>> Announcer: 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, talking a lot about data, 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, beneath the semiconductors, there are companies who provide semiconductor equipment. One of those companies is Applied Materials and Amudha Nadesan is here, he's a senior manager at Applied Materials, symbol AMAT. Welcome Amudha, thanks for coming on theCUBE. >> Yeah, thank you, thank you for inviting me. >> 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. >> Dave: Getting your hands dirty. Give us 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's core business is always the hardware which is the semiconductor and display equipment manufacturer, so every new chip that was kind of manufactured, or any new display equipment displays coming out, that's manufactured using the Applied tool actually. We are the software world 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 software, 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 for final 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. Right so, our products need to be up and running all the time, actually. If our product stops, the production line will stop, actually, right if the production line stops, then there'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. >> Right. >> So the mobility, presumably, is a productivity aspect. So people can work at home on the weekends, or wherever they are, teasing of course. Virtualization, getting more out of, that's an asset utilization play. And prediction, that's using machine intelligence to predict failures, optimize the equipment, maybe you could describe what's behind each of those. >> Yeah, I'll kind of go one by one, actually. All of our products, they are like at least twenty, thirty years old, actually. They have been all big clients, actually, running on desktops and laptops, actually. Right 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 what the mobility is. So we are kind of trying to model the latest technologies like Angular and STML 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 taking up 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 close to 25 to 30 software products that kind of completely automate that manufacturing line, actually. 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 gigs of just log files, actually, and those log files have lot of critical information within the log file, and when we didn't have the Splunk two years back, what we would do is, whenever there is a problem in our customer production line, it allows 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 are to come up with the resolution to kind of fix the problem, and then it takes months, sometimes. I worked on a problem, even for six months to kind of bring a resolution to it, and the customers are very upset, actually. >> Yeah, it's interesting, go back to your earlier statements, you know, 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 they could be doing if they're not looking after and doing all these manual things. You've been there 22 years, 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 down time at least once or twice a year, actually, so when they have a down time, if it's a small customer, they take a loss of about 10 K per hour, actually. So and if it is a medium, then probably 100 K, if it's a large, then it's 1 million actually, per hour. I have experience in the last 22 years, I've experienced at least, a customer has one to two down times a year, sometimes even more than that, actually. So after we implemented Splunk, the last two years, one of our customers we are remotely monitoring, we never had a down time, so that itself is a big success, actually, but we are not done with it yet, actually, we are continuing to innovate with Splunk on the log monitor. >> Make sure I understood what you said. So, rough rules of thumb, these things vary, we always understand that, but you say in small customers, when their down time, you said $10,000 an hour, medium $100,000 an hour, large customer's a million dollars, and probably up with 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 impact it, actually, when the production line stops, but when you go for a kind of go for a medium size, they have more tools, 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, right, the math is pretty simple to calculate but when they have a down time like that, do they try and 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 by seven. >> And you can't get more time in a day. >> Yeah, they can't make it over the weekend, actually it's already running 24 by seven, and when the production line stops, that means it's a revenue loss for them, and then also their operators are sitting idle actually. >> Dave: These are companies with a fab, right? >> These are companies with fab actually. >> Which is a multi-billion dollar investment oftentimes, right? >> Yes, yes. Name any semiconductor companies like Intel or Samsung, they're all using Applied tools to run their manufacturing. >> And when they're down, it's right in the bottom line. >> Yes, that's right, and they all use our softwares to kind of like completely automate their factory end to end, actually. >> Can you directly attribute the lack of down time, the reduction in that down time, to Splunk? >> That's right, actually, yeah. At least one of the customers we are remotely monitoring right now, those customers are monitored using Splunk. We are, right now, scaling up with more and more customers for the remote monitoring. >> The other thing 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, right now we are just using the basic machine learning algorithms that are available from Splunk for kind of doing the anomaly detection, our outlier detection, our trend analysis. So we are expecting to kind of introduce more and more machine learning algorithms that can accurately predict the servers going down, that can kind of give us more lead time to kind of proactively address the issues before the user can see an impact, actually. Currently, most of the time it is kind of more reactive, 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. >> Your role is customer facing, is that right? Your software is customer facing? Or 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 adapting it, and there are other business units now who are kind of receiving 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 company, you know, $35 billion market cap, why did it take you so long to find out about Splunk and adopt 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. Right 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're 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, right? So that's where we started investing on all, we started looking at many technologies, and one of the first technologies to adapt was the Splunk, actually. And then especially we kind of came up with this remote monitoring concept where most of our customers are, the small customers, I would say, they did not have their own IT organization, right so whenever they had a down, they had to kind of literally log a call and they had 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 then they said, we don't have our own IT organization, why don't you kind of take the IT responsibilities off, keep making sure those softwares are kind of up and running all the time? So that's the time when we went to Splunk, and we got it, we implemented it, we tested it, and we are kind of seeing a good success with it, actually. >> And do you guys buy this as a subscription, or is it a perpetual license? Or how do you guys do that? >> It is a perpetual license, yeah, we have an on-prem. That's another concern with our customers, because they want to 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 are to host the Splunk, on-prem, and kind of transfer all the data from our customers through a secure FTP, bring it to our on-prem Splunk servers and do all the analytics, actually. >> We've heard Splunk and many other companies this year and for the last couple of years talking about AI and ML. Does that resonate with you, those 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 kind of want to go more deeper into that, actually, so that currently our prediction, whatever we have built up 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 93% accuracy, 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 algorithms 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 data, the modeling, iterations, just time, right? You'll eventually get there. Amudha, thanks very much for coming to theCUBE, it was great to hear your story. Last question is, we hear this story of Splunk, I call it land and expand. >> Right. >> We have, you know, one use case, and then there are other use cases, is that your situation? You've only been a customer for a couple 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 IT, 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, actually, so all those things we want to do, actually, that's the area we want to kind of more expand with, we will really kind of add value to our customers, actually. >> Amudha Nadesan from Applied Materials, thanks so much for coming on theCUBE, appreciate your time. >> Yeah, thank you. >> Alright, 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. 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