 Hi, and welcome to another CUBE Conversation. Today we're at BMC Helix's Immersion Days and the Santa Clara Marriott and Santa Clara California and we're having a great series of conversations about the convergence of digital services and operations management. And one of the most important features of that is how do you utilize analytics? Analytics is on the tip of everybody's tongues these days, but it's being applied in marketing and sales. Kind of the cobbler's children that aren't getting the same treatment are in fact the IT organization. So what we're going to do in this next CUBE Conversation is learn more about how IT analytics is beginning to transform IT and facilitating this convergence of digital services and operations management. And to do that, we've got Gaurav Ravari who is the president or co-founder and CEO of Numerify. Gaurav, welcome to the CUBE. Thank you Peter, pleasure to be here. So Gaurav, tell us a little bit about Numerify. Let's start there. Sure, I know I liked in your opening statement you talked about IT in terms of its own use of analytics being a little like a situation where the cobbler's children don't have any shoes because IT stood up pretty powerful analytical applications for the CMO, the CFO, VP of sales, et cetera, but not for IT itself. And so we think, yes, it's ironic, but it's also untenable. And it's untenable because in the age of digital transformation where you're opening up digital channels for revenue generation and the like and customer engagement, IT is really moving up from the basement to the boardroom. So you have CEOs who worry now about things like availability, about sort of a speed of innovation with quality, and things like that. And so to be able to have a decision support system, a system of intelligence, if you will, that across rank and file of IT, across the plan-build-run lifecycle, across the entire IT estate from infrastructure to apps to business services, gives you recommendations and intelligent insights on how to improve the quality of your work, the health of your systems, the reduced the risk of your systems. That we felt was an idea whose time had come. And so that's why we got started with Numerify. And we rolled out a bunch of targeted analytical applications across areas like project analytics, DevOps analytics, service analytics, asset analytics and the like. And so it's sort of a string of pearls that you can deploy across your IT organization and it's interconnected. So you can ask cross-cutting questions as well. So that's in a nutshell, the story on Numerify and its vision. So Gaurav, I've been within approximate to IT or in IT for a long time now. And it's not that we didn't have reporting because IT was always doing reporting. We report on stoplights, projects, whatever they were. But I think what you're saying is something a little bit more fundamental. It's really, can we do a better job of really capturing the resources that are creating value for the business, understand how to deploy them more successfully. And we're not just talking about the infrastructure. We're talking a lot about the people. Have I got that right? You hit that nail on the head there. Ultimately, IT is a business. And you have to, if you want to face sort of the epic challenges and opportunities of tomorrow that IT alone can really take on, you have to understand the people, process, project, and product dimensions of the IT business. And so what that means is, if you want to drive down your INO costs from roughly 72% of IT budget, which is what it was the average today, to 50%, which is the gold standard, that's half a trillion dollars for the G2K, right? And you wanna take that savings and reinvest it in agility, in faster app dev with higher quality, right? How do you do that without tapping into things like automation and the use of analytics to drive down your INO costs rationally and increase your dev, your development velocity intelligently, right? So that's why analytics has a huge role to play. But also, it's gotta be, if I can interrupt you, it's gotta be that you have to start with visibility into what resources are generating the greatest return. Why are they generating that return? Why are other resources not generating return? And seeing how all that gets connected across the range of activities that an IT organization is performing on behalf of the business, right? Yeah, no, exactly. I think the how is really about getting that visibility across sources. And it's a non-trivial problem to do that when you have a plethora of sources that were never built to talk to one another, right? You may want to, for example, within IT understand the total open work on each person's plate, right? So they may have a bunch of incident resolution work that they're doing and the signal from that comes from a BMC or a service now. And yet they may be pulled into app dev work, which the signal is coming from a JIRA or a CA. How do you pull that together into a single dashboard that gives you that view of what everyone's working on? And then you can make decisions like goodness with so much unplanned work that's gone Fred's way. There is no way that the epic that he's involved with is going to be completed on time. So I have project risk, I have release risk as a result, I may even have attrition risk. And so the ability to pull together data into a single model, answer the visibility question to the point you make, and then go the next step of predicting likely outcomes, that's the magic and that's the use of analytics to sort of transform IT into operating in a far more intelligent paradigm than it has thus far. Other tools have attempted to do this, but they attempted to basically be the soup to nuts tool. So they forced users to install agents everywhere that there was a single process model that was expected to be employed to administer all kinds of different resources. There were very significant limits on how you consider application development or application management, for example. Why is numerify different? Yeah, what we've tried to do is really take a leaf from the page and books of those who've sort of succeeded in this endeavor before. So if you look at, you know, the solutions that a CMO might have at her fingertips or a CFO might have, right? Fundamentally, it's about pulling data into existing systems, not requiring a change of behavior, but pulling data from existing systems into a canonical model, into a standard sort of analytical data model, that runs on sort of in a dedicated stack. And then you basically have this layer of descriptive, prescriptive, and predictive analytics sort of folded in. And that's the approach we've taken, where we say, hey look, there is such a thing as a change management system that doesn't go away. We would like to mind the accumulated history of all the changes you've ever put into production by tapping into your IT service management system, and then your upstream dev and test system, because change is often a piece of code. It began its life somewhere in a dev cycle. So how many times was that piece of code rolled back, tested, how many times did it fail the testing cycle? Who worked on it? What's been their success rate thus far? And then with respect to the change itself, in the past, how often has a change like this failed? You know, if changes were done on a weekend through a combination of an onshore and offshore team, is that implicated in failed changes in the past? And then downstream of the change, in the past, was there a decline in performance or usage or availability as gleaned from your monitoring tools? So we pull data from all these sources without requiring you to re-instrument them into a standard model, and then for every upcoming change, we tell you, hey, this one is a risky change. Go look at it, send it back for further testing. Hey, this one is a low risk change, push it to production directly. And so, inherently, the ability to pull data from multiple existing sources into a standard data model and have best practice reports and insights sort of a layer on top, that's the approach we've taken. Well, okay, I really like this. Let me see if I can summarize something that you just said. So, numerify is not immediately antagonistic to anything that anybody has within the shop. But it starts from a proposition that, look, what you're doing is working or not, but let's start from a premise that you're doing something now. Let's learn more about it. Let's then ask, can we do it better? Yes or no? And you have the analytics to do that. And if it should be replaced, can you actually get to the point of that you can actually indicate or suggest how and when to replace something? Yeah, that's a fabulous question. I think, you know, increasingly what we're seeing is that our customers are pulling us in the direction of making active recommendations on decisions that they could potentially make. Such as, you know, you may wanna consider consolidating a certain class of applications because, you know, given its revenue and usage, the amount of support burden associated with it is too high. Hey, you might want to take a more refined and data-driven, insight-driven approach to asset retirement because, you know, this whole, you know, everything that's Lenovo and five years old must go is too blunt an instrument. You know, retire those assets that are the most error-prone and keep alive those assets that still have useful life in front of them. And that process may by itself be extremely expensive and very limited returns. Precisely, precisely. So the ability to transcend now from just visibility and dashboards to providing active recommendations for every action along the way, you know, project risk, release risk, attrition risk, change risk, service quality risk, et cetera, we see that as the vision for us, you know? It's, so the use of AI, not just for automation, you know, sort of, which clearly the AI ops field is investing in, but also the use of AI for decision support, for providing you with intelligent recommendations across the full sphere of activities that IT undertakes. Gaurav Vavari from the, from Numerify. Thanks very much for being on theCUBE. My pleasure, thank you. Once again, this has been a CUBE conversation from BMC Helix's Immersion Days. And we look forward to seeing you again. Thanks for listening.