>>Hi and welcome to another Cube conversation today were BMC Felix's Immersion Days and the Senate Clara Marry on Santa Clara, California We're having a great series of conversations about the convergence of digital service's and operations management on one of the most important features of that is How do you realise Analytics Analytics is on? The tip of everybody's tongue is these days, but it's being applied marketing and sales >>kind of the >>surely cobbler's children that aren't getting the same treatment or, in fact, the IittIe organization. So what we're gonna do in this next few conversation is learn more about how I t analytics is beginning to transform I t. And facilitating this convergence of digital service is in operations management. And to do that, we've got Gore over Bari. Who's the president's or co founder on CEO of numeric fi. Welcome to the Cube. >>Thank you, Peter. Pleasure to be here. >>So, Gaurav, tell us a little about new verify. Let's start there. >>Sure. Yeah, I know. I liked, you know, in your opening statement, he talked about I t in terms of its own use of analytics being a little like, you know, a situation where the cobbler's children don't have any shoes because I t stood up pretty powerful analytical applications for the CMO, the CFO, VP of sales, et cetera, but not for writing 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, I t. Is really moving up from the basement to the boardroom, right? 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 ah, system of intelligence, if you will, that across rank and file off i t across the plan bill run life cycle across the entire idea. State from infrastructure to ABS to Business Service's gives you recommendations and intelligent insights on how to, you know, improve the quality of your work, the health of your systems to reduce the risk of your systems that we felt it was an idea whose time had come on. So that's why we got started with the Mer if I and we rolled out a bunch of targeted analytical applications across areas like Project Analytics develops analytic service and lyrics, Asset Analytics and the like. And so it's sort of a string of purse that you can deploy across your I T organization and its interconnected s so you can ask cross getting questions as well. So that's in a nutshell. The story in America, Fi in its vision. >>So, Garv, I've been within a proximate to i t You're in I t for a long time now. And it's not that we didn't have reporting because I t was always doing reporting. We have poured on no stop lights projects wherever 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 or successfully We're not just talking about the infrastructure. We're talking a lot about people. I got that right. >>You hit that nail on the head there. Ultimately, you know it is a business, and you have to if you want to face sort of the epic challenges and opportunities off tomorrow that I t alone can really take on. You have to understand the people process, project and product dimensions of the I t business. And so what that means is, if you want to drive down your iron oh, costs from, you know, roughly 72% of I t budget, which is what it was. The average today to 50% is the gold standard. That's half a trillion dollars for the G two K, right? And you want to take that savings and reinvest it in agility in foster app. David. Higher quality, right? How do you do that without tapping into things like automation and the use of analytics to drive down your ire no cost rationally and increase your dev your development velocity intelligently? Right? So that's where analytics has a huge role to play. >>But also it's got to be fucking interrupt you. It's gotta be that you have to have. You have to start with visibility. Yep, into what resource is are generating the greatest return? Yeah. Why air they generating that return? Why are other resources not generating return? Yeah, and seeing how all that gets connected across the range of activities that a nightie organization is performing on behalf of the business. >>Yeah, I know 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. You may want to, for example, with an I t. Understand. You know, 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 data and the signal from that comes from a B, M, C or a service now and yet they may be pulled into apt of work, which the signal is coming from Ajira or a C A. 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, you know, be completed on time. So I have Project Chris. I have released 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, too. You're to the point you make and then go the next step off predicting likely outcomes. That's the magic. And that's the use of analytics to sort of trance for my tea into, um, you know, 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 Thio install agents everywhere that there was a single process model that was expected to be employed to administer all kinds of different resources. There are very significant limits on how you considered application development application management, For example, Why is numeric five different? >>Yeah, what we've tried to do is really take ah leaf from the page on books of those who have set up succeeded in this endeavor before. So if you look at you know the solutions that a CMO might have it 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 surfing. Ah, a dedicated stack on. Then you basically have this layer off descriptive, prescriptive and predictive analytics sort of folded in on. That's the approach we've taken where we say, Hey, look, we want 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 service management system and then your upstream Devon test system. Because change is often a piece of court, it began its life somewhere in a in a death cycle. So how many times was that piece of court rollback tested? How many times that it failed 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 unsure in offshore team, is that implicated in a failed changes in the past and then downstream of the change in the past, you know, Was it a decline in performance or usage availability as gleaned from your monitoring tools? So we pull all 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 lower exchange pusher to production directly and so inherently thehe bility toe pull data from multiple existing sources into a standard data model and have best practice reports and insights sort of layer on top. That's the approach taken. >>Well, look, I really like this. Uh, let me let me see if I can summarize something you just said So Numeric fei is not immediately antagonistic to anything that anybody has with the shop. That it starts from a proposition. That look what you're doing is working or not, But let's start from across from a premise. It you're doing something now. Let's learn more about it. Let's then asked Can we do it better? Yes or no? You have the intelligence 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 off, making active recommendations on decisions that they could potentially make such as, you know, you may want to consider consolidating a certain class of applications because, you know, given its revenue and usage, the amount of support button associated with it is too high here. You might want to take a more refined and data driven, inside driven approach to asset retirement because you know this whole, >>you know, >>everything that Lenovo, in five years old Moscow is too blunt an instrument, you know, retired those assets that are the most error prone and keep alive those assets that still have useful life >>And that process, maybe itself be extremely expensive, very limited returns >>precisely precisely. So the ability to transcend now from just visibility on dashboards to providing active recommendations for every action along the way, you know, project race release risk, patrician risk, change, risk service quality risk, et cetera. We see that as as the as the vision for us. You know, it's the use of a I not just for automation, you know, sort of Ah, which clearly the ops field is investing in, but also the use of a i for decision support for providing you with intelligent recommendations across the full sphere of activities that I t undertakes. >>Grove Ari from the from the verify. Thanks very much for being on the Cube. >>My pleasure. Thank you. >>Once again, this has been a cute conversation from BMC. Helix is immersion days and the we look forward to seeing you again. Thanks for listening.