How AI Enhances Human Collaboration and Drives Innovation with Nishit Sahay of Marvell Technology

Sure, absolutely. And it's perfect timing because a couple of years ago, when I would say semiconductor, people would give me blank stares back saying, 'What is semiconductor?' And I used to have a joke that most people think we just make bad conductors. It's halfway. So what we do is, we are one of the top semiconductor companies in the world. And in fact, if you look at anything AI lately, somewhere or the other is powered by our technology. But beyond that, we actually make chips or semiconductor devices for carriers. A lot of 5G network has our components in it. Cars, we are big in the automotive world, but also what you call the enterprise market. So if, like I'm a CIO, I buy a lot of stuff, switches and network connectors and whatnot, a lot of that has Marvell component in it. But the place where we are really differentiating lately is the data center. So we have the data center business, but within that, we are a key player in the AI accelerated computing space. So that's what Marvell does on a day-to-day. You might not be seeing our brand when you use things, but be assured, most of what you use has somewhere or the other Marvell technology powering it. You have over 10,000 IP, so it's a very IP-centric, very engineering-focused company, and in the semi space, one of the most respected companies that's out there.

Yeah, so when it comes to our customers, it's a very close-knit relationship that you'd see with a company like Marvell. I mean, there are different types of products that we build. You rightly said, in some cases we actually do custom products for the customer. It's we're collaborating with them to define this is how the product should be, and then we are building it all the way through, and again in a deep collaboration with the customer. So it's a very, very high-touch relationship in those areas. And in some areas, we have mass market, so we do it through distributors and all that because Marvell has great chips and everybody wants it. But if you really look at it, when at Marvell, most of our customers are big customers, and you're talking about heavy infrastructure investment that they're doing in whether it's all the markets that I talked about. So when it comes to the relationship, our customers tend to be very knowledgeable about the product because it's a B2B relationship. And you see, that's what I see in the technology world. Marvell is a very big brand name, but you see in a regular commercial world, people might not be as aware unless until you're in Bay Area.

Yeah, that's a great question. So the problem statements that we are dealing with somewhat is determined by the industry we are in, also determined where we are geopolitically, because technology industry is very widespread. And then finally, that you mentioned the layering of customers and the products that we build. So it's a very complex supply chain that we have to deal with too. So I would say five, six years ago when we started to mature in our data strategy, it was very metrics-driven, very finance-driven, that we need to get our numbers right and we need to have the right data feeding into our ecosystem so that we can get that more efficiently in front of business. Now if you look at the history of the company, we scaled up pretty fast. We did some very strategic large-size acquisitions, we brought in a lot of different flavors of technologies into our portfolio to make it more complete, and then we expanded fairly decently. So in those cases, a lot of data we needed were decision support data. We need financial data, data around your customers, data around your products. So we started with a pure BI-type strategy. So you have a request on the other end, we'll figure out where the data is and then we'll pipe it through. And of course, we use that opportunity to also clean the sources, because again, if you look at the journey we have been through, Marvell has done a lot of work pretty much in all angles within the company. I mean, not outside, we're doing great stuff, but when you talk about technology foundations, we've been doing quite a bit of work on cleaning things up to scale. So we did a lot of work on the source side as well, and those things are good incremental improvements that we made. And like I said, the support systems that we built were pretty useful when we did the scaling up through acquisitions. Now a couple of years ago, we started to drive what people call data-driven transformations. So now we're looking at larger data sets across the company that we had to bring together to make key decisions. So any decision, Marvell is a very data-driven company, always been. And most cases, there's some whole request from the other side saying, 'Okay, I have to make a decision on a regular basis,' and Marvell ecosystem expects you to have the right data in front of people. Now that kind of slows me down in my decision-making because I can't present that data to the stakeholders on time. So we used to go chase that kind of data, chase the kind of decision-driving factors that people have, and then go all the way backwards. But then we started to look at the whole ecosystem we have and how individually all of them are working versus how they're working together, something like an integrated business planning. So we started to stage data not just based on requirement but based on the business process, end-to-end business process. And there you build the ecosystem where you can ask any question and it's not like a six-months project every time you have a requirement to get the data through. So that is, I would say, stage two. Stage three, which we are in, is a little bit more complicated. Now we are getting outside the realm of your structured data, things normally which are clean in a company if you do your upstream process right. Now you're getting into a wide variety of data because we want to bring engineering knowledge to our systems. So with that, now we're looking into data governance, more advanced data pipeline, all the key latest and greatest that you can think about, data mesh or data lakehouses. So now this is a journey that we are on, and a lot of that is going to be just your basic decision-making process, but a lot of it is actually going to be feeding into our AI pipeline that we're building very aggressively in the company.

No, that's an excellent point. And in fact, one of our learnings was sometimes it's okay to leave the source the way it is because the return on cleaning that up is probably not very good. Now, the way we, again in terms of maturity, the initial set of source cleaning that we were doing, it was also essential for the company. You have to have your ERP processes done well, your supply chain processes done well, your CRM processes done well. That's mandatory for a company to be efficient. So there it made sense for us to go to the source and clean it. This requires big cultural support from the company. So if your company has a culture of collaboration, if your company understands the value of data, the value of clean processes, then it's much easier to drive. So for us, a lot of our job was much easier because Marvell does have that culture. And there we had an alignment saying, sometimes you have a process which is not the cleanest process in the world, but does the job. So in those cases, just have a good control process around it so you know that if this is happening, at least your data set that's coming out is the right data set, because ultimately you're making decisions based on that data. In some cases, we automated the system to make it fully foolproof and robust. Now, good thing there was we had enough time early on, because if you look at six, seven years ago, things were not so dynamic. They're moving at a certain pace. Our path was pretty much defined by us. So if Marvell wanted to move at a particular pace, we move at a particular pace, and which Marvell moved very fast. So that's why we moved fast on the data cleaning side too. Then comes 2019 timeframe where looks like everything was thrown up in air and anything is a variable that you can think of. Geopolitical situations got in, pandemic got in, technology world was upside down with a supply chain crisis, and then there's a huge unprecedented demand that we saw in silicon space, then a huge unprecedented drop of silicon space. Remember, I came in. So now the question here is, with that kind of dynamics that you have surrounding you, where do you invest and what do you prioritize? So that is the piece again. So we are still very outcome-driven. So while I talked about looking at the entire end-to-end business process, but that was for an outcome that we are trying to drive. So for example, for us, we want to have a very efficient supply chain, we want to provide a very good customer service and drive our top line and bottom line to the perfect point. So it's that's an outcome, and then you're looking at the end-to-end business process, and then you're looking at the end-to-end data models and trying to put that in place. So that is a journey which will change as and when the company's strategy changes, which at a macro level it doesn't change that often. Where the one earlier that I talked about, you had more time in hand because your things didn't change that frequently.

That is true. So I think initial part of our work was more on non-engineering aspect of the work, the company, like I said, the supply chains, SG&A functions. Because apart from being an engineering-focused company, we're also a growth company. So it's a company which is going to scale into new markets, scale into new set of customers, and really go up in the volume. So a lot of work was in that area. So even though we are engineering-focused company, we are a business that we have to run very efficiently. And if you look at the problem statements that Marvell has been facing, while we're doing great work in terms of the technology that we are in, the nodes that we are advancing ourselves, and we rely a lot on our ecosystem, our supplier ecosystem. So we are a fabless company, so it is very critical for us to have a very strong partnership around us, and it's a global partnership. Most of the manufacturing is not located in one particular zone, it's very spread out. We also do specialized parts, so it's not like even though we have the usual list of suppliers that you would hear more semiconductor companies have, we also have some specialized ones, especially in our modules area. Now you're talking about very complex supply chain. And you talked about all these factors that actually touches the supply chain, global factors, India-US-China relationship, or you can name it. And for us, we have to make quick decisions around that and have a good visibility. So actually before the decision side of it, you have to understand what's going on so that you can decide certain things. And going back to your initial point, initially, or a lot of companies right now, what they do is somebody says, 'I want this report, I want this data, I want it to be presented in this format.' And one of the common problem statements people face is when the information is in front of them, they change their requirement. The typical IT grief, you had a business requirements document, you completely changed it as soon as they deployed the report. But there's a legitimate reason, you don't know what you want till you see the data. So that is the approach that we are now taking, is let's get the data in front of people in the right business process to align with their business strategy, and then we'll figure out how they want to visualize it, how they see it, how they want to slice and dice it.

Yeah, actually, that is a mountain of a problem statement that we realized that we had to solve. So Marvell sits on a lot of data. Even though we are not a B2C company, B2C company tends to have a lot more data than we do, but we have a lot of technology data that's sitting in front of us. Now for companies like us, again, if we have to really take advantage of the AI revolution, the real meat is when we bring our context, our knowledge into it. Now the question is, how do we bring it? And that's where the unstructured data part of it came into being. So it's not so much about reporting, but it's more to do to feed in these AI pipelines that we are developing. Now, coming up with where we are with it, we are in a 'what should we do' mode, because when you start to drill down into this problem statement, you're looking at it from various different angles. Companies like us are very good at external security, this is an example. We do excellent source system security, but as soon as you move the data across, how would you track if you're doing things right? So that's one example on the security angle. But now if you compound that with the compliance need that most of the geographies that we actually operate in, whether it's US, Europe, and lately in India and Vietnam and China, China of course has been very popular in that area in terms of their strict requirements and compliance. When you bring this content in and feed an AI pipeline, all of a sudden your data protection, your data compliance requirements just becomes multifold. Now what we are doing is we are trying to understand what we have. So there are two things. One is like we're building these AI pipelines, we're building these AI systems, and as we feed them, we actually look at the data and make sure that everything is clean. But now we realize if you had to scale it up, right now most companies are in a build state, you're piloting things for 100 users, 200 users, you're curating data for those engines. But if you want to scale up, if you want to feed in large amount of data for the AI system to really move the needle, you have to look at data foundations. So think about, most companies have data all over the company. Most companies don't think about data quality when they're feeding data because it didn't matter to them. If you're writing a technical spec, let's take that as an example. Everybody knows this is the final version of the technical spec which has to be used by developer and the product is built, then who cares about the technical spec till a revision is required? Now you might have 100 versions of the technical spec and one is the one that you really want to use to feed in your pipeline. Which one? So you have the redundancy issue that I just brought in, in the data itself or in the spec itself. You might start putting in people's information, like a requirement coming from XYZ, and all of a sudden you have personal information in the spec that you never thought of. It's not, again in this example, it's not HR data, you're talking about technical data. And then you might have some customer information embedded in it, because you have a customer requirement coming in, and you have customer's data in it. All unstructured, all in this format. Looking at the tag or the metadata of the document, you can figure out that this problem actually exists. Then you're starting to feed it into these knowledge graphs where everything just comes together in a vector format, and now you try to figure out controls on top of it. Now that's very difficult. So for us, understanding our data, understanding the ownership, understanding the redundancy of it, and trying to figure out at domain level how do we control the quality, how do we control the compliance, how do we tag the data, how do we classify the data, that is the problem statement that we have started to work on. We're building teams around it. The first thing you have to do is you have to hire a data officer, which is what we did. So now we're building teams around it, we're working with the security team, working with the compliance team, and more importantly, working with the domains, the business unit and the functional leads, to have a program structure in place, and then we'll prioritize certain things too so that those things are coming. But this is where we are. We have recognized the problem statement.

No, that is true. And I would say to a certain extent, for enterprise-type companies, the awareness has not been there. So a lot in B2C companies, I would say, in these matters, they were a little bit more mature. They realize they're actually intentionally having the nouns in their content. For us, again, it's an old company, we've been here for almost 30 years. This kind of sensitivity or maturity has grown over a period of time. So we're talking about a lot of content, a lot of nouns in it, using your term, it's a good term, I'm actually going to steal it. And then understanding the impact of it, that's huge.

Well, I don't see that even replacing a single person, quite frankly. I mean, and some of it is going to be an opinion. None of such technologies ever replaced people, it changed the way they function for sure, and it's just going to scale up. There are certain things we just never did, we didn't do it at the right pace. And that's what is going to change, because these technologies, at least where they are, they're not autonomous in nature. And if any company is trying to make it autonomous, I feel they're on a very wrong path. Because first, if your technology is going to be replacing your employees, then you wouldn't have those employees help you build the technology. And until they're in the loop, this is not going to work. That's the first thing. The second thing is the feasibility of the deployment itself, if their strategy is replacing people. The third thing is, none of where we are, we talk about high accuracy percentage of 90%. That's a huge accuracy in AI world. Well, you still have 10% wrong, so you need to have human in the loop. And the fourth thing is, it's not the technology itself, but it's a mix of how your business process, your people, and this data support ecosystem comes along with the technology which makes a business transformation happen. So we are looking at transformation here. When you talk about replacing headcounts, when you talk about organizational efficiency, these are incremental process. In IT world, we've been automating things to do that ever since I've started working in IT. But this is something different. This is something about doing more, doing it better. And that's the approach, at least what we have taken, is not talk about employee headcount reduction, but look at how we can actually accelerate certain things. How can we accelerate product development? How can we make our decision process faster? So that's the approach we have taken, and we took it very consciously, given where this technology is and what we need to make it more successful.

Yeah, it's not so secret actually, most of it is very obvious. So first thing I would say, what helped me the most in my career, I always asked for help. So if I'm stuck somewhere, normally I would just look around people who can come in and you can jump in and help. It's actually amazing how people actually love doing that, they actually prefer doing that. And I worked in companies with various cultures, and Marvell is known for its amazing culture and collaborative environment. And before that is a good company, but when I was working for SPC, I had worked with a lot of customers too, and not everybody had the best culture in the world. But then still, when you ask for help, when you ask them to participate in problem solving, it's a human tendency to do that. And that has always helped me. So normally what I'd do is I'll pick up a function which is hard to deliver, and then I'd look for all those people who can come around and help me do it and build a team which can actually work together and solving that problem. And then that normally results to a good success. And when you have these success lined up and everybody is rooting for your success, then normally you are successful. So that has helped me. And every time I tried to, some cases you want to be a lone wolf and do things because you think you're really fast, and I fell on my face right away and correct course and this got back to the thing that always worked. So taking right help at the right time, collaborating with the ecosystem or with the stakeholders, with my technology partners, with vendors, some cases with the customers, to make a goal successful, that has always helped me.

No, that's, and by the way, you do have to fight your human nature to ask for help. I'm one of those people who would be losing, when driving, I have no idea where I am before the GPS will come in, but never stop to ask somebody. So it was also work on my end to realize, okay, this is the right thing to do, because you're working towards a larger goal than yourself. So let's say if the company is driving big business transformation or system transformation or big acquisition, it's not about you, it's about a larger goal than you. So that is a perfect time to go ask for help, and people like to work on those things. And that goes into the collaboration side of it. And of course, when you're asking for help, when people are helping, the reverse has to be true too. When they ask for help, you should be ready for doing that. You can't be saying that too busy, come another day. So you have to give back some too. The second bit I would say is collaboration, but collaboration is again a very overused term. I would say communication along with a collaboration. So you can talk horizontally. People normally talk well vertically, whether to their team or to their management. When I say normally, the ones at least on the executive layer. But I feel the horizontal communication is not always perfect. And that is something I work towards. And that requires us to sit with the person, understand what they do, empathize with their work, in that process also gain some knowledge in the area so you can talk in the common terms. And that has been fairly helpful in my career growth.

Awesome. Great talking to you.