 From around the globe, it's 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 it can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin and I'm joined by A.J. Vihora, 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. It's good to be back. Great to see you, Lisa. Likewise, very socially distant, of course, as we are. Lisa, we're 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. 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 also focused a lot on performance. It'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 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 regex 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 by it. Sounds exciting. AJ from the CEO's level, what's going on? Well, I think just building on that, what Lester was just mentioning there, 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. Lester collaboration already with Oracle in government, in insurance and in banking. And we're excited because we get to have an impact. It's real 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 concerns as well. 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, 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 is 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 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 what enterprise companies are doing now or are they not quite there yet? Well, at least I think every organization is going at its pace, but markets are changing, the economy and the speed at which some of the changes in the economy are happening is compelling more businesses to look at being more digital in how they serve their own customers. So what we're seeing 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 hardwire 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 into a lot of 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 manually 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 the bowl of rice with the chopsticks one grain at a time. What you'd wanna 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 IOTAHO 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 questions or what shape is that data in? How is it structured? What's dependent on an 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, 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, and I'm going to take you through a story here, and I want to introduce Amanda. Amanda's not like 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 but should know about various different kinds of databases. And Amanda's just tasking with understanding all of this so that they can embark on her data journey program. So Amanda 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. Property names or column names or have ambiguous names like attribute one and attribute two or maybe date one and date two. So Amanda's starting to struggle, even though she's got tools to visualize and look at these databases, she still knows she's got a long road ahead. And with 2,000 databases in her large enterprise, yes, it's going to be a long journey. But Amanda's 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 the owner to find what the data means. And she's tracking all this information. Well, 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 because there's still ambiguities in the data. With IOTAHO, 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 IOTAHO, 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. So that may be indicative of the 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 two. Through observation, we can see that date one, 99% of the time is less than date two. 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 date one is less 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 IOTAHO. Going even a step further, you can take your favorite data science solution productionize it and incorporate it into our next version as a, what we call a worker process to do your own bespoke analytics. Bespoke analytics. Iceland, Lester, thank you. So AJ, 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 think and how do this bring into life a little bit Lisa is just to talk through a case today. We pulled something together. I know it's available for download, but in a well-known telecommunications media company, they had a lot of the issues that Lester just spoke about, lots of teams of Amanda's super bright data practitioners and maybe looking to get more productivity out of their day and deliver a good result for their own customers or cell phone subscribers and broadband users. So some of the examples that we can see here is how we went about auto generating a lot of that understanding of that data within hours. So Amanda had a data catalog populated automatically, a business glossary built up and could really then start to see, 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 tax versus customer operations, have different access or permissions to that data. And what we've been able to do there is to build up that picture to see, how does data move across the internal organization across the state and monitor that over time for improvement. So we've taken it from being a 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 talents in that telco organization and the folks that work there with data. Okay, Ajay, so 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 ROI. How do customers measure ROI? What are they seeing with IOTA host 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 have been today with a lot of the larger vendors and technologies. So what a CIO, an economic buyer, really needs to be certain of is, how quickly can I get that ROI? And 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 where we've been able to have that impact. And in this playbook that we've pulled together the before and after picture, really shows those savings that can be delivered 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 who has access to it. So Lisa, 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 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 and normalizing that across your data estate. So you couple this with the ontologies that are unique to your business scenarios and algorithms and you basically go across them 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 columns, named columns, but I can write them in terms of the tags. Using that automated pattern recognition where we observed that the loan start should be before the loan, being able to automate that as 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 are increased that are, I should say decreased that time to value that AJ 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 ambiguities 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 might ask the user said, can you help me identify what date one and date two are in this table? Turns out there are a start date and an end date for a loan. That gets remembered, cycled into the machine learning. So if I start to see this pattern of date one, date two elsewhere, I'm gonna say, is it start date and end date? And bringing all these things together with all this automation is really what's key to enabling this data governance. Your data governance program. Great, thanks, Lester. And AJ, I wanna wrap things up with something that you mentioned in the beginning about what you guys are doing with Oracle. Take us up by telling us what you're doing there, how are you guys working together? Yeah, I think those of us who've worked in IT for many years, we've learned to trust Oracle's technology, they're shifting now to a hybrid on-prem cloud generation two platform, which is exciting and their existing customers and new customers moving to Oracle on a journey. So Oracle came to us and said, 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 and 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, in hours, days and weeks rather than many months is opening up the eyes to chief data officers, CIOs 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 are keen to drive through. And helping businesses transform digitally is so incredibly important in this time as we look to things changing in 2021. AJ 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. Thank you. Thanks again, Lisa.