 Live from Orlando, Florida. It's theCUBE. Covering .conf18, brought to you by Splunk. Hi everybody, welcome back to Orlando. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise. My name is Dave Vellante. I'm here with my co-host Stu Miniman. This is day one of .conf18, Splunk's big user conference. You know, we're talking a lot about AI at these conferences. We're talking a lot about data. So one of the enablers is semiconductors. The power of semiconductors and the cheap storage have enabled people to ingest a lot of data. And when you look into the supply chain, you know, beneath the semiconductors, there are companies who provide semiconductor equipment. One of those companies is Applied Materials and Amuda Nadeisan is here. He's a senior manager at Applied Materials, symbol AMAT. Welcome Amuda, thanks for coming on theCUBE. Yeah, thank you. Thank you for inviting me. So as you're welcome. So as I say, there's a semiconductor boom going on right now, which is obviously a great tailwind for your business. You're on the data side, obviously. Right. Getting your hands dirty. Do you have a sense of your role and we'll get into it? Yeah, so I'm a senior manager in the software group of the Applied Materials, actually. So Applied Score Business is always the hardware which is the semiconductor and display equipment manufacturing, right? So every new chip that is kind of manufactured or any new display equipment, display is coming out, right? That's manufactured using the Applied Tool, actually. We are the software group that kind of interfaces with the Applied Tools. So we get all the data from the Applied Tools and Non-Applied Tools and we kind of do all the analytics using our softwares, actually. So I'm kind of the technology group leader within the automation products group. So we are responsible for bringing in the new technologies into our products, actually. And our products, now we are kind of trying to align with the industry 4.0 principles. So we are trying to bring in all the new technologies like mobility, virtualization, IoT, then predictive monitoring, predictive analytics, all these new technologies we are trying to kind of bring into our products right now. So I know that certainly the tolerances in the semiconductor business are so tight. And given that you're manufacturing semiconductor equipment and providing software associated with that, is it your job to try to analyze the performance and the efficacy of the equipment and feed that back to your engineers and your customers in a collaborative mode? What's the outcome that your team is trying to drive? So my team's main responsibility is to kind of maintain that finite availability for all the data that is coming from the tools into our products, actually. So our products need to be up and running all the time, actually. If our product stops, the production line will stop, actually. If the production line stops, then it's going to be a big business impact, actually. So that's where we are kind of trying to leverage all these new technologies so we can really kind of run our software with finite availability, actually. You mentioned three things, mobility, virtualization, prediction, there may be others. So the mobility, presumably, is a productivity aspect, so people can work at home on the weekends. Well, wherever they are, I'm teasing, of course. Virtualization, getting more out of, that's an asset utilization play. And prediction, that's sort of using machine intelligence to predict what, failures, optimize the equipment. Maybe you could describe what's behind each other. Yeah, I'll kind of go one by one, actually. So all of our products, they are like at least 20, 30 years old, actually. They have been all like thick clients, actually, running on desktops and laptops, actually. So now we are kind of trying to bring the user experience where the end users who are using the UI for our products, they can get a good experience and that can kind of improve the productivity. So that's with the mobility. So we are trying to kind of move to the latest technologies like Angular and a HSTML file for our product UI, actually. And with respect to the virtualization, we have been kind of running our softwares on physical servers, actually, in an enterprise fashion and that is kind of, it's kind of taking up a lot of cost, actually. So we are kind of getting into this virtualization world where we can kind of reduce the TCO of our assets, actually. That is running all these softwares. Help connect the dots with us as to how Splunk fits into your environment. Oh, okay. So we just got into Splunk just two years back, actually. We have like close to 25 to 30 software products that kind of completely automates that manufacturing line, actually, right? All these products, they generate so much of logs, actually, on a daily basis. If you take in a year, they kind of generate about 100 gig of just log files, actually, right? And those log files have a lot of critical information within the log file. And when we didn't have the Splunk two years back, what we will do is we will always, whenever there is a problem in our customer production line, we'll ask them to kind of FTP those logs, actually. And then we have to kind of manually go and scan through all those logs and identify the issue, actually. Sometimes, even to identify the issue, it takes about like a week, actually, right? And after we identify the issue, we have to come up with a resolution to kind of fix the problem, actually. And then it takes months. Sometimes I worked on a problem even for six months to kind of bring a resolution to it, actually. And the customers are very upset, actually. Yeah, it's interesting. Go back to your early statements. We've talked for years, decades, our whole careers about how important uptime is. And then you talk about your people and there's a lot more efficient things that they could be doing if they're not running after and doing all this manual things. You've been there 22 years, you know. What is something like Splunk? How do you measure that, the success of the outcome of using a tool like that? Yeah, so right now we can see the success immediately because we have implemented Splunk and we are kind of remotely monitoring our production lines, at least five customers. Right now we are remotely monitoring them. Every customer, they have a downtime at least once or twice a year, actually. So when they have a downtime, if it's a small customer, they take a loss of about 10K per hour, actually. So, and if it is a medium, then probably 100K, if it's a large, then it's 1 million, actually, per hour. I have experienced, in the last 22 years, I've experienced at least a customer has one to two downtimes a year, sometimes even more than that, actually. So after we implemented Splunk, in the last two years, whatever customers we are remotely monitoring, we never had a downtime. So that itself is a big success, actually. But we are not done with it yet, actually. We are continuing to kind of innovate with Splunk, actually, on the log monitor. I'm sure you understood what you said. So, rough rules of thumb. These things vary, we always understand that, but you're saying small customers, when they're downtime, you said $10,000 an hour, medium $100,000 an hour, large customers a million dollars and probably up. Right, yes. Huge companies. Yes. Yeah, it really kind of depends upon, when I say a small customer, they have less number of tools, actually, which means they have less number of operators. So, less number of people impacted, actually, when the production line stops. But when you kind of go for a medium size, they have, like, more tools, right? More people are working with those tools. They don't have to work, which means, right, it's a disruption, actually, in the production line. And if it's a large fab, there are more number of operators, actually, working in the production line. So that's how we kind of calculate the loss, actually. When they have, I mean, right, you can just, the math is pretty simple to calculate, but when they have a downtime like that, do they try to make it up on the weekends, or can they not do that, because people have lives, or they are already actually running 24-7? It's already running 24-7. So they can't get any more time in a day? Yeah, they can't make it over the weekend, actually. It's already running 24-7, and when the production line stops, that means it's a revenue loss for them, and then also their operators are sitting idle, actually. These are companies with a fab, right? These are companies with fab, actually. And it's just a multi-billion-dollar investment, oftentimes, right? Yes, yes. You name any semiconductor companies, like Intel or Samsung, they're all using applied tools, actually. Yeah, of course, right. To run their manufacturing line. And when they're down, it's right bottom line. Yes, that's right. And they all use our softwares to kind of completely automate their factory end-to-end, actually. And you directly attribute the lack of downtime, the reduction in that downtime, to Splunk? That's right, actually, yeah. At least, whatever the customers we are remotely monitoring right now, that those customers are monitored using Splunk. We are right now scaling up with more and more customers for the remote monitoring, actually. The other thing that you said is you're starting to innovate even more with Splunk. Maybe you can elaborate a little on that. Yeah, we are trying to kind of, we are, right now, we are just using the basic machine learning algorithms that are available from Splunk for kind of doing the anomaly detection or outlier detection or trend analysis, actually. So we are expecting to kind of introduce more and more machine learning algorithms that can accurately predict the servers going down, actually, right? That can kind of give us more lead time to kind of proactively address the issues before the user can see an impact, actually, right? Currently, most of the time, it is kind of more reactive, actually. We see the issue and then we kind of react to it. We want to be more proactive and that is where Splunk is playing a big role, actually. And your role is customer-facing. Is that right? Your software is customer-facing? Are you guys using this internally as well? We are using both internally. Right now it is customer-facing, but our IT organization, after seeing the success with how we are kind of monitoring our customers, they are also kind of adopting it. And there are other business units now who are kind of receiving a lot of data from these tools, actually. Like the sensor data from the tools, they are also kind of trying to use Splunk and see how they can kind of predict the issues in the tool more proactively or accurately. Splunk is not a new company. I'm just curious. And Applied Materials is obviously a huge, huge company. You know, $35 billion market cap. Why did it take you so long to find out about Splunk and adopt Splunk? Splunk, was it just organizational? Was it your processes are so delicate and hardened? I wonder if you could explain. Yeah, so that's a very good question, actually. So, only in the last two years, we have started investing more on the R&D, especially on the software products, actually. Mostly the investment was on the hardware products where they want to kind of improve the productivity, they want to kind of improve the testing methodology, all those things. Most of the investment was going to the hardware components. So they were not even looking at all these software innovations that were happening. So, last two years, they are kind of investing more on the software groups, actually, which they want to kind of bring it or kind of take it to that industry 4.0 revolution, actually. So that's where we started investing on all. We started looking at many technologies and one of the first technology to adapt was the Splunk, actually. And then, especially, we kind of came up with this remote monitoring concept where most of our customers or the small customers, I would say, they did not have their own IT organization, right? So whenever they had a down, they have to kind of literally log a call and they have to wait for us to kind of come in, fix their problem and it took days, actually. And they took a big impact because of that. So, and then they said that we don't have our own IT organization. Why don't you kind of take the IT responsibilities of keep making sure those softwares are kind of up and running all the time. So that's the time when we went to kind of Splunk and we got it, we implemented it, we tested it, and we are kind of seeing a good success with it, actually. And you guys buy this as a subscription or is it a perpetual license or how do you guys do it? It's a perpetual license, actually. And we have an on-prem. That's another concern with our customers because they want to kind of make sure their IP does not go out, actually. They don't want to put anything on the cloud. This is for every semiconductor companies. They are not there on the cloud yet, actually. So that's why we have to host Splunk on-prem and kind of transfer all the data from our customer through a secure FTP, bring it to our on-prem Splunk servers and do all the analytics, actually. All right, okay. Hurt Splunk in many other companies this year, for the last couple of years, talking about AI and ML. Does that resonate with you, the sort of solutions that you think you'll be looking for, that kind of functionality? How does that play into your environment? That's right, actually. So we are trying to kind of get into that. We have, to a certain extent, we are kind of already into the machine learning algorithms, actually, but we want to kind of go more deeper into that, actually, so that currently our prediction, whatever we have developed in-house, actually, our prediction algorithms can predict only 60%, actually. So that's the accuracy we could get, but we want to get somewhere in the 90% or 95% accuracy, so which means we have to get more, we have to get more on the accuracy part, actually, right? We have to get more accurate machine learning algorithm developed, actually. So that is where we are trying to kind of see if the platform can kind of provide more of this machine learning algorithms which can predict more accurately, actually, the problem. So that's the data, that's data, the modeling, iterations, just time, right? You'll eventually get there. Amrita, thanks very much for coming on theCUBE. It was great to hear your story. Last question is, we hear this story of Splunk, I call it land and expand. We have one use case and then there are other use cases. Is that your situation? You've only been a customer for a couple of years now. Do you see using Splunk potentially in other areas? Yes, we are trying to kind of expand to other areas. Right now we started with remote monitoring. We are going to use it for our IT, right? Our IT is going to use it and then we want to kind of go to the predictive analytics, actually. That means we want to kind of look at the tool data, like the data that is coming from the sensors, from the tool, we want to kind of do the analytics and then make sure that we can predict the problems, we can predict the maintenance that we need to do, so all those things we want to do, actually. That's the area we want to kind of more expand, where we will really kind of add value to our customers, actually. Amuta Nadesan from Applied Materials, thanks so much for coming on theCUBE. Appreciate your time. Thank you. All right, keep it right there, everybody. We'll be back with our next guest. I'm Dave Vellante, he's Stu Miniman. We'll be right back. You're watching theCUBE from splunk.conf18.