>> 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)

>>from around the globe. It's the Cube presenting adaptive data governance brought >>to you by >>Iota Ho. In this next segment, we're gonna be talking to you about getting to know your data. And specifically you're gonna hear from two folks at Io Tahoe. We've got enterprise account execs Evita Davis here, as well as Enterprise Data engineer Patrick Simon. They're gonna be sharing insights and tips and tricks for how you can get to know your data and quickly on. We also want to encourage you to engage with Sabina and Patrick. Use the chat feature to the right, send comments, questions or feedback so you can participate. All right, Patrick Sabetta, take it away. All right. >>Thanks, Lisa. Great to be here as Lisa mentioned guys. I'm the enterprise account executive here in Ohio. Tahoe you Pat? >>Yeah. Hey, everyone so great to be here. A said My name's Patrick Samit. I'm the enterprise data engineer here at Iota Ho. And we're so excited to be here and talk about this topic as one thing we're really trying to perpetuate is that data is everyone's business. >>I couldn't agree more, Pat. So, guys, what patent? I patent. I've actually had multiple discussions with clients from different organizations with different roles. So we spoke with both your technical and your non technical audience. So while they were interested in different aspects of our platform, we found that what they had in common was they wanted to make data easy to understand and usable. So that comes back. The pats point off being everybody's business because no matter your role, we're all dependent on data. So what Pan I wanted to do today was wanted toe walk. You guys through some of those client questions, slash pain points that we're hearing from different industries and different roles and demo how our platform here, like Tahoe, is used for automating those, uh, automating Dozier related tasks. So with that said, are you ready for the first one, Pat? >>Yeah, Let's do it. >>Great. So I'm gonna put my technical hat on for this one, So I'm a data practitioner. I just started my job. ABC Bank. I have over 100 different data sources. So I have data kept in Data Lakes, legacy data, sources, even the cloud. So my issue is I don't know what those data sources hold. I don't know what data sensitive, and I don't even understand how that data is connected. So how can I talk to help? >>Yeah, I think that's a very common experience many are facing and definitely something I've encountered in my past. Typically, the first step is to catalog the data and then start mapping the relationships between your various data stores. Now, more often than not, this has tackled through numerous meetings and a combination of Excel and something similar to video, which are too great tools in their own part. But they're very difficult to maintain. Just due to the rate that we are creating data in the modern world. It starts to beg for an idea that can scale with your business needs. And this is where a platform like Io Tahoe becomes so appealing. You can see here visualization of the data relationships created by the I Ho Tahoe service. Now, what is fantastic about this is it's not only laid out in a very human and digestible format in the same action of creating this view, the data catalog was constructed. >>Um, So is the data catalog automatically populated? Correct. Okay, so So what? I'm using iota. Hope at what I'm getting is this complete, unified automated platform without the added cost, of course. >>Exactly. And that's at the heart of Iota Ho. A great feature with that data catalog is that Iota Ho will also profile your data as it creates the catalog, assigning some meaning to those pesky column Underscore ones and custom variable underscore tents that are always such a joy to deal with. Uh, now, by leveraging this interface, we can start to answer the first part of your question and understand where the core relationships within our data exists. Personally, I'm a big fan of this >>view, >>as it really just helps the i b naturally John to these focal points that coincide with these key columns following that train of thought. Let's examine the customer I D column that seems to be at the center of a lot of these relationships. We can see that it's a fairly important column as it's maintaining the relationship between at least three other tables. Now you notice all the connectors are in this blue color. This means that their system defined relationships. But I hope Tahoe goes that extra mile and actually creates thes orange colored connectors as well. These air ones that are machine learning algorithms have predicted to be relationships. Uh, and you can leverage to try and make new and powerful relationships within your data. So I hope that answers the first part of your question. >>Eso So this is really cool. And I can see how this could be leverage quickly. Now. What if I added new data sources or your multiple data sources and needed toe? Identify what data sensitive. Can I Oh, Tahoe, Detect that. >>Yeah, definitely. Within the i o ta platform. There already over 300 pre defined policies such as HIPAA, ferpa, C, c, p, a and the like. One can choose which of these policies to run against their data along for flexibility and efficiency and running the policies that affect organization. >>Okay, so so 300 is an exceptional number. I'll give you that. But what about internal policies that apply to my organization? Is there any ability for me to write custom policies? >>Yeah, that's no issue. And is something that clients leverage fairly often to utilize this function when simply has to write a rejects that our team has helped many deploy. After that, the custom policy is stored for future use to profile sensitive data. One then selects the data sources they're interested in and select the policies that meet your particular needs. The interface will automatically take your data according to the policies of detects, after which you can review the discoveries confirming or rejecting the tagging. All of these insights are easily exported through the interface, so one can work these into the action items within your project management systems. And I think this lends to the collaboration as a team can work through the discovery simultaneously. And as each item is confirmed or rejected, they can see it ni instantaneously. All this translates to a confidence that with iota how you can be sure you're in compliance. >>Um, so I'm glad you mentioned compliance because that's extremely important to my organization. >>So >>what you're saying when I use the eye a Tahoe automated platform, we'd be 90% more compliant that before were other than if you were going to be using a human. >>Yeah, definitely. The collaboration and documentation that the iota ho interface lends itself to can really help you build that confidence that your compliance is sound. >>Does >>that answer your question about sense of data? >>Definitely so. So path. I have the next question for you. So we're planning on migration on guy. Have a set of reports I need to migrate. But what I need to know is that well, what what data sources? Those report those reports are dependent on and what's feeding those tables? >>Yeah, it's a fantastic questions to be toe identifying critical data elements, and the interdependencies within the various databases could be a time consuming but vital process and the migration initiative. Luckily, Iota Ho does have an answer, and again, it's presented in a very visual format. >>So what I'm looking at here is my entire day landscape. >>Yes, exactly. >>So let's say I add another data source. I can still see that Unified 3 60 view. >>Yeah, One feature that is particularly helpful is the ability to add data sources after the data lineage. Discovery has finished along for the flexibility and scope necessary for any data migration project. If you only need need to select a few databases or your entirety, this service will provide the answers. You're looking for this visual representation of the connectivity makes the identification of critical data elements a simple matter. The connections air driven by both system defined flows as well as those predicted by our algorithms, the confidence of which, uh can actually be customized to make sure that they're meeting the needs of the initiative that you have in place. Now, this also provides tabular output in case you need it for your own internal documentation or for your action items, which we can see right here. Uh, in this interface, you can actually also confirm or deny the pair rejection the pair directions along to make sure that the data is as accurate as possible. Does that help with your data lineage needs? >>Definitely. So So, Pat, My next big question here is So now I know a little bit about my data. How do I know I can trust it? So what I'm interested in knowing really is is it in a fit state for Meteo use it? Is it accurate? Does it conform to the right format? >>Yeah, that's a great question. I think that is a pain point felt across the board, be it by data practitioners or data consumers alike. another service that iota hope provides is the ability to write custom data quality rules and understand how well the data pertains to these rules. This dashboard gives a unified view of the strength of these rules, and your dad is overall quality. >>Okay, so Pat s o on on the accuracy scores there. So if my marketing team needs to run, a campaign can read dependent those accuracy scores to know what what tables have quality data to use for our marketing campaign. >>Yeah, this view would allow you to understand your overall accuracy as well as dive into the minutia to see which data elements are of the highest quality. So for that marketing campaign, if you need everything in a strong form, you'll be able to see very quickly with these high level numbers. But if you're only dependent on a few columns to get that information out the door, you can find that within this view, uh, >>so you >>no longer have to rely on reports about reports, but instead just come to this one platform to help drive conversations between stakeholders and data practitioners. I hope that helps answer your questions about that quality. >>Oh, definitely. So I have another one for you here. Path. So I get now the value of IATA who brings by automatically captured all those technical metadata from sources. But how do we match that with the business glossary? >>Yeah, within the same data quality service that we just reviewed. One can actually add business rules detailing the definitions and the business domains that these fall into. What's more is that the data quality rules were just looking at can then be tied into these definitions, allowing insight into the strength of these business rules. It is this service that empowers stakeholders across the business to be involved with the data life cycle and take ownership over the rules that fall within their domain. >>Okay, so those custom rules can I apply that across data sources? >>Yeah. You can bring in as many data sources as you need, so long as you could tie them to that unified definition. >>Okay, great. Thanks so much bad. And we just want to quickly say to everyone working in data, we understand your pain, so please feel free to reach out >>to us. We >>are website the chapel. Oh, Arlington. And let's get a conversation started on how iota Who can help you guys automate all those manual task to help save you time and money. Thank you. Thank >>you. Erin. >>Impact. If I could ask you one quick question, how do you advise customers? You just walk in this great example This banking example that you and city to talk through. How do you advise customers get started? >>Yeah, I think the number one thing that customers could do to get started with our platform is to just run the tag discovery and build up that data catalog. It lends itself very quickly to the other needs you might have, such as thes quality rules as well as identifying those kind of tricky columns that might exist in your data. Those custom variable underscore tens I mentioned before >>last questions to be to anything to add to what Pat just described as a starting place. >>Um, no, I think actually passed something that pretty well, I mean, just just by automating all those manual tasks, I mean, it definitely can save your company a lot of time and money, so we we encourage you just reach out to us. Let's get that conversation started. >>Excellent. Savita and Pat, Thank you so much. We hope you have learned a lot from these folks about how to get to know your data. Make sure that it's quality so that you can maximize the value of it. Thanks for watching.

>> Presenter: From around the globe, it's theCUBE. Presenting Adaptive Data Governance, brought to you by Io-Tahoe. >> Our next segment here is an interesting panel, you're going to hear from three gentlemen, about Adaptive Data Governance. We're going to talk a lot about that. Please welcome Yusef Khan, the global director of data services for Io-Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and Gudron Van Der Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this panel. (indistinct) >> All right, Santiago, we're going to start with you. Can you talk to the audience a little bit about the First Bank of Nigeria and its scale? This is beyond Nigeria, talk to us about that. >> Yes. So First Bank of Nigeria was created 125 years ago, it's one of the oldest, if not the oldest bank in Africa. And because of the history, it grew, everywhere in the region, and beyond the region. I'm currently based in London, where it's kind of the European headquarters. And it really promotes trade finance, institutional banking, corporate banking, private banking around the world, in particular in relationship to Africa. We are also in Asia, in the Middle East. And yes, and is a very kind of active bank in all these regions. >> So Santiago, talk to me about what adaptive data governance means to you, and how does it helps the First Bank of Nigeria to be able to innovate faster with the data that you have. >> Yes I like that concept of adaptive data governance, because it's kind of, I would say, an approach that can really happen today with the new technology before it was much more difficult to implement. So just to give you a little bit of context, I used to work in consulting for 16-17 years before joining the First Bank of Nigeria. And I saw many organizations trying to apply different type of approaches in data governance. And the beginning, early days was really kind of (indistinct), where you top down approach, where data governance was seen as implement a set of rules, policies and procedures, but really from the top down. And is important, it's important to have the battle of your sea level, of your director, whatever is, so just that way it fails, you really need to have a complimentary approach, I often say both amount, and actually, as a CEO I'm really trying to decentralized data governance, really instead of imposing a framework that some people in the business don't understand or don't care about it. It really needs to come from them. So what I'm trying to say is that, data basically support business objectives. And what you need to do is every business area needs information on particular decisions to actually be able to be more efficient, create value, et cetera. Now, depending on the business questions they have to show, they will need certain data sets. So they need actually to be able to have data quality for their own, 'çause now when they understand that, they become the stewards naturally of their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also in power and be actually in a way flexible to adapt the different questions that they have in order to be able to respond to the business needs. And I think that is where these adaptive data governance starts. Because if you want, I'll give you an example. In the bank, we work, imagine a Venn diagram. So we have information that is provided to finance, and all information to risk, or information for business development. And in this Venn diagram, there is going to be part of that every circle that are going to kind of intersect with each other. So what you want as a data governance is to help providing what is in common, and then let them do their own analysis to what is really related to their own area as an example, nationality. You will say in a bank that will open an account is the nationality of your customer, that's fine for final when they want to do a balance sheet an accounting or a P&L, but for risk, they want that type of analysis plus the net nationality of exposure, meaning where you are actually exposed as a risk, you can have a customer that are on hold in the UK, but then trade with Africa, and in Africa they're exposing their credit. So what I'm trying to say is they have these pieces in common and pieces that are different. Now I cannot impose a definition for everyone. I need them to adapt and to bring their answers to their own business questions. That is adaptive data governance. And all that is possible because we have and I was saying at the very beginning, just to finalize the point, we have new technologies that allow you to do these metadata classification in a very sophisticated way that you can actually create analytics of your metadata. You can understand your different data sources, in order to be able to create those classifications like nationalities and way of classifying your customers, your products, et cetera. But you will need to understand which areas need, what type nationality or classification, which others will mean that all the time. And the more you create that understanding, that intelligence about how your people are using your data you create in a way building blocks like a label, if you want. Where you provide them with those definitions, those catalogs you understand how they are used or you let them compose like Lego. They would play their way to build their analysis. And they will be adaptive. And I think the new technologies are allowing that. And this is a real game changer. I will say that over and over. >> So one of the things that you just said Santiago kind of struck me in to enable the users to be adaptive, they probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the user so that they can be adaptive? >> Yeah, that's a really good question. And that goes along with that type of approach. I was saying in a way more and more business users want autonomy, and they want to basically be able to grab the data and answers their question. Now, when you have that, that's great, because then you have demand. The business is asking for data. They're asking for the insight. So how do you actually support that? I will say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, of course, technology is one of the biggest winners without technology we couldn't have been working remotely. Without this technology, where people can actually log in from their homes and still have a market data marketplaces where they self serve their information. But even beyond that, data is a big winner. Data because the pandemic has shown us that crisis happened, but we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore and we need to adapt and we need to be flexible. How do we do that? With data. As a good example this, every country, every government, is publishing everyday data stats of what's happening in the countries with the COVID and the pandemic so they can understand how to react because this is new. So you need facts in order to learn and adapt. Now, the companies that are the same. Every single company either saw the revenue going down, or the revenue going very up for those companies that are very digital already now, it changed the reality. So they needed to adapt, but for that they needed information in order to think and innovate and try to create responses. So that type of self service of data, (indistinct) for data in order to be able to understand what's happening when the construct is changing, is something that is becoming more of the topic today because of the pandemic, because of the new capabilities of technologies that allow that. And then, you then are allowed to basically help, your data citizens, I call them in organization. People that know their business and can actually start playing and answer their own questions. So these technologies that gives more accessibility to the data, that gives some cataloging so we can understand where to go or where to find lineage and relationships. All this is basically the new type of platforms or tools that allow you to create what I call a data marketplace. So once you create that marketplace, they can play with it. And I was talking about new culture. And I'm going to finish with that idea. I think these new tools are really strong because they are now allowing for people that are not technology or IT people to be able to play with data because it comes in the digital world they are useful. I'll give you an example with all your stuff where you have a very interesting search functionality, where you want to find your data and you want to self serve, you go there in that search, and you actually go and look for your data. Everybody knows how to search in Google, everybody searching the internet. So this is part of the data culture, the digital culture, they know how to use those tools. Now similarly, that data marketplace is in Io-Tahoe, you can for example, see which data sources are mostly used. So when I'm doing an analysis, I see that police in my area are also using these sources so I trust those sources. We are a little bit like Amazon, when you might suggest you what next to buy, again this is the digital kind of culture where people very easily will understand. Similarly, you can actually like some type of data sets that are working, that's Facebook. So what I'm trying to say is you have some very easy user friendly technologies that allows you to understand how to interact with them. And then within the type of digital knowledge that you have, be able to self serve, play, collaborate with your peers, collaborate with the data query analysis. So its really enabling very easily that transition to become a data savvy without actually needing too much knowledge of IT, or coding, et cetera, et cetera. And I think that is a game changer as well. >> And enabling that speed that we're all demanding today during these unprecedented times. Gudron I wanted to go to you, as we talk about in the spirit of evolution, technology's changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >> Yeah, thank you. Well, Oracle Digital is a business unit at Oracle EMEA. And we focus on emerging countries, as well as low end enterprises in the mid market in more developed countries. And four years ago, they started with the idea to engage digital with our customers via central hubs across EMEA. That means engaging with video having conference calls, having a wall, agreeing wall, where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today. And this helps us to engage with our customers in the way we're already doing. And then about my team. The focus of my team is to have early stage conversations with our customers on digital transformation and innovation. And we also have a team of industry experts who engage with our customers and share expertise across EMEA. And we we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important. So we can help the customer and for the customer means that we will help them with our technology and our resources to achieve their goals. >> It's all about outcomes. Right Gudron? So in terms of automation, what are some of the things Oracle is doing there to help your clients leverage automation to improve agility so that they can innovate faster? Which on these interesting times it's demanding. >> Yeah. Thank you. Well, traditionally, Oracle is known for their databases, which has been innovated year over year since the first launch. And the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90%, with a multimodal converged database, and machine learning based automation for full lifecycle management. Our database is self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security and itself repairing the ultimate failure detection, failover and repair. And the question is for our customers, what does it mean? It means they can focus on their business instead of maintaining their infrastructure and their operations. >> That's absolutely critical. Yusef, I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that, but we know that whether we're talking about data management, or digital transformation. A one size fits all approach doesn't work to address the challenges that the business has. That the IT folks have. As you are looking to the industry, with what Santiago told us about First Bank of Nigeria, what are some of the changes that you're seeing that Io-Tahoe has seen throughout the industry? >> Well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data, where you have almost a data policeman who tells you what you can and cannot do just doesn't work anymore. It's too slow, it's too result intensive. Data Management, data governance, digital transformation itself, it has to be collaborative. And it has to be an element of personalization today to users. In the environment we find ourselves in now, it has to be about enabling self service as well. A one size fits all model when it comes to those things around data doesn't work. As Santiago was saying, it needs to be adaptive to how the data is used and who is using it. And in order to do this, companies, enterprises, organizations really need to know their data. They need to understand what data they hold, where it is, and what the sensitivity of it is. They can then in a more agile way, apply appropriate controls and access so that people themselves are in groups within businesses are agile and can innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >> Yet a one size fits all terms doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Yusef, talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do before. >> I guess one of the key ones is around. Just it's automatically being able to classify terabytes of data or even petabytes of data across different sources to find duplicates, which you can then remediate and delete. Now, with the capabilities that Io-Tahoe offers, and Oracle offers, you can do things not just with a five times or 10 times improvement, but it actually enables you to do project for stock that otherwise would fail, or you would just not be able to do. Classifying multi terabyte and multi petabyte estates across different sources, formats, very large volumes of data. In many scenarios, you just can't do that manually. We've worked with government departments. And the issues there as you'd expect are the result of fragmented data. There's a lot of different sources, there's a lot of different formats. And without these newer technologies to address it, with automation and machine learning, the project isn't doable. But now it is. And that could lead to a revolution in some of these businesses organizations. >> To enable that revolution now, there's got to be the right cultural mindset. And one, when Santiago was talking about those really kind of adopting that and I think, I always call that getting comfortably uncomfortable. But that's hard for organizations to do. The technology is here to enable that. But when you're talking with customers, how do you help them build the trust and the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive that kind of attack in the culture? >> It's really good question, because it can be quite scary. I think the first thing we'd start with is to say, look, the technology is here, with businesses like Io-Tahoe, unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting, being agile around it and trying new ways of doing things. If you don't want to get left behind. And Santiago, and the team at FBN, are a great example of embracing it, testing it on a small scale and then scaling up. At Io-Tahoe we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with the customer, pick a use case, install the application, analyze data, drive out some some quick wins. So we worked in the last few weeks of a large energy supplier. And in about 20 days, we were able to give them an accurate understanding of their critical data elements, help them apply data protection policies, minimize copies of the data, and work out what data they needed to delete to reduce their infrastructure spend. So it's about experimenting on that small scale, being agile, and then scaling up in a in a kind of very modern way. >> Great advice. Santiago, I'd like to go back to you. Is we kind of look at, again, that topic of culture, and the need to get that mindset there to facilitate these rapid changes. I want to understand kind of last question for you about how you're doing that. From a digital transformation perspective, we know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges that we're all going through? >> That's a really interesting transition, I would say. I was mentioning, just going back to some of the points before to transition these I said that the new technologies allowed us to discover the data in a new way to blog and see very quickly information, to have new models of (indistinct) data, we are talking about data (indistinct), and giving autonomy to our different data units. Well, from that autonomy, they can then compose and innovate their own ways. So for me now we're talking about resilience. Because, in a way autonomy and flexibility in our organization, in our data structure, we platform gives you resilience. The organizations and the business units that I have experienced in the pandemic, are working well, are those that actually, because they're not physically present anymore in the office, you need to give them their autonomy and let them actually engage on their own side and do their own job and trust them in a away. And as you give them that they start innovating, and they start having a really interesting idea. So autonomy and flexibility, I think, is a key component of the new infrastructure where you get the new reality that pandemic shows that yes, we used to be very kind of structure, policies, procedures, as they're important, but now we learn flexibility and adaptability at the same site. Now, when you have that, a key other components of resiliency is speed, of course, people want to access the data and access it fast and decide fast, especially changes are changing so quickly nowadays, that you need to be able to, interact and iterate with your information to answer your questions quickly. And coming back maybe to where Yusef was saying, I completely agree is about experimenting, and iterate. You will not get it right the first time, especially that the world is changing too fast. And we don't have answers already set for everything. So we need to just go play and have ideas fail, fail fast, and then learn and then go for the next. So, technology that allows you to be flexible, iterate, and in a very fast agile way continue will allow you to actually be resilient in the way because you're flexible, you adapt, you are agile and you continue answering questions as they come without having everything said in a stroke that is too hard. Now coming back to your idea about the culture is changing in employees and in customers. Our employees, our customers are more and more digital service. And in a way the pandemic has accelerated that. We had many branches of the bank that people used to go to ask for things now they cannot go. You need to, here in Europe with the lockdown you physically cannot be going to the branches and the shops that have been closed. So they had to use our mobile apps. And we have to go into the internet banking, which is great, because that was the acceleration we wanted. Similarly, our employees needed to work remotely. So they needed to engage with a digital platform. Now what that means, and this is, I think the really strong point for the cultural change for resilience is that more and more we have two type of connectivity that is happening with data. And I call it employees connecting to data. The session we're talking about, employees connecting with each other, the collaboration that Yusef was talking about, which is allowing people to share ideas, learn and innovate. Because the more you have platforms where people can actually find themselves and play with the data, they can bring ideas to the analysis. And then employees actually connecting to algorithms. And this is the other part that is really interesting. We also are a partner of Oracle. And Oracle (indistinct) is great, they have embedded within the transactional system, many algorithms that are allowing us to calculate as the transactions happen. What happened there is that when our customers engage with algorithms, and again, with Io-Tahoe as well, the machine learning that is there for speeding the automation of how you find your data allows you to create an alliance with the machine. The machine is there to actually in a way be your best friend, to actually have more volume of data calculated faster in a way to discover more variety. And then, we couldn't cope without being connected to these algorithms. And then, we'll finally get to the last connection I was saying is, the customers themselves engaging with the connecting with the data. I was saying they're more and more engaging with our app and our website and they're digitally serving. The expectation of the customer has changed. I work in a bank where the industry is completely challenged. You used to have people going to a branch, as I was saying, they cannot not only not go there, but they're even going from branch to digital to ask to now even wanting to have business services actually in every single app that they are using. So the data becomes a service for them. The data they want to see how they spend their money and the data of their transactions will tell them what is actually their spending is going well with their lifestyle. For example, we talk about a normal healthy person. I want to see that I'm standing, eating good food and the right handle, healthy environment where I'm mentally engaged. Now all these is metadata is knowing how to classify your data according to my values, my lifestyle, is algorithms I'm coming back to my three connections, is the algorithms that allow me to very quickly analyze that metadata. And actually my staff in the background, creating those understanding of the customer journey to give them service that they expect on a digital channel, which is actually allowing them to understand how they are engaging with financial services. >> Engagement is absolutely critical Santiago. Thank you for sharing that. I do want to wrap really quickly. Gudron one last question for you. Santiago talked about Oracle, you've talked about it a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle, what you guys are doing there to help your customers get the resilience that they have to have to be. To not just survive, but thrive. >> Yeah. Well, Oracle has a cloud offering for infrastructure, database, platform service, and the complete solutions offered at SaaS. And as Santiago also mentioned, we are using AI across our entire portfolio, and by this will help our customers to focus on their business innovation and capitalize on data by enabling your business models. And Oracle has a global coverage with our cloud regions. It's massively investing in innovating and expanding their cloud. And by offering cloud as public cloud in our data centers, and also as private clouds with clouded customer, we can meet every sovereignty and security requirement. And then this way, we help people to see data in new ways. We discover insights and unlock endless possibilities. And maybe one one of my takeaways is, if I speak with customers, I always tell them, you better start collecting your data now. We enable this by this like Io-Tahoe help us as well. If you collect your data now you are ready for tomorrow. You can never collect your data backwards. So that is my takeaway for today. >> You can't collect your data backwards. Excellent Gudron. Gentlemen, thank you for sharing all of your insights, very informative conversation. All right. This is theCUBE, the leader in live digital tech coverage. (upbeat music)

>>from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>And we're back with the data automation. Siri's. In this episode, we're gonna learn more about what I owe Tahoe is doing in the field of adaptive data governance how it can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin, and I'm joined by a J. Bihar on the CEO of Iot Tahoe and Lester Waters, the CEO of Bio Tahoe. Gentlemen, it's great to have you on the program. >>Thank you. Lisa is good to be back. >>Great. Staley's >>likewise very socially distant. Of course as we are. Listen, we're gonna start with you. What's going on? And I am Tahoe. What's name? Well, >>I've been with Iot Tahoe for a little over the year, and one thing I've learned is every customer needs air just a bit different. So we've been working on our next major release of the I O. Tahoe product. But to really try to address these customer concerns because, you know, we wanna we wanna be flexible enough in order to come in and not just profile the date 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, uh, extend the product without building a new version of the product. We wanted to be able to have plausible modules. We also focused a lot on performance. That's very important with the bulk of data that we deal with that we're able to pass through that data in a single pass and do the analytics that are needed, whether it's, uh, 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 MAWR dimensions than we've ever done before. We're able to do data quality without doing a Nen initial rejects for, for example, just out of the box. So I think it's all of these things were coming together to form our next version of our product. We're really excited by it, >>So it's exciting a J from the CEO's level. What's going on? >>Wow, I think just building on that. But let's still just mentioned there. It's were growing pretty quickly with our partners. And today, here with Oracle are excited. Thio explain how that shaping up lots of collaboration already with Oracle in government, in insurance, on in banking and we're excited because we get to have an impact. It's real satisfying to see how we're able. Thio. Help businesses transform, Redefine what's possible with their data on bond. Having I recall there is a partner, uh, to lean in with is definitely helping. >>Excellent. We're gonna dig into that a little bit later. Let's 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 I t out to the business. And to do that, you've got to you've got Thio. 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 concerns itself. 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 could make their educated decisions on on what they need to do to achieve those business outcomes. >>Ah, >>lot of 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, the start date of alone must always be before the end date of alone 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 is the intersection of three circles, Really. It's the technical metadata coming together with policies and rules and coming together with the business ontology ease that are that are unique to that particular business. And this all of this. 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, Angie, help me understand this. Is this book enterprise companies are doing now? Are they not quite there yet. >>Well, you know, Lisa, I think every organization is is going at its pace. But, you know, markets are changing the economy and the speed at which, um, some of the changes in the economy happening is is compelling more businesses to look at being more digital in how they serve their own customers. Eh? So what we're seeing is a number of trends here from heads of data Chief Data Officers, CEO, stepping back from, ah, one size fits all approach because they've tried that before, and it it just hasn't worked. They've spent millions of dollars on I T programs China Dr Value from that data on Bennett. And they've ended up with large teams of manual processing around data to try and hardwire these policies to fit with the context and each line of business and on that hasn't worked. So the trends that we're seeing emerge really relate. Thio, How do I There's a chief data officer as a CEO. Inject more automation into a lot of these common tax. Andi, you know, we've been able toc that impact. I think the news here is you know, if you're trying to create a knowledge graph a data catalog or Ah, business glossary. And you're trying to do that manually will stop you. You don't have to do that manually anymore. I think best example I can give is Lester and I We we like Chinese food and Japanese food on. If you were sitting there with your chopsticks, you wouldn't eat the 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 to enjoy that meal before it gets cold. Andi, that's similar to how we're able to help the 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 eso Lester, how then does iota who go about doing this and enabling customers to achieve this? >>Let me, uh, let me show you a little story have here. So if you take a look at the challenges the most customers have, they're very similar, but every customers on a different data journey, so but it all starts with what data do I have? What questions or what shape is that data in? Uh, how is it structured? What's dependent on it? Upstream and downstream. Um, 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 oracle. Maybe they're doing some data governance changes on bits about enabling this. So if you look at these challenges and I'm gonna take you through a >>story here, E, >>I want to introduce Amanda. Man does not live like, uh, anyone in any large organization. She's looking around and she just sees stacks of data. I mean, different databases, the one she knows about, the one she doesn't know about what should know about various different kinds of databases. And a man is just tasking with understanding all of this so that they can embark on her data journey program. So So a man who goes through and she's great. I've got some handy tools. I can start looking at these databases and getting an idea of what we've got. Well, as she digs into the databases, she starts to see that not everything is as clear as she might have hoped it would be. You know, property names or column names, or have ambiguous names like Attribute one and attribute to or maybe date one and date to s Oh, man is starting to struggle, even though she's get tools to visualize. And look what look at these databases. She still No, she's got a long road ahead. And with 2000 databases in her large enterprise, yes, it's gonna be a long turkey but Amanda Smart. So she pulls out her trusty spreadsheet to track all of her findings on what she doesn't know about. She raises a ticket or maybe tries to track down the owner to find what the data means. And she's tracking all this information. Clearly, this doesn't scale that well for Amanda, you know? So maybe organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well because they're still ambiguities in the data with Iota ho. 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 U. S. Social Security number and attribute to looks like a I c D 10 medical code. And we do this by using anthologies and dictionaries and algorithms to help identify the underlying data and then tag it. Key Thio Doing, uh, 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 contain the same data. And by going through this exercise with a Tahoe, not only can we identify the data, but we also could 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. So there may be that may be indicative of a data quality issues, so we try to find those kind 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 to through Ah, observation. We can see that date 1 99% of the time is less than date, too. 1% of the time. It's not probably indicative of a data quality issue, but going a step further, we can also build a business rule that says Day one is less than date to. And so then when it pops up again, we can quickly identify and re mediate that problem. So these are the kinds of things that we could do with with iota going even a step further. You could take your your favorite data science solution production ISAT and incorporated into our next version a zey what we call a worker process to do your own bespoke analytics. >>We spoke analytics. Excellent, Lester. Thank you. So a J talk us through some examples of where you're putting this to use. And also what is some of the feedback from >>some customers? But I think it helped do this Bring it to life a little bit. Lisa is just to talk through a case study way. Pull something together. I know it's available for download, but in ah, well known telecommunications media company, they had a lot of the issues that lasted. You spoke about lots of teams of Amanda's, um, super bright data practitioners, um, on baby looking to to get more productivity out of their day on, deliver a good result for their own customers for cell phone subscribers, Um, on broadband users. So you know that some of the examples that we can see here is how we went about auto generating a lot of that understanding off that data within hours. So Amanda had her data catalog populated automatically. A business class three built up on it. Really? Then start to see. Okay, where do I want Thio? Apply some policies to the data to to set in place some controls where they want to adapt, how different lines of business, maybe tax versus customer operations have different access or permissions to that data on What we've been able to do there is, is to build up that picture to see how does data move across the entire organization across the state. Andi on monitor that overtime for improvement, so have taken it from being a reactive. Let's do something Thio. Fix something. Thio, 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. Um, on from there. Taking a proactive approach is a real smart use of of the talents in in that telco organization Onda folks that worked there with data. >>Okay, Jason, dig 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 our ally. How do customers measure are? Why? What are they seeing with iota host >>solution? Yeah, right now that the big ticket item is time to value on. And I think in data, a lot of the upfront investment cause quite expensive. They have been today with a lot of the larger vendors and technologies. So what a CEO and economic bio really needs to be certain of is how quickly can I get that are away. I think we've got something we can show. Just pull up a before and after, and it really comes down to hours, days and weeks. Um, where we've been able Thio have that impact on in this playbook that we pulled together before and after picture really shows. You know, those savings that committed a bit through providing data into some actionable form within hours and days to to drive agility, but at the same time being out and forced the controls to protect the use of that data who has access to it. So these are the number one thing I'd have to say. It's time on. We can see that on the the graphic that we've just pulled up here. >>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, >>Azaz, we see Mitt Emmanuel day. The days of manual effort are so I think you know 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 ontology, that air unique to your business. There is no algorithms, and you basically go across and you identify and tag tag that data that allows for the next steps toe happen. So now I can write business rules not in terms of columns named columns, but I could write him in terms of the tags 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 will thes air steps that increased that are, I should say, decrease that time to value that A. J talked about and then, lastly, a couple of machine learning because even with even with great automation and being able to profile all of your data and getting a good understanding, that brings you to a certain point. But there's still ambiguities in the data. So, for example, I might have to columns date one and date to. I may have even observed the date. One should be less than day two, but I don't really know what date one and date to our other than a date. So this is where it comes in, and I might ask the user said, >>Can >>you help me identify what date? One and date You are in this in this table. Turns out they're a start date and an end date for alone That gets remembered, cycled into the machine learning. So if I start to see this pattern of date one day to elsewhere, I'm going to say, Is it start dating and date? And these Bringing all these things together with this all this automation is really what's key to enabling this This'll data governance. Yeah, >>great. Thanks. Lester and a j wanna wrap things up with something that you mentioned in the beginning about what you guys were 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 i t for many years we've We've learned Thio trust articles technology that they're shifting now to ah, hybrid on Prohm Cloud Generation to platform, which is exciting. Andi on their existing customers and new customers moving to article on a journey. So? So Oracle came to us and said, you know, we can see how quickly you're able to help us change mindsets Ondas mindsets are locked in a way of thinking around operating models of I t. That there may be no agile and what siloed on day wanting to break free of that and adopt a more agile A p I at driven approach. A lot of the work that we're doing with our recall no is around, uh, accelerating what customers conduce with understanding their data and to build digital APS by identifying the the underlying data that has value. Onda at the time were able to do that in in in hours, days and weeks. Rather many months. Is opening up the eyes to Chief Data Officers CEO to say, Well, maybe we can do this whole digital transformation this year. Maybe we can bring that forward and and transform who we are as a company on that's driving innovation, which we're excited about it. I know Oracle, a keen Thio to drive through and >>helping businesses transformed digitally is so incredibly important in this time as we look Thio things changing in 2021 a. J. 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 our Oi. Thanks so much, guys. >>Thank you. Thanks again, Lisa. >>In a moment, we'll look a adaptive data governance in banking. This is the Cube, your global leader in high tech coverage. >>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 in high tech digital coverage. >>Our next segment here is an interesting panel you're gonna hear from three gentlemen about adaptive data. Governments want to talk a lot about that. Please welcome Yusuf Khan, the global director of data services for Iot Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and good John Vander Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this in this panel. Great >>to be >>tried for me. >>Alright, Santiago, we're going to start with you. Can you talk to the audience a little bit about the first Bank of Nigeria and its scale? This is beyond Nigeria. Talk to us about that. >>Yes, eso First Bank of Nigeria was created 125 years ago. One of the oldest ignored the old in Africa because of the history he grew everywhere in the region on beyond the region. I am calling based in London, where it's kind of the headquarters and it really promotes trade, finance, institutional banking, corporate banking, private banking around the world in particular, in relationship to Africa. We are also in Asia in in the Middle East. >>So, Sanjay, go talk to me about what adaptive data governance means to you. And how does it help the first Bank of Nigeria to be able to innovate faster with the data that you have? >>Yes, I like that concept off adaptive data governor, because it's kind of Ah, I would say an approach that can really happen today with the new technologies before it was much more difficult to implement. So just to give you a little bit of context, I I used to work in consulting for 16, 17 years before joining the president of Nigeria, and I saw many organizations trying to apply different type of approaches in the governance on by the beginning early days was really kind of a year. A Chicago A. A top down approach where data governance was seeing as implement a set of rules, policies and procedures. But really, from the top down on is important. It's important to have the battle off your sea level of your of your director. Whatever I saw, just the way it fails, you really need to have a complimentary approach. You can say bottom are actually as a CEO are really trying to decentralize the governor's. Really, Instead of imposing a framework that some people in the business don't understand or don't care about it, it really needs to come from them. So what I'm trying to say is that data basically support business objectives on what you need to do is every business area needs information on the detector decisions toe actually be able to be more efficient or create value etcetera. Now, depending on the business questions they have to solve, they will need certain data set. So they need actually to be ableto have data quality for their own. For us now, when they understand that they become the stores naturally on their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also empower and be actually, in a way flexible to adapt the different questions that they have in orderto be able to respond to the business needs. Now I cannot impose at the finish for everyone. I need them to adapt and to bring their answers toe their own business questions. That is adaptive data governor and all That is possible because we have. And I was saying at the very beginning just to finalize the point, we have new technologies that allow you to do this method data classifications, uh, in a very sophisticated way that you can actually create analitico of your metadata. You can understand your different data sources in order to be able to create those classifications like nationalities, a way of classifying your customers, your products, etcetera. >>So one of the things that you just said Santa kind of struck me to enable the users to be adaptive. They probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the users so that they can be adaptive. >>More and more business users wants autonomy, and they want to basically be ableto grab the data and answer their own question. Now when you have, that is great, because then you have demand of businesses asking for data. They're asking for the insight. Eso How do you actually support that? I would say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, off course, technology is one of the biggest winners without technology. We couldn't have been working remotely without these technologies where people can actually looking from their homes and still have a market data marketplaces where they self serve their their information. But even beyond that data is a big winner. Data because the pandemic has shown us that crisis happened, that we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore that we need to adapt and we need to be flexible. How do we do that with data. Every single company either saw the revenue going down or the revenue going very up For those companies that are very digital already. Now it changed the reality, so they needed to adapt. But for that they needed information. In order to think on innovate, try toe, create responses So that type of, uh, self service off data Haider for data in order to be able to understand what's happening when the prospect is changing is something that is becoming more, uh, the topic today because off the condemning because of the new abilities, the technologies that allow that and then you then are allowed to basically help your data. Citizens that call them in the organization people that no other business and can actually start playing and an answer their own questions. Eso so these technologies that gives more accessibility to the data that is some cataloging so they can understand where to go or what to find lineage and relationships. All this is is basically the new type of platforms and tools that allow you to create what are called a data marketplace. I think these new tools are really strong because they are now allowing for people that are not technology or I t people to be able to play with data because it comes in the digital world There. Used to a given example without your who You have a very interesting search functionality. Where if you want to find your data you want to sell, Sir, you go there in that search and you actually go on book for your data. Everybody knows how to search in Google, everybody's searching Internet. So this is part of the data culture, the digital culture. They know how to use those schools. Now, similarly, that data marketplace is, uh, in you can, for example, see which data sources they're mostly used >>and enabling that speed that we're all demanding today during these unprecedented times. Goodwin, I wanted to go to you as we talk about in the spirit of evolution, technology is changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >>Yeah, Thank you. Um, well, Oracle Digital is a business unit that Oracle EMEA on. We focus on emerging countries as well as low and enterprises in the mid market, in more developed countries and four years ago. This started with the idea to engage digital with our customers. Fear Central helps across EMEA. That means engaging with video, having conference calls, having a wall, a green wall where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today, and this helps us to engage with our customers in the way we were already doing and then about my team. The focus of my team is to have early stage conversations with our with our customers on digital transformation and innovation. And we also have a team off industry experts who engaged with our customers and share expertise across EMEA, and we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important so we can help the customer and for the customer means that we will help them with our technology and our resource is to achieve their goals. >>It's all about outcomes, right? Good Ron. So in terms of automation, what are some of the things Oracle's doing there to help your clients leverage automation to improve agility? So that they can innovate faster, which in these interesting times it's demanded. >>Yeah, thank you. Well, traditionally, Oracle is known for their databases, which have bean innovated year over year. So here's the first lunch on the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90% with a multi medal converts, database and machine learning based automation for full life cycle management. Our databases self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security, and it's self repairing the automates failure, detection fail over and repair. And then the question is for our customers, What does it mean? It means they can focus on their on their business instead off maintaining their infrastructure and their operations. >>That's absolutely critical use if I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that. But we know that whether we're talking about beta management or digital transformation, a one size fits all approach doesn't work to address the challenges that the business has, um that the i t folks have, as you're looking through the industry with what Santiago told us about first Bank of Nigeria. What are some of the changes that you're seeing that I owe Tahoe seeing throughout the industry? >>Uh, well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data where you have almost a data Policeman who tells you what you can and can't do, just doesn't work anymore. It's too slow. It's too resource intensive. Uh, data management data, governments, digital transformation itself. It has to be collaborative on. There has to be in a personalization to data users. Um, in the environment we find ourselves in. Now, it has to be about enabling self service as well. Um, a one size fits all model when it comes to those things around. Data doesn't work. As Santiago was saying, it needs to be adapted toe how the data is used. Andi, who is using it on in order to do this cos enterprises organizations really need to know their data. They need to understand what data they hold, where it is on what the sensitivity of it is they can then any more agile way apply appropriate controls on access so that people themselves are and groups within businesses are our job and could innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >>Yeah, that one size fits all term just doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Used to talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do >>before it's it's automatically being able to classify terabytes, terabytes of data or even petabytes of data across different sources to find duplicates, which you can then re mediate on. Deletes now, with the capabilities that iota offers on the Oracle offers, you can do things not just where the five times or 10 times improvement, but it actually enables you to do projects for Stop that otherwise would fail or you would just not be able to dio I mean, uh, classifying multi terrible and multi petabytes states across different sources, formats very large volumes of data in many scenarios. You just can't do that manually. I mean, we've worked with government departments on the issues there is expect are the result of fragmented data. There's a lot of different sources. There's lot of different formats and without these newer technologies to address it with automation on machine learning, the project isn't durable. But now it is on that that could lead to a revolution in some of these businesses organizations >>to enable that revolution that there's got to be the right cultural mindset. And one of the when Santiago was talking about folks really kind of adapted that. The thing I always call that getting comfortably uncomfortable. But that's hard for organizations to. The technology is here to enable that. But well, you're talking with customers use. How do you help them build the trust in the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive the kind of a tech in the culture? >>It's really good question is because it can be quite scary. I think the first thing we'd start with is to say, Look, the technology is here with businesses like I Tahoe. Unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting being agile around it, Andi trying new ways of doing things. Uh, if you don't wanna get less behind that Santiago on the team that fbn are a great example off embracing it, testing it on a small scale on, then scaling up a Toyota, we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with a customer. Picky use case, install the application, uh, analyzed data. Drive out Cem Cem quick winds. So we worked in the last few weeks of a large entity energy supplier, and in about 20 days, we were able to give them an accurate understanding of their critical data. Elements apply. Helping apply data protection policies. Minimize copies of the data on work out what data they needed to delete to reduce their infrastructure. Spend eso. It's about experimenting on that small scale, being agile on, then scaling up in a kind of very modern way. >>Great advice. Uh, Santiago, I'd like to go back to Is we kind of look at again that that topic of culture and the need to get that mindset there to facilitate these rapid changes, I want to understand kind of last question for you about how you're doing that from a digital transformation perspective. We know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges and changes that we're all going through? >>The new technologies allowed us to discover the dating anyway. Toe flawed and see very quickly Information toe. Have new models off over in the data on giving autonomy to our different data units. Now, from that autonomy, they can then compose an innovator own ways. So for me now, we're talking about resilience because in a way, autonomy and flexibility in a organization in a data structure with platform gives you resilience. The organizations and the business units that I have experienced in the pandemic are working well. Are those that actually because they're not physically present during more in the office, you need to give them their autonomy and let them actually engaged on their own side that do their own job and trust them in a way on as you give them, that they start innovating and they start having a really interesting ideas. So autonomy and flexibility. I think this is a key component off the new infrastructure. But even the new reality that on then it show us that, yes, we used to be very kind off structure, policies, procedures as very important. But now we learn flexibility and adaptability of the same side. Now, when you have that a key, other components of resiliency speed, because people want, you know, to access the data and access it fast and on the site fast, especially changes are changing so quickly nowadays that you need to be ableto do you know, interact. Reiterate with your information to answer your questions. Pretty, um, so technology that allows you toe be flexible iterating on in a very fast job way continue will allow you toe actually be resilient in that way, because you are flexible, you adapt your job and you continue answering questions as they come without having everything, setting a structure that is too hard. We also are a partner off Oracle and Oracle. Embodies is great. They have embedded within the transactional system many algorithms that are allowing us to calculate as the transactions happened. What happened there is that when our customers engaged with algorithms and again without your powers, well, the machine learning that is there for for speeding the automation of how you find your data allows you to create a new alliance with the machine. The machine is their toe, actually, in a way to your best friend to actually have more volume of data calculated faster. In a way, it's cover more variety. I mean, we couldn't hope without being connected to this algorithm on >>that engagement is absolutely critical. Santiago. Thank you for sharing that. I do wanna rap really quickly. Good On one last question for you, Santiago talked about Oracle. You've talked about a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle. What you guys were doing there to help your customers get the resilience that they have toe have to be not just survive but thrive. >>Yeah. Oracle has a cloud offering for infrastructure, database, platform service and a complete solutions offered a South on Daz. As Santiago also mentioned, We are using AI across our entire portfolio and by this will help our customers to focus on their business innovation and capitalize on data by enabling new business models. Um, and Oracle has a global conference with our cloud regions. It's massively investing and innovating and expanding their clouds. And by offering clouds as public cloud in our data centers and also as private cloud with clouded customer, we can meet every sovereignty and security requirements. And in this way we help people to see data in new ways. We discover insights and unlock endless possibilities. And and maybe 11 of my takeaways is if I If I speak with customers, I always tell them you better start collecting your data. Now we enable this partners like Iota help us as well. If you collect your data now, you are ready for tomorrow. You can never collect your data backwards, So that is my take away for today. >>You can't collect your data backwards. Excellently, John. Gentlemen, thank you for sharing all of your insights. Very informative conversation in a moment, we'll address the question. Do you know your data? >>Are you interested in test driving the iota Ho platform kick Start the benefits of data automation for your business through the Iota Ho Data Health check program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iota ho. Look time with a data engineer to learn more and see Io Tahoe in action from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>In this next segment, we're gonna be talking to you about getting to know your data. And specifically you're gonna hear from two folks at Io Tahoe. We've got enterprise account execs to be to Davis here, as well as Enterprise Data engineer Patrick Simon. They're gonna be sharing insights and tips and tricks for how you could get to know your data and quickly on. We also want to encourage you to engage with the media and Patrick, use the chat feature to the right, send comments, questions or feedback so you can participate. All right, Patrick Savita, take it away. Alright. >>Thankfully saw great to be here as Lisa mentioned guys, I'm the enterprise account executive here in Ohio. Tahoe you Pat? >>Yeah. Hey, everyone so great to be here. I said my name is Patrick Samit. I'm the enterprise data engineer here in Ohio Tahoe. And we're so excited to be here and talk about this topic as one thing we're really trying to perpetuate is that data is everyone's business. >>So, guys, what patent I got? I've actually had multiple discussions with clients from different organizations with different roles. So we spoke with both your technical and your non technical audience. So while they were interested in different aspects of our platform, we found that what they had in common was they wanted to make data easy to understand and usable. So that comes back. The pats point off to being everybody's business because no matter your role, we're all dependent on data. So what Pan I wanted to do today was wanted to walk you guys through some of those client questions, slash pain points that we're hearing from different industries and different rules and demo how our platform here, like Tahoe, is used for automating Dozier related tasks. So with that said are you ready for the first one, Pat? >>Yeah, Let's do it. >>Great. So I'm gonna put my technical hat on for this one. So I'm a data practitioner. I just started my job. ABC Bank. I have, like, over 100 different data sources. So I have data kept in Data Lakes, legacy data, sources, even the cloud. So my issue is I don't know what those data sources hold. I don't know what data sensitive, and I don't even understand how that data is connected. So how can I saw who help? >>Yeah, I think that's a very common experience many are facing and definitely something I've encountered in my past. Typically, the first step is to catalog the data and then start mapping the relationships between your various data stores. Now, more often than not, this has tackled through numerous meetings and a combination of excel and something similar to video which are too great tools in their own part. But they're very difficult to maintain. Just due to the rate that we are creating data in the modern world. It starts to beg for an idea that can scale with your business needs. And this is where a platform like Io Tahoe becomes so appealing, you can see here visualization of the data relationships created by the I. O. Tahoe service. Now, what is fantastic about this is it's not only laid out in a very human and digestible format in the same action of creating this view, the data catalog was constructed. >>Um so is the data catalog automatically populated? Correct. Okay, so So what I'm using Iota hope at what I'm getting is this complete, unified automated platform without the added cost? Of course. >>Exactly. And that's at the heart of Iota Ho. A great feature with that data catalog is that Iota Ho will also profile your data as it creates the catalog, assigning some meaning to those pesky column underscore ones and custom variable underscore tents. They're always such a joy to deal with. Now, by leveraging this interface, we can start to answer the first part of your question and understand where the core relationships within our data exists. Uh, personally, I'm a big fan of this view, as it really just helps the i b naturally John to these focal points that coincide with these key columns following that train of thought, Let's examine the customer I D column that seems to be at the center of a lot of these relationships. We can see that it's a fairly important column as it's maintaining the relationship between at least three other tables. >>Now you >>notice all the connectors are in this blue color. This means that their system defined relationships. But I hope Tahoe goes that extra mile and actually creates thes orange colored connectors as well. These air ones that are machine learning algorithms have predicted to be relationships on. You can leverage to try and make new and powerful relationships within your data. >>Eso So this is really cool, and I can see how this could be leverage quickly now. What if I added new data sources or your multiple data sources and need toe identify what data sensitive can iota who detect that? >>Yeah, definitely. Within the hotel platform. There, already over 300 pre defined policies such as hip for C, C, P. A and the like one can choose which of these policies to run against their data along for flexibility and efficiency and running the policies that affect organization. >>Okay, so so 300 is an exceptional number. I'll give you that. But what about internal policies that apply to my organization? Is there any ability for me to write custom policies? >>Yeah, that's no issue. And it's something that clients leverage fairly often to utilize this function when simply has to write a rejects that our team has helped many deploy. After that, the custom policy is stored for future use to profile sensitive data. One then selects the data sources they're interested in and select the policies that meet your particular needs. The interface will automatically take your data according to the policies of detects, after which you can review the discoveries confirming or rejecting the tagging. All of these insights are easily exported through the interface. Someone can work these into the action items within your project management systems, and I think this lends to the collaboration as a team can work through the discovery simultaneously, and as each item is confirmed or rejected, they can see it ni instantaneously. All this translates to a confidence that with iota hope, you can be sure you're in compliance. >>So I'm glad you mentioned compliance because that's extremely important to my organization. So what you're saying when I use the eye a Tahoe automated platform, we'd be 90% more compliant that before were other than if you were going to be using a human. >>Yeah, definitely the collaboration and documentation that the Iot Tahoe interface lends itself to really help you build that confidence that your compliance is sound. >>So we're planning a migration. Andi, I have a set of reports I need to migrate. But what I need to know is, uh well, what what data sources? Those report those reports are dependent on. And what's feeding those tables? >>Yeah, it's a fantastic questions to be toe identifying critical data elements, and the interdependencies within the various databases could be a time consuming but vital process and the migration initiative. Luckily, Iota Ho does have an answer, and again, it's presented in a very visual format. >>Eso So what I'm looking at here is my entire day landscape. >>Yes, exactly. >>Let's say I add another data source. I can still see that unified 3 60 view. >>Yeah, One future that is particularly helpful is the ability to add data sources after the data lineage. Discovery has finished alone for the flexibility and scope necessary for any data migration project. If you only need need to select a few databases or your entirety, this service will provide the answers. You're looking for things. Visual representation of the connectivity makes the identification of critical data elements a simple matter. The connections air driven by both system defined flows as well as those predicted by our algorithms, the confidence of which, uh, can actually be customized to make sure that they're meeting the needs of the initiative that you have in place. This also provides tabular output in case you needed for your own internal documentation or for your action items, which we can see right here. Uh, in this interface, you can actually also confirm or deny the pair rejection the pair directions, allowing to make sure that the data is as accurate as possible. Does that help with your data lineage needs? >>Definitely. So So, Pat, My next big question here is So now I know a little bit about my data. How do I know I can trust >>it? So >>what I'm interested in knowing, really is is it in a fit state for me to use it? Is it accurate? Does it conform to the right format? >>Yeah, that's a great question. And I think that is a pain point felt across the board, be it by data practitioners or data consumers alike. Another service that I owe Tahoe provides is the ability to write custom data quality rules and understand how well the data pertains to these rules. This dashboard gives a unified view of the strength of these rules, and your dad is overall quality. >>Okay, so Pat s o on on the accuracy scores there. So if my marketing team needs to run, a campaign can read dependent those accuracy scores to know what what tables have quality data to use for our marketing campaign. >>Yeah, this view would allow you to understand your overall accuracy as well as dive into the minutia to see which data elements are of the highest quality. So for that marketing campaign, if you need everything in a strong form, you'll be able to see very quickly with these high level numbers. But if you're only dependent on a few columns to get that information out the door, you can find that within this view, eso >>you >>no longer have to rely on reports about reports, but instead just come to this one platform to help drive conversations between stakeholders and data practitioners. >>So I get now the value of IATA who brings by automatically capturing all those technical metadata from sources. But how do we match that with the business glossary? >>Yeah, within the same data quality service that we just reviewed, one can actually add business rules detailing the definitions and the business domains that these fall into. What's more is that the data quality rules were just looking at can then be tied into these definitions. Allowing insight into the strength of these business rules is this service that empowers stakeholders across the business to be involved with the data life cycle and take ownership over the rules that fall within their domain. >>Okay, >>so those custom rules can I apply that across data sources? >>Yeah, you could bring in as many data sources as you need, so long as you could tie them to that unified definition. >>Okay, great. Thanks so much bad. And we just want to quickly say to everyone working in data, we understand your pain, so please feel free to reach out to us. we are Website the chapel. Oh, Arlington. And let's get a conversation started on how iota Who can help you guys automate all those manual task to help save you time and money. Thank you. Thank >>you. Your Honor, >>if I could ask you one quick question, how do you advise customers? You just walk in this great example this banking example that you instantly to talk through. How do you advise customers get started? >>Yeah, I think the number one thing that customers could do to get started with our platform is to just run the tag discovery and build up that data catalog. It lends itself very quickly to the other needs you might have, such as thes quality rules. A swell is identifying those kind of tricky columns that might exist in your data. Those custom variable underscore tens I mentioned before >>last questions to be to anything to add to what Pat just described as a starting place. >>I'm no, I think actually passed something that pretty well, I mean, just just by automating all those manual task. I mean, it definitely can save your company a lot of time and money, so we we encourage you just reach out to us. Let's get that conversation >>started. Excellent. So, Pete and Pat, thank you so much. We hope you have learned a lot from these folks about how to get to know your data. Make sure that it's quality, something you can maximize the value of it. Thanks >>for watching. Thanks again, Lisa, for that very insightful and useful deep dive into the world of adaptive data governance with Iota Ho Oracle First Bank of Nigeria This is Dave a lot You won't wanna mess Iota, whose fifth episode in the data automation Siri's in that we'll talk to experts from Red Hat and Happiest Minds about their best practices for managing data across hybrid cloud Inter Cloud multi Cloud I T environment So market calendar for Wednesday, January 27th That's Episode five. You're watching the Cube Global Leader digital event technique

(upbeat music) >> Hey, everyone. Welcome to theCUBE's coverage of the International Women's Showcase for 2022. I'm your host, Lisa Martin. I'm pleased to welcome Nancy Wong, the general manager of Data Protection and Governance at AWS to the program. Nancy, it's great to have you. >> Thanks so much for having me Lisa, and you know, I really hope that this is hopefully the last year that we'll be celebrating International Women's Day all virtually. >> I agree. I agree. Well, we're going in that right direction globally. So let's cross our fingers. Talk to me a little bit about your role at AWS and what you do there. >> Sure. So as a GM of AWS Data Protection and Governance, a lot of, we tackle quite a few problems that our biggest customers face, right? When they think about, "How do I manage my data?" Right. Especially in this digital world. And speaking of the pandemic, how much data has been generated by consumers, by devices, by systems, by servers? How do you protect all of that data? Right. Especially we hear about cyber crime, cyber attacks. Right. Data breaches. It's really important to make sure that all of our customers have a coherent strategy around not just management, right, but also protection and really how you govern your data. Right. And there's just so many awesome conversations that my team and I have had lately with CSOs or chief technology officers on this topic, as it evolves. >> Data protection is so critical. It's one of my favorite topics to talk about, cybersecurity as well. Talk to me about what it means though if we keep this at a bit of a different level to be an operator within the the big ecosystem that is AWS. >> Yeah. And that's actually one of the the favorite aspects of my role. Right. Which is, you know, I get to innovate every day on behalf of my customers. For example, I love having one-on-one dialogues. I love having architecture conversations where we brainstorm. Right. And so those type of conversations help inform how we deliver and develop products. And so in an operator role, right, for the the women in the audience today, is it really gives you that perspective into not just how, what type of products do you want to build that delight your customers but also from an engineering. Right. And a bottom line perspective of, well how do you make this happen? Right. How do you fund this? And how do you plan out your development milestones? >> What are, tell me a little bit about your background and then what makes women in technology such an important initiative for you to stand behind? >> Absolutely. So I'm so proud today to see that the number of women or the percentage of women enrolled at engineering curriculums just continue to rise. Right. And especially as someone who went through an engineering degree in her undergraduate studies, that was not always the case. Right. So oftentimes, you know, I would look around the classroom and be the only woman on the lab bench or only woman in a CS classroom. And so when you have roles in tech, specifically, that require an undergraduate degree in computer science or a degree in engineering, that helps to, or that only serves to really reduce the population of eligible candidates. Right. Who then, if you look at that pool of eligible candidates who then you can invest and accelerate through the career ladder to become leaders in tech, well that's where you may end up with a representation issue. Right. And that's why we have, for example, so few women leaders in tech that we can look up to as role models. And that's really the problem or the gap that I'm very passionate about solving. And also, Lisa, I'm really excited to tell you a little bit more about advancing women in tech, which is a 501c3 nonprofit organization that I started to tackle this exact problem. >> Talk to me about that, cause it's one of the things that you bring up is, you know, we always say when we're having conversations like this, we can't be what we can't see. We need to be able to see those female leaders. To your point, there aren't a ton in comparison to the male leaders. So talk to me about advancing women in technology, why you founded this, and what you guys are accomplishing. >> Absolutely. So it's been such a personal journey as well. Just starting this organization called Advancing Women In Tech because I started it in 2017. Right. So when I really was, you know, just starting out as a product manager, I was at another big tech company at the time. And what I really realized, right, is looking around you know, I had so many, for example, bosses, managers, peer leaders, who were really invested in growing me as a product manager and growing my tech and career. And this is right after I'd made the transition from the federal government into big tech. What that said though, looking around, there weren't that many women tech leaders that I could look up to, or get coffee, or just have a mentoring conversation. And quickly I realized, well, it's not so much that women can't do it. Right. It's the fact that we're not advancing enough women into leadership roles. And so really we have to look at why that is. Right. And we, you know, from a personal perspective, one contribution towards that angle is upskilling. Right. So if you think about what skills one needs as one climbs a career ladder, whether that's your first people management role, or your first manager manager's role, or obviously for bigger leaders when they start managing thousands, tens of thousands of individuals, well all of that requires different skills. And so learning those skills about how to manage people, how to motivate your teams effectively, super, super important. And of course on the other side, and one that I'm, you know, near to dear to me is that of mentorship and executive sponsorship because you can have all the skills in the world, right. And especially with digital learning and AWIT is very involved with Coursera and AWS in producing and making those resources readily available and accessible. Well, if you don't have those opportunities, if you don't have mentors and sponsors who are well to push you or give you a step ladder to those roles, well you're still not going to get there. Right. And so, that's why actually, if you look at the AWIT mission, it's really those two pillars working very closely together to help advance women into leadership roles. >> The idea of mentorship and sponsorship is so critical. And I think a lot of people don't understand the difference between a mentor and a sponsor. How do you define that difference and how do you bring them into the organization so that they can be mentors and sponsors? >> Yeah, absolutely. And there's, you know, these two terms are often used today so interchangeably that I do get a lot of questions around, well, what is the difference? Right. And how does, let's say a mentor become a sponsor? So, maybe just taking a few steps back, right. When you have let's say questions around compensation or, "Hey I have some job offers, which ones do I consider?" And you ask someone a question or advice, well that person's likely your mentor. Right? And typically a mentor is someone who you can ask those questions on a repeated basis. Who's very accessible to you. Well, a sponsor takes that a few steps forward in the sense that they are sponsoring you into a role or into a project or initiative that you on your own may not be able to achieve. And by doing so, I think what really differentiates a sponsor from a mentor is that the sponsor will actually put their own reputation on the line. Right. They're using their own political capital in order to make sure that you get into that role, you get into that room. Right. And that's why it's so key, for example, especially if you have that relationship already with a person who's your mentor, you're able to ask questions or advice from, to convert them into a sponsor so that you can accelerate your career. >> Great definition, description, and great recommendations for converting mentors to sponsors. You know, I only learned the difference about a mentor and a sponsor a few years ago at another women in tech event that I was hosting. And I thought, "It's brilliant. It makes perfect sense." We need more people to understand the difference, the synergies, and how to promote mentors to sponsors. Talk to me now about advancing women in tech plus the power of AWS. How are they helping this nonprofit to really accelerate? >> Sure. So from an organization perspective, right, there's many women, for example, across the the tech companies who are part of Advancing Women In Tech, obviously Amazon of course as an employee has a very large community within who's part of AWIT. But we also have members across the tech industry from startups to VC firms to of course, Google, Microsoft, and Netflix. You name it. With that said, you know, what AWS has done with AWIT is actually very special in the sense that if you go to the Coursera platform, coursera.org/awit you can see our two Coursera specializations. Four courses each that go through the real world product management fundamentals. Or the business side, the technical skills, and even interviewing for mid-career product management roles. And the second specialization, which I'm super excited to share today, is actually geared towards getting folks ramped up and prepared to successfully pass the Cloud Practitioner's Exam, which is one of the industry recognized standards about understanding the AWS Cloud and being functional in the AWS Cloud. This summer, of course, and I'm sharing kind of a sneak peek announcement that I'll be making tomorrow with the University of Pennsylvania, is that we're kicking off a program for the masters of CIS program, or the Computer Information Systems Master students, to actually go through this Coursera specialization, which is produced by AWIT, sponsored by AWS, and AWS Training and Certifications has so generously donated exam vouchers for these students so that they can then go on and be certified in the AWS Cloud. So that's one just really cool collaboration that we are doing between AWS and AWIT to get more qualified folks in the door in tech jobs, and hopefully at jobs in AWS. >> That's a great collaboration. What are some of the goals in terms of metrics, the number of women that you want to get into the program and complete the program? What are some of those on your radar? >> Absolutely. So one of the reasons, of course, that the Master's of CIS Program, the University of Pennsylvania caught my eye, not withstanding, I graduated from there, but also that just the statistics of women enrolled. Right. So what's really notable about this program is it's entirely online, which as a university creating a Master's degree fully online, well, it takes a ton of resources from the university, from the faculty. And what's really special about these students is that they're already full-time adult professionals, which means that they're working a full-time job, they might be taking care of family obligations, and they're still finding time to advance themselves, to acquire a Master's degree in CS. And best of all, 42% of these students are women. Right. And so that's a number that is multiples of what we're finding in engineering curriculums today. And so my theory is, well if you go to a student population that is over 40%, 42 to be exact percent women, and enable these women to be certified in AWS Cloud, to have direct interview prep and mentorship from AWS software development leaders, well, that greatly increases their chances of getting a full-time role, right, at AWS. Right. At which then we can help them advance their careers to further and further roles in software development. >> So is this curriculum also open to women who aren't currently in tech to be able to open the door for them to get into tech and STEM fields? >> Absolutely. And so in my bad and remiss in mentioning, which is students of this Master's in CS Program are actually students not from tech already. So they're not in a tech field. And they did not have a degree in CS or even engineering as part of their undergraduate studies. So it's truly folks who are outside of tech, that are 42% women, that we're getting into the tech industry with this collaboration between AWS, AWIT, and the University of Pennsylvania. >> That's outstanding to get them in from completely different fields into tech. >> Absolutely. >> How do you help women have the confidence to say, "I want to try this." Cause if we think about every company today is a tech company. It's a data company. It has to be to be competitive. You know, the pandemic taught us that everything we're able to do online and digitally, for example, but how do you help women get the confidence to say, "Okay, I'm going to go from a completely different field into tech." >> Absolutely. So if we, you know, define tech of course as big tech or, you know, now the main companies, right, I myself made that transition, which is why it is a topic near and dear to me because I can personally speak to my journey because I didn't start my career out in tech. Right. Yes. I studied engineering. But with that said, my first full-time job out of college was with the federal government because I wanted to go and build healthdata.gov, right, which gave folks a lot of access to the healthcare data, roles, right, that existed within the U.S. government and the CMS, NIH, you know, CDC, so on and so forth. But that was quite a big change from then taking a product management job at Google. Right. And so how did I make that change? Well, a lot of it came from, you know, the mentors that I had. Right. What I call my personal board of directors who gave me that confidence. And sure, I mean even today, I still have imposter syndrome where, you know, I think, "Am I good enough." Right. "Should I be leading this organization," right, "of data protection and governance." But I think what it boils down to is, you know, inner confidence. Right. And goes back to those two pillars of having the right skills and also the right mentors and sponsors who are willing to help sponsor you into those opportunities and help sponsor you to success. >> Absolutely. Great advice and recommendations. Thanks for sharing your background, Nancy, it's outstanding to see where you started to where you are now and also to what you're enabling for so many other females to get into tech with the AWIT program combined with AWS and UPenn. Exciting stuff. Can't wait to talk to you next year to see where you guys go from here. >> Absolutely Lisa. And what I'm really looking forward to sharing with you next year is the personal testimonials of other women who have gone through the AWIT, the AWS, the UPenn Program and have gotten their tech jobs and also promotions. >> That sounds like a great thing to look forward to. I'm looking forward to that. Nancy, thanks so much for your time and the insight that you shared. >> Thanks so much for having me, Lisa. >> My pleasure. For Nancy Wong, I'm Lisa Martin. You're watching theCUBE's coverage of the International Women's Showcase 2022. (upbeat music)

(Upbeat intro music) >> Thanks guys. Hi everybody. Welcome back to The Spheres. My name is Dave Vellante and you're watching theCubes continuous coverage of AWS storage day. I'm really excited to bring on Wayne Duso. Wayne is the vice-president of AWS Storage Edge and Data Governance Services. Wayne, two Boston boys got to come to Seattle to see each other. You know. Good to see you, man. >> Good to see you too. >> I mean, I'm not really from Boston. The guys from East Boston give me crap for saying that. [Wayne laughs] That my city, right? You're a city too. >> It's my city as well I'm from Charlestown so right across the ocean. >> Charlestown is actually legit Boston, you know I grew up in a town outside, but that's my city. So all the sports fan. So, hey great keynote today. We're going to unpack the keynote and, and really try to dig into it a little bit. You know, last 18 months has been a pretty bizarre, you know, who could have predicted this. We were just talking to my line about, you know, some of the permanent changes and, and even now it's like day to day, you're trying to figure out, okay, you know, what's next, you know, our business, your business. But, but clearly this has been an interesting time to say the least and the tailwind for the Cloud, but let's face it. How are customers responding? How are they changing their strategies as a result? >> Yeah. Well, first off, let me say it's good to see you. It's been years since we've been in chairs across from one another. >> Yeah. A couple of years ago in Boston, >> A couple of years ago in Boston. I'm glad to see you're doing well. >> Yeah. Thanks. You too. >> You look great. (Wayne Laughs) >> We get the Sox going. >> We'll be all set. >> Mm Dave you know, the last 18 months have been challenging. There's been a lot of change, but it's also been inspiring. What we've seen is our customers engaging the agility of the Cloud and appreciating the cost benefits of the Cloud. You know, during this time we've had to be there for our partners, our clients, our customers, and our people, whether it's work from home, whether it's expanding your capability, because it's surging say a company like zoom, where they're surging and they need more capability. Our cloud capabilities have allowed them to function, grow and thrive. In these challenging times. It's really a privilege that we have the services and we have the capability to enable people to behave and, execute and operate as normally as you possibly can in something that's never happened before in our lifetimes. It's unprecedented. It's a privilege. >> Yeah. I mean, I agree. You think about it. There's a lot of negative narrative, in the press about, about big tech and, and, and, you know, the reality is, is big tech has, has stood and small tech has stepped up big time and we were really think about it, Wayne, where would we be without, without tech? And I know it sounds bizarre, but we're kind of lucky. This pandemic actually occurred when it did, because had it occurred, you know, 10 years ago it would have been a lot tougher. I mean, who knows the state of vaccines, but certainly from a tech standpoint, the Cloud has been a savior. You've mentioned Zoom. I mean, you know, we, productivity continues. So that's been, been pretty key. I want to ask you, in you keynote, you talked about two paths to, to move to the Cloud, you know, Vector one was go and kind of lift and shift if I got it right. And then vector two was modernized first and then go, first of all, did I get that right? And >> Super close and >> So help me course correct. And what are those, what are those two paths mean for customers? How should we think about that? >> Yeah. So we want to make sure that customers can appreciate the value of the Cloud as quickly as they need to. And so there's, there's two paths and with not launches and, we'll talk about them in a minute, like our FSX for NetApp ONTAP, it allows customers to quickly move from like to like, so they can move from on-prem and what they're using in terms of the storage services, the processes they use to administer the data and manage the data straight onto AWS, without any conversion, without any change to their application. So I don't change to anything. So storage administrators can be really confident that they can move. Application Administrators know it will work as well, if not better with the Cloud. So moving onto AWS quickly to value that's one path. Now, once they move on to AWS, some customers will choose to modernize. So they will, they will modernize by containerizing their applications, or they will modernize by moving to server-less using Lambda, right? So that gives them the opportunity at the pace they want as quickly or as cautiously as they need to modernize their application, because they're already executing, they're already operating already getting value. Now within that context, then they can continue that modernization process by integrating with even more capabilities, whether it's ML capabilities or IOT capabilities, depending on their needs. So it's really about speed agility, the ability to innovate, and then the ability to get that flywheel going with cost optimization, feed those savings back into betterment for their customers. >> So how did the launches that you guys have made today and even, even previously, do they map into those two paths? >> Yeah, they do very well. >> How so? Help us understand that. >> So if we look, let's just run down through some of the launches today, >> Great. >> And we can, we can map those two, those two paths. So like we talked about FSX for NetApp ONTAP, or we just like to say FSX for ONTAP because it's so much easier to say. [Dave laughs] >> So FSX for ONTAP is a clear case of move. >> Right >> EBS io2 Block Express for Sand, a clear case of move. It allows customers to quickly move their sand workloads to AWS, with the launch of EBS direct API, supporting 64 terabyte volumes. Now you can snapshot your 64 terabyte volumes on-prem to already be in AWS, and you can restore them to an EBS io2 Block Express volume, allowing you to quickly move an ERP application or an Oracle application. Some enterprise application that requires the speed, the durability and the capability of VBS super quickly. So that's, those are good examples of, of that. In terms of the modernization path, our launch of AWS transfer managed workflows is a good example of that. Manage workflows have been around forever. >> Dave: Yeah. >> And, and customers rely on those workflows to run their business, but they really want to be able to take advantage of cloud capabilities. They want to be able to, for instance, apply ML to those workflows because it really kind of makes sense that their workloads are people related. You can apply artificial intelligence to them, >> Right >> This is an example of a service that allows them to modify those workflows, to modernize them and to build additional value into them. >> Well. I like that example. I got a couple of followup questions, if I may. Sticking on the machine learning and machine intelligence for a minute. That to me is a big one because when I was talking to my line about this is this, it's not just you sticking storage in a bucket anymore, right? You're invoking other services: machine intelligence, machine learning, might be database services, whatever it is, you know, streaming services. And it's a service, you know, there it is. It's not a real complicated integration. So that to me is big. I want to ask you about the block side of things >> Wayne: Sure >> You built in your day, a lot of boxes. >> Wayne: I've built a lot of boxes. >> And you know, the Sand space really well. >> Yeah. >> And you know, a lot of people probably more than I do storage admins that say you're not touching my Sand, right? And they just build a brick wall around it. Okay. And now eventually it ages out. And I think, you know, that whole cumbersome model it's understood, but nonetheless, their workloads and our apps are running on that. How do you see that movement from those and they're the toughest ones to move. The Oracle, the SAP they're really, you know, mission critical Microsoft apps, the database apps, hardcore stuff. How do you see that moving into the Cloud? Give us a sense as to what customers are telling you. >> Storage administrators have a hard job >> Dave: Yeah >> And trying to navigate how they move from on-prem to in Cloud is challenging. So we listened to the storage administrators, even when they tell us, No. we want to understand why no. And when you look at EBS io2 Block Express, this is in part our initial response to moving their saying into the Cloud super easily. Right? Because what do they need? They need performance. They need their ability. They need availability. They need the services to be able to snap and to be able to replicate their Capa- their storage. They need to know that they can move their applications without having to redo all they know to re-plan all they work on each and every day. They want to be able to move quickly and confidently. EBS io2 Block Express is the beginning of that. They can move confidently to sand in the Cloud using EBS. >> Well, so why do they say 'no'? Is it just like the inherent fear? Like a lawyer would say, don't do that, you know, don't or is it just, is it, is it a technical issue? Is it a cultural issue? And what are you seeing there? >> It's a cultural issue. It's a mindset issue, but it's a responsibility. I mean, these folks are responsible for the, one of the most important assets that you have. Most important asset for any company is people. Second most important asset is data. These folks are responsible for a very important asset. And if they don't get it right, if they don't get security, right. They don't get performance right. They don't get durability right. They don't get availability right. It's on them. So it's on us to make sure they're okay. >> Do you see it similar to the security discussion? Because early on, I was just talking to Sandy Carter about this and we were saying, you remember the CIA deal? Right? So I remember talking to the financial services people said, we'll never put any data in the Cloud. Okay they got to be one of your biggest industries, if not your biggest, you know customer base today. But there was fear and, and the CIA deal changed that. They're like, wow CIA is going to the Cloud They're really security conscious. And that was an example of maybe public sector informing commercial. Do you see it as similar? I mean there's obviously differences, but is it a sort of similar dynamic? >> I do. I do. You know, all of these ilities right. Whether it's, you know, durability, availability, security, we'll put ility at the end of that somehow. All of these are not jargon words. They mean something to each persona, to each customer. So we have to make sure that we address each of them. So like security. And we've been addressing the security concern since the beginning of AWS, because security is job number one. And operational excellence job number two. So, a lot of things we're talking about here is operational excellence, durability, availability, likeness are all operational concerns. And we have to make sure we deliver against those for our customers. >> I get it. I mean, the storage admins job is thankless, but the same time, you know, if your main expertise is managing LUNs, your growth path is limited. So they, they want to transform. They want to modernize their own careers. >> I love that. >> It's true. Right? I mean it's- >> Yeah. Yeah. So, you know, if you're a storage administrator today, understanding the storage portfolio that AWS delivers will allow you, and it will enable you empower you to be a cloud storage administrator. So you have no worry because you're, let's take FSX for ONTAP. You will take the skills that you've developed and honed over years and directly apply them to the workloads that you will bring to the Cloud. Using the same CLIs, The same APIs, the same consoles, the same capabilities. >> Plus you mentioned you guys announced, you talked about AWS backup services today, announced some stuff there. I see security governance, backup, identity access management, and governance. These are all adjacency. So if you're a, if you're a cloud storage administrator, you now are going to expand your scope of operations. You, you know, you're not going to be a security, Wiz overnight by any means, but you're now part of that, that rubric. And you're going to participate in that opportunity and learn some things and advance your career. I want to ask you, before we run out of time, you talked about agility and cost optimization, and it's kind of the yin and the yang of Cloud, if you will. But how are these seemingly conflicting forces in sync in your view. >> Like many things in life, right? [Wayne Laughs] >> We're going to get a little spiritually. >> We might get a little philosophical here. [Dave Laughs] >> You know, cloud announced, we've talked about two paths and in part of the two paths is enabling you to move quickly and be agile in how you move to the Cloud. Once you are on the Cloud, we have the ability through all of the service integrations that we have. In your ability to see exactly what's happening at every moment, to then cost optimize, to modernize, to cost optimize, to improve on the applications and workloads and data sets that you've brought. So this becomes a flywheel cost optimization allows you to reinvest, reinvest, be more agile, more innovative, which again, returns a value to your business and value to your customers. It's a flywheel effect. >> Yeah. It's kind of that gain sharing. Right? >> It is. >> And, you know, it's harder to do that in a, in an on-prem world, which everything is kind of, okay, it's working. Now boom, make it static. Oh, I want to bring in this capability or this, you know, AI. And then there's an integration challenge >> That's true. >> Going on. Not, not that there's, you know, there's differences in, APIs. But that's, to me is the opportunity to build on top of it. I just, again, talking to my line, I remember Andy Jassy saying, Hey, we purposefully have created our services at a really atomic level so that we can get down to the primitives and change as the market changes. To me, that's an opportunity for builders to create abstraction layers on top of that, you know, you've kind of, Amazon has kind of resisted that over the years, but, but almost on purpose. There's some of that now going on specialization and maybe certain industry solutions, but in general, your philosophy is to maintain that agility at the really granular level. >> It is, you know, we go back a long way. And as you said, I've built a lot of boxes and I'm proud of a lot of the boxes I've built, but a box is still a box, right? You have constraints. And when you innovate and build on the Cloud, when you move to the Cloud, you do not have those constraints, right? You have the agility, you can stand up a file system in three seconds, you can grow it and shrink it whenever you want. And you can delete it, get rid of it whenever you want back it up and then delete it. You don't have to worry about your infrastructure. You don't have to worry about is it going to be there in three months? It will be there in three seconds. So the agility of each of these services, the unique elements of all of these services allow you to capitalize on their value, use what you need and stop using it when you don't, and you don't have the same capabilities when you use more traditional products. >> So when you're designing a box, how is your mindset different than when you're designing a service? >> Well. You have physical constraints. You have to worry about the physical resources on that device for the life of that device, which is years. Think about what changes in three or five years. Think about the last two years alone and what's changed. Can you imagine having been constrained by only having boxes available to you during this last two years versus having the Cloud and being able to expand or contract based on your business needs, that would be really tough, right? And it has been tough. And that's why we've seen customers for every industry accelerate their use of the Cloud during these last two years. >> So I get that. So what's your mindset when you're building storage services and data services. >> So. Each of the surfaces that we have in object block file, movement services, data services, each of them provides very specific customer value and each are deeply integrated with the rest of AWS, so that when you need object services, you start using them. The integrations come along with you. When, if you're using traditional block, we talked about EBS io2 Block Express. When you're using file, just the example alone today with ONTAP, you know, you get to use what you need when you need it, and the way that you're used to using it without any concerns. >> (Dave mumbles) So your mindset is how do I exploit all these other services? You're like the chef and these are ingredients that you can tap and give a path to your customers to explore it over time. >> Yeah. Traditionally, for instance, if you were to have a filer, you would run multiple applications on that filer you're worried about. Cause you should, as a storage administrator, will each of those applications have the right amount of resources to run at peak. When you're on the Cloud, each of those applications will just spin up in seconds, their own file system. And those file systems can grow and shrink at whatever, however they need to do so. And you don't have to worry about one application interfering with the other application. It's not your concern anymore. And it's not really that fun to do. Anyway. It's kind of the hard work that nobody really you know, really wants to reward you for. So you can take your time and apply it to more business generate, you know, value for your business. >> That's great. Thank you for that. Okay. I'll I'll give you the last word. Give us the bumper sticker on AWS Storage day. Exciting day. The third AWS storage day. You guys keep getting bigger, raising the bar. >> And we're happy to keep doing it with you. >> Awesome. >> So thank you for flying out from Boston to see me. >> Pleasure, >> As they say. >> So, you know, this is a great opportunity for us to talk to customers, to thank them. It's a privilege to build what we build for customers. You know, our customers are leaders in their organizations and their businesses for their customers. And what we want to do is help them continue to be leaders and help them to continue to build and deliver we're here for them. >> Wayne. It's great to see you again. Thanks so much. >> Thanks. >> Maybe see you back at home. >> All right. Go Sox. All right. Yeah, go Sox. [Wayne Laughs] All right. Thank you for watching everybody. Back to Jenna Canal and Darko in the studio. Its Dave Volante. You're watching theCube. [Outro Music]

(lively music) >> Thank you, Frank, for kicking us off, setting the stage, and providing the vision for the Snowflake Data Cloud. Hi, everyone, I hope you're all doing well and staying safe. Thank you for joining me at the Snowflake Summit today to dive into the role of the Data Cloud in mobilizing data at Disney Streaming. Together, we're going to discuss data governance and how to leverage some of the unique benefits of Snowflake's data platform to unlock business value for better customer experiences. I am Anita Lynch, Vice President of Data Governance at Disney Streaming, home of Disney+. I fell in love with technology at an early age. My family is originally from Chicago and we came to the Bay Area when my dad's sales career led him to Silicon Valley. Because of the exciting advancements he saw in the devices he sold and the engineers he worked with, I am so fortunate that my father created the early opportunities for me to learn about technology, like starting to code when I was 10. Decades later, over the course of my career spanning tech startups, business school, strategy consulting, and leading data at global enterprises, I have learned it is not enough to create a technology solution. It takes a real understanding of what problems your customers are trying to solve, and what resources or capabilities they can mobilize to do it. Today, this is the focus of my career in data. At Disney Streaming, we pride ourselves on delighting our customers. We commit each day to bringing beloved characters, timeless stories, and epic sporting events to a global audience. I am one member of a global data team at Disney Streaming, continuing to work through these challenging times for our world. We are deeply appreciative to be able to continue doing our part to deliver the entertainment people love on Disney+, including my new, personal favorite series, "The Mandalorian." It is important to all of us that we maintain our viewers' highest level of trust. As our data volume grows continuously on a daily basis, we need to ensure data is compliant, secure, and well-governed. Therefore, how we execute is critical. Our work ensures our business is guiding decisions with high-quality data. Doing this empowers us to challenge convention and innovate, which brings us to the role of the organization I lead at Disney Streaming. I lead data governance, which includes instrumentation, compliance, integrations, and data architecture. Collectively, we are responsible for the value, protection, and mobilization of data for Disney+. With data volumes in the thousands of petabytes after just one year and global teams depending on us to be able to perform their analysis, data science modeling, and machine learning, it is critical to maintain compliance protocols and governance standards. However, our approach to locking down the data and limiting access without becoming a blocker to critical information needs is key. Poorly informed business decisions could ultimately lead to suboptimal customer experiences. Recognizing this, I've established eight operating principles to maintain a balance between technology, people, and process. Data lifecycle, stewardship, and data quality together define the mechanisms by which we maintain, measure, and improve the value of data as an asset. Regulatory compliance and data access establish key partnerships with our legal and information security to help us ensure data complies with internal and external legal guidelines in each region. Auditability, traceability, and risk management ensure we monitor, educate, influence, and enforce best practices. And lastly, data sharing, which serves to socialize valuable datasets and shared definitions in a secure, easy way that allows us to keep pace with the fast-moving and rapidly changing nature of our world today. Principles serve only as guardrails. In real practice, we measure the value data governance delivers based on these six, quantifiable goals for the teams we serve. Underpinning all of them is the Snowflake Data Cloud. It is our platform to store, secure, integrate, and mobilize data across the organization. It enables us to make compliant data accessible for teams to collaborate without copying, moving, or reprocessing. Going beyond the notion of a single source of truth, Snowflake's Data Cloud allows us to truly have a single copy of the data, plus the ability to scale to support a near-unlimited number of concurrent users without contention for resources, and the flexibility to prioritize or deprioritize compute workloads where concurrency matters less than our ability to manage cost. What does this mean to me? Put simply, it means the ability to support business intelligence, analytics, data science, and machine learning use cases on-demand, exceeding expectations for speed and performance where they matter without sacrificing anything on governance. And that is how we deliver value through data governance for Disney+. Data sharing is at the heart of how we make this work. We'll look at three important use cases, data clean rooms that enable restricted data sharing, data discovery that ensures data is easily found and understood, and partner data management for collaboration outside of our team. Data sharing creates the opportunity to access the power of the integrated dataset in an environment that ensures both quality and compliance. Let's start with data clean rooms and the example of restricted data sharing. Better understanding the interests and preferences of our audience through analysis is how we improve experiences for our customers, such as in-app personalization or making a recommendation on what to watch. The challenge is to mobilize the right data as it is needed while blocking distribution of any data that is not required, preventing the disclosure of sensitive information and prohibiting the merging of data that should not be combined. Simultaneously, while we seek to deliver compliance, we also want to avoid the typical process delays and enormous manual repetitive work that often comes with it. Data clean rooms enable the secure sharing of data, again, without creating copies, the combining of datasets without PII or sensitive information, and the restricting of queries by use of parameterized inputs and filtered query outputs, so only permissible data can be extracted. Outlining in advance how data will be used properly ensures consistency and execution of our compliance workflows and improves transparency on constraints, so teams don't waste their valuable time. This accelerates our ability to act on data insights. Decisions can be made for the benefit of our customers. For example, for me on Disney+, I would see right away the season two trailer for "The Mandalorian," including exciting scenes with Baby Yoda, more formerly known to some of you as the Child. Sometimes unintended data silos arise due to architectural complexities. In a traditional model for data infrastructure, complexity can evolve over time as various teams need to access, integrate, and transform data from different data sources in ways that uniquely serve their specific stakeholders. This proliferation in the analytical supply chain could result in multiple instances of copying, loading, and transforming the same data and introduce significant risks to data quality throughout the system, such as a lack of traceability. For example, changing one data pipeline may create unforeseen consequences in the calculations that occur in downstream tables and reports with no clear resolution. In the spirit of challenging convention to innovate, we knew we had to do better. With the Snowflake Data Cloud, our teams are able to discover the data sources they need through a centrally organized platform for data management and data sharing. Each user knows the data visible to them is available to them. They know they can trust it, and they know how it can properly be used to drive broader customer insights. And if a team wants to share their insights for further collaboration, they can easily publish those datasets to the Data Cloud, where they benefit from the protection of our managed platform, making sure all governance protocols are in place, including who can access for what purpose and at what level of granularity. This facilitates data sharing without the administration worry that comes with sharing files. And since there is one single copy, future updates happen at once for all consumers of the data, keeping it fresh for everyone without sacrificing business continuity. Finally, data sharing improves the performance of our partner relationships with the same degree of simplicity. In this model, our partner teams can also participate in the Data Cloud by invitation to access data specifically shared to them. Or conversely, a partner can request to share their data, and upon authorization for quality and compliance, we can safely publish that data, making it simultaneously available to all the right teams who need it. As a thought exercise, one way for us to envision making it easier to work with partners is in the way we collect and analyze data from media serving and content distribution networks. Today, customer stream Disney+ on more than 13 different types of devices. Their streaming is made possible through a collection of services that vary by geography and consumer choice. Better understanding the experience for an individual client may require integration of data collected across the unique combination of services available to that customer. To better serve our content and delight our customers, data-driven analysis to detect anomalies and service impacts might benefit from a data management platform for partner data that requires a high level of data governance similar to what we do today through our Snowflake Data Cloud. Now in closing, data is at the core of our mission at Disney Streaming to delight our customers. And when it comes to data governance, we strive to always hold ourselves to the highest standard. With the Data Cloud, we power our business with a single source of truth. As we grow, it enables data sharing with data governance at massive scale and performance. I will also leave you with this often quoted African proverb I like. "If you want to go fast, go alone. But if you want to go far, go together." We share an important cultural value. Commitment to innovation accelerated our ability to address unique use cases and the successful growth of Disney+. It was both the technology and the commitment to meet our data governance needs that has resulted in more than just another cloud data platform. We have a solution that works for us. Thank you for joining me on this journey, and thank you to Snowflake for the ongoing partnership. With the product keynote coming up next, I'm excited to see how future innovation will continue to enable us to challenge convention going forward.

>> Hello everybody and welcome back to the cubes coverage of the Snowflake Data Cloud Summit 2020. We're tracking the rise of the Data Cloud, and fresh off the keynotes here, Frank Slootman, the chairman and CEO of Snowflake and Anita Lynch, the vice president of Data Governance at Disney Streaming Services. Folks, welcome. >> Thank you Thanks for having us, Dave, >> I need a Disney plus awesome. You know, we signed up early, watched all the Marvel movies, Hamilton, the new Pixar Movie saw, I haven't gotten into the Mandalorian yet, your favorite, but, (woman laughing) really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud, because I never liked the term Enterprise Data Warehouse, what you're doing is so different from the sort of that legacy world that I've known all these years, but start with why the Data Cloud, what problems are you trying to solve? And maybe some of the harder challenges you're seeing. >> Yeah I know, you know we've come a long way in terms of workload execution, right? In terms of scale and performance, you know, concurrent execution, we really taken the lid off sort of the physical constraints that have existed on these types of operations. But there's one problem, that we're not yet solving. And that is the siloing and bunkering of data. And especially in a data is locked in application it is locked data centers, is locked in cloud regions, incredibly hard for data science teams to really, you know, unlock the true value of data. When you can address patterns that exist across a data set. So we're perpetuate, a status we've had forever since the beginning of computing. If we don't start to crack that problem now we have that opportunity. But the notion of a Data Cloud is like basically saying, look folks, you know, we have to start unsiloing and unlocking the data, and bring it into a place, you know, where we can access it, you know, across all these parameters and boundaries that have historically existed. It's very much a step level function. Now the customers have always looked at things one workload at a time, that mentality really has to go. You really have to have a Data Cloud mentality, as well as a workload orientation towards managing data. >> Anita was great hearing your role at Disney, and your keynote, and the work you're doing, the governance work, and you're serving a great number of stakeholders, enabling things like data sharing, you got really laser focused on trust, compliance, privacy. Is this idea of a data clean room is really interesting. You maybe you can expand on some of these initiatives here and share what you're seeing as some of the biggest challenges to success. And of course, the opportunities that you're unlocking. >> Sure. I mean, in my role, leading Data Governance, it's really critical to make sure that all of our stakeholders, not only know what data is available and accessible to them, they can also understand really easily and quickly, whether or not the data that they're using is for the appropriate use case. And so, that's a big part of how we scale data governance, and a lot of the work that we would normally have to do manually, is actually done for us through the data clean rooms. >> Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos, and obviously you can relate to that, having been in the data business for awhile, but wonder if you can elucidate on that. >> Sure. I mean, data complexities are going to evolve over time in any traditional data architecture, simply because you often have different teams at different periods in time, trying to analyze and gather data across a whole lot of different sources. And the complexity that just arises out of that, is, due to the different needs of specific stakeholders. There are time constraints, and quite often it's not always clear, how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down those silos, is, allow individuals to see upfront how much value they're going to get from the data, by knowing that it's trustworthy right away . By knowing that it's something that they can use in their specific use case right away. And by ensuring that essentially, as they're continuing to kind of scale the use cases that they're focused on, they're no longer required to make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world. >> Yeah, for sure. I mean copy creek, cause it'd be the silent killer. Frank I followed you for a number of years. You're a big thinker. You and I have had a lot of conversations about the near term, mid term and long term. I wonder if you could talk about, you know, when your keynote, you talked about eliminating silos, and connecting across data sources, which really powerful concept, but it really only, if people are willing and able to connect and collaborate, where do you see that happening? Maybe what are some of the blockers there? >> Well, there's certainly a natural friction there. I still remember when we first started to talk to Salesforce, you know, they had, discovered that we were a top three destination of Salesforce data and they were wondering, you know, why that was? And the reason is of course that people take Salesforce data, push it to Snowflake, because they want to overlay it with data outside of Salesforce. You know what it is Adobe or any 6other marketing dataset. And then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS, is always like, no, we're an Island, we're a planet onto ourselves. Everybody needs to come with us as opposed to, we go to a different platform to run these types of processes. It's no different for thee public cloud vendor. They did only, they have, you know, massive moats around, you know, their storage to, you know, to really prevent data from leaving their orbit. So there is natural friction in terms of for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on the power and potential of data, unless we allow it to come together. Snowflake is the platform that allows that to happen. You know, we were pleased with our relationship with Salesforce because they did appreciate, you know, why this was important and why this was necessary. And we think, you know, other parts of the industry will gradually come around to it as well. So the idea of a Data Cloud has really come. Right, people are recognizing, you know, why does this matters now. It's not going to happen overnight. It is a step what will function a very big change in mentality and orientation. You know? >> Yeah. It's almost as though the sussification of our industry sort of repeated some of the application silos, and build a heart on to and all the processes of(mummers) Okay, here we go. And you're really trying to break that aren't you? >> Yep, exactly. >> Anita, again, I want to come back to this notion of governance. It's so important. It's the first rule in your title, and it really underscores the importance of this. You know, Frank was just talking about some of the hurdles and this is a big one. I mean we saw this in the early days of big data where governance was this afterthought. It was like bolted on kind of wild west. I'm interested in your governance journey. And maybe you can share a little bit about what role Snowflake has played there in terms of supporting that agenda, and kind of what's next on that journey. >> Sure. Well, you know, I've led data teams, in numerous ways over my career, this is the first time, that I've actually had the opportunity to focus on governance. And what it's done, is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >> Well, I mean, a big part of what you were talking about, at least my inference in your talk, was really that the business folks didn't have to care about, your wonder about they cared about it, but they don't have to wonder about, and about the privacy concerns, et cetera, you've taken care of all that. It's sort of transparent to them. >> Yeah, that's right. Absolutely. So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical, to ensuring you know, that we're able to do this. We don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access. And it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data, we focus on the infrastructure, and making sure that we've architected for scale , and all of these are really important components of our strategy. >> So I have a question maybe each of you can answer it. I sort of see this, our industry moving from products to then, to the platforms and platforms even evolving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing, but, but maybe Frank, you can start and Anita you can add onto Frank's answer. You obviously both passionate about the use of data and trying to do so in a responsible way, that's critical, but it's also going to have business impact. Frank, where's this passion come from on your side? And how are you putting into action in your own organization? >> Well, you know, I'm really going to date myself here, but you know many years ago, you know, I saw the first glimpse of multidimensional databases that were used for reporting really on IBM mainframes. And it was extraordinarily difficult. We didn't even have the words back then in terms of data, warehouses and business, all these terms didn't exist. People just knew that they want to have a more flexible way of reporting and being able to pivot data, dimensionally, all these kinds of things. And I just by whatever this predates, you know, windows 3.1, which really, you know, set off the whole sort of graphical, you know, way of dealing with systems, which there's not whole generations of people that don't know any different. Right? So I've lived the pain of this problem, and sort of had a front row seat, to watching this transpire over a very long period of time. And that's one of the reasons, you know, why I'm here because I finally seen, you know, a glimpse of, you know, I also, as an industry fully, just unleashing and unlocking the potential. We're not at a place where the technology is ahead of people's ability to harness it. Right. Which we'd never been there before. Right. It was always like we wanted to do things and technology wouldn't let us, it's different now. I mean, people are just, heads are spinning with what's now possible, which is why you see Marcus evolve, you know, very rapidly right now, we were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on in the world. The world's changing right in front of your eyes right now, >> So Anita maybe you could add on to what Frank just said and share some of the business impacts, and outcomes that are notable since you've really applied your love of data and maybe touch culture, data culture. Any words of wisdom for folks in the audience who might be thinking about embarking on a Data Cloud journey, similar to what you've been on. >> Yeah Sure. I think for me, I fell in love with technology first, and then I fell in love with data and I fell in love with data because of the impact that data can have, on both the business, and the technology strategy. And so it's sort of that nexus between all three. And in terms of my career journey and some of the impacts that I've seen. I think with the advent of the Cloud, you know, before, well, how do I say this? Before the cloud actually became so prevalent and such a common part of the strategy that's required, it was so difficult, you know, so painful. It took so many hours to actually, be able to calculate, you know, the volumes of data that we had. Now we have that accessibility. And then on top of it, with the Snowflake Data Cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have to have multiple copies of the data. And I think, moving beyond some of the traditional mechanisms for measuring business impact, has only been possible with the volumes of data that we have available to us today. And it's just, it's phenomenal to see the speed at which we can operate and really, truly understand our customer's interests and their preferences, and then tailor the experiences that they really want and deserve for them. It's been a great feeling to get to this point in time. >> That's fantastic. So, Frank, I got to ask you to do so in your spare time you decided to write a book am loving it. I have a signed copy, so I'm going to have to send it back and have you sign it. But, and I love the inside baseball. It's just awesome. So really appreciate that. So, but why did you decide to write a book? >> Well, there were a couple of reasons. Obviously we thought it was an interesting tale to tell for anybody, you know, who is interested in, you know, what's going on? How did this come about? You know, or the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, you know, because this is such a step function, it's so non incremental, we felt like we really needed quite a bit of real estate to really lay out, what the full narrative and context is. And, you know, we thought, you know, books titled the rise of the Data Cloud. That's exactly what it is. And we're trying to make the case for that mindset, that mentality, that strategy, because all of us, you know, I think as an industry we're at risk of, you know, persisting, perpetuating, you know, where we've been since the beginning of computing. So we're really trying to make a pretty forceful case for look, you know, there's an enormous opportunity out there, but there's some choices you have to make along the way. >> Guys, we got to leave it there. Frank. I know you and I are going to talk again, Anita, I hope we have a chance to meet face to face and in the cube live someday, your phenomenal guest and what a great story. Thank you both for coming on. Thanks Dave, >> Thank you >> You're welcome to keep it right there, buddy. We'll be back with the next guest right after this short break. (upbeat music)

>> Announcer: From around the globe, it's theCube with digital coverage of MIT, Chief Data Officer and Information Quality Symposium brought to you by Silicon Angle Media. >> Hi, I'm Stu Miniman and this is the seventh year of theCubes coverage of the MIT, Chief Data Officer and Information Quality Symposium. We love getting to talk to these chief data officers and the people in this ecosystem, the importance of data, driving data-driven cultures, and really happy to welcome to the program, first time guests Eileen Vitrine, Eileen is the Chief Data Officer for the United States Air Force, Eileen, thank you so much for joining us. >> Thank you Stu really excited about being here today. >> All right, so the United States Air Force, I believe had it first CDO office in 2017, you were put in the CDO role in June of 2018. If you could, bring us back, give us how that was formed inside the Air force and how you came to be in that role. >> Well, Stu I like to say that we are a startup organization and a really mature organization, so it's really about culture change and it began by bringing a group of amazing citizen airman reservists back to the Air Force to bring their skills from industry and bring them into the Air Force. So, I like to say that we're a total force because we have active and reservists working with civilians on a daily basis and one of the first things we did in June was we stood up a data lab, that's based in the Jones building on Andrews Air Force Base. And there, we actually take small use cases that have enterprise focus, and we really try to dig deep to try to drive data insights, to inform senior leaders across the department on really important, what I would call enterprise focused challenges, it's pretty exciting. >> Yeah, it's been fascinating when we've dug into this ecosystem, of course while the data itself is very sensitive and I'm sure for the Air Force, there are some very highest level of security, the practices that are done as to how to leverage data, the line between public and private blurs, because you have people that have come from industry that go into government and people that are from government that have leveraged their experiences there. So, if you could give us a little bit of your background and what it is that your charter has been and what you're looking to build out, as you mentioned that culture of change. >> Well, I like to say I began my data leadership journey as an active duty soldier in the army, and I was originally a transportation officer, today we would use the title condition based maintenance, but back then, it was really about running the numbers so that I could optimize my truck fleet on the road each and every day, so that my soldiers were driving safely. Data has always been part of my leadership journey and so I like to say that one of our challenges is really to make sure that data is part of every airmans core DNA, so that they're using the right data at the right level to drive insights, whether it's tactical, operational or strategic. And so it's really about empowering each and every airman, which I think is pretty exciting. >> There's so many pieces of that data, you talk about data quality, there's obviously the data life cycle. I know your presentation that you're given here at the CDO, IQ talks about the data platform that your team has built, could you explain that? What are the key tenants and what maybe differentiates it from what other organizations might have done? >> So, when we first took the challenge to build our data lab, we really wanted to really come up. Our goal was to have a cross domain solution where we could solve data problems at the appropriate classification level. And so we built the VAULT data platform, VAULT stands for visible, accessible, understandable, linked, and trustworthy. And if you look at the DOD data strategy, they will also add the tenants of interoperability and secure. So, the first steps that we have really focused on is making data visible and accessible to airmen, to empower them, to drive insights from available data to solve their problems. So, it's really about that data empowerment, we like to use the hashtag built by airmen because it's really about each and every airman being part of the solution. And I think it's really an exciting time to be in the Air Force because any airman can solve a really hard challenge and it can very quickly wrap it up rapidly, escalate up with great velocity to senior leadership, to be an enterprise solution. >> Is there some basic training that goes on from a data standpoint? For any of those that have lived in data, oftentimes you can get lost in numbers, you have to have context, you need to understand how do I separate good from bad data, or when is data still valid? So, how does someone in the Air Force get some of that beta data competency? >> Well, we have taken a multitenant approach because each and every airman has different needs. So, we have quite a few pathfinders across the Air Force today, to help what I call, upscale our total force. And so I developed a partnership with the Air Force Institute of Technology and they now have a online graduate level data science certificate program. So, individuals studying at AFIT or remotely have the opportunity to really focus on building up their data touchpoints. Just recently, we have been working on a pathfinder to allow our data officers to get their ICCP Federal Data Sector Governance Certificate Program. So, we've been running what I would call short boot camps to prep data officers to be ready for that. And I think the one that I'm most excited about is that this year, this fall, new cadets at the U.S Air Force Academy will be able to have an undergraduate degree in data science and so it's not about a one prong approach, it's about having short courses as well as academe solutions to up skill our total force moving forward. >> Well, information absolutely is such an important differentiator(laughs) in general business and absolutely the military aspects are there. You mentioned the DOD talks about interoperability in their platform, can you speak a little bit to how you make sure that data is secure? Yet, I'm sure there's opportunities for other organizations, for there to be collaboration between them. >> Well, I like to say, that we don't fight alone. So, I work on a daily basis with my peers, Tom Cecila at the Department of Navy and Greg Garcia at the Department of Army, as well as Mr. David Berg in the DOD level. It's really important that we have an integrated approach moving forward and in the DOD we partner with our security experts, so it's not about us doing security individually, it's really about, in the Air Force we use a term called digital air force, and it's about optimizing and building a trusted partnership with our CIO colleagues, as well as our chief management colleagues because it's really about that trusted partnership to make sure that we're working collaboratively across the enterprise and whatever we do in the department, we also have to reach across our services so that we're all working together. >> Eileen, I'm curious if there's been much impact from the global pandemic. When I talk to enterprise companies, that they had to rapidly make sure that while they needed to protect data, when it was in their four walls and maybe for VPN, now everyone is accessing data, much more work from home and the like. I have to imagine some of those security measures you've already taken, but have there anything along those lines or anything else that this shift in where people are, and a little bit more dispersed has impacted your work? >> Well, the story that I like to say is, that this has given us velocity. So, prior to COVID, we built our VAULT data platform as a multitenancy platform that is also cross-domain solution, so it allows people to develop and do their problem solving in an appropriate classification level. And it allows us to connect or pushup if we need to into higher classification levels. The other thing that it has helped us really work smart because we do as much as we can in that unclassified environment and then using our cloud based solution in our gateways, it allows us to bring people in at a very scheduled component so that we maximize, or we optimize their time on site. And so I really think that it's really given us great velocity because it has really allowed people to work on the right problem set, on the right class of patient level at a specific time. And plus the other pieces, we look at what we're doing is that the problem set that we've had has really allowed people to become more data focused. I think that it's personal for folks moving forward, so it has increased understanding in terms of the need for data insights, as we move forward to drive decision making. It's not that data makes the decision, but it's using the insight to make the decision. >> And one of the interesting conversations we've been having about how to get to those data insights is the use of things like machine learning, artificial intelligence, anything you can share about, how you're looking at that journey, where you are along that discovery. >> Well, I love to say that in order to do AI and machine learning, you have to have great volumes of high quality data. And so really step one was visible, accessible data, but we in the Department of the Air Force stood up an accelerator at MIT. And so we have a group of amazing airmen that are actually working with MIT on a daily basis to solve some of those, what I would call opportunities for us to move forward. My office collaborates with them on a consistent basis, because they're doing additional use cases in that academic environment, which I'm pretty excited about because I think it gives us access to some of the smartest minds. >> All right, Eileen also I understand it's your first year doing the event. Unfortunately, we don't get, all come together in Cambridge, walking those hallways and being able to listen to some of those conversations and follow up is something we've very much enjoyed over the years. What excites you about being interact with your peers and participating in the event this year? >> Well, I really think it's about helping each other leverage the amazing lessons learned. I think that if we look collaboratively, both across industry and in the federal sector, there have been amazing lessons learned and it gives us a great forum for us to really share and leverage those lessons learned as we move forward so that we're not hitting the reboot button, but we actually are starting faster. So, it comes back to the velocity component, it all helps us go faster and at a higher quality level and I think that's really exciting. >> So, final question I have for you, we've talked for years about digital transformation, we've really said that having that data strategy and that culture of leveraging data is one of the most critical pieces of having gone through that transformation. For people that are maybe early on their journey, any advice that you'd give them, having worked through a couple of years of this and the experience you've had with your peers. >> I think that the first thing is that you have to really start with a blank slate and really look at the art of the possible. Don't think about what you've always done, think about where you want to go because there are many different paths to get there. And if you look at what the target goal is, it's really about making sure that you do that backward tracking to get to that goal. And the other piece that I tell my colleagues is celebrate the wins. My team of airmen, they are amazing, it's an honor to serve them and the reality is that they are doing great things and sometimes you want more. And it's really important to celebrate the victories because it's a very long journey and we keep moving the goalposts because we're always striving for excellence. >> Absolutely, it is always a journey that we're on, it's not about the destination. Eileen, thank you so much for sharing all that you've learned and glad you could participate. >> Thank you, STU, I appreciate being included today. Have a great day. >> Thanks and thank you for watching theCube. I'm Stu Miniman stay tuned for more from the MIT, CDO IQ event. (lively upbeat music)

>> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this a CUBE conversation. >> Hello, and welcome to this CUBE conversation, I'm John Furrier, host of theCUBE here, in our Palo Alto, California studios, here with our quarantine crew. We're getting all the remote interviews during this time of COVID-19. We've got two great remote guests here, Rachini Moosavi who's the Executive Director of Analytical Services and Data Governance at UNC Healthcare, and Sonya Jordan, Enterprise Analytics Manager of Data Governance at UNC Health. Welcome to theCUBE, thanks for coming on. >> Thank you. >> Thanks for having us. >> So, I'm super excited. University of North Carolina, my daughter will be a freshman this year, and she is coming, so hopefully she won't have to visit UNC Health, but looking forward to having more visits down there, it's a great place. So, thanks for coming on, really appreciate it. Okay, so the conversation today is going to be about how data and how analytics are helping solve problems, and ultimately, in your case, serve the community, and this is a super important conversation. So, before we get started, talk about UNC Health, what's going on there, how you guys organize, how big is it, what are some of the challenges that you have? >> SO UNC Health is comprised of about 12 different entities within our hospital system. We have physician groups as well as hospitals, and we serve, we're spread throughout all of North Carolina, and so we serve the patients of North Carolina, and that is our primary focus and responsibility for our mission. As part of the offices Sonya and I are in, we are in the Enterprise Analytics and Data Sciences Office that serves all of those entities and so we are centrally located in the triangle area of North Carolina, which is pretty central to the state, and we serve all of our entities equally from our Analytics and Data Governance needs. >> John: You guys got a different customer base, obviously you've got the clinical support, and you got the business applications, you got to be agile, that's what it's all about today, you don't need to rely on IT support. How do you guys do that? What's the framework? How do you guys tackle that problem of being agile, having the data be available, and you got two different customers, you got all the compliance issues with clinical, I can only imagine all the regulations involved, and you've got the business applications. How do you handle those? >> Yeah, so for us in the roles that we are in, we are fully responsible for more of the data and analytics needs of the organization, and so we provide services that truly are balanced across our clinician group, so we have physicians, and nurses, and all of the other ancillary clinical staff that we support, as well as the operational needs as well, so revenue cycle, finance, pharmacy, any of those groups that are required in order to run a healthcare system. So, we balance our time amongst all of those and for the work that we take on and how we continuously support them is really based on governance at the end of the day. How we make decisions around what the priorities are and what needs to happen next, and requires the best insights, is really how we focus on what work we do next. As for the applications that we build, in our office, we truly only build analytical applications or products like visualizations within Tableau as well as we support data governance platforms and services and so we provide some of the tools that enable our end users to be able to interact with the information that we're providing around analytics and insights, at the end of the day. >> Sonya, what's your job? Your title is Analytics Manager of Data Governance, obviously that sounds broad but governance is obviously required in all things. What is your job, what is your day-to-day roles like? What's your focus? >> Well, my day-to-day operations is first around building a data governance program. I try to work with identifying customers who we can start partnering with so that we can start getting documentation and utilizing a lot of the programs that we currently have, such as certification, so when we talk about initiatives, this is one of the initiatives that we use to partner with our stakeholders in order to start bringing visibilities to the various assets, such as metrics, or universes that we want to certify, or dashboards, algorithm, just various lists of different types of assets that we certify that we like to partner with the customers in order for them to start documenting within the tools, so that we can bring visibility to what's available, really focusing on data literacy, helping people to understand what assets are available, not only what assets are available, but who owns them, and who own the asset, and what can they do with it, making sure that we have great documentation in order to be able to leverage literacy as well. >> So, I can only imagine with how much volume you guys are dealing from a data standpoint, and the diversity, that the data warehouse must be massive, or it must be architected in a way that it can be agile because the needs, of the diverse needs. Can you guys share your thoughts on how you guys look on the data warehouse challenge and opportunity, and what you guys are currently doing? >> Well, so- >> Yeah you go ahead, Rachini. >> Go ahead, Sonya. >> Well, last year we implemented a tool, an enterprise warehouse, basically behind a tool that we implemented, and that was an opportunity for Data Governance to really lay some foundation and really bring visibility to the work that we could provide for the enterprise. We were able to embed into probably about six or seven of the 13 initiatives, I was actually within that project, and with that we were able to develop our stewardship committee, our data governance council, and because Rachini managed Data Solutions, our data solution manager was able to really help with the architect and integration of the tools. >> Rachini, your thoughts on running the data warehouse, because you've got to have flexibility for new types of data sources. How do you look at that? >> So, as Sonya just mentioned, we upgraded our data warehouse platform just recently because of these evolving needs, and like a lot of healthcare providers out there, a lot of them are either one or the other EMRs that are top in the market. With our EMR, they provide their own data warehouse, so you have to factor almost the impact of what they bring to the table in with an addition to all of those other sources of data that you're trying to co-mingle and bring together into the same data warehouse, and so for us, it was time for us to evolve our data warehouse. We ended up deciding on trying to create a virtual data warehouse, and in doing so, with virtualization, we had to upgrade our platform, which is what created that opportunity that Sonya was mentioning. And by moving to this new platform we are now able to bring all of that into one space and it's enabled us to think about how does the community of analysts interact with the data? How do we make that available to them in a secure way? In a way that they can take advantage of reusable master data files that could be our source of truth within our data warehouse, while also being able to have the flexibility to build what they need in their own functional spaces so that they can get the wealth of information that they need out of the same source and it's available to everyone. >> Okay, so I got to ask the question, and I was trying to get the good stuff out first, but let's get at the reality of COVID-19. You got pre-COVID-19 pandemic, we're kind of in the middle of it, and people are looking at strategies to come out of it, obviously the world will be changed, higher with a lot of virtualization, virtual meetings, and virtual workforce, but the data still needs to be, the business still needs to run, but data will be changing different sources, how are you guys responding to that crisis because you're going to be leaned on heavily for more and more support? >> Yeah it's been non-stop since March (laughs). So, I'm going to tell you about the reporting aspects of it, and then I'd love to turn it over to Sonya to tell you about some of the great things that we've actually been able to do to it and enhance our data governance program by not wasting this terrible event and this opportunity that's come up. So, with COVID, when it kicked off back in March, we actually formed a war room to address the needs around reporting analytics and just insights that our executives needed, and so in doing so, we created within the first week, our first weekend actually, our first dashboard, and within the next two weeks we had about eight or nine other dashboards that were available. And we continuously add to that. Information is so critical to our executives, to our clinicians, to be able to know how to address the evolving needs of COVID-19 and how we need to respond. We literally, and I'm not even exaggerating, at this very moment we have probably, let's see, I think it's seven different forecasts that we're trying to build all at the same time to try and help us prepare for this new recovery, this sort of ramp up efforts, so to your point, it started off as we're shutting down so that we can flatten the curve, but now as we try to also reopen at the same time while we're still meeting the needs of our COVID patients, there's this balancing act that we're trying to keep up with and so analytics is playing a critical factor in doing that. >> Sonya, your thoughts. First of all, congratulations, and action is what defines the players from the pretenders in my mind, you're seeing that play out, so congratulations for taking great action, I know you're working hard. Sonya, your thoughts, COVID, it's putting a lot of pressure? It highlights the weaknesses and strengths of what's kind of out there, what's your thoughts? >> Well, it just requires a great deal of collaboration and making sure that you're documenting metrics in a way where you're factoring true definition because at the end of the day, this information can go into a dashboard that's going to be visualized across the organization, I think what COVID has done was really enhanced the need and the understanding of why data governance is important and also it has allowed us to create a lot of standardization, where we we're standardizing a lot of processes that we currently had in correct place but just enhancing them. >> You know, not to go on a tangent, but I will, it's funny how the reality has kind of pulled back, exposed a lot of things, whether it's the remote work situation, people are VPNing, not under provision with the IT side. On the data side, everyone now understands the quality of the data. I mean, I got my kids talking progression analysis, "Oh, the curves are all wrong," I mean people are now seeing the science behind the data and they're looking at graphs all the time, you guys are in the visualization piece, this really highlights the need of data as a story, because there's an impact, and two, quality data. And if you don't have the data, the story isn't being told and then misinformation comes out of it, and this is actually playing out in real time, so it's not like it's just a use case for the most analytics but this again highlights the value of proposition of what you guys do. What's your personal thoughts on all this because this really is playing out globally. >> Yeah, it's been amazing how much information is out there. So, we have been extremely blessed at times but also burdened at times by that amount of information. So, there's the data that's going through our healthcare system that we're trying to manage and wrangle and do that data storytelling so that people can drive those insights to very effective decisions. But there's also all of this external data that we're trying to be able to leverage as well. And this is where the whole sharing of information can sometimes become really hard to try and get ahead of, we leverage the Johns Hopkins data for some time, but even that, too, can have some hiccups in terms of what's available. We try to use our State Department of Health and Human Services data and they just about updated their website and how information was being shared every other week and it was making it impossible for us to ingest that into our dashboards that we were providing, and so there's really great opportunities but also risks in some of the information that we're pulling. >> Sonya, what's your thoughts? I was just having a conversation this morning with the Chief of Analytics and Insight from NOA which is the National Oceanic Administration, about weather data and forecasting weather, and they've got this community model where they're trying to get the edges to kind of come in, this teases out a template. You guys have multiple locations. As you get more democratized in the connection points, whether it's third-party data, having a system managing that is hard, and again, this is a new trend that's emerging, this community connection points, where I think you guys might also might be a template, and your multiple locations, what's your general thoughts on that because the data's coming in, it's now connected in, whether it's first-party to the healthcare system or third-party. >> Yeah, well we have been leveraging our data governance tool to try to get that centralized location, making sure that we obtain the documentations. Due to COVID, everything is moving very fast, so it requires us to really sit down and capture the information and when you don't have enough resources in order to do that, it's easy to miss some very important information, so really trying to encourage people to understand the reason why we have data governance tools in order for them to leverage, in order to capture the documentation in a way that it can tell the story about the data, but most of all, to be able to capture it in a way so that if that person happened to leave the organization, we're not spending a lot of time trying to figure out how was this information created, how was this dashboard designed, where are the requirements, where are the specifications, where are the key elements, where does that information live, and making sure we capture that up front. >> So, guys, you guys are using Informatica, how are they helping you? Obviously, they have a system they're getting some great feedback on, how are you using Informatica, how is it going, and how has that enabled you guys to be successful? >> Yeah, so we decided on Informatica after doing a really thorough vetting of all of the other vendors in the industry that could provide us these services. We've really loved the capabilities that we've been able to provide to our customers at this point. It's evolving, I think, for us, the ability to partner with a group like Prominence, to be able to really leverage the capabilities of Informatica and then be really super, super hyper focused on providing data literacy back to our end users and making that the full intent of what we're doing within data governance has really enabled us to take the tools and make it something that's specific to UNC Health and the needs that our end users are verbalizing and provide that to them in a very positive way. >> Sonya, they talk about this master catalog, and I've talked to the CEO of Informatica and all their leaders, governance is a big part of it, and I've always said, I've always kind of had a hard time, I'm an entrepreneur, I like to innovate, move fast, break things, which is kind of not the way you work in the data world, you don't want to be breaking anything, so how do you balance governance and compliance with innovation? This has been a key topic and I know that you guys are using their enterprise data catolog. Is that helping? How does that fit in, is that part of it? >> Well, yeah, so during our COVID initiatives and building these telos dashboards, these visualizations and forecast models for executive leaders, we were able to document and EMPower you, which we rebranded Axon to EMPower, we were able to document a lot of our dashboards, which is a data set, and pretty much document attributes and show lineage from EMPower to EDC, so that users would know exactly when they start looking at the visualization not only what does this information mean, but they're also able to see what other sources that that information impacts as well as the data lineage, where did the information come from in EDC. >> So I got to ask the question to kind of wrap things up, has Informatica helped you guys out now that you're in this crisis? Obviously you've implemented before, now that you're in the middle of it, have you seen any things that jumped out at you that's been helpful, and are there areas that need to be worked on so that you guys continue to fight the good fight, come out of this thing stronger than before you came in? >> Yeah, there is a lot of new information, what we consider as "aha" moments that we've been learning about, and how EMPower, yes there's definitely a learning curve because we implemented EDC and EMPower last year doing our warehouse implementation, and so there's a lot of work that still needs to be done, but based on where we were the first of the year, I can say we have evolved tremendously due to a lot of the pandemic issues that arised, and we're looking to really evolve even greater, and pilot across the entire organization so that they can start leveraging these tools for their needs. >> Rachini you got any thoughts on your end on what's worked, what you see improvements coming, anything to share? >> Yeah, so we're excited about some of the new capabilities like the marketplace for example that's available in Axon, we're looking forward to being able to take advantage of some of these great new aspects of the tool so that we can really focus more on providing those insights back to our end users. I think for us, during COVID, it's really been about how do we take advantage of the immediate needs that are surfacing. How do we build all of these dashboards in record-breaking time but also make sure that folks understand exactly what's being represented within those dashboards, and so being able to provide that through our Informatica tools and service it back to our end users, almost in a seamless way like it's built into our dashboards, has been a really critical factor for us, and feeling like we can provide that level of transparency, and so I think that's where as we evolve that we would look for more opportunities, too. How do we make it simple for people to get that immediate answers to their questions, of what does the information need without it feeling like they're going elsewhere for the information. >> Rachini, thank you so much for your insight, Sonya as well, thanks for the insight, and stay safe. Sonya, behind you, I was pointing out, that's your artwork, you painted that picture. >> Yes. >> Looks beautiful. >> Yes, I did. >> You got two jobs, you're an artist, and you're doing data governance. >> Yes, I am, and I enjoy painting, that's how I relax (laughs). >> Looks great, get that on the market soon, get that on the marketplace, let's get that going. Appreciate the time, thank you so much for the insights, and stay safe and again, congratulations on the hard work you're doing, I know there's still a lot more to do, thanks for your time, appreciate it. >> Thank you. >> Thank you. >> It's theCUBE conversation, I'm John Furrier at the Palo Alto studios, for the remote interviews with Informatica, I'm John Furrier, thanks for watching. (upbeat music)

>> Presenter: From theCUBE Studios in Palo Alto and Boston, connecting with Dot leaders all around the world. This is a CUBE conversation. >> Everyone welcome to theCUBE studio here in Palo Alto. I'm John Furrier, host of theCUBE. We're here with our quarantine crew. We've been here for three months quarantining but we're getting the stories out. We're talking to all of our favorite guests and most important stories in technologies here remotely and we have a great conversation in store for you today with Amit Walia CEO of Informatica. Cube alumni, frequent guest of theCUBE, now, the CEO of Informatica. Amit, great to see you. Thank you for coming on this CUBE conversation. >> Good to see you John. It's different to be doing this like this versus being in the studio with you but I'm glad that we could leverage technology to still talk to each other. >> You're usually right here, right next to me, but I'm glad to get you remotely at least and I really appreciate you. You always have some great commentary and insights. And Amit, before we get into the real meaty stuff that I'd love around the data, I want to get your thoughts on this COVID-19 crisis. It's a new reality, it's highlighted as we've been reporting on SiliconANGLE for the past few months. The at scale problems that people are facing but it's also an opportunity. People are sheltered in place, there's a lot of anxiety on what their work environment is going to look like but the world still runs. Your thoughts on the current crisis and how you're looking at it, how you're navigating it as a leader. >> No doubt, it is a very unique situation we all live in. We've never all faced something like this. So I think first of all, I'll begin by expressing my prayers for anyone out there who has been impacted by it and of course, a huge round of thank you to all the heroes out there at the front lines. The healthcare workers, the doctors, the nurses (mumbles) so we can't forget that. These are very unique situations but as you said, let's not forget that this is a health crisis first and then it becomes an economic crisis. And then, as you said there is a tremendous amount of disruption and (mumbles) I think all of them will go through some phases and I think you can see already while there is disruption in front of us, you see the digital contents of organizations who are ready for that have definitely faced it lot better but as obviously the ones that have been somewhat in the previous generations, let's just say business models or technologies models are struggling through it. So there is a lot data chain. I think they're still learning. We're absolutely still learning and we will continue to learn til the end of this year and we'll come out very different for the next decade for sure. >> If anyone who's watching goes to YouTube on the SiliconANGLE CUBE and look at your videos over the years, we've been talking about big data and these transformational things. It's been an inside the industry kind of discussion. Board room for your clients and your business and Informatica but I think this is now showing the world this digital transformation. The future has been pulled forward faster than people have been expecting it and innovation strategy has been on paper, maybe some execution but now I think it's apparent to everyone that the innovation strategy needs to start now because of this business model impact, the economic crisis is exposed. The scale of opportunities and challenges, there will be winners and losers and projects still need to get done or reset or reinvented to come out of this with growth. So this is going to be the number one conversation. What are your thoughts around this? >> No, so I've talked to hundreds of customers across the globe and we see the same thing. In fact actually, in some ways as we went through this, something very profound dawned on me. We, John, talked about digital transformation for the last few years and clearly digital transformation will accelerate but as I was talking to customers, I came to this realization that we actually haven't digitally transformed. To be honest, what happened in the last three to four years is that it was more digital modernization. A few apps got tweaked, a few front-ends got tweaked but if you realize, it was more digital modernization, not transformation because in my opinion, there are four aspects to digital transformation. You think of new products and services, you think of new models of engaging with your customers, you think of absolutely new operating models and you think of fundamentally new business models. That's a whole rewrite of an organization, which is not just creating a new application out there, fundamental end to end transformation. My belief is, our belief is that, now starts a whole new era of transformation, digital transformation. We've just gone through digital modernization. >> Well, that's a great point and the business model impacts create... And in times of these inflection points, and again, you're a student of history in the tech industry, PC revolution, TCP IP. These are big points in time. They're not transitions. The big players tend to win the transitions. When you have a transformation, it's a Cambrian explosion of new kinds of capabilities. This is really, I would agree with your point but I think it's going to be a Cambrian explosion because the business model forcing function is there. How do you see it play, 'cause you're in the middle of all this, 'cause you guys are the control plane for data in the industry as a company. You enable these new apps. Could you share your-- >> So, we see a lot of that and I think the way to think about it, I think first of all, you said it right. This is a step function changing orbit. This is a whole new... You get to a new curve, you go to a different model. It's a whole new equation you're hiking for the curve you're going to be on. It's not just changing the gradient of the curve you've been on, this is going to be a whole journey. And when we think of the new world of digital transformation, there are four elements that are taught. First of all, it has to be strategic. It has to be Board, CEO, executive topped down, fundamentally across the whole organization, across every function of an organization. Second one you talked about scale. I believe this is all about innovating at scale. It's not about, hey, let me go put a new application in some far plans of my business. You've got to innovate at scale, end to end change does not happen in bits and pieces. Third one, this is cloud native, absolutely cloud native. If there was any minuscule of doubt, this is taking it away. Cloud nativity is the fundamental differentiator and the last but not the least is digital natives, which is where everybody wants to go become a digitally transformed company that are data-led. You got to make data-led decisions. So for competence, strategic mindset, innovation at scale cloud nativity and being data-led is going to define digital transformation. >> I think that encapsulates absolutely innovation strategy. I agree with you 100%, that's really insightful. I want to also get your thoughts on some things that you're talking about and you have always had some really kind of high level conversations around this and theCUBE has been a very social organization. We'd love to be that social construct between companies and audiences but you use a term, the digital transformation, the soul of digital transformation is data 4.0. This idea of having a soul is interesting because the apps all have personalization built in. You have CLAIRE, you've been doing CLAIRE AI for a while. So this idea of social organizations, a soul is kind of an interesting piece of metadata you're putting out in the messaging. What do you mean by that? How can digital transmission have a soul? >> I think we talked about it a lot and I think it just came to me that, look at the end of the day, any transformation is so fundamental to anything that anybody does and I think if you think about, you can go to a fundamental transformation that is just qualitative, it's qualitative and quantitative. It's about a human body, it's about a human body transforming itself and then something doesn't have a soul, John, it does not have life. It cannot truly move to the next paradigm. So I believe that, any transformation has to have a soul and the digital world is all about data. So obviously, we believe that we're walking into a data-for-data world where, as I said, the four pillars of digital transformation would be data-led and I believe data is the soul of that transformation and data itself is moving into a new paradigm. You've heard us talk about 1.0, 2.0, 3.0, and this is the new world of 4.0, a data 4.0 which basically is all about cloud nativity, intelligent automation, AI powered, focusing on data, trust in data ethics and operations and innovation at scale. When you bring these elements together, then that enables digital transformation to happen on the shoulders of data 4.0, which in my opinion, is the soul of digital transformation. >> All right, so just rewind on data 4.0 for a minute. Pretend I'm a CIO, I'm super busy. I don't have time to read up about it. Give me the bottom line, what is data 4.0? Describe it to me in basic terms, is it just an advancement, acceleration? What's the quick elevator pitch on 4.0, data 4.0? >> Very simple?. We're all walking into a world where we're going to be digital. Digital means that we're basically going to be creating tons of data. By the way, and data is everywhere. It's not just within the four walls of us. It's basically what I call transaction and interaction and with the scale and volume of data increasing, the complexity of it increasing. We want to make decisions. I say, tomorrow's decision, today and with data that is available to us yesterday, so I can be better at that decision. So we need intelligence, we need automation, we need flexibility, which is where AI comes in. These are all very fundamental rewrites of the technology stack to enable a fundamental business transformation. So in that world, data is front and center and you look at the amount of data we are going to collect, the whole concept of data ethics and data trust become very important, not just Goodwill governance, governance is important but data privacy, data trust becomes very important. Then we're going to do things like contact tracing, it's very important for the society but the ethics, trust and privacy of what you and I will give to the government is going to become very much important. So to me, that world that we go in, every enterprise has to think data first, data led, build an infrastructure to support the business in that context and then, as I said, then the soul, which is data will give life to digital transformation. >> That's awesome. Love the personalization and the soul angle on it. I always believe that you guys had that intelligent automation fabric and to me, you said earlier, cloud native is apparent to everyone now. I think out of all this crisis, I think the one thing that's not going to be debated anymore is that cloud native is the operating model. I think that's pretty much a done deal at this point. So having this horizontally scalable data, you know I've been on this rant for years. I think that's the killer app. I think having horizontally scalable data is going to enable a lot, souls and more life. So I got to ask you the real, the billion dollar question. I'm a customer of yours or prospect or a large enterprise. I'm seeing what's happening at scale, provisioning of VPNs for 100% employees at home, except for the most needed workers. I now see all the things I need to either process, I need to cancel and projects that double down on. I still got to go out and build my competitive advantage. I still have to run my business. So I need to really start deploying right out of the gate data centric, data first, virtual first, whatever you want to call it, the new reality first, this inflection point. What do I do? What is the things that you see as projects or playbook recipes that people could implement? >> First of all, we see a very fundamental reevaluation of the entire business model. In fact, we have this term that we're using now that we have to think of business has a business 360 and if I think about it in this new world, that the businesses that stood the test is that had basically what I call, a digital supply chain or in a very digital scalable way of interacting with their customers, being able to engage with their customers. A digital fabric often making sure that they can bring their product and services to the customers very quickly or in some cases, if they were creating new products and services, they had the ability for a whole new supply chain to reach that end customer. And of course, a business model that is flexible so they dont obviously, they can cater to the needs of their customers. So in all of these worlds, customers are a building digital, scalable data platforms and when I say platforms, it's not about some monolithic platform. These are, as you and I have talked about, very modular microservices based platform that reside on what we call metadata. Data has to be the soul of the digital enterprise. Metadata is the nervous system, that makes it all work. That's the left brain, right brain, that makes it all work, which is where we put AI on top. AI that works for the customers and then they leverage it but AI applied to that metadata allows them to be very flexible, nimble and make these decisions very rapidly, whether they are doing analytics for tomorrow's offering to be brought in front of a customer or understanding the customer better to give them something that appeals to them in changing times or to protect the customer's data or to provide governance on top of it. Anything that you would like to do has to ride on top of what I call a, AI led metadata driven platform that can scale horizontally. >> Okay, so I got to go to the next level on this, which is, okay, you got me on that. I hear what you're saying, I agree, great. But I got to put my developers to work and I got insight, I got analytics teams, I got competencies but Amit, my complexities don't go away. I still got compliance at scale, I got governance at scale but I also got, now my developers not just to get analytical insight, there's great dashboards and there's great analytic data out there, you guys do a good job there. I got to get my developers coding so I can get that agility of the data into the apps for visualization in the app or having a key ingredient of the software. How do I do that? What's your answer to that one? >> So, that's a critic use case. If you think about it, for a developer, one of the biggest challenge for analytics project is how do I bring all the data that is in sites across the enterprise so then I can put it in any kind of visualization analytics tool and things are happening at scale. An enterprise is spread across the globe. It's so many different data sources available everywhere. Again, what we've done is that as a part of the data platform when you focus upon the metadata, that allows you to go to one place where you can have full access to all of the data assets that are available across (mumbles). Do you remember at theCUBE years ago, we unveiled the launch of our enterprise data catalog, which as I said, was the Google for enterprise data through metadata. Now, developers don't have to go start wasting their time, trying to find whether data has (mumbles), through the catalog that CLAIRE is in-built, they have access to it. They can start putting that to work and figuring out how do I take different kinds of data? How do I put it in some data times tool? Through which we have the in-built integrations. Do what I call the valuable last mile work, which is where the intelligence is needed from them versus spend their energy trying to figure out where good data, clean data, all kinds of data sets. We have eliminated all of that complexity with the help of metadata data platform, CLAIRE, to let the developers do what I call value-added productive work. >> Amit, final question for you. I know you talk to customers a lot, you're always on the road, you got a great product background, that's where you came from, good mix understanding of the business but now your customers and prospects are trynna put the fires out. The big room that... No one's going to talk about their kitchen appliances when the house is burning down and in some cases on the business model side or if it's a growth strategy, they're going to put all their energies where the action is. So getting mind share with them is going to be very difficult. How are you as a leader and how is Informatica getting in front of these folks and saying, "Look, I know things are tough "but we're an important supplier for you." How do you differentiate? How are you going to get that mind share? What are some of those conversations? 'Cause this is really the psychology of the marketplace right now, the buyer and the customer. >> Well, first of all, obviously we had to adapt to reach our customers in a different way because, virtually based just like you and I are chatting right now and to be candid, our teams were fantastic in being able to do it. We've actually already had multiple pretty big sides of it. In fact, the first week before we started (mumbles), we had set up the MDM and Data Governance Summit up in New York and we expected thousands of customers to come there, ask them (mumbles) virtual and we did it virtually and we had three times more people attend the virtual event. It was much easier for people who attended from the confines of their living room. So we'd gone 100% virtual and good news is, that our customers are heavily engaged. We've actually had more participation of customers coming and attending our events. We've had obviously our customers speaking, talking about how they've created value. In light of that next week, we have the big event which we're calling, CLAIREview named after ClAIRE AI engine. It's basically a beautiful net-filled tech experience. We'll have a keynote, we'll have seasons and episodes, people can do bite-sized viewing at their own leisure. We'll talk about all kinds of transformation. In fact, we have Scott Guthrie who runs all of Azure and Cloud at Microsoft as a part of my Keynote. We have two great customers, CDO at XXL and a CEO of GDR nonprofit that does (mumbles) on diabetes work talk about the data journeys. We have Martin Byer from Gardner. So we've been able to pivot and our customers are heavily engaged because data is a P-zero or a P-one activity for them to invest in. So we haven't seen any drop-off in customer engagement with us and we've been very blessed that we have a very loyal and a very high retention rate customer base. >> Well, I would expect that being the center of the value proposition, where we've always said data has been. One more final question since this just popped in my head. You and I have been talking about the edge for years. Certainly now the edge is exposed, we all know what the edge is, it's working at home. It's the human, it's me, it's my IOT devices. More than ever, the edge is now the new perimeter. It's the edge and now the edges is there. There's something that you've been talking a while. This is another part of data fabric that's important. Your view on this new edge that's now visualized by everybody, realized this immersion. What's your thoughts on the edge? >> Oh, I think the edge is real now. You and me chatted about that almost four years ago and I (mumbles). Look, think of it this way. Think of how security is going to change. There's no more data center to which we route our traffic anymore. It's sitting over there somewhere where no human beings is going to have access. People are connecting to all kinds of cloud application directly from their offices or living rooms or their cultures and the world of security has to change in that context. And people are more going to be more, enterprise (mumbles) are more worried about, hey, how do I make sure that that data centric, privacy and security is there in my device and that connects to the third party cloud vendors versus I can't transfer traffic to mine, everything to my VPN. So the edge is going to become a lot more compute intensive as well as it will require a lot of the elements that are, to be honest, used to be data center centric. We have to lighten them and bring them to the edge so enterprises can feel assured and working because at the end of the day, they have to run a business by the standards that an enterprise is held to. So you will see a ton of innovation, by the way, robotics. Robotics is going to make edge even more interesting in live view. So I see the next couple of years, heavy IOP edge computing, just like the clients that are modeled to mainframe that the PC became like a mainframe in terms of compute capacity. I guarantee at the desktop, compute capacity will go down to the edge and we're going to see that happen in the next five years or so. >> The edge is the new data centers. I always say, it's the land is the way, the way is the land. Amit, great to see you and thanks for sharing and I'm sorry, we can't do it in person but this has been like a fireside chat meets CUBE interview, remote. Thanks for spending the time and sharing your insights and we've always had great interviews at your events, virtual again, this year. We're going to spread it out over time, good call. Thanks for coming on, I appreciate it. >> Thanks, John, take care. >> Okay, Amit, CEO of Informatica, always great to get the conversation updates from him on the industry and what Informatica, as at the center of the value proposition data 4.0. This is really the new transformation, not transition, data science, data, data engineering, all happening. theCUBE with our remote interviews, bringing you all the coverage here from our Palo Alto studios, I'm John Furrier. Thanks for watching. (gentle music)

>> live from Boston, Massachusetts. It's the Cube covering AWS reinforce 2019. Brought to you by Amazon Web service is and its ecosystem partners. >> Okay, welcome back. It was two cubes. Live coverage in Boston, Massachusetts, for Amazon Web services reinforces A W s, his first inaugural conference around security, cloud security and all the benefits of security vendors of bringing. We're here with a man who runs the marketplace and more. Dave McCann Cube, alumni vice president of migration, marketplace and control surfaces. That's a new tail you were that you have here since the last time we talked. Lots changed. Give us the update. Welcome to the Cube. >> Great to be back, ma'am. Believe it's seven months of every event. >> Feels like this. Seven years. You know, you've got a lot new things happening. >> We do >> explain. You have new responsibility. You got the marketplace, which we talked about a great product solutions. What else do you have? >> So we've obviously been expanding our service portfolio, right? So either us is launching. New service is all the time. We have a set of service is a road in the migration of software. So I run. No, the immigration Service's team and interesting. We were sitting in Boston, and that's actually headquartered 800 yards down the road. So there's a set of surfaces around the tools to help you as a CEO. Move your applications onto the clothes. Marketplace is obviously where we want you to find short where you need to buy. And then once you get into the topic of governance, we had one product called Service Catalog and reinvent. We announced a new product. That was a preview called Control. Yesterday we went to G A full availability off control, Terror and Control term service catalog together are in the government space, but we're calling them control service is because it's around controlling the access off teams to particular resources. So that's control service. >> What people moving into the cloud and give us a sense of the the workload. I know you see everything but any patterns that you can see a >> lot of patterns and merging and migration, and they are very industry specific. But there are some common patterns, so you know we're doing migrations and frozen companies were weighed and professional service is run by. Todd Weatherby is engaged in hundreds of those migrations. But we also have no over 70 partners that we've certified of migration partners. Migration partners are doing three times as many migrations as our old professional service is. Team are doing so in collection. There's a lot going on there, one of the common patterns. First of all, everybody is moved a Web development other websites have done. They're all running on the AWS know what they're doing is they're modernizing new applications. So the building in Europe or bring enough over moving onto containers. So it was a lie that ran on a sever server on. As they move into the clothes, they're gonna reshape the throw away. Some of the court brief the court up into micro service is on. Deploy out, Let's see on E. C s, which is continuing. There's a lot of application organization, and then on the migration side, we're seeing applications clearly were migrating a lost a lot of ASAP. So the big partners like Deloitte and Accenture are doing a C P migrations, and we've done a lot of ASAP migrations. And then there are other business applications are being moved with particular software vendors. You know there's a company here in Boston called Pegasystems. They do a world leading workflow platform. We've worked with Pagan, and we have migrated loss of paga warped floors in dozens of paying customers up on the float. >> You innovated on the marketplace, which is where people buy so they can contract with software. So now you got moving to the cloud, buying on the cloud, consuming the cloud and then governing it and managing that aspect all under one cohesive unit. That's you. Is that good? >> Yeah, it's a good way to think about it. It's a san of engineering teams with Coleman purpose for the customer. So you know, one of the things we do AWS is we innovate a lot, and then we organize the engineering teams around a common customer needs. So we said, above all of the computer stories service is on. We pay attention to the application layer. We described the application, So if you think of a migration service is says, I've actually got a service called Discovery, I crawl over your servers and I find what you have way. Then what we do is we have a tool that says, Are you gonna bring and move the till. So you have to build a business case. We just bought a company in Canada called TSA Logic. They had a Super Two for building a business case that said, what would this absolutely running with either of us. >> So is the need of the business case. What's the courtney that you guys have focused on? What was that? >> So, interestingly, we run more Windows Server and the clothes when Microsoft. So you actually have to business keys here. So many windows servers are running on print. What does it look like when a run on either the U. S. And T s so logic? Really good, too. And we find our customers using it. That says, Here's your own prim Windows server configuration with an app on run the mortal What would it look like when it runs on AWS? >> But why would you just do that with a spreadsheet? What? What is the T s so logic do that you couldn't do especially >> well? First of all, you want to make a simple too Somebody has to go run a spreadsheet. They've turned it into a tool that a business years Ercan used a sales person you could use on. They've built on top of a database. So it's got a rich set of choices. You are richer than you put in. A special with a U IE is intuitive, and you're gonna learn it in 20 minutes. I'm not gonna have you made up >> this date in their best practice things like that that you can draw a library >> of what's going down, and it keeps the data store of all the ones we've done. So we're turning that into two. Were giving Old Toller solution architect. >> Well, you got a good thing going on with the marketplace. Good to see you wrapping around those needs there. I gotta ask for the marketplace. Just give us the latest stats. How many subscriptions air in the marketplace these days? What's the overall number in the marketplace? It's >> pretty exciting. Way decided just at San Francisco to announce that we now have over 1,000,000 active subscriptions in the marketplace, which is a main boggling number on its own 1,000,000 subscriptions. Ice of Scrape. Within those subscriptions, we've got over 240 foes and active accounts, you know, and the audience doors you could be an enterprise with 100 cases and in an enterprise. What we typically see is that there are seven or eight teams that are buying or using software, so we'll have seven or eight accounts that have the right to subscribe. So you could be a one team and you're in another team you're buying B I tools. You're buying security tools. So those accounts on what? We're announcing the show for the first time ever. Its security is we have over 100,000 security subscriptions. That's a while. That's a big number. Some companies only have 100 customers, and the market, please. Our customers are switched on 100,000 security. So >> many product listings is that roughly it's just security security. At 300 >> there's over 100 listings. Thing is a product with a price okay on a vendor could be Let's see Paolo off networks or crowdstrike or trains or semantic or McAfee or a brand new company like Twist located of Israel. These companies might have one offer or 20 offers, so we have over 800 offers from over 300. Vendors were having new vendors every week. >> That's the next question. How many security app developers are eyes? Do you have over 300? 300? Okay. About 100. Anyway, I heard >> this morning from Gartner that they believe that are over 1000 security vendors. So I'm only 30% done. I got a little work >> tonight. How >> do you >> govern all this stuff? I was a customer. Sort of Make sure that they're in compliance. >> Great question. Steven Smith yesterday was talking about governance once she moved things on the clothes. It's very elastic. You could be running it today, not running a tomato, running it in I d running in Sydney. So it's easy to fire up running everywhere. So how did the governance team of a company nor watch running where you know, you get into tagging, everything has to be tagged. Everything has to have a cord attached to it. And then you do want to control who gets to use what I may have bought about a cuter appliance. But I don't know that I gave you rates to use it, right, so we could have border on behalf of the company. But I need to grant you access. So we launched a couple of years ago. Service catalog is our first governance to and yesterday we went into full release over new to call the control tower. >> Right. What you announced way reinvents >> preview. And yesterday we went to Jenny. What control does is it Natural Owes me to set up a set of accounts. So if you think of it, your development team, you've got David Kay and tested and the product ain't your brand new to the company. I'm a little worried. What, you're going to get up. You >> don't want to give him the keys to the kingdom, >> so I'm actually going to grant you access to a set of resources, and then I'm gonna apply some rules, or what we call God reels is your brand. You you haven't read my manual, you're in the company. So I'm gonna put a set of God reels on you to make sure that you follow our guide length >> Just training. And so is pressing the wrong button, that kind of thing. So I gotta ask you I mean, on the buying side consumption. I heard you say in a talk upstairs on Monday. You have a buyer, buyer, lead, engineering teams and cellar Let engineering, which tells me that you got a lot of innovation going on the marketplace. So the results are obviously they mention the listings. But one of the trends that's here security conference and it was proper is ecosystems importance in monetization. So back in the old days, Channel partners were a big part of the old computer industry. You're essentially going direct with service listings, which is great. How does that help the channel? Is there sinking around channel as a buyer opportunity? How do you How does that work with the market? Is what your thinking around the relationship between the scale of a simplicity and efficiency, the marketplace with the relationships the channel partners may have with their customers? And how do you bridge that together? What's the thinking >> you've overstayed? Been around a long time? >> Uh, so you have 90 Sydney? Well, the channels have been modernizes the nineties. You think about a >> long time. It's really interesting when we conceived Market please candidly. Way didn't put the channel in marketplace, and in retrospect, that was a miss. Our customers are big customers or small customers. Trust some of the resellers. Some resellers operates surely on price. Some resellers bring a lot of knowledge, even the biggest of the global 2000 Fortune 100. They have a prepared advisor. Let's take a company record. You often got 700 security engineers that are blue chip companies in America trusts or they buy the software the adoptive recommends. So mark it, please really didn't accommodate for Let's Pick another One in Europe, it would be computer center. So in the last two years we've dedicated the data separate engineering team were actually opened up. A team in a different city on their sole customer is a reseller. And so we launch this thing called Consulting Partner Private offer. And so now you're Palo. Also, for your trained, you can authorize active or serious or s h I to be the re sailor at this corporation, and they can actually negotiate the price, which is what a role resellers do. They negotiate price in terms, so we've actually true reseller >> write software for fulfillment through the marketplace. Four partners which are now customers to you now so that they could wrap service is because that's something we talk to. People in the Channel number one conversation is we love the cloud. But how do I make money and that is Service is right. They all want to wrap Service's around, So okay, you guys are delivering this. Is that my getting that right? You guys are riding a direct link in tow marketplace for partners, and they could wrap service is around there, >> will you? Seeing two things? First of all, yes. We're lowering the resale of to sell the software for absolutely. So you re sailor, you can quote software you build rebuild for you so that I become the billing partner for a serious or a billing partner for active on active can use marketplace to fulfill clothes software for their customers. Dan Burns to see you about pretty happy. You crossed the line into a second scenario, which is condone burns attached. Service is on. Clearly, that's a use case we hear usually would we hear use cases way end up through feeling that a little, little not a use case I have enabled, but we've done >> what you're working on It. We've had what the customer. How does the reseller get into the marketplace? What kind of requirements are there. Is it? Is it different than some of your other partners, or is it sort of a similar framework? >> They have to become an approved resale or so First of all, they have to be in a peon partner. I mean, we work tightly with a p N e p M screens partners for AWS. So Josh Hoffman's team Terry Wise, his team, whole part of team screen. The reseller we would only work with resellers are screened and approved by the PM Wants the AP en approved way have no set up a dedicated program team. They work with a reseller with trained them what's involved. Ultimately, however, the relationship is between Splunk in a tree sailor, a five and a three sailor named after a tree sailor or Paulo trend or Croat straight. So it's up to the I S V to tail us that hey, computer centers my reseller. I don't control that relationship. A fulfillment agent you crow strike to save resellers, and I simply have to meet that work so that I get the end customer happy. >> So your enabler in that instance, that's really no, I'm >> really an engine, even team for everybody engineer for the Iast way, engineer for the buyer. And they have to engineer for the re. So >> you have your hands in a lot of the action because you're in the middle of all this marketplace and you must do a lot of planning. I gotta ask you the question and this comes up. That kind of put on my learning all the Amazon lingo covering reinvent for eight years and covering all the different events. So you gotta raise the bar, which is an internal. You keep innovating. Andy Jassy always sucks about removing the undifferentiated heavy lifting. So what is the undifferentiated heavy lifting that you're working toe automate for your customers? >> Great questions. Right now there's probably three. We'll see what the buyer friction is, and then we'll talk about what the sale of friction is. The buyer frustration that is, undifferentiated. Heavy lifting is the interestingly, it's the team process around choosing software. So a couple of customers were on stage yesterday right on those big institutions talked about security software. But in order for an institution to buy that software, there are five groups involved. Security director is choosing the vendor, but procurement has to be involved. Andre. No procurement. We can't be left out the bit. So yesterday we did. The integration to Cooper is a procurement system. So that friction is by subscribing marketplace tied round. Match it with appeal because the p O is what goes on the ledgers with the company. A purchase order. So that has to be a match in purchase order for the marketplace subscription. And then engineers don't Tidwell engineers to always remember you didn't tag it. Hi, this finance nowhere being spent. So we're doing work on working service catalog to do more tagging. And so the buyer wants good tagging procurement integrated. So we're working on a walk slow between marketplace service catalog for procurement. >> Tiring. So you've kind of eliminated procurement or are eliminating procurement as a potential blocker, they use another. Actually, we won't be >> apart for leading procurement. VPs want their V piece of engineering to be happy. >> This is legal. Next. Actually, Greek question. We actually tackled >> legal. First, we did something called Enterprise Code tracked and our customer advisory board Two years ago, one of our buyers, one of our customers, said we're gonna be 100 vendors to deploy it. We're not doing 100 tracks. We've only got one lawyer, You know, 6000 engineers and one lawyer. Well, lawyers, good cord is quickly. So we've created a standard contract. It take stain to persuade legal cause at risk. So we've got a whole bunch of corporations adopting enterprise contract, and we're up to over 75 companies adopting enterprise contract. But legal is apartment >> so modernizing the procurement, a key goal >> procurement, legal, security, engineering. And then the next one is I t finance. So if you think of our budgets on their course teams on AWS, everything needs to be can become visible in either of US budgets. And everything has become visible in course exporter. So we have to call the rate tags. >> I heard a stat that 6,000,000 After moving to the cloud in the next 6,000,000 3 to 5 years, security as a focus reinforces not a summit. It's branded as a W s reinforce, just like reinvents. Same kind of five year for security. What's your impression of the show so far? No, you've been highly active speaking, doing briefing started a customer's burn, the midnight oil with partners and customers What's that? What's your vibe of the show? What's your takeaway? What's the most important thing happening here? What's your what's your summary? >> So I always think you get the truth in the booth. Cut to the chase. I made a customer last night from a major media company who we all know who's in Los Angeles. His comment was weeks, either. These expectations wasn't she wanted to come because he goes to reinvent. Why am I coming to Boston in June? Because I'm gonna go to reinvent November on this. The rates of security for a major media company last night basically said, I love the love. The subject matter, right? It's so security centric. He actually ended up bringing a bunch of people from his team on, and he loves the topics in the stations. The other thing he loved was everybody. Here is insecurity, reinvent. There's lots of people from what's the functions, But everybody here is a security professional. So that was the director of security for a media company. He was at an event talking to one of the suppliers, the marketplace. I asked this president of a very well known security vendor and I said. So what's your reaction to reinforce? And he said, Frankly, when you guys told me it was coming, we didn't really want the bother. It's the end of the quarter. It's a busy time of year. It's another event, he said. I am sure glad we came on. He was standing talking to these VP of marketing, saying, We want to bring more people, make sure, So he's overjoyed. His His comment was, when I go to Rio event 50,000 people but only 5% of their own security. I can't reinforce everybody's insecurity >> in Houston in 2020. Any inside US tow? Why Houston? I have no clue what I actually think >> is really smart about the Vineyard, and this is what a customer said Last night. I met a customer from Connecticut who isn't a load to travel far. They don't get to go to reinvent in Vegas. I think what we did when we came to Boston way tapped into all the states that could drive. So there are people here who don't get to go to reinvent. I think when we go to Houston, we're going to get a whole bunch of takes its customers. Yeah, you don't get a flight to Vegas. So I think it's really good for the customer that people who don't get budget to travel >> makes sense on dry kind of a geographic beograd. The world >> if we're expanding the customers that can learn. So from an education point of view, we're just increase the audience that we're teaching. Great, >> Dave. Great to have you on. Thanks for the insights and congratulations on the new responsibility as you get more coz and around marketplace been very successful. 1,000,000 subscriptions. That's good stuff again. They were >> you reinvented and >> a couple of months, Seven days? What? We're excited. I love covering the growth of the clouds. Certainly cloud security of his own conference. Dave McCann, Vice president Marketplace Migration and Control Service is controlled cattle up. How they how you how you move contract and governed applications in the future. All gonna be happening online. Cloud Mr. Q coverage from Boston. They just reinforced. We right back with more after this short break

[Narrator] Live from Las Vegas! It's theCUBE covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone to theCUBE's live coverage of Informatica World, here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We're joined by Sally Jenkins. She is the executive vice president and CMO here at Informatica. Thank you so much for coming on theCUBE, Sally. >> Oh you're welcome, thank you for having me. Its nice to see you all again. >> So congrats on a great show, we're going to get to the stats of the show, but the framework of Informatica World is built around these four customer journeys. Next Gen analytics, Cloud Hybrid, 360 engagement, Data Governance and Privacy. Can you tell our viewers a little bit about how this framework reflects what you're hearing from customers and their priorities >> Yes absolutely, Rebecca and yes, you got the right and in the right order, thank you. So, we started this journey with our customers and trying to understand how do they want to be spoken to. What business problems are they solving? And how do they categorize them, if you will. And so, we've been validating these are the right journeys with our customers over the past few years. So everything that you see here at Informatica World is centered around those journeys. The breakouts, our keynotes, all the signage here in our solutions expo. So, its all in validation of how our customers think, and those business problems they're solving. >> So the show, 2600 attendees from 44 countries, 1200 sessions. What's new, what's new and exciting. >> Oh, gosh, there's so many things that are new this year. And one other stat you forgot, 92 customers presenting in our Breakouts. So our customers love to hear from other customers. As to what journeys they're on, what problems their solving. Those are record numbers for us. Record number of partners sponsoring. We've got AWS, we've got Google, we've got Microsoft, we've got the up and comers, that we're calling in the Cloud and AI Innovation zone. So people like DataBricks and Snowflake. We wanted to highlight these up and comer partners, what we call our ecosystem partners. Along with the big guys. You know, we're the Switzerland of data. We play with everybody. We play nicely with everybody. A lot of new things there. A few other things that are new, direct feedback from our customers last year. They said we want you to tell us which breakouts we should go to. Or what work shops should we attend. So we rolled out two things this year. One's called the Intelligent Scheduler. That's where we ask customers what journey are they on. What do they want to learn about. And then we make a smart recommendation to them about what their agenda should look like while they're here. >> You're using the data. >> Yes, AI, we're involving AI, and making the recommendations out to our customers. In addition, our customers said we want to connect with other customers that are like us, on their journeys, so we can learn from them. So we launched we called the Intelligent Connect and again this is part of our app. Which, our app's not new, but what we've done with our app this year is new. We've added gamification, in fact as part of the AI and Cloud Innovation zone, we are asking our customers and all of our attendees to vote on who they think is the one with the best innovation. They're using our app to use voting. They can win things, so there's lots of gaming. There's social that's involved in that, so the app's new. We're taking adavantage of day four. We usually end around lunchtime on day four, this year we're going all in, all day workshops, so that our practitioners can actually roll up their sleeves and get started working with our software. And our ecosystem partners are also leading a lot of those workshops. So a lot that's new this year. And as I mentioned, the Cloud and AI Innovation zone, that's new it's like a booth within a booth here on the solutions expo floor. So this is the year of new, for sure. >> You know one of the things that's been impressive, I was talking with Anil and also Bruce Chizen, who is a board member, The bets you guys have made is impressive. You look back, and this our tenth year in theCUBE, so we go to a lot of events, 100s events in a year, over 100 events over 10 years. We've seen this story with you guys, this is now our fourth year doing theCUBE here. And the story has not changed, its been early moves, big bets. Cloud, early. Going private to see this next big wave. AI, early before everyone else. This is really kind of showing, and I think the ecosystem part is on stage with Databricks, with Snowflake. Really kind of point to a new cast of characters in the ecosystem. >> That's right. >> You're seeing not just the classic enterprise, 'cause you guys have great big, large enterprises that you do business with. That want to be SAS like, they want the agility, they want all those great things but now you have Cloud. The markets seems to have changed. This is an ecosystem opportunity. >> That's right. >> Can you share what's new? Because you see Amazon, Google and Azure, at the cloud, you got On-Premise, you now Edge and IoT, everything's happening with data. Hard, complex, what's new, what's the ecosystem benefit? Can you just share some color commentary around how you guys view that as a company. >> Yeah, thanks, John, and that's a good question. I'm glad you're pointing out that our whole go to market motion is evolving. It's not changing it's evolving because we want to work with our customers in whatever environment they want to work in. So if they're working in a cloud environment, we want to make sure we're there with our cloud ecosystem partners. And it doesn't matter who, cause like I said, we work with everybody, we work nicely with everybody. So we are tying in our cloud ecosystem partners as it makes sense based on what our customer needs are. As well as our GSI partners. So we've got Accentra's here. They brought 35 people to Informatica World this year. We play nicely with Accentra, Deloitte, Cognizant, Capgemini so we really are wanting to make sure that we're doing what makes sense with our customer and working with those partners that our customers want to work with. >> Well I think one of the observations we've made on theCUBE and we said in our opening editorial segment this morning, and we're asking the question about the skill gaps, which we'll get into with you in second, but these big partners from the Global System Integraters to even indirect channel partners, whether they're software developers and or channel partners. They all are now enabled and are mandated to create value. >> Yes, that's right. >> And if they can't get to the value, those projects aren't going to get funded and they're not going to get renewed And so we've seen with the Hadoop cycle of just standing up infrastructure for infrastructure sake isn't going to fly. You got to get to the value. And data, the business that you're in, is the heart of it. >> Well, data's at the heart of it. That's why we're sitting at a really nice sweet spot, because data will always be relevant. And the theme of the conference here is data needs AI and AI needs data. So we're always going to be around. But like I said, I feel like we're sitting right in the middle of it. And we're helping our customers solve really complex problems. And again, like I said if we need to pull in a GSI partner for implementation, we'll do that we've got close to 400,000 people around the world, trained on how to use Informatica solutions. So we're poised and we are ready to go. >> We were talking before we came on camera. We were sitting there catching up, Sally. And I always make these weird metaphors and references, but I think you guys are in an enabling business. It reminds me of VMware, when virtualization came in. Because what that did was, it changed the game on what servers were from a physical footprint, but also changed the economics and change the development landscape. This seems to be the same kind of pattern we're seeing in data where you guys are providing an operational model with technical capabilities. Ecosystem lift, different economics. So kind of similar, and VMware had a good run. >> We'll take that analogy, John, thank you. >> What's your reaction? Do you see it that way? >> Yeah I do, and it all comes back to the journeys that we talk about right. Because our customers, they're never on just one journey. Most of them are on multiple journeys, that they are deploying at the same time. And so as they uncover insights around one journey, it could lead them to the next. So it really comes back to that and data is at the center of all that. >> I want to ask about the skills gap. And this is a problem that the technology industry is facing on a lot of different levels I want to hear about Informatica's thoughts on this. And what you're doing to tackle this problem. And also what kinds of initiatives you're starting around this. >> Well, I'm glad you asked because it's actually top of mind for us. So Informatica is taking a stance in managing the future, so that we can get rid of the skills gap in the future. And last year we launched a program we call the Next 25. That's where we are investing in middle school aged students for the next seven years. Its starts in 6th grade and takes them all the way through high school. They are part of a STEM program, in fact we partnered with Akash middle school here in Las Vegas. Cause we wanted to give back to the local communities since we spend so much time here. And so these kids who are part of the STEM program take part in what we call the Next 25. Where we help them understand beyond academics what they need to learn about in order to be ready for college. Whether that's social skills, or teamwork, or just how do we help them build the self confidence, so it goes beyond the academics. But one of the things that we're talking about tomorrow, is what's next as part of STEM. Cause we all know they're very good at STEM. And so we've engaged with one of the professors at UNLV to talk about what does she see as a gap when she sees middle school students and high school students coming to college and so that's where she recognizes that coding is so important. So we've got a big announcement that we're making tomorrow for the Next 25 kids around coding. >> Its interesting, cause we could talk about this all day, cause my daughter just graduated from Cal, so its fresh in my mind, but I was pointed out at the graduation ceremony on Saturday that the first ever class at University of California Berkley, graduated a data science, they graduated their inaugural class. That goes to show you how early it is. The other thing we're hearing also on these interviews as well as others, that the aperture or the surface area for opportunities isn't just technical. >> Right >> You could be pre med and study machine learning and computer science. There's so much more to it. What do you see just anecdotally or from a personal standpoint and professional, key skills that you think people should hone in on? What dials should they turn? More math, more coding, more cognitive, more social emotional, What do you see as skills they can tailor up for their-- >> Well so let's just start with the data scientist. We know LinkedIn has identified that there are 150,000 job openings just for data scientist in the US alone. So what's more interesting than that, is four times that are available for data engineers. And for the first time ever, data engineers' starting salaries are paying more than starting salaries on Wall Street. So, there's a huge opportunity, just in the data engineering area and the data scientist area. Now you can take that any which way you want. I'm in marketing and we use data all day long to make decisions. You don't have to be, you don't have to go down the engineering path. But you definitely have to have a good understanding of data and how data drives your next decisions, no matter what field you're in. >> And its also those others skills that you were talking about, particularly with those middle school kids, it is the collaboration and the team work and all of those too. >> It does, again, it goes beyond academics. These kids are brilliant. Most of them are 7th or 8th grade. But nothing holds them back, and that's exactly what we're trying to inspire within. So we have them solving big global problems. And you'll hear as they talk about how they're approaching this. They work in teams of five. And they realize to solve huge problems they need to start small and local. So some of these big global problems they're working on, like eradicating poverty, they're starting at the local shelters here in Las Vegas to see how they can start small and make a difference. And this is all on their own, I have folks on my team who are junior genius counselors with them, but that is really to foster some of the conversations. All the new ideas are coming directly from the kids. >> My final question is obviously for the folks who couldn't make it here, watching, know you guys, what's the theme of the show because the news right out of the gate is obviously the big cloud players. That's the key. And the new breed of partners, Snowflake, Databricks as an example. Hallway conversations that I'm hearing, can kind of be geeky and customer focused around "where do I store my data?" so you're seeing a range of conversations. What is the theme this year? What's different this year, or what more the same? Where are you doubling down? What's going on here for the show? What's the main content? >> Well so this is our 20th Informatica World if you can believe that. We've been around for 26 years, but this is our 20th Informatica World. And several years ago we started with the disruptive power of data. Then last year we talked about how we help our customers disrupt intelligently. And this year the theme is around ClAIrity Unleashed. You can tell the theme has been that we've been talking about for the past three years is all underpinned with AI. So it is all about how AI needs data and data needs AI. And how we help bring clarity to our customer's problems through data. >> And a play on words, ClAIr, your AI to clarity. >> Exactly, AI is at the center of our Intelligent data platform. So it is a play on AI but that is where ClAIrity Unleashed comes from. >> Terrific, thank you so much for coming on theCube, Sally. Its great having you. >> Great, thanks Rebecca. Thanks, John. >> Thank you. >> Nice to see you all. >> I'm Rebecca Knight for John Furrier. We will have more from Informatica World, stay tuned. (upbeat pop outro)

>> Live from San Francisco, it's theCUBE covering IBM Think 2019, brought to you by IBM. >> Welcome back everyone, live here in Moscone North in San Francisco, it's theCUBE's exclusive coverage of IBM Think 2019. I'm John Furrier, Dave Vellante. We're bringing down all the action, four days of live coverage. We've got two great guests here, Elenita Elinon, Executive Director of Quantitative Research at JP Morgan Chase, and John Thomas, Distinguished Engineer and Director of the Data Science Elite Team... great team, elite data science team at IBM, and of course, JP Morgan Chase, great innovator. Welcome to theCUBE. >> Welcome. >> Thank you very much. >> Thank you, thank you, guys. >> So I like to dig in, great use case here real customer on the cutting edge, JP Morgan Chase, known for being on the bleeding edge sometimes, but financial, money, speed... time is money, insights is money. >> Absolutely. Yes. >> Tell us what you do at the Quantitative Group. >> Well, first of all, thank you very much for having me here, I'm quite honored. I hope you get something valuable out of what I say here. At the moment, I have two hats on, I am co-head of Quantitative Research Analytics. It's a very small SWAT, very well selected group of technologists who are also physicists and mathematicians, statisticians, high-performance compute experts, machine learning experts, and we help the larger organization of Quantitative Research which is about 700-plus strong, as well as some other technology organizations in the firm to use the latest, greatest technologies. And how we do this is we actually go in there, we're very hands-on, we're working with the systems, we're working with the tools, and we're applying it to real use cases and real business problems that we see in Quantitative Research, and we prove out the technology. We make sure that we're going to save millions of dollars using this thing, or we're going to be able to execute a lot on this particular business that was difficult to execute on before because we didn't have the right compute behind it. So we go in there, we try out these various technologies, we have lots of partnerships with the different vendors, and IBM's been obviously one of few, very major vendors that we work with, and we find the ones that work. We have an influencing role as well in the organization, so we go out and tell people, "Hey, look, "this particular tool, perfect for this type of problem. "You should try it out." We help them set it up. They can't figure out the technology? We help them out. We're kind of like what I said, we're a SWAT team, very small compared to the rest of the organization, but we add a lot of value. >> You guys are the brain trust too. You've got the math skills, you've got the quantitative modeling going on, and it's a competitive advantage for your business. This is like a key thing, a lot of new things are emerging. One of things we're seeing here in the industry, certainly at this show, it's not your yesterday's machine learning. There's certainly math involved, you've got cognition and math kind of coming together, deterministic, non-deterministic elements, you guys are seeing these front edge, the problems, opportunities, for you guys. How do you see that world evolving because you got the classic math, school of math machine learning, and then the school of learning machines coming together? What kind of problems do you see these things, this kind of new model attacking? >> So we're making a very, very large investment in machine learning and data science as a whole in the organization. You probably heard in the press that we've brought in the Head of Machine Learning from CMU, Manuela Veloso. She's now heading up the AI Research Organization, JP Morgan, and she's making herself very available to the rest of the firm, setting strategies, trying different things out, partnering with the businesses, and making sure that she understands the use case of where machine learning will be a success. We've also put a lot of investments in tooling and hiring the right kinds of people from the right kinds of universities. My organization, we're changing the focus in our recruiting efforts to bring in more data science and machine learning. But, I think the most important thing, in addition to all that investment is that we, first and foremost, understand our own problems, we work with researchers, we work with IBM, we work with the vendors, and say, "Okay, this is the types of problems, "what is the best thing to throw at it?" And then we PoC, we prove it out, we look for the small wins, we try to strategize, and then we come up with the recommendations for a full-out, scalable architecture. >> John, talk about the IBM Elite Program. You guys roll your sleeves up. It's a service that you guys provide with your top clients. You bring in the best and you just jump in, co-create opportunities together, solving problems. >> That is exactly right. >> How does this work? What's your relationship with JP Morgan Chase? What specific use case are you going after? What are the opportunities? >> Yeah, so the Data Science Elite Team was setup to really help our top clients in their AI journey, in terms of bringing skills, tools, expertise to work collaboratively with clients like JP Morgan Chase. It's been a great partnership working with Elenita and her team. We've had some very interesting use cases related to her model risk management platform, and some interesting challenges in that space about how do you apply machine learning and deep learning to solve those problems. >> So what exactly is model risk management? How does that all work? >> Good question. (laughing) That's why we're building a very large platform around it. So model risk is one of several types of risk that we worry about and keep us awake at night. There's a long history of risk management in the banks. Of course, there's credit risk, there's market risk, these are all very well-known, very quantified risks. Model risk isn't a number, right? You can't say, "this model, which is some stochastic model "it's going to cost us X million dollars today," right? We currently... it's so somewhat new, and at the moment, it's more prescriptive and things like, you can't do that, or you can use that model in this context, or you can't use it for this type of trade. It's very difficult to automate that type of model risk in the banks, so I'm attempting to put together a platform that captures all of the prescriptive, and the conditions, and the restrictions around what to do, and what to use models for in the bank. Making sure that we actually know this in real time, or at least when the trade is being booked, We have an awareness of where these models are getting somewhat abused, right? We look out for those types of situations, and we make sure that we alert the correct stakeholders, and they do something about it. >> So in essence, you're governing the application of the model, and then learning as you go on, in terms of-- >> That's the second phase. So we do want to learn at the moment, what's in production today. Morpheus running in production, it's running against all of the trading systems in the firm, inside the investment bank. We want to make sure that as these trades are getting booked from day to day, we understand which ones are risky, and we flag those. There's no learning yet in that, but what we've worked with John on are the potential uses of machine learning to help us manage all those risks because it's difficult. There's a lot of data out there. I was just saying, "I don't want our Quants to do stupid things," 'cause there's too much stupidity happening right now. We're looking at emails, we're looking at data that doesn't make sense, so Morpheus is an attempt to make all of that understandable, and make the whole workflow efficient. >> So it's financial programming in a way, that's come with a whole scale of computing, a model gone astray could be very dangerous? >> Absolutely. >> This is what you're getting at right? >> It will cost real money to the firm. This is all the use-- >> So a model to watch the model? So policing the models, kind of watching-- >> Yes, another model. >> When you have to isolate the contribution of the model not like you saying before, "Are there market risks "or other types of risks--" >> Correct. >> You isolate it to the narrow component. >> And there's a lot of work. We work with the Model Governance Organization, another several hundred person organization, and that's all they do. They figure out, they review the models, they understand what the risk of the models are. Now, it's the job of my team to take what they say, which could be very easy to interpret or very hard, and there's a little bit of NLP that I think is potentially useful there, to convert what they say about a model, and what controls around the model are to something that we can systematize and run everyday, and possibly even in real time. >> This is really about getting it right and not letting it get out of control, but also this is where the scale comes in so when you get the model right, you can deploy it, manage it in a way that helps the business, versus if someone throws the wrong number in there, or the classic "we've got a model for that." >> Right, exactly. (laughing) There's two things here, right? There's the ability to monitor a model such that we don't pay fines, and we don't go out of compliance, and there's the ability to use the model exactly to the extreme where we're still within compliance, and make money, right? 'Cause we want to use these models and make our business stronger. >> There's consequences too, I mean, if it's an opportunity, there's upside, it's a problem, there's downside. You guys look at the quantification of those kinds of consequences where the risk management comes in? >> Yeah, absolutely. And there's real money that's at stake here, right? If the regulators decide that a model's too risky, you have to set aside a certain amount of capital so that you're basically protecting your investors and your business, and the stakeholders. If that's done incorrectly, we end up putting a lot more capital in reserve than we should be, and that's a bad thing. So quantifying the risks correctly and accurately is a very important part of what we do. >> So a lot of skillsets obviously, and I always say, "In the money business, you want the best nerds." Don't hate me for saying that... the smartest people. What are some of the challenges that are unique to model risk management that you might not see in sort of other risk management approaches? >> There are some technical challenges, right? The volume of data that you're dealing with is very large. If you are building... so at the very simplistic level, you have classification problems that you're addressing with data that might not actually be all there, so that is one. When you get into time series analysis for exposure prediction and so on, these are complex problems to handle. The training time for these models, especially deep learning models, if you are doing time series analysis, can be pretty challenging. Data volume, training time for models, how do you turn this around quickly? We use a combination of technologies for some of these use cases. Watson Studio running on power hardware with GPUs. So the idea here is you can cut down your model training time dramatically and we saw that as part of the-- >> Talk about how that works because this is something that we're seeing people move from manual to automated machine learning and deep learning, it give you augmented assistance to get this to the market. How does it actually work? >> So there is a training part of this, and then there is the operationalizing part of this, right? At the training part itself, you have a challenge, which is you're dealing with very large data volumes, you're dealing with training times that need to be shrunk down. And having a platform that allows you to do that, so you build models quickly, your data science folks can iterate through model creation very quickly is essential. But then, once the models have been built, how do you operationalize those models? How do you actually invoke the models at scale? How do you do workflow management of those models? How do you make sure that a certain exposure model is not thrashing some other models that are also essential to the business? How do you do policies and workflow management? >> And on top of that, we need to be very transparent, right? If the model is used to make certain decisions that have obvious impact financially on the bottom line, and an auditor comes back and says, "Okay, you made this trade so and so, why? What was happening at that time?" So we need to be able to capture and snapshot and understand what the model was doing at that particular instant in time, and go back and understand the inputs that went into that model and made it operate the way it did. >> It can't be a black box. >> It cannot be, yeah. >> Holistically, you got to look at the time series in real time, when things were happening and happened, happening, and then holistically tie that together. Is that kind of the impact analysis? >> We have to make our regulars happy. (laughing) That's number one, and we have to make our traders happy. We, as quantitative researchers, we're the ones that give them the hard math and the models, and then they use it. They use their own skillsets too to apply them, but-- >> What's the biggest needs that your stakeholders on the trading side want, and what's the needs on the compliance side, the traders want more, they want to move quickly? >> They're coming from different sides of it. Traders want to make more money, right? And they want to make decisions quickly. They want all the tools to tell them what to do, and for them to exercise whatever they normally exercise-- >> They want a competitive advantage. >> They want that competitive advantage, and they're also... we've got algo-trades as well, we want to have the best algo behind our trading. >> And the regulator side, we just want to make sure laws aren't broken, that there's auditing-- >> We use the phrase, "model explainability," right? Can you explain how the model came to a conclusion, right? Can you make sure that there is no bias in the model? How can you ensure the models are fair? And if you can detect there is a drift, what do you do to correct that? So that is very important. >> Do you have means of detecting sort of misuse of the model? Is that part of the governance process? >> That is exactly what Morpheus is doing. The unique thing about Morpheus is that we're tied into the risk management systems in the investment bank. We're actually running the same exact code that's pricing these trades, and what that brings is the ability to really understand pretty much the full stack trace of what's going into the price of a trade. We also have captured the restrictions and the conditions. It's in the Python script, it's essentially Python. And we can marry the two, and we can do all the checks that the governance person indicated we should be doing, and so we know, okay, if this trade is operating beyond maturity or a certain maturity, or beyond a certain expiry, we'll know that, and then we'll tag that information. >> And just for clarification, Morpheus is the name of the platform that does the-- >> Morpheus is the name of the model risk platform that I'm building out, yes. >> A final question for you, what's the biggest challenge that you guys have seen from a complexity standpoint that you're solving? What's the big complex... You don't want to just be rubber-stamping models. You want to solve big problems. What are the big problems that you guys are going after? >> I have many big problems. (laughing) >> Opportunities. >> The one that is right now facing me, is the problem of metadata, data ingestion, getting disparate sources, getting different disparate data from different sources. One source calls it a delta, this other source calls it something else. We've got a strategic data warehouse, that's supposed to take all of these exposures and make sense out of it. I'm in the middle because they're there, probably at the ten-year roadmap, who knows? And I have a one-month roadmap, I have something that was due last week and I need to come up with these regulatory reports today. So what I end up doing is a mix of a tactical strategic data ingestion, and I have to make sense of the data that I'm getting. So I need tools out there that will help support that type of data ingestion problem that will also lead the way towards the more strategic one, where we're better integrated with this-- >> John, talk about how you solve the problems? What are some of the things that you guys do? Give the plug for IBM real quick, 'cause I know you guys got the Studio. Explain how you guys are helping and working with JP Morgan Chase. >> Yeah, I touched upon this briefly earlier, which is from the model training perspective, Watson Studio running on Power hardware is very powerful, in terms of cutting down training time, right? But you've got to go beyond model building to how do you operationalize these models? How do I deploy these models at scale? How do I define workload management policies for these models, and connecting to their backbone. So that is part of this, and model explainability, we touched upon that, to eliminate this problem of how do I ingest data from different sources without having to manually oversee all of that. We need to manually apply auto-classification at the time of ingestion. Can I capture metadata around the model and reconcile data from different data sources as the data is being brought in? And can I apply ML to solve that problem, right? There is multiple applications of ML along this workflow. >> Talk about real quick, comment before we break, I want to get this in, machine learning has been around for a while now with compute and scale. It really is a renaissance in AI, it's great things are happening. But what feeds machine learning is data, the cleaner the data, the better the AI, the better the machine learning, so data cleanliness now has to be more real-time, it's less of a cleaning group, right? It used to be clean the data, bring it in, wrangle it, now you got to be much more agile, use speed of compute to make sure that you're qualifying data before it comes in, these machine learning. How do you guys see that rolling out, is that impacting you now? Are you thinking about it? How should people think about data quality as an input in machine learning? >> Well, I think the whole problem of setting up an application properly for data science and machine learning is really making sure that from the beginning, you're designing, and you're thinking about all of these problems of data quality, if it's the speed of ingestion, the speed of publication, all of that stuff. You need to think about the beginning, set yourself up to have the right elements, and it may not all be built out, and that's been a big strategy I've had with Morpheus. I've had a very small team working on it, but we think ahead and we put elements of the right components in place so data quality is just one of those things, and we're always trying to find the right tool sets that will enable use to do that better, faster, quicker. One of the things I'd like to do is to upscale and uplift the skillsets on my team, so that we are building the right things in the system from the beginning. >> A lot of that's math too, right? I mean, you talk about classification, getting that right upfront. Mathematics is-- >> And we'll continue to partner with Elenita and her team on this, and this helps us shape the direction in which our data science offerings go because we need to address complex enterprise challenges. >> I think you guys are really onto something big. I love the elite program, but I think having the small team, thinking about the model, thinking about the business model, the team model before you build the technology build-out, is super important, that seems to be the new model versus the old days, build some great technology and then, we'll put a team around it. So you see the world kind of being a little bit more... it's easier to build out and acquire technology, than to get it right, that seems to be the trend here. Congratulations. >> Thank you. >> Thanks for coming on. I appreciate it. theCUBE here, CUBE Conversations here. We're live in San Francisco, IBM Think. I'm John Furrier, Dave Vellante, stay with us for more day two coverage. Four days we'll be here in the hallway and lobby of Moscone North, stay with us.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Hello, everyone, welcome back. This is theCUBE's exclusive coverage of Informatica World 2018. It's our fourth year covering Informatica on the front lines. Every year it gets bigger and bigger. I'm John Furrier, the host of theCUBE, with Peter Burris, my co-host, with some, chief analyst at Wikibon and SiliconANGLE on theCUBE. Our next guest is theCUBE alumni Amit Walia, who's been on many times, even before he was president. Now he's the president of products and strategic ecosystems for Informatica. Great to see you, great to have you on. Congratulations on your keynote. Thanks for stopping by. >> Thanks, John, glad to be here. Always good to be back. >> You're super, well, I love talking with you because one, you know, the business is growing. You've been in the product side, you guys are all great product folks. And this, they're shipping products. It's not like it's, like, vaporware. It's, like, great stuff. Now Azure deal was announced. But now the timing of the data play with Switzerland, we talked about this fabric, better time than ever. This year, you got data lakes turned into data swamps last year. This year it's about governance and catalog. Good timing. What's your assessment? Give us your point of view from the keynote, timing, product. >> Well, I mean, I think you're exactly right. We see that it's a unique time, and it was building over the last couple of years. So, you know, we have this phrase that this is a data 3.0 world where data has become its own thing. It's no more captive to an application or a database. Those days are gone. And I think in data 3.0 world, I think we talked about it this morning in my keynote, that, you know, customers have to step back and think differently. You can't just do the same old things and expect to be different, and especially as they're driving digital transformations. So we introduced this concept of system thinking 3.0, where as you're thinking about a data, you have to think about it as a platform. A nimble platform, not a ERP-ish platform. Think of it at scale. It's doubling every year. >> Yeah. >> Think of it metadata in, metadata out. Let AI assist you. You know, you've got to have, we as humans are just going to be swamped with so much data we can't process it. And last, very important thesis, as you all know, is that governance, security, and privacy have to be design principles. They cannot be an afterthought. >> Last year you announced CLAIRE AI component of the system. >> Amit: Yeah. >> How has that evolved this year? I mean, I know it was a strategic centerpiece for you guys. Obviously the catalog is looking really strong right now, a lot of buzzing to show around the enterprise catalog. Where is the AI, the CLAIRE piece fitting in? Can you just give us the update on CLAIRE? >> Well, CLAIRE's come a long way. Basically part of every product we have. So it manifests itself probably most holistically in the catalog, but whether in the data lake, it's in the context of surfacing data, discovering data, giving recommendations of data to an analyst in a very business user context, all in the context of an MBM, giving you relationship discovery of, let's say, John, who you are, into who you are. So it is in Secure@Source helping anomaly detection happen. So CLAIRE has now made its way into every product. But as you said, the one product where it basically surfaces itself in its full bloom is the catalog, which, by the way, has been the fastest growing product in Informatica's history. One year since launch, it has just gone, taken off. >> Well, presumably there's a relationship. Sorry, John. Presumably there's relationship there. Catalogs have been around for years, but they've been very, very difficult to build and sustain and maintain. CLAIRE presumably is providing a capability that removes a lot of the drudgery associated with catalogs, and that's one of the things that's making it possible. Have I got that right? >> No, yeah, absolutely. And actually, building the new catalog also has been a hard thing. So in some ways building it for scale has been a massive common sense problem that we've been solving for the last three, four years. You know, collecting metadata across the full enterprise is a non-trivial activity, so it was never done across the enterprise ever. If you remember when I was here last time, our vision for the catalog was very simple. We want to be the Google for enterprise data... >> Peter: Yeah. >> ...through metadata. >> And that's what we were able to do through the catalog. But as you rightfully said, it's very hard to consume it if you don't write AI to help it. That's where CLAIRE made a very big road. So the UI's very straightforward. It's a Google UI, and any business user can, with the help of CLAIRE, start using it. >> But it persists. >> Yes. >> So unlike just putting a search term in and getting a page of stuff back, a catalog has to persist. >> Has a persistence, exactly. >> And so describe, now that you have that in place with CLAIRE, as John asked, where does it go? >> Solving use cases. Actually, I'll give you a little preview. Tomorrow I do the closing keynote, and usually what I do, the closing keynote is all about features. So actually, it's a whole demo on CLAIRE where we're taking CLAIRE to the whole next level. As a great example, you know, building data supply chains, you know, it's a manual activity that you have to do. With the help of the catalog, we actually understand the system architecture. So if you want to add new sources of data or change anything you want to do, you don't have to go through those steps again. We will service it to you and we'll tell you what to do. In fact, tomorrow I'll show what we call a self-integrating system. It'll happen by itself. You have to just go and say whether I agree or not agree and the machine learns. Next time it gets smarter and smarter. Or in the context of governance. If a new policy comes up in an enterprise, the biggest challenge is how do I even know what the impact of the new policy is? Look at GDPR right now. So with the help of CLAIRE, we can understand across the entire enterprise what would be the impact of that policy across different functions and what the gaps are. Those are the kind of places we are taking CLAIRE towards more bigger business-driven initiatives. In fact, tomorrow there'll be a whole demo on that one. >> I mean, GDPR is interesting because it really exposes who's ready. >> Yeah. >> Who has had invested their, the engineering in data, understands the data. So that's clear. We're seeing some, and it's also a shot across the bow of companies saying, look, you got to think strategically around your data. We talk about this all the time with you guys, so it's not new to us, but it is new to the fact that some people are right now sitting there going, oh no, I need to do something. >> Amit: Yeah. >> How is Informatica going to help me if I have a GDPR awakening of, oh man, I got to do something. >> You know, GDPR... >> Do I just call you up and do the, roll in the catalog? Do I... >> That's a great place to begin, by the way. So GDPR, by the way, is a data problem. So GDPR is not necessarily a compliance/security problem, because you want to understand which data pass through boundaries, who's accessing it. It's a true data problem. So today, I mean, in fact, at Informatica World, we have customers like PayPal talking about their journey with us on GDPR. And so you begin with the catalog, and then we have three products that help in the GDPR journey, the catalog, Secure@Source, and the Data Governance Axon product. And again, each company's GDPR implications are slightly different, and companies, as I said, like MasterCard, like PayPal, that are using our products to run their GDPR activity right now, it's a... So we are seeing that going through the roof. And in fact, one of the big use cases for catalog has been in the context of governance and GDPR. >> I want to talk about the trends on, that are impacting you guys. Again, I was saying earlier that it's a tailwind for you guys. The timing's perfect. Multi-cloud, hybrid cloud. I'd say hybrid cloud's probably in its second year, maybe third year hype, but now multi-cloud is real. You have announced a Azure relationship. You guys have a growing ecosystem opportunity. >> Amit: Yeah. >> How are you guys looking at it? 'Cause it's really emergent. It's happening right now. How are you guys targeting the ecosystem, whether it's business development partnerships, joint product development go to market, and/or on the business side? What's the orientation, what's the posture? Are you guy taking a certain approach, expecting certain growth? What's the update on the ecosystem, the global partner landscape? >> You know, the way we think about ourselves is that we've been the Switzerland of data always. And customers, actually, I always say it's always customer-backed. >> John: Yeah. >> If you solve for the customer, everything goes good. Customers expect us to do that. And customers are going to be in a heterogeneous world. Nobody's going to ever pick one stack. You know, you all know, right, there are customers who are still, larger devices still running mainframe for some processing, and they are already using new platforms for IoT, so they have to somehow manage this entire transition, and there will be multi-cloud, cloud hybrid world. So they naturally expect us to be a Switzerland of data across the board, and that's our overall strategy. We will always be there for them. In that context, we work with, we have learned the art of working with their ecosystem. >> John: Yeah. >> So you saw Azure today, and we are very close partners of hundreds of customers. Amazon, hundreds of customers. Google's coming up. So those are common. So we, Adobe, tomorrow you'll have Adobe. >> John: So you're cool with all the cloud players. >> And, you know, I always look at it this way. If you solve for the customer, everybody will work with you, and I think we're doing meaningful work. So that's helping our strategy. But what we have done two very different things with that. We've gone deep in terms of product integration. I mean, you saw today. We are making it easy from a customer experience point of view to get these jobs done, right? If you are spinning up a data warehouse in the cloud, you don't want to repeat the mistakes of the last 20 years. So now it's five clicks, you should be good to go. >> John: Yeah. >> That's an area we've invested a lot to make sure that those experiences are a lot simpler and easier and very native. >> We had Bruce Chizen on earlier. He was implying that you guys have significant R&D, and he was trying to get me to get you the number. I think it was on Twitter. I think I'll ask Neal. I think he's out there already. But it's not so much the numbers. It's about the investment and the mindset you guys have for R&D. I know you had, went with a private equity company. >> Amit: Yes. >> We talked about that. >> You guy are growing. >> So this is a growth company. >> Amit: Yeah. >> You need R&D. >> Absolutely. >> What is the priority? How are you looking at that? How would you talk to the industry and customers about your R&D priorities? >> Well, I think we've been very blessed, and I think our investors, and I think Bruce, when we sit in a board meeting, you know, we always joke around. They have never skimped on investing in products. And I think that we've been, our belief is that we are the innovation leader in our markets. There is a massive opportunity in front of us to obviously capitalize on, and the only way you do it where you innovate, and innovate means we invest. And I tell you we've been very fortunate that the investment in products has continuously increased every year. I mean, this year, forget just the products and technologies. We made, John, double digit million dollar investments in building a brand-new hosting architecture across the world, in Americas, NMEI and APJ, and we benchmarked ourselves against the Amazons and the Azures of the world, not our competitors. So not just products, but taking the cloud infrastructure across the globe, most secure, most... >> So your own infrastructure. >> Absolutely. >> Well, I mean, we run our own stuff. >> Yeah. >> But we leverage both AWS and Azure in that context. But our goal is that we can be in the countries because data should not leave some of those countries. We comply to the biggest regulations. So we've made lots of investment, and hence we can also innovate and get into new product categories. I mean, you see we have a whole new cloud architecture out there. Catalog, security, these are all brand new markets that actually, some of them have all come out since we went private. Actually more innovation has come out of Informatica since we went private than in the three years previous to going private. >> So, you know, let's play a game. Let's say that the catalog, doing very well. Let's say that you, working with Microsoft, working with AWS, you're actually successful at establishing a standard... >> Amit: Yeah. >> ...for how we think about data catalogs in a hybrid, multi-cloud world. Combine that with R&D and products. If you have, in a data-first world, where the next generation of applications are going to be data-first, that catalog gives you an inside edge to an enormous number of new application forms. >> Amit: Yeah. >> How far does Informatica go? >> Well, that's a great question. I mean, I think, I generally believe that in some ways, we are barely scratching the opportunity in front of us. I mean, none of us have seen where this world will go. I mean, who would have imagined, think of all the trends that have happened. Look at the world of social, where it has brought us to bear. I generally think that, look, each company that I talk to, each customer I talk to, and I talk to hundreds of customers across the earth, they all want to become a tech company. They all want to be an Amazon or a Google. And they realize that they will not become an Amazon Google by replicating them. The best way they can become an Amazon Google is to figure out all of the data they have and start using it, right? >> Institutionalizing their work around their data. >> Exactly. So that's where the catalog becomes very handy. It's a great first step to begin that. And in that context, there are Fortune 5000, there's Fortune 10,000, there are mid-market customers. I think we have just literally scratched the surface of that. >> Do you envision catalog-driven applications... >> Amit: Oh, absolutely. >> ...that get into, with the Informatica brand on them? >> Oh, so we actually have, so a great point. We actually made the catalog rest API-driven. So there are customers who are building their applications on the catalog. In fact, I'll give you a preview of that tomorrow. I'll show a demo where Cognizant took our catalog, took CLAIRE within the catalog, used Microsoft's chatbot to create a complete third-party custom application called the Data Concierge, where you can go ask for data. So it's Microsoft chatbot, our CLAIRE engine, and a custom app written by... So the world where I see is that it will be, that is a central nervous system of the platform, and enough custom apps will be written in time. >> It's a real enabler. So I got to ask, and I know we got not a lot of time left, I mean, but I want to get thoughts on cloud native. >> Amit: Yeah. >> 'Cause you have, with containers, you don't have to kill the old to bring in the new. And what you guys are doing is with on-prem and some of the coolness, ease of use around getting the data kind of cataloged in with the metadata, you're enabling potentially developers. Where does this lead us with containers, microservices, service meshes, 'cause that's right around the corner. >> It's happening as we speak. I mean, so we rewrote the cloud platform as I just talked about. It's completely microservices-based, completely. We had to, we had a whole cloud platform. We basically said we're going to rewrite the whole thing. Microservices-based. And it's containerized. So the idea is that A, microservices give you agility, as we all very well know. We can innovate a lot faster. And with the help of containers, you can just rapidly scale, I mean, rapidly deploy. You can test. Dev becomes a whole lot easy. The, I mean, today's cycle is so short. Customers want to do things rapidly. So we are just really helping them be able to do that. >> So you see the data actually being an input into the development process... >> Oh, absolutely. >> ...via microservices and your service mesh. >> I mean, if you don't do that, you don't know what you're building. >> It's going to be a data-first world. My, going back to my point, I think there's an opportunity for you guys to then go to the marketplace with some thought leadership about what does it mean to build data-first applications. Historically we start with a process and we imagine what the data structure's going to look like, we put it in the database, and then there's all the plumbing about interaction and integration. You guys are saying get your data assets, get your data objects rendered inside the catalog and think about the new ways you can put them to work, and you think of your code... >> Amit: Yeah. >> ...as the mechanism by which that happens. >> Flips everything on its ear. >> Amit: Yeah. >> It's a data-first world, and a data-first approach to building applications seems like it's an appropriate next conversation. >> That, I agree with that, and that's a big opportunity, and obviously there's a task at hand to make sure we can help educate everyone to get there. And I think, you know, it'll take some time, but of course that's the, anything which is easy is not interesting. It's a hard problem that where you basically, you solve and you kind of make it a big industry. >> I mean, it's great to see you. We feel like we've been following the journey of the success of you guys. We've been talking, go back four years. >> Amit: Yeah. >> You can go back to thecube.net, look at the tape. You can see the conversations. You guys stayed on task. Great product team, very, you guys are kicking some butt out there. Congratulations. Final question for you. Put you on the spot. Biggest surprise this year for you. What's, obviously the catalog, you mentioned it's been taking off. What surprised you? Anything jump out in terms of successes, speed bumps in the road, architecture trends? What's the big surprise? >> You know, I think I'm actually very warmed up by seeing, I talked about the day zero. You know, it is a data-driven world where we see so many customers looking to come here. We've become the biggest data conference of the industry. In fact, we were reflecting, Informatica World has become the biggest accumulation of people who think data-first. And I think that has been more than any technology. To me, at the end of the day, look, as much technology will come and stay, I'm a big believer it's people that make the difference. >> John: Yeah. >> And I've been seeing all of those people here, seeing them make contributions, learn, and drive change has been my biggest, not only a positive surprise, but biggest, you know, gratification that I've seen at Informatica World. >> And the emphasis of not having such a big hype. I mean, getting excited about new technology is one thing, but the rubber's got to hit the road. You've got to have real performance, real software... >> Yeah. >> ...real results. >> 'Cause the pressure of scale fast, time to market... >> ...all that stuff. >> Right. >> Congratulations, great to see you. Amit Walia, president here at Informatica on products and strategic ecosystems. I'm sure he's going to continue to be busy over the next year when we see him certainly at our next theCUBE event. Amit, great to see you. I'm John Furrier, Peter Burris, live here at Informatica World 2018. It's the largest data-first conference on the planet We'll be right back with more after this short break. (musical sting)