hoping out which run those processes within hours of getting started And, um, Bill that picture, visualize that picture and bring it to life. You know, the PR Oh, I that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on GCB or a migration to any other clouds such as AWS or a multi cloud landscape right off the map. >>A. J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have you. >>Thank you, David. Look who is smoking in >>now. We want to bring in the customer perspective. We have a great conversation with Paul Damico, senior vice president data architecture, Webster Bank. So keep it right there. >>Utah Data automated Improve efficiency, Drive down costs and make your enterprise data work for you. Yeah, we're on a mission to enable our customers to automate the management of data to realise maximum strategic and operational benefits. We envisage a world where data users consume accurate, up to date unified data distilled from many silos to deliver transformational outcomes, activate your data and avoid manual processing. Accelerate data projects by enabling non I t resources and data experts to consolidate categorize and master data. Automate your data operations Power digital transformations by automating a significant portion of data management through human guided machine learning. Yeah, get value from the start. Increase the velocity of business outcomes with complete accurate data curated automatically for data, visualization tours and analytic insights. Improve the security and quality of your data. Data automation improves security by reducing the number of individuals who have access to sensitive data, and it can improve quality. Many companies report double digit era reduction in data entry and other repetitive tasks. Trust the way data works for you. Data automation by our Tahoe learns as it works and can ornament business user behavior. It learns from exception handling and scales up or down is needed to prevent system or application overloads or crashes. It also allows for innate knowledge to be socialized rather than individualized. No longer will your companies struggle when the employee who knows how this report is done, retires or takes another job, the work continues on without the need for detailed information transfer. Continue supporting the digital shift. Perhaps most importantly, data automation allows companies to begin making moves towards a broader, more aspirational transformation, but on a small scale but is easy to implement and manage and delivers quick wins. Digital is the buzzword of the day, but many companies recognized that it is a complex strategy requires time and investment. Once you get started with data automation, the digital transformation initiated and leaders and employees alike become more eager to invest time and effort in a broader digital transformational agenda. Yeah, >>everybody, we're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise Data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Nice to see you too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the >>bank. Yeah, Webster Bank is regional, Boston. And that again in New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated bank regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community. And, um, are really moving forward. Technology lives. Currently, today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on off something to offer that >>at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change >>the ability to give the customer what they need at the time when they need it? And what I mean by that is that we have, um, customer interactions and multiple weights, right? And I want to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look and also to be able to offer them the next best offer for them. >>Part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity >>exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. >>Do you see the potential to increase the data sources and hence the quality of the data? Or is that sort of premature? >>Oh, no. Um, exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of runnin system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into, like, an s three bucket Where that data king, we can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake Good, um, utilize that data or we can give it out to our market. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on, and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. I have eight engineers, data architects, they database administrators, right, um, and then data traditional data forwarding people, Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of read regiment that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things. This is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data, and we read the data flows and data redundancy and things like that and help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, Yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. >>In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure, and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing ai or machine intelligence into the data pipeline is really how you're attacking automation, right? >>Exactly. So you're able to let's say that I have I have seven cause lines of business that are asking me questions. And one of the questions I'll ask me is, um, we want to know if this customer is okay to contact, right? And you know, there's different avenues so you can go online to go. Do not contact me. You can go to the bank And you could say, I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said Okay to contact the other one says, You know, just for one to pray all these, you know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another of analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say yes we already have that documentation. Here it is. And this is where you can find where the customer has said, You know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. I'm using Iot typos eight automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not, um a It's an on prem. It's an oracle database. Um, and it's 15 years old, so it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>What's your vision or your your data driven organization? >>Um, I want for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers. >>That's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that's a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Next, we'll talk with Lester Waters, who's the CTO of Iot Toe cluster takes us through the key considerations of moving to the cloud. >>Yeah, right. The entire platform Automated data Discovery data Discovery is the first step to knowing your data auto discover data across any application on any infrastructure and identify all unknown data relationships across the entire siloed data landscape. smart data catalog. Know how everything is connected? Understand everything in context, regained ownership and trust in your data and maintain a single source of truth across cloud platforms, SAS applications, reference data and legacy systems and power business users to quickly discover and understand the data that matters to them with a smart data catalog continuously updated ensuring business teams always have access to the most trusted data available. Automated data mapping and linking automate the identification of unknown relationships within and across data silos throughout the organization. Build your business glossary automatically using in house common business terms, vocabulary and definitions. Discovered relationships appears connections or dependencies between data entities such as customer account, address invoice and these data entities have many discovery properties. At a granular level, data signals dashboards. Get up to date feeds on the health of your data for faster improved data management. See trends, view for history. Compare versions and get accurate and timely visual insights from across the organization. Automated data flows automatically captured every data flow to locate all the dependencies across systems. Visualize how they work together collectively and know who within your organization has access to data. Understand the source and destination for all your business data with comprehensive data lineage constructed automatically during with data discovery phase and continuously load results into the smart Data catalog. Active, geeky automated data quality assessments Powered by active geek You ensure data is fit for consumption that meets the needs of enterprise data users. Keep information about the current data quality state readily available faster Improved decision making Data policy. Governor Automate data governance End to end over the entire data lifecycle with automation, instant transparency and control Automate data policy assessments with glossaries, metadata and policies for sensitive data discovery that automatically tag link and annotate with metadata to provide enterprise wide search for all lines of business self service knowledge graph Digitize and search your enterprise knowledge. Turn multiple siloed data sources into machine Understandable knowledge from a single data canvas searching Explore data content across systems including GRP CRM billing systems, social media to fuel data pipelines >>Yeah, yeah, focusing on enterprise data automation. We're gonna talk about the journey to the cloud Remember, the hashtag is data automate and we're here with Leicester Waters. Who's the CTO of Iot Tahoe? Give us a little background CTO, You've got a deep, deep expertise in a lot of different areas. But what do we need to know? >>Well, David, I started my career basically at Microsoft, uh, where I started the information Security Cryptography group. They're the very 1st 1 that the company had, and that led to a career in information, security. And and, of course, as easy as you go along with information security data is the key element to be protected. Eso I always had my hands and data not naturally progressed into a roll out Iot talk was their CTO. >>What's the prescription for that automation journey and simplifying that migration to the cloud? >>Well, I think the first thing is understanding what you've got. So discover and cataloging your data and your applications. You know, I don't know what I have. I can't move it. I can't. I can't improve it. I can't build upon it. And I have to understand there's dependence. And so building that data catalog is the very first step What I got. Okay, >>so So we've done the audit. We know we've got what's what's next? Where do we go >>next? So the next thing is remediating that data you know, where do I have duplicate data? I may have often times in an organization. Uh, data will get duplicated. So somebody will take a snapshot of the data, you know, and then end up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer, and you can see where that will go. And trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to sort of understand where all your redundant data is? So when you go to the cloud, maybe you have an opportunity here to do you consolidate that that data, >>then what? You figure out what to get rid of our actually get rid of it. What's what's next? >>Yes, yes, that would be the next step. So figure out what you need. What, you don't need you Often times I've found that there's obsolete columns of data in your databases that you just don't need. Or maybe it's been superseded by another. You've got tables have been superseded by other tables in your database, so you got to kind of understand what's being used and what's not. And then from that, you can decide. I'm gonna leave this stuff behind or I'm gonna I'm gonna archive this stuff because I might need it for data retention where I'm just gonna delete it. You don't need it. All were >>plowing through your steps here. What's next on the >>journey? The next one is is in a nutshell. Preserve your data format. Don't. Don't, Don't. Don't boil the ocean here at music Cliche. You know, you you want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables in which they sent the columns and the way they're named. So some degree, you are gonna be doing a lift and ship, but it's an intelligent lift and ship. The >>data lives in silos. So how do you kind of deal with that? Problem? Is that is that part of the journey? >>That's that's great pointed because you're right that the data silos happen because, you know, this business unit is start chartered with this task. Another business unit has this task and that's how you get those in stance creations of the same data occurring in multiple places. So you really want to is part of your cloud migration. You really want a plan where there's an opportunity to consolidate your data because that means it will be less to manage. Would be less data to secure, and it will be. It will have a smaller footprint, which means reduce costs. >>But maybe you could address data quality. Where does that fit in on the >>journey? That's that's a very important point, you know. First of all, you don't want to bring your legacy issues with U. S. As the point I made earlier. If you've got data quality issues, this is a good time to find those and and identify and remediate them. But that could be a laborious task, and you could probably accomplish. It will take a lot of work. So the opportunity used tools you and automate that process is really will help you find those outliers that >>what's next? I think we're through. I think I've counted six. What's the What's the lucky seven >>Lucky seven involved your business users. Really, When you think about it, you're your data is in silos, part of part of this migration to cloud as an opportunity to break down the silos. These silence that naturally occurs are the business. You, uh, you've got to break these cultural barriers that sometimes exists between business and say so. For example, I always advise there's an opportunity year to consolidate your sensitive data. Your P I. I personally identifiable information and and three different business units have the same source of truth From that, there's an opportunity to consolidate that into one. >>Well, great advice, Lester. Thanks so much. I mean, it's clear that the Cap Ex investments on data centers they're generally not a good investment for most companies. Lester really appreciate Lester Water CTO of Iot Tahoe. Let's watch this short video and we'll come right back. >>Use cases. Data migration. Accelerate digitization of business by providing automated data migration work flows that save time in achieving project milestones. Eradicate operational risk and minimize labor intensive manual processes that demand costly overhead data quality. You know the data swamp and re establish trust in the data to enable data signs and Data analytics data governance. Ensure that business and technology understand critical data elements and have control over the enterprise data landscape Data Analytics ENABLEMENT Data Discovery to enable data scientists and Data Analytics teams to identify the right data set through self service for business demands or analytical reporting that advanced too complex regulatory compliance. Government mandated data privacy requirements. GDP Our CCP, A, e, p, R HIPPA and Data Lake Management. Identify late contents cleanup manage ongoing activity. Data mapping and knowledge graph Creates BKG models on business enterprise data with automated mapping to a specific ontology enabling semantic search across all sources in the data estate data ops scale as a foundation to automate data management presences. >>Are you interested in test driving the i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program? Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iot. Top Click on the link and connect with the data engineer to learn more and see Iot Tahoe in action. Everybody, we're back. We're talking about enterprise data automation. The hashtag is data automated and we're going to really dig into data migrations, data migrations. They're risky, they're time consuming and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So let's set up the problem a little bit. And then I want to get into some of the data said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>I think I mean, all migrations have to start with knowing the facts about your data. Uh, and you can try and do this manually. But when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. >>So I want to dig into this migration and let's let's pull up graphic. It will talk about We'll talk about what a typical migration project looks like. So what you see, here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this, uh and then use if I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones That Blue Bar is the time to test so you can see the second step. Data analysis. It's 24 weeks so very time consuming, and then let's not get dig into the stuff in the middle of the fine print. But there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom, and you can see hi is that sort of brown and and you could see a number of data analysis data staging data prep, the trial, the implementation post implementation fixtures, the transition to be a Blu, which I think is business as usual. >>The key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers, and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the basis of the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and probably the area that risk you could be. Stack your project team of lots and lots of people to do the next phase, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis, um, manual processes, as we all know, on the layer of trying to relate data that's in different grocery stores relating individual tables and columns, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools. Aziz said earlier the people who understand some of those systems may have left a while ago. CEO even higher risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data met on the data flow has been generated automatically, much less time and effort and much less cars stopped. >>Yeah. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the sort of Cisco said accelerated by Iot, Tom. Okay, great. And we're gonna talk about this, but look, what happens to the operational risk. A dramatic reduction in that, That that graph and then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis, data staging data prep trialling post implementation fixtures in transition to be a you all those went from high labor intensity. So we've now attacked that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its price States catalog. If you like, imagine trying to do that manually, you need to go into every individual data store. You need a DB, a business analyst, reach data store. They need to do an extract of the data. But it on the table was individually they need to cross reference that with other data school, it stores and schemers and tables you probably with the mother of all Lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. I'm not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data in the first place, get rid of duplication that sounds migrate to cloud store where things are really accessible. It's easy to build connections and to use native machine learning tools. You well, on the way up to the maturity card, you can start to use some of the more advanced applications >>massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Much appreciated. Yeah, yeah, yeah, yeah

>> Narrator: From around the globe its "theCUBE" presenting Adaptive Data Governance, brought to you by Io-Tahoe. >> And we're back with the Data Automation series. In this episode we're going to learn more about what Io-Tahoe is doing in the field of adaptive data governance, how can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin and I'm joined by Ajay Vohora the CEO of Io-Tahoe, and Lester Waters the CTO of Io-Tahoe. Gentlemen it's great to have you on the program. >> Thank you Lisa is good to be back. >> Great to see you Lisa. >> Likewise, very seriously this isn't cautious as we are. Lester were going to start with you, what's going on at Io-Tahoe, what's new? >> Well, I've been with Io-Tahoe for a little over the year, and one thing I've learned is every customer needs are just a bit different. So we've been working on our next major release of the Io-Tahoe product and to really try to address these customer concerns because we want to be flexible enough in order to come in and not just profile the data and not just understand data quality and lineage, but also to address the unique needs of each and every customer that we have. And so that required a platform rewrite of our product so that we could extend the product without building a new version of the product, we wanted to be able to have pluggable modules. We are also focused a lot on performance, that's very important with the bulk of data that we deal with and we're able to pass through that data in a single pass and do the analytics that are needed whether it's a lineage data quality or just identifying the underlying data. And we're incorporating all that we've learned, we're tuning up our machine learning, we're analyzing on more dimensions than we've ever done before, we're able to do data quality without doing an initial reggie expert for example, just out of the box. So I think it's all of these things are coming together to form our next version of our product and We're really excited about. >> Sounds exciting, Ajay from the CEOs level what's going on? >> Wow, I think just building on that, what Lester just mentioned now it's we're growing pretty quickly with our partners, and today here with Oracle we're excited to explain how that's shaping up lots of collaboration already with Oracle, and government in insurance and in banking. And we're excited because we get to have an impact, it's really satisfying to see how we're able to help businesses transform and redefine what's possible with their data. And having Oracle there as a partner to lean in with is definitely helping. >> Excellent, we're going to dig into that a little bit later. Lester let's go back over to you, explain adaptive data governance, help us understand that. >> Really adaptive data governance is about achieving business outcomes through automation. It's really also about establishing a data-driven culture and pushing what's traditionally managed in IT out to the business. And to do that, you've got to enable an environment where people can actually access and look at the information about the data, not necessarily access the underlying data because we've got privacy concern system, but they need to understand what kind of data they have, what shape it's in, what's dependent on it upstream and downstream, and so that they can make their educated decisions on what they need to do to achieve those business outcomes. A lot of frameworks these days are hardwired, so you can set up a set of business rules, and that set of business rules works for a very specific database and a specific schema. But imagine a world where you could just say, you know, (tapping) the start date of a loan must always be before the end date of a loan, and having that generic rule regardless of the underlying database, and applying it even when a new database comes online and having those rules applied, that's what adaptive data governance about. I like to think of it as the intersection of three circles, really it's the technical metadata coming together with policies and rules, and coming together with the business ontologies that are unique to that particular business. And bringing this all together allows you to enable rapid change in your environment, so, it's a mouthful adaptive data governance, but that's what it kind of comes down to. >> So Ajay help me understand this, is this what enterprise companies are doing now or are they not quite there yet? >> Well, you know Lisa I think every organization is going at his pace, but markets are changing economy and the speed at which some of the changes in the economy happening is compelling more businesses to look at being more digital in how they serve their own customers. So what we're saying is a number of trends here from heads of data, chief data officers, CIO stepping back from a one size fits all approach because they've tried that before and it just hasn't worked. They've spent millions of dollars on IT programs trying to drive value from that data, and they've ended up with large teams of manual processing around data to try and hard-wire these policies to fit with the context and each line of business, and that hasn't worked. So, the trends that we're seeing emerge really relate to how do I as a chief data officer, as a CIO, inject more automation and to allow these common tasks. And we've been able to see that impact, I think the news here is if you're trying to create a knowledge graph, a data catalog, or a business glossary, and you're trying to do that manually, well stop, you don't have to do that manual anymore. I think best example I can give is Lester and I we like Chinese food and Japanese food, and if you were sitting there with your chopsticks you wouldn't eat a bowl of rice with the chopsticks one grain at a time, what you'd want to do is to find a more productive way to enjoy that meal before it gets cold. And that's similar to how we're able to help organizations to digest their data is to get through it faster, enjoy the benefits of putting that data to work. >> And if it was me eating that food with you guys I would be not using chopsticks I would be using a fork and probably a spoon. So Lester how then does Io-Tahoe go about doing this and enabling customers to achieve this? >> Let me show you a little story here. So if you take a look at the challenges that most customers have they're very similar, but every customer is on a different data journey, so, but it all starts with what data do I have, what shape is that data in, how is it structured, what's dependent on it upstream and downstream, what insights can I derive from that data, and how can I answer all of those questions automatically? So if you look at the challenges for these data professionals, you know, they're either on a journey to the cloud, maybe they're doing a migration to Oracle, maybe they're doing some data governance changes, and it's about enabling this. So if you look at these challenges, I'm going to take you through a story here, and I want to introduce Amanda. Amanda is not Latin like anyone in any large organizations, she is looking around and she just sees stacks of data, I mean, different databases the one she knows about, the ones she doesn't know about but should know about, various different kinds of databases, and Amanda is this tasking with understanding all of this so that they can embark on her data journey program. So Amanda goes through and she's great, (snaps finger) "I've got some handy tools, I can start looking at these databases and getting an idea of what we've got." But when she digs into the databases she starts to see that not everything is as clear as she might've hoped it would be. Property names or column names have ambiguous names like Attribute one and Attribute two, or maybe Date one and Date two, so Amanda is starting to struggle even though she's got tools to visualize and look at these databases, she's still knows she's got a long road ahead, and with 2000 databases in her large enterprise, yes it's going to be a long journey. But Amanda is smart, so she pulls out her trusty spreadsheet to track all of her findings, and what she doesn't know about she raises a ticket or maybe tries to track down in order to find what that data means, and she's tracking all this information, but clearly this doesn't scale that well for Amanda. So maybe the organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well 'cause there's still ambiguities in the data. With Io-Tahoe what we do is we actually profile the underlying data. By looking at the underlying data, we can quickly see that Attribute one looks very much like a US social security number, and Attribute two looks like a ICD 10 medical code. And we do this by using ontologies, and dictionaries, and algorithms to help identify the underlying data and then tag it. Key to doing this automation is really being able to normalize things across different databases so that where there's differences in column names, I know that in fact they contain the same data. And by going through this exercise with Io-Tahoe, not only can we identify the data, but we also can gain insights about the data. So for example, we can see that 97% of that time, that column named Attribute one that's got US social security numbers, has something that looks like a social security number. But 3% of the time it doesn't quite look right, maybe there's a dash missing, maybe there's a digit dropped, or maybe there's even characters embedded in it, that may be indicative of a data quality issues, so we try to find those kinds of things. Going a step further, we also try to identify data quality relationships. So for example we have two columns, one date one date two, through observation we can see the date one 99% of the time is less than date two, 1% of the time it's not, probably indicative of the data quality issue, but going a step further we can also build a business rule that says date one is actually than date two, and so then when it pops up again we can quickly identify and remediate that problem. So these are the kinds of things that we can do with Io-Tahoe. Going even a step further, we can take your favorite data science solution, productionize it, and incorporate it into our next version as what we call a worker process to do your own bespoke analytics. >> Bespoke analytics, excellent, Lester thank you. So Ajay, talk us through some examples of where you're putting this to use, and also what is some of the feedback from some customers. >> Yeah, what I'm thinking how do you bring into life a little bit Lisa lets just talk through a case study. We put something together, I know it's available for download, but in a well-known telecommunications media company, they have a lot of the issues that lasted just spoke about lots of teams of Amanda's, super bright data practitioners, and are maybe looking to get more productivity out of their day, and deliver a good result for their own customers, for cell phone subscribers and broadband users. So, there are so many examples that we can see here is how we went about auto generating a lot of that old understanding of that data within hours. So, Amanda had her data catalog populated automatically, a business glossary built up, and maybe I would start to say, "Okay, where do I want to apply some policies to the data to set in place some controls, whether I want to adapt how different lines of business maybe tasks versus customer operations have different access or permissions to that data." And what we've been able to do that is to build up that picture to see how does data move across the entire organization, across the state, and monitor that over time for improvement. So we've taken it from being like reactive, let's do something to fix something to now more proactive. We can see what's happening with our data, who's using it, who's accessing it, how it's being used, how it's being combined, and from there taking a proactive approach is a real smart use of the tanons in that telco organization and the folks that work there with data. >> Okay Ajay, so digging into that a little bit deeper, and one of the things I was thinking when you were talking through some of those outcomes that you're helping customers achieve is ROI. How do customers measure ROI, What are they seeing with Io-Tahoe solution? >> Yeah, right now the big ticket item is time to value. And I think in data a lot of the upfront investment costs are quite expensive, they happen today with a lot of the larger vendors and technologies. Well, a CIO, an economic buyer really needs to be certain about this, how quickly can I get that ROI? And I think we've got something that we can show just pull up a before and after, and it really comes down to hours, days, and weeks where we've been able to have that impact. And in this playbook that we put together the before and after picture really shows those savings that committed a bit through providing data into some actionable form within hours and days to drive agility. But at the same time being able to enforce the controls to protect the use of that data and who has access to it, so atleast the number one thing I'd have to say is time, and we can see that on the graphic that we've just pulled up here. >> Excellent, so ostensible measurable outcomes that time to value. We talk about achieving adaptive data governance. Lester, you guys talk about automation, you talk about machine learning, how are you seeing those technologies being a facilitator of organizations adopting adaptive data governance? >> Well, as we see the manual date, the days of manual effort are out, so I think this is a multi-step process, but the very first step is understanding what you have in normalizing that across your data estate. So, you couple this with the ontologies that are unique to your business and algorithms, and you basically go across it and you identify and tag that data, that allows for the next steps to happen. So now I can write business rules not in terms of named columns, but I can write them in terms of the tags. Using that automated pattern recognition where we observed the loan starts should be before the loan (indistinct), being able to automate that is a huge time saver, and the fact that we can suggest that as a rule rather than waiting for a person to come along and say, "Oh wow, okay, I need this rule, I need this rule." These are steps that increase, or I should say decrease that time to value that Ajay talked about. And then lastly, a couple of machine learning, because even with great automation and being able to profile all your data and getting a good understanding, that brings you to a certain point, but there's still ambiguity in the data. So for example I might have two columns date one and date two, I may have even observed that date one should be less than date two, but I don't really know what date one and date two are other than a date. So, this is where it comes in and I'm like, "As the user said, can you help me identify what date one and day two are in this table?" It turns out they're a start date and an end date for a loan, that gets remembered, cycled into machine learning step by step to see this pattern of date one date two. Elsewhere I'm going to say, "Is it start date and end date?" Bringing all these things together with all this automation is really what's key to enable this data database, your data governance program. >> Great, thanks Lester. And Ajay I do want to wrap things up with something that you mentioned in the beginning about what you guys are doing with Oracle, take us out by telling us what you're doing there, how are you guys working together? >> Yeah, I think those of us who worked in IT for many years we've learned to trust Oracle's technology that they're shifting now to a hybrid on-prem cloud generation 2 platform which is exciting, and their existing customers and new customers moving to Oracle are on a journey. So Oracle came to us and said, "Now, we can see how quickly you're able to help us change mindsets," and as mindsets are locked in a way of thinking around operating models of IT that are maybe not agile or more siloed, and they're wanting to break free of that and adopt a more agile API driven approach with their data. So, a lot of the work that we're doing with Oracle is around accelerating what customers can do with understanding their data and to build digital apps by identifying the underlying data that has value. And the time we're able to do that in hours, days, and weeks, rather than many months is opening up the eyes to chief data officers, CIO is to say, "Well, maybe we can do this whole digital transformation this year, maybe we can bring that forward and transform who we are as a company." And that's driving innovation which we're excited about, and I know Oracle keen to drive through. >> And helping businesses transform digitally is so incredibly important in this time as we look to things changing in 2021. Ajay and Lester thank you so much for joining me on this segment, explaining adaptive data governance, how organizations can use it, benefit from it, and achieve ROI, thanks so much guys. >> Thanks you. >> Thanks again Lisa. (bright music)

>> Commentator: From around the globe, it's theCUBE with digital coverage of Enterprise Data Automation. An event series brought to you by Io-Tahoe. >> Hi everybody, we're back, we're talking about Enterprise Data Automation. The hashtag is data automated, and we're going to really dig into data migrations. Data migrations are risky, they're time consuming and they're expensive. Yusef Khan is here, he's the head of partnerships and alliances at Io-Tahoe, coming again from London. Hey, good to see you, Yusef, thanks very much. >> Thank Dave, great guy. >> So your role is interesting. We're talking about data migrations, you're going to head of partnerships, what is your role specifically and how is it relevant to what we're going to talk about today? >> Well, I work with the various businesses, such as cloud companies, systems integrators, companies that sell operating systems, middleware, all of whom are often quite well embedded within a company IT infrastructures and have existing relationships, because what we do fundamentally makes migration to the cloud easier and data migration easier, there are lots of businesses that are interested in partnering with us some were interested in partnering with. >> So let's set up the problem a little bit and then I want to get into some of the data. You know, you said that migrations are risky, time consuming, expensive, they're often times a blocker for organizations to really get value out of data. Why is that? >> Ah, I think I mean, all migrations have to start with knowing the facts about your data and you can try and do this manually but when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate. So they'll have everything from on premise mainframes, they may have stuff which is partly in the clouds but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. Now their understanding of what they have, is often quite limited because you can try and draw manual maps but they're out-of-date very quickly, every time data changes, the manual map set a date and people obviously leave organizations all the time. So that kind of tribal knowledge gets built up is limited as well. So you can try and map all that manually, you might need a DBA, database analyst or a business analyst and they might go in and explore the data for you. But doing that manually is very very time consuming. This can take teams of people months and months or you can use automation, just like Webster Bank did with Io-Tahoe and they managed to do this with a relatively small team in a timeframe of days. >> Yeah, we talked to Paul from Webster Bank, awesome discussion. So I want to dig in to this migration, then let's pull up a graphic that we'll talk about, what a typical migration project looks like. So what you see here it's very detailed, I know, it's a bit of an eye test but let me call your attention to some of the key aspects of this and then Yusef, I want you to chime in. So at the top here, you see that area graph, that's operational risk for typical migration project and you can see the timeline and the milestones, that blue bar is the time to test, so you can see the second step data analysis it's taking 24 weeks, so you know, very time consuming and then let's not get dig into the stuff in the middle of the fine print but there's some real good detail there but go down the bottom, that's labor intensity in the bottom and you can see high is that sort of brown and you can see a number of data analysis, data staging, data prep, the trial, the implementation, post implementation fixtures, the transition to BAU, which I think is Business As Usual. Those are all very labor intensive. So what are your takeaways from this typical migration project? What do we need to know Yusef? >> I mean, I think the key thing is, when you don't understand your data upfront, it's very difficult to scope and to set up a project because you go to business stakeholders and decision makers and you say, "okay, we want to migrate these data stores, we want to put them into the cloud most often", but actually, you probably don't know how much data is there, you don't necessarily know how many applications it relates to, you don't know the relationships between the data, you don't know the flow of the data so the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and alleviate that risk, you probably stack your project team with lots and lots of people to do the next phase, which is analysis and so you've set up a project which is got to pretty high cost. The bigger the project, the more people the heavier the governance obviously and then in the phase where they're trying to do lots and lots of manual analysis. Manual analysis, as we all know and the idea of trying to relate data that's in different data stores, relating individual tables and columns are very, very time consuming, expensive if you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use third party tools. As I said earlier, the people who understand some of those systems may have left a while ago and so you are in a high risks, high cost situation from the off and the same thing sort of develops through the project. What you find with Io-Tahoe is that we're able to automate a lot of this process from the very beginning, because we can do the initial data discovery run for example automatically, so you very quickly have an automated view of the data, a data map and the data flow has been generated automatically, much less time and effort and much less cost of money. >> Okay, so I'm going to bring back that first chart and I want to call your attention to again, that area graph, the blue bars and then down below that labor intensity and now let's bring up the same chart, but with a sort of an automation injection in here and now so you now see the sort of essence celebrated by Io-Tahoe. Okay, great, we're going to talk about this but look what happens to the operational risk, a dramatic reduction in that graph and then look at the bars, the bars, those blue bars, you know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. All these were high, data analysis, data staging, Data Prep, trial, post implementation fixtures in transition to BAU. All those went from high labor intensity, so we've now attacked that and gone to low labor intensity, explain how that magic happened. >> Ah, let's take the example of a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about that data and its price data catalog, if you like. Imagine trying to do that manually, you need to go into every individual data store, you need a DBA and the business analyst for each data store, they need to do an extract of the data, they need to put tables individually, they need to cross reference that with other data stores and schemas and tables, you've probably end up with the mother of all Excel spreadsheets and it would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of these things is, it accelerates the ability to automate, but in some cases it also makes it possible for enterprise customers with legacy systems, take banks, for example, they quite often end up staying on mainframe systems that they've had in place for decades, and not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data that isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems or success systems that are out of their support. Go back to the data catalog example. Whatever you discover in data discovery has to persist in a tool like a data catalog and so we automate data catalogs including our own, we can also feed others but we have our own. The only alternative to this kind of automation is to build out this very large project team of business analysts, of DBAs, project managers, process analysts, to gather all the data, to understand that the process of gathering the data is correct, to put it in the repository, to validate it, etcetera, etcetera. We've got into organizations and we've seen them, ramp up teams of 20 30 people, cost of 2, 3, 4 million pounds a year and a timeframe of 15 to 20 years, just to try and get a data catalog done and that's something that we can typically do in a timeframe of months if not weeks and the differences is using automation and if you do what I've just described in this manual situation, you make migrations to the cloud prohibitively expensive, whatever saving you might make from shutting down your legacy data stores, will get eaten up by the cost of doing it unless you go with a more automated approach. >> Okay, so the automated approach reduces risk because you're not going to, you know, you're going to stay on project plan, ideally, you know, it's all these out of scope expectations that come up with the manual processes that kill you in the rework and then that data catalog, people are afraid that their family jewels data is not going to make it through to the other side. So, that's something that you're addressing and then you're also not boiling the ocean, you're really taking the pieces that are critical and the stuff that you don't need, you don't have to pay for as part of this process. >> It's a very good point. I mean, one of the other things that we do and we have specific features to do, is to automatically analyze data for duplication at a row-level or record level and redundancy at a column level. So as you say, before you go into migration process, you can then understand actually, this stuff here is duplicated, we don't need it. Quite often, if you put data in the cloud, you're paying obviously for storage space or for compute time, the more data you have in there is duplicated, that's pure cost you should take out before you migrate. Again, if you're trying to do that process of understanding was duplicated manually of 10s or 100s of data stores, it will take you months if not years, you use machine learning to do it in an automatic way and it's much much quicker. I mean, there's nothing I'd say about the net cost and benefit of Io-Tahoe. Every organization we work with has a lot of money existing sunk cost in there IT, so they'll have your IP systems like Oracle or data lakes which they've spent good time and money investing in. What we do by enabling them to transition everything to their strategic future repositories, is accelerate the value of investment and the time to value that investment. So we are trying to help people get value out of their existing investments and data estate, close down the things that they don't need and enable them to go to a kind of brighter and more present future. >> Well, I think as well, you know, once you're able to and this is a journey, we know that but once you're able to go live and you're infusing sort of a data mindset, a data oriented culture, I know it's somewhat buzzwordy, but when you when you see it in organizations, you know it's real and what happens is you dramatically reduce that and cycle time of going from data to actually insights, data is plentiful but insights aren't and that is what's going to drive competitive advantage over the next decade and beyond. >> Yeah, definitely and you can only really do that if you get your data state cleaned up in the first place. I've worked with and managed teams of data scientists, big data engineers, business analysts, people who are pushing out dashboards and are trying to build machine learning applications. You'll know you have the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is, and cleaning data, which really you don't want a highly paid data scientist doing with their time but if you sort out your data set in the first place, get rid of duplication, perhaps migrate to a cloud store where things are more readily accessible and it's easy to build connections and to use native machine learning tools, you're well on the way up the maturity curve and you can start to use some of those more advanced applications. >> Yusef, what are some of the prerequisites maybe the top, you know, few that are two or three that I need to understand as a customer to really be successful here? I mean, there's, is it skill sets? Is it, mindset, leadership buy-in? What do I absolutely need to have to make this successful? >> Well, I think leadership is obviously key, being able to sort of set the vision for people is obviously key. One of the great things about Io-Tahoe though, is you can use your existing staff to do this work if you use our automation platform, there's no need to hire expensive people. Io-Tahoe is a no code solution, it works out of the box, you just connect to source and then your existing staff can use it. It's very intuitive and easy to use, user interface is only to invest vast amounts with large consultancies, who may well charging the earth and you are actually a bit of an advantage if you've got existing staff who are close to the data, who are subject matter experts or use it because they can very easily learn how to use the tool and then they can go in and they can write their own data quality rules and they can really make a contribution from day one. When we go into organizations and we connect all of the great things about the whole experience via Io-Tahoe is we can get tangible results back within the day. Usually within an hour or two, were able to say, okay, we started to map the relationships here. Here's a data map of the data that we've analyzed and here are some thoughts on what your sensitive data is, because it's automated, because it's running algorithms across data and that's what people really should expect. >> And you know this because you're dealing with the ecosystem, we're entering a new era of data and many organizations to your point, they just don't have the resources to do what Google and Amazon and Facebook and Microsoft did over the past decade to become you know, data dominant, you know, trillion dollar market cap companies. Incumbents need to rely on technology companies to bring that automation, that machine intelligence to them so they can apply it. They don't want to be AI inventors, they want to apply it to their businesses. So and that's what really was so difficult in the early days of so called Big Data, you had this just too much complexity out there and now companies like Io-Tahoe are bringing you know, tooling and platforms that are allowing companies to really become data driven. Your final thoughts, please Yusef. >> But that's a great point, Dave. In a way it brings us back to where it began in terms of partnerships and alliances. I completely agree, a really exciting point where we can take applications like Io-Tahoe and we can go into enterprises and help them really leverage the value of these type of machine learning algorithms and AI. We work with all the major cloud providers, AWS, Microsoft Azure, Google Cloud Platform, IBM, Red Hat, and others and we really, I think, for us, the key thing is that we want to be the best in the world at Enterprise Data Automation. We don't aspire to be a cloud provider or even a workflow provider but what we want to do is really help customers with their data, with our automated data functionality in partnership with some of those other businesses so we can leverage the great work they've done in the cloud, the great work they've done on workflows, on virtual assistants and in other areas and we help customers leverage those investments as well but our heart we're really targeted at just being the best enterprise, data automation business in the world. >> Massive opportunities not only for technology companies but for those organizations that can apply technology for business advantage, Yusef Khan, thanks so much for coming on theCUBE. >> Pretty much appreciated. >> All right, and thank you for watching everybody. We'll be right back right after this short break. (upbeat music)