>>LA Las Vegas. It's the cube covering AWS reinvent 2019, brought to you by Amazon web services and they don't care along with its ecosystem partners. >>Fine. Oh, welcome back here to Las Vegas. We are alive at AWS. Reinvent a lot with Justin Warren. I'm John Walls day one of a jam pack show. We had great keynotes this morning from Andy Jassy, uh, also representatives from Goldman Sachs and number of other enterprises on this stage right now we're gonna talk about data. It's all about data with IO Tahoe, a couple of the companies, representatives, CEO H J for horror. Jorge J. Thanks for being with us. Thank you Joan. And uh, Lester waters is the CSO at IO Tahoe. Leicester. Good afternoon to you. Thanks for being with us. Thank you for having us. CJ, you brought a football with you there. I see. So you've come prepared for a sport sport. I love it. All right. But if this is that your booth and your, you're showing here I assume and exhibiting and I know you've got a big offering we're going to talk about a little bit later on. First tell us about IO Tahoe a little bit to inform our viewers right now who might not be too familiar with the company. >>Sure. Well, our background was dealing with enterprise scale data issues that were really about the complexity, the amount of data and different types of data. So 2014 around when we're in stealth, kind of working on our technology, uh, the, a lot of the common technologies around them were Apache base. So Hadoop, um, large enterprises that were working with like a GE, Comcast had a cow help us come out of stealth in 2017. Uh, and grave, it's gave us a great story of solving petabyte scale data challenges, uh, using machine learning. So, uh, that manual overhead, that more and more as we look at, uh, AWS services, how do we drive the automation and get the value from data, uh, automation. >>It's gotta be the way forwards. All right, so let's, let's jump onto that then. Uh, on, on that notion, you've got this exponential growth in data, obviously working off the edge internet of things. Um, all these inputs, right? And we have so much more information at our disposal. Some of it's great, some of it's not. How do we know the difference, especially in this world where this exponential increase has happened. Lester, I mean, just tackle that for, from a, uh, from a company perspective and identifying, you know, first off, how do we ever figure out what do we have that's that valuable? Where do we get the value out of that, right? And then, um, how do we make sense of it? How do we put it into practice? >>Yeah. So I think not most enterprises have a problem with data sprawl. There's project startup, we get a block of data and then all of a sudden the new, a new project comes along, they take a copy of that data. There's another instance of it. Then there's another instance for another project. >>And suddenly these different data sources become authoritative and become production. So now I have three, four, or five different instances. Oh, and then there's the three or four that got canceled and they're still sitting around. And as an information security professional, my challenge is to know where all of those pieces of data are so that, so that I can govern it and make sure that the stuff I don't need is gotten rid of it deleted. Uh, so you know, using the IO Tahoe software, I'm able to catalog all of that. I'm able to garner insights into that data using the, the nine patent pending algorithms that we have, uh, to, to find that, uh, to do intelligent tagging, if you will. So, uh, from my perspective, I'm very interested in making sure that I'm adhering to compliance rules. So the really cool thing about the stuff is that we go and tag data, we look at it and we actually tie it to lines of regulations. So you could go CC CCPA. This bit of text here applies to this. And that's really helpful for me as an information security professional because I'm not necessarily versed on every line of regulation, but when I can go and look at it handily like that, it makes it easier for me to go, Oh, okay, that's great. I know how to treat that in terms of control. So that for, that's the important bit for me. So if you don't know where your data is, you can't control it. You can't monitor it. >>Governance. Yeah. The, the knowing where stuff is, I'm familiar with a framework that was developed at Telstra back in Australia called the five no's, which is about exactly that. Knowing where your data is, what is it, who has access to it? Cause I actually being able to cattle on the data then like knowing what it is that you have. This is a mammoth task. I mean that's, that's hard enough 12 years ago. But like today with the amount of data that's actually actively being created every single day, so how, how does your system help CSOs tackle this, this kind of issue and maybe less listed. You can, you can start off and then, then you can tell us a bit more of yourself. >>Yeah, I mean I'll start off on that. It's a, a place to kind of see the feedback from our enterprise customers is as that veracity and volume of data increases. The, the challenge is definitely there to keep on top of governing that. So continually discovering that new data created, how is it different? How's it adding to the existing data? Uh, using machine learning and the models that we create, whether it's anomaly detection or classifying the data based on certain features in the data that allows us to tag it, load that in our catalog. So I've discovered it now we've made it accessible. Now any BI developer data engineer can search for that data in a catalog and make something from it. So if there were 10 steps in that data mile, we definitely sold the first four or five to of bring that momentum to getting value from that data. So discovering it, catalog it, tagging the data to make it searchable, and then it's free to pick up for whatever use case is out there, whether it's migration, security, compliance, um, security is a big one for you. >>And I would also add too, for the data scientists, you know, knowing all the assets they have available to them in order to, to drive those business value insights that they're so important these days. For companies because you know, a lot of companies compete on very thin margins and, and, and having insights into their data and to the way customers can use their data really can make, make or break a company these days. So that's, that's critical. And as Aja pointed out, being able to automate that through, through data ops if you will, uh, and drive those insights automatically is great. Like for example, from an information security standpoint, I want to fingerprint my data and I want to feed it into a DLP system. And so that, you know, I can really sort of keep an eye out if this data is actually going out. And it really is my data versus a standard reject kind of matching, which isn't the best, uh, techniques. So >>yeah. So walk us through that in a bit more detail. So you mentioned tagging is essentially that a couple of times. So let's go into the details a little bit about what that, what that actually means for customers. My understanding is that you're looking for things like a social security number that could be sitting somewhere in this data. So finding out where are all these social security numbers that I may not be aware of and it could be being shared with someone who shouldn't have access to that, but it is there, is that what it is or are they, are there other kinds of data that you're able to tag that traditional purchase? >>Yeah. Was wait straight out of the box. You've got your um, PII or personally, um, identifiable information, that kind of day that is covered under the CCPA GDPR. So there are those standards, regulatory driven definitions that is social security number name, address would fall under. Um, beyond that. Then in a large enterprise, you've got a clever data scientists, data engineers you through the nature of their work can combine sets of data that could include work patterns, IDs, um, lots of activity. You bring that together and that suddenly becomes, uh, under that umbrella of sensitive. Um, so being able to tag and classify data under those regulatory policies, but then is what and what could be an operational risk to an organization, whether it's a bank, insurance, utility, health care in particular, if you work in all those verticals or yeah, across the way, agnostic to any vertical. >>Okay. All right. And the nature of being able to do that is having that machine learning set up a baseline, um, around what is sensitive and then honing that to what is particular to that organization. So, you know, lots of people will use ever sort of seen here at AWS S three, uh, Aurora, Postgres or, or my sequel Redshift. Um, and also different ways the underlying sources of that data, whether it's a CRM system, a IOT, all of those sources have got nuances that makes every enterprise data landscape just slightly different. So China make a rules based, one size fits all approach is, is going to be limiting, um, that the increase your manual overhead. So customers like GE, Comcast, um, that move way beyond throwing people at the problem, that's no longer possible. Uh, so being smart about how to approach this, classifying the data, using features in the data crane, that metadata as an asset just as an eight data warehouse would be, allows you to, to enable the rest of the organization. >>So, I mean, you've talked about, um, you know, deriving value and identifying value. Um, how does ultimately, once you catalog your tag, what does this mean to the bottom line of terms of ROI? How does AWS play into that? Um, you know, why am I as, as a, as a company, you know, what value am I getting out of, of your abilities with AWS and then having that kind of capability. >>Yeah. We, we did a great study with Forester. Um, they calculated the ROI and it's a mixture of things. It's that manual personnel overhead who are locked into that. Um, pretty unpleasant low productivity role of wrangling with data for want of a better words to make something of it. They'd much rather be creating the dashboards that the BI or the insights. Um, so moving, you know, dozens of people from the back office manual wrangling into what's going to make difference to the chief marketing officer and your CFO bring down the cost of served your customer by getting those operational insights is how they want to get to working with that data. So that automation to take out the manual overhead of the upfront task is an allowing that, that resource to be better deployed onto the more interesting productive work. So that's one part of the ROI. >>The other is with AWS. What we've found here engaging with the AWS ecosystem is just that speed of migration to AWS. We can take months out of that by cataloging what's on premise and saying, huh, I date aside. So our data engineering team want to create products on for their own customers using Sage maker using Redshift, Athena. Um, but what is the exact data that we need to push into the cloud to use those services? Is it the 20 petabytes that we've accumulated over the 20 last 20 years? That's probably not going to be the case. So tiering the on prem and cloud, um, base of that data is, is really helpful to a data officer and an information architect to set themselves up to accelerate that migration to AWS. So for people who've used this kind of system and they've run through the tagging and seen the power of the platform that you've got there. So what are some of the things that they're now able to do once they've got these highly qual, high quality tagged data set? >>So it's not just tagging too. We also do, uh, we do, we do, we do fuzzy, fuzzy magic so we can find relationships in the data or even relationships within the data in terms of duplicate. So, so for example, somebody, somebody got married and they're really the same, you know, so now there's their surname has changed. We can help companies find that, those bits of a matching. And I think we had one customer where we saved about, saved him about a hundred thousand a year in mailing costs because they were sending, you know, to, you know, misses, you know, right there anymore. Her name was. And having the, you know, being able to deduplicate that kind of data really helps with that helps people save money. >>Yep. And that's kind of the next phase in our journey is moving beyond the tag in the classification is uh, our roadmap working with AWS is very much machine learning driven. So our engineering team, uh, what they're excited about is what's the next model, what's the next problem we can solve with AI machine learning to throw at the large scale data problem. So we'll continually be curating and creating that metadata catalog asset. So allow that to be used as a resource to enable the rest of the, the data landscape. >>And I think what's interesting about our product is we really have multiple audiences for it. We've got the chief data officer who wants to make sure that we're completely compliant because it doesn't want that 4% potential fine. You know, so being able to evidence that they're having due diligence and their data management will go a long way towards if there is a breach because zero days do happen. But if you can evidence that you've really been, been, had a good discipline, then you won't get that fine or hopefully you won't get a big fine. And that the second audience is going to be information security professionals who want to secure that perimeter. The third is going to be the data architects who are trying to, to uh, to, you know, manage and, and create new solutions with that data. And the fourth of course is the data scientists trying to drive >>new business value. >>Alright, well before we, we, we, we um, let y'all take off, I want to know about, uh, an offering that you've launched this week, uh, apparently to great success and you're pretty excited about just your space alone here, your presence here. But tell us a little bit about that before you take off. >>Yeah. So we're here also sponsoring the jam lounge and everybody's welcome to sign up. It's, um, a number of our friends there to competitively take some challenges, come into the jam lounge, use our products, and kind of understand what it means to accelerate that journey onto AWS. What can I do if I show what what? Yeah, give me, give me an idea about the blog. You can take some chances to discover data and understand what data is there. Isn't there fighting relationships and intuitively through our UI, start exploring that and, and joining the dots. Um, uh, what, what is my day that knowing your data and then creating policies to drive that data into use. Cool. Good. And maybe pick up a football along the way so I know. Yeah. Thanks for being with us. Thank you for half the time. And, uh, again, the jam lounge, right? Right, right here at the SAS Bora AWS reinvent. We are alive. And you're watching this right here on the queue.