 From around the globe, it's theCUBE with digital coverage of enterprise data automation and event series brought to you by Io Tahoe. Okay, we're back. Welcome back to data automated. A.J. Vihora is CEO of Io Tahoe. A.J., good to see you. How are things in London? Things are doing well. Things are doing well. We're making progress. I good to see you. Hope you're doing well and pleasure being back here on theCUBE. Yeah, it's always great to talk to you. We're talking enterprise data automation. As you know, within our community, we've been pounding the whole data ops conversation. A little different, though. We're going to dig into that a little bit. But let's start with A.J., how you've seen the response to COVID and I'm especially interested in the role that data has played in this pandemic. Yeah, absolutely. I think everyone's adapting both socially and in business. The customers that I speak to, day in, day out that we partner with, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring that we can serve our customers to help their customers. And the adaptation that's happening here is trying to be more agile, trying to be more flexible. And there's a lot of pressure on data, a lot of demand on data to deliver more value to the business, to serve that customer. Yeah, I mean data, machine intelligence, and cloud are really three huge factors that have helped organizations in this pandemic. And the machine intelligence or AI piece, that's what automation is all about. How do you see automation helping organizations evolve maybe faster than they thought they might have to? For sure. I think the necessity of these times, as they say, there's a lot of demand on doing something with data. Data, a lot of businesses talk about being data driven. It's interesting. I sort of look behind that when we work with our customers and it's all about the customer. The my peers, CEOs, investors, shareholders. The common theme here is the customer and that customer experience starts and ends with data. Being able to move from a point that is reacting to what the customer is expecting and taking it to that step forward where you can be proactive to serve what that customer is expectation to. That's definitely come alive now with the current time. Yeah, so as I said, we've been talking about data ops a lot. The idea of being DevOps applied to the data pipeline. But talk about enterprise data automation. What is it to you and how is it different from data ops? Yeah, great question. Thank you. I think we're all familiar. We've got more and more awareness around DevOps as it's applied to processes, methodologies that have become more mature over the past five years around DevOps that managing change, managing application life cycles, managing software development. DevOps has been great for breaking down those silos between different roles, functions and bringing people together to collaborate. And we definitely see that those tools, those methodologies, those processes, that kind of thinking, lending itself to data with data ops is exciting. We're excited about that and shifting the focus from being IT versus business users to who are the data producers and who are the data consumers? A lot of cases it can sit in many different lines of business. So with data ops, those methods, those tools, those processes, what we look to do is build on top of that with data automation. It's the nuts and bolts of the algorithms, the models behind machine learning, the functions. That's where we invest our R&D and bringing that into build on top of the methods, the ways of thinking that break down those silos and injecting that automation into the business processes that are going to drive a business to serve its customer. It's a layer beyond DevOps, data ops, taking you to that point where where I like to think about it is, the automation behind the automation we can take, I'll give you an example of a bank where we've done a lot of work to move them into accelerating their digital transformation. And what we're finding is that as we're able to automate the jobs related to data and managing that data and serving that data, that's going into them as a business, automating their processes for their customer. So it's definitely having a compound effect. Yeah, I mean, I think that data ops for a lot of people is somewhat new, the whole DevOps, the data ops thing is good and it's a nice framework, good methodology. There's obviously a level of automation in there and collaboration across different roles, but it sounds like you're talking about sort of supercharging it, if you will, the automation behind the automation. Organizations talk about being data-driven. You hear that sort of thrown around a lot. A lot of times people will sit back and say, we don't make decisions without data. Okay, but really being data-driven is there's a lot of aspects there. There's cultural, but it's also putting data at the core of your organization, understanding how it affects monetization. And as you know well, silos have been built up, whether it's through M&A, data sprawl, outside data sources. So I'm interested in your thoughts on what data-driven means and specifically how Io Tahoe plays there. Yeah, I'm sure, I'll be happy to talk about that for you, David. We've come a long way in the last three or four years. We started out with automating some of those simple to codify, but have a high impact on an organization across the data lake, across the data warehouse. Those data related tasks that help classify data. And a lot of our original patterns and IP portfolio that were built up is very much around that automating, classifying data across different sources and then being able to serve that for some purpose. So originally, some of those simpler challenges that we help our customers solve were around data privacy. I've got a huge data lake here. I'm a telecoms business. So I've got millions of subscribers. Quite often, Chief Data Office challenges, how do I cover the operational risk here where I got so much data, I need to simplify my approach to automating classifying that data. Reason is you can't do that manually. You can't throw people at it. And the scale of that is prohibitive. Quite often, if you were to do it manually, by the time you've got a good picture of it, it's already out of date. Then starting with those simple challenges that we've been able to address, we've then gone on and built on that to say, what else do we serve? What else do we serve for the Chief Data Officer, Chief Marketing Officer and the CFO? And in these times where those decision makers are looking for, have a lot of choices in the platform options that they take, the tooling, they're very much looking for that Swiss Army knife. Being able to do one thing really well is great. But more and more where that cost pressure challenge is coming in, it's about how do we offer more across the organization, bring in those business, lines of business activities that depend on data. So not just with IT. So we like, in theCUBE sometimes we like to talk about, okay, what is it and then how does it work and what's the business impact? We kind of covered what it is. I'd love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So guys, if you'd bring that up, I wonder, AJ, if you could tell us kind of, what is the secret sauce behind IO Tahoe? And if you could take us through this slide. Sure, I mean, right there in the middle that the heart of what we do, it is the intellectual property that we've built up over time that takes from heterogeneous data sources, your Oracle relational database, your mainframe, your day-to-day and increasingly, APIs and devices that produce data and creates the ability to automatically discover that data, classify that data. After it's classified then have the ability to form relationship across those different source systems, silos, different lines of business. And once we've automated that, then we can start to do some cool things such as put some context and meaning around that data. So it's moving it now from being data-driven and increasingly, well, we have really smart, bright people in our customer organizations who want to do some of those advanced knowledge tasks, data scientists and, you know, quants in some of the banks that we work with. The onus is on then putting everything we've done there with automation, pacifying it, relationship, understanding data quality, the policies that you can apply to that data and putting it in context. Once you've got the ability to empower a professional who's using data to be able to put that data in context and search across the entire enterprise estate, then they can start to do some exciting things and piece together the tapestry, the fabric across their different system. Could be CRM, ELP systems such as SAP and some of the newer cloud databases that we work with, Snowflake is a great one. Yes, so this is, you're describing sort of one of the reasons why there's so many stovepipes in organizations because data is kind of locked into these silos of applications. And I also want to point out that, you know, previously to do discovery, to do that classification that you talked about to form those relationships, to glean context from data, a lot of that, if not most of that, in some cases all of that would have been manual. And of course, it's out of date so quickly that nobody wants to do it because it's so hard. So this again is where automation comes into the idea of really becoming data driven. Sure, I mean, the efforts, if I look back maybe five years ago, we had a prevalence of data lake technologies at the cutting edge and those have started to converge and move to some of the cloud platforms that we worked with, which is Google and AWS. And I think very much as you've said it, those manual attempts to try and grasp what is such a complex challenge at scale quickly runs out of steam. Because once you've got your hat, once you've got your fingers on the details of what's in your data estate, it's changed. You know, you've onboarded a new customer, you've signed up a new partner, a customer has adopted a new product that you've just launched and that slew of data keeps coming. So it's keeping pace with that. The only answer really is some form of automation. And what we've found is if we can tie automation with what I said before, the expertise, the subject matter expertise that sometimes goes back many years within an organization's people, that augmentation between machine learning, AI and that knowledge that sits within inside the organization really tends to unlock a lot of value in data. Yeah, so you know, well, AJ, you can't be as a smaller company, all things to all people, but the ecosystem is critical. You're working with AWS, you're working with Google, you got Red Hat, IBM as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? Yeah, that's fundamental. I mean, when I came in sweet, Biotaho here as a CEO, one of the trends that I wanted us to be part of was being open, having an open architecture that allowed one thing that was close to my heart, which was as a CEO, a CIO, where you've got a budget vision and you've already made investments into your organization. And some of those are pretty long-term bets. They could be going out five, 10 years sometimes with a CRM system, training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly plug in using APIs that were available to a lot of that, some investment and the cost that has already gone into managing an organization's IT for business users to perform. So part of the reason why we've been able to be successful with some of our partners like Google, AWS and increasingly a number of technology players such as Red Hat, MongoDB is another one we're doing a lot of good work with and Snowflake here is, it's those investments have been made by the organizations that are our customers. And we want to make sure we're adding to that and they're leveraging the value that they've already committed to. Okay, so we've talked about kind of what it is and how it works. Now I want to get into the business impact. I would say what I would be looking for from this would be, can you help me lower my operational risk? I've got tasks that I do, many are sequential, some are in parallel, but can you reduce my time to task and can you help me reduce the labor intensity and ultimately my labor cost so I can put those resources elsewhere and then ultimately I want to reduce the end cycle time because that is going to drive telephone number ROI. So am I missing anything? Can you do those things? Maybe you could give us some examples of the ROI and the business impact. Yeah, I mean the ROI David is built upon on three things that I've mentioned. It's a combination of leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM and putting that to work because the customers that we work with have made those choices. On top of that, it's ensuring that we have got the automation that is working right down to the level of data at a column level or at a file level. So we don't deal with metadata, it's being very specific to be at the most granular level. So as we run our processes and the automation, classification, tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome. It could be a customer who wants that experience on a mobile device, a tablet or face-to-face within a store. Being able to provision the right data and enable our customers to do that for their customers with the right data that they can trust at the right time just in that real-time moment where decision or an action is being expected. That's driving the ROI to be in some cases 20 X plus and that's really satisfying to see that kind of impact. It's taking years down to months and in many cases, months of work down to days and in some cases hours, the time to value. I'm impressed with how quickly out of the box with very little training a customer can pick up our tool and use features such as search, data discovery, knowledge graph and identifying duplicates and redundant data straight off the bat within hours. Well, it's why investors are interested in this space. I mean, they're looking for a big total available market. They're looking for a significant return. 10 X is, you got to have 10 X, 20 X is better. So that's exciting and obviously strong management and strong team. I want to ask you about people and culture. So you got people process technology. We've seen with this pandemic that the processes are really unpredictable and the technology has to be able to adapt to any process, not the reverse. You can't force your process into some static software. So that's very, very important. But at the end of the day, you got to get people on board. So I wonder if you could talk about this notion of culture and a data driven culture. Yeah, that's so important. I mean, current times is forcing the necessity of the moment to adapt. But as we start to work our way through these changes and adapt and work with our customers to adapt to these changing economic times, what we're seeing here is the ability to have the technology complement in a really smart way what those business users and IT knowledge workers are looking to achieve together. So I'll give you an example. We have quite often with the data operations teams in the companies that we are partnering with have a lot of inbound inquiries on a day-to-day level of I really need this set of data because I think it can help my data scientist run a particular model or what would happen if we combine these two different silos of data and get some enrichment going. Now those requests can sometimes take weeks to realize what we've been able to do with the power of our search technology is to get those answers being addressed by the business users themselves. And now with our customers, they're coming to the data and IT folks saying, hey, I've now built something in a development environment. Why don't we see how that can scale up with these sets of data? I don't need terabytes of it. I know exactly the columns and the features in the data that I'm going to use. And that could sound a lot of wastage in time. And that could sound a lot of wastage in time and cut to innovate. Well, that's huge. I mean, the whole notion of self-service and the lines of business actually feeling like they have ownership of the data as opposed to IT or some technology group owning the data because then you've got data quality issues or if it doesn't line up with their agenda you're going to get a lot of finger pointing. So that is a really important piece of it. I'll give you a last word, AJ. What are your final thoughts, if you would? Yeah, we're excited to be beyond this path. And I think we've got some great customer examples here where we're having a real impact in a really fast pace whether it's helping them migrate to the cloud, helping them clean up their legacy data lake. And quite often now the conversation is around data quality as more of the applications that we enable to work more proficiently could be data, RPA, could be robotic process automation, a lot of the APIs that are now available in the cloud platforms. A lot of those are dependent on data quality and being able to automate for business users to take accountability of being able to look at the trend of their data quality over time and get those signals is really driving trust. And that trust in data is helping in turn, the IT teams, the data operations team are with do more and more quickly. So it comes back to culture, being able to apply the technology in such a way that it's visual, it's intuitive and helping just like DevOps has with IT, data ops, putting intelligence in at the data level to drive that collaboration. We're excited. You know, you remind me of something. I lied, I don't want to go yet if it's okay. I know we're tight on time, but you mentioned migration to the cloud and I'm thinking about the conversation with Paula from Webster Bank. What's the mean show? Migrations are, you know, they're a nasty word for organizations. So all right, and we saw this with Webster, how are you able to help minimize the migration pain and why is that something that you guys are good at? Yeah, I mean, there are many large successful companies that we've worked with Webster's a great example where, you know, I'd like to give you the analogy where you've got a lot of like people in your teams, if you're running a business as a CEO and it's a bit like a living brain, but imagine if those different parts of your brain were not connected, that would certainly diminish how you're able to perform. So what we're seeing particularly with migration is where banks, retailers, manufacturers have grown over the last 10 years through acquisition and through different initiatives to drive customer value. That sprawl in their data estate hasn't been fully dealt with. It's sometimes been a good thing to leave whatever you've acquired or created in situ aside by side with that legacy mainframe and your Oracle ERP. And what we're able to do very quickly with that migration challenge is shine a light on all the different parts of data application at the column level or at the file level, if it's a data lake and show an enterprise architect, a CDO, how everything's connected where there may not be any documentation. The bright people that created some of those systems have long since moved on or retired or been promoted into other roles. And within days being able to automatically generate and keep refreshed the state of that data across that landscape and put it into context then allows you to look at a migration from a confidence that you're dealing with the fact rather than what we've often seen in the past is teams of consultants and business analysts and data analysts spend months getting an approximation and a good idea of what it could be in the current state and try their very best to map that to the future, target state. Now with our Tahoe being able to run those processes within hours of getting started and build that picture, visualize that picture and bring it to life. The ROI starts off the back with finding data that should have been deleted, data that there's copies of and being able to allow the architect, whether it's we are working on GCP or in migration to any of the clouds such as AWS or a multi-cloud landscape quite often now. We're seeing, yeah. That visibility is key to sort of reducing operational risk and giving people confidence that they can move forward and being able to do that and update that on an ongoing basis means you can scale. Ajay Vohorat, thanks so much for coming on theCUBE and sharing your insights and your experiences. Great to have you. Thank you, David. Look forward to talking again. Well, bye. All right, and keep it right there, everybody. We're here with data automated on theCUBE. This is Dave Vellante and we'll be right back for a short break.