Resilient Edge: A Business Vitality Podcast from Deloitte

00:00:02 Som Suresh: You see headlines everywhere, where it says AI is the future. A lot of these successful companies, they treat data as a product. So instead of IT owning a monolithic data lake, we need to push a lot of the ownership towards the business domains. But obviously it comes with a lot of common traps and struggles. The first one is data swamps. There was a big movement in the market where everybody had aspirations to go after big data that resulted in a large unmanaged data lake, which are now data swamps. The second one I'm seeing is: shiny object syndrome. So a lot of the decisions are now technology-led versus business-led. So a lot of our clients get stuck in POC purgatory. You build something really cool, everyone is so excited about it, but it's not really integrated into your day-to-day business process.

00:00:46 Chip Kleinheksel: Hello and welcome to season two of Resilient Edge, a Business Vitality Podcast from Deloitte. I'm Chip Kleinheksel, the Chief Technology officer of Deloitte's Global SAP alliance. In this series, we explore some of the most pressing business transformation issues that organizations are contending with today. The question at the heart of our series, how do companies and executives go from thinking about change to actually making it happen at scale? I'm joined by world-class experts at Deloitte, SAP and beyond to help me answer this question.

We're focused on three central themes this season: artificial intelligence, of course, that's my specialty and was before it was even cool, the energy resources and industrial sector, an industry in a moment of change, and today's topic, data, and how to make it actually useful. A timely topic considering data is the fuel for so many innovations, including AI.

Data poses the biggest opportunity and the greatest challenge for a lot of companies at the moment. My colleague, Deloitte Principal, Michael Gretzko succinctly summarized the problem at hand as too much data, but starved for insights. Data is or should be an essential ingredient in decision-making and taking action in most areas of business today. AI, sustainability and supply chains are just a few examples, but it's often not. Instead, it's the most persuasive or loud talker in the room, convincing people to go in a direction they think is best, or we rely on data gathered through surveys which don't reflect ground realities a good deal of the time. Why is this the case when we should have a surplus of data available to us?

An SAP report found that 55% of global business and tech leaders cite poor data quality as their biggest challenge, and nearly half of them pointed to the difficulty of harmonizing data across ecosystems as a key reason that innovation stalls. So the problem is twofold, trusting the data, is it good? Is it clean? Is it from a valid source? And how do I cull the noise from the signal? In this episode we'll explore what data architectures and tools companies can use to solve this problem and get what they really need out of their data.

To help me, I'm joined by two very experienced guides, Satya Jayadev, the Vice President and Chief Information Officer at semiconductor manufacturer Skyworks Solutions. Welcome to the Resilient Edge, Satya.

00:03:05 Satya Jayadev: Thanks for having me.

00:03:06 Chip Kleinheksel: And Som Suresh, Deloitte's SAP Analytics Sales Lead. Hey Som, how are you?

00:03:10 Som Suresh: Hey, Chip. Really great to be here.

00:03:12 Chip Kleinheksel: Awesome. Great to have both of you. Satya, could you tell us a little bit about yourself and Skyworks?

00:03:17 Satya Jayadev: Skyworks is a high-performance analog semiconductor organization. So we make analog semiconductors that actually connect people, places and things through applications within aerospace, automotive, broadband, infrastructure, connected home, all the way to the wearables market. So anywhere you see wireless connectivity, we play a role there and as the demand for the ubiquitous connectivity expands, our products become more and more amplified within the market space. So that's a very exciting space to be in. And semiconductors are also the bellwether for the markets. So great to be a CIO in an organization that's so fast-paced.

00:03:54 Chip Kleinheksel: Absolutely. And Som, can you give a quick introduction about you and what you do within Deloitte?

00:03:59  Som Suresh: Happy to, Chip. So I'm a leader in our Deloitte consulting SAP offering, and my day job is really delivering a lot of value-driven transformations around data analytics and AI and how we can use it to drive better outcomes for a lot of our clients. When I'm not doing that, my other job is I spend a lot of my time thinking about the broader ecosystem where we look at SAP and some of our other alliance partners to make sure that we bring the best of the product innovations to our clients to make sure that we are consistently driving value for them.

00:04:29 Chip Kleinheksel: Love it. And Som, you'll get a kick out of this. The listeners can't see this, but I had a bet with folks that I guaranteed, even though this was audio only, you'd still have a pocket square on today, and stunned to see that you don't.

00:04:41 Som Suresh:  I had to go casual today.

00:04:43 Chip Kleinheksel: I love it. That's great. Well, Som, let's start with the lay of the land here. What are you seeing in the market when it comes to how companies are using data, the good and the bad?

00:04:52 Som Suresh: It's a tale of two very different outcomes. The easy answer is everybody thinks it's about data volume or complexity, but really, Chip, it's about the strategy, culture and governance. So it's a very interesting spin on it. There are a couple areas where I see a lot of leaders and clients winning in the market when it comes to data. The first aspect is these are the organizations that treat data as a core business asset instead of a technical byproduct. And that requires a mindset shift. You're building systems that act on data rather than building a system that just displays data. So we got to move away from that.

The second aspect is the idea of a business data fabric is super paramount, because we understand our client's landscapes are so complex, they don't just rely on SAP data. There's data from a variety of different sources. So having that unified view in a business data fabric has been pretty paramount in winning in the market.

And the third aspect is a lot of these successful companies, they treat data as a product. So instead of IT, owning a monolithic data lake, we need to push a lot of the ownership towards the business domains. But obviously it comes with a lot of common traps and struggles, which I'm sure you've also seen. The first one is data swamps. There was a big movement in the market where everybody had aspirations to go after big data that resulted in a large unmanaged data lake, which are now data swamps, where most of the folks are spending their time wrangling data and finding clean data, rather than using it.

The second one I'm seeing is, which I love, is the shiny object syndrome. You see headlines everywhere where it says AI is the future. So a lot of the decisions are now technology-led versus business-led. So a lot of our clients get stuck in POC purgatory. You build something really cool, everyone is so excited about it, but it's not really integrated into your day-to-day business process. For example, it doesn't really change how an inventory planner makes a decision in S4 using an AI insight.

And the last piece, which I think, and I'll keep bringing this back, is what I call the last-mile problem of anything related to data, which is really, you may have great solutions, you may have a great strategy, but if you're bad at solving the adoption problem where people aren't following or adopting the solutions, that's going to make it very, very difficult for you to really embrace and implement a holistic data strategy.

00:07:03 Chip Kleinheksel: Satya, does that align with what you've been witnessing so far, particularly in your own company as well as what you're just seeing out there in the marketplace?

00:07:10 Satya Jayadev: Absolutely. I think Som, he actually brought up some really good points, especially when you mentioned data as a strategic business asset, it's just not a static asset. There is a huge correlation between data and time. If you take a small example, the time to close your books on a quarterly basis has shrunk significantly. Now we're talking about hours to close books rather than days to close books. So I think speed and agility becomes more and more important as you start looking at your business the way that you compete, and so data has a profound effect on that. And the importance of good, clean data is more important.

A decade ago we said we need to democratize data. Data is important. And then we said we need to democratize analytics, so IT should be a toolbox. Anybody should be able to use a tool. And now we are saying we need to democratize AI and all that. So democratizing, as we start opening the aperture for the business to also become a part of IT and IT to become a part of the business, the most important ingredient there is data. That's the one that actually helps business to make decisions, to run things faster, more nimble.

So to me, AI is only half the game, data is the other half. And getting these two together is extremely critical. And I think as the use of technology such as SAP and the combination of these, as we build our own modern data estate, going away from a very monolithic architecture, becomes extremely critical for organizations, as that is to me the holy grail of where decisions are being made.

00:08:42 Chip Kleinheksel: A couple of things you said that really resonate. First is speed and agility. It's funny, for ages, we would say real-time insights. But we all knew it wasn't truly real-time. Well, in the world of AI, we're as close as we've ever gotten to that, but the only way you have real-time insights is if you have good data and good access to the data that you have.

And then Satya, your point of AI is only half the equation, data is the other, I look at it as, and I think you're spot-on, I think there is the company's need, the modern applications and processes to be able to transact in a way that they can leverage AI. They need the data and easy access to it so that they can actually get good outcomes with AI, and then AI to sit on top. If you don't have all three of those things together, you're going to struggle a little bit. And so really, really good stuff.

It seems despite massive investments of time and money, some business data projects don't always yield the results that they're envisioned to. Som, what are some of the challenges you've either personally ran into or you've seen in the marketplace as challenges?

00:09:48  Som Suresh:  I think I can break it down into maybe three broad categories or buckets, which hopefully will resonate even with you, Satya, right? So the number one thing that I've noticed is when you have a lack of a value lens, this is really the most common cause I see for why some of these things don't pan out. When you get the why of why you're doing it and the eventual what capabilities you want to bring, wrong from the beginning, your initiatives are built on a very shaky ground. So here's a great example. So something goes wrong. Let's say you have an additional cost overrun. The immediate answer is, well, we need to use AI to go fix it. Well, that's a very solution, looking for a problem mentality, versus something that's a little bit more business led.

What if the scenario was something like, we are spending too much money on expedited freight. We need a system to predict potential shortages 30 days out so we can avoid it. So you're starting with the why and the business problem rather than slapping on a technical solution right off the bat. The second piece, garbage in, garbage out. It's a very unfortunate reason why that principle has continued to stand the test of time, because we see it all the time with the promise of AI, when you really don't have clean data that Satya was talking about earlier, you're not going to get the value that you need from any of your initiatives. So clean data, high quality data becomes incredibly important. And I'm a technologist, but I always like to pay attention to the people side of things. 

So I'm going to go back to that last mile concept that I spoke about where you can have the perfect strategy, you can have the best world-class technology within your organization, but if you're not solving for the human element, the initiatives will absolutely fail, especially in the age of AI, the idea of a human-centric aspect with the human in the loop becomes more and more important. And I always like to say, look, it's better to get alignment over agreement. And I think that kind of mentality to bring the folks together to adopt your solutions is some of the challenges that I see a lot of our clients run into.

00:11:43 Satya Jayadev: I'll just address one thing that Som brought up. He mentioned garbage in, garbage out. I think in the age of AI, I will double down on that and say garbage in, is garbage out squared. I think it's very important to know that the quality of data determines the quality of your AI models or your ML models that you build. You feed it bad data, it's going to multiply several times the output that you're going to get. In the age of AI, every nail looks like an AI nail, especially when you have an AI hammer in your hand. There's a very inherent run towards AI to fix things that we wouldn't have thought about AI to fix.

00:12:16 Chip Kleinheksel: Satya, how do companies like yours look to harness the data you have? Data can be a means to an end, but how do you know what questions to ask of the data or how to define the task?

00:12:26 Satya Jayadev: At Skyworks, we are looking at avenues to multiply the use of AI in areas where we feel we can get a competitive edge. Our AI strategy is actually three-pronged. It is productivity, differentiation and disruption, the way that we see it. And we are very clear in terms of what goes into what bucket and how do we classify that and how do we look at projects, how do we look at ideas? We have a good intake process today where we get a lot of good ideas coming into our organizations primarily by a lot of folks, our engineers and our operations folks are very good about thinking outside of the box, because the high-tech industry is very nuanced, and I think the brain power that we have is just amazing in terms of how people think about their own work and think about how their products can be a whole lot better.

So those ideas, when they come through, we are looking at how we could use AI or build ML models that can help make our products a whole lot faster, whole lot better. Can it be a force multiplier? Can one engineer be five engineers? Can 10 project managers be 20, 50 project managers? So I think the force multiplication is true and it's truly a game changer the way that we see, especially in the agentic world.

And then on the other side, we are also a manufacturing house. So we want to understand, how can we make our products better? How can we reduce the time to market? What can we do that can give us a more competitive edge? Circuit designs today are also driven or powered by AI. There is also an inherent need to power our own supply chains to use AI effectively. There's a lot of gravity towards quality data. Data hygiene is extremely key for us.

And the good thing for us is that we got some of our foundational transformation done. So our processes are harmonized, single instances of our ERP systems already in place. So the importance is to harness this data, is to create that single source of truth, which is extremely critical for us. And once you have that, then now moving towards what we need to do or exposing that to our own data scientists, to our own business becomes critical for us. And that's where we are in the business of. We were lucky that we have got into this game early on, because we knew that in order for us to scale the peak, you've got to go in a step-by-step in terms of how we wanted to get there. We want to make sure that everything came together in terms of getting our process towards making effective decisions, using good, clean data powered by our AI, ML models. And so it's all coming in terms of how we can harness all of that together.

00:14:59 Chip Kleinheksel: I love it. And as you said, garbage in can be garbage out squared if you don't have good data hygiene.

00:15:05  Som Suresh: We're going to need some T-shirts made.

00:15:06 Chip Kleinheksel: That's right. 

00:15:07 Satya Jayadev: I actually mentioned this to our engineers, and they said, “Well, the square root of garbage in garbage out,” and then it got into a very hairy spot. 

00:15:15 Chip Kleinheksel: That's when we get to be too nerdy.

So how do you turn operational data into revenue generating products or insights? What are the ingredients you need?

00:15:27  Som Suresh: I mean, I love this question too because we're actually almost talking about what is known as the modern data economy. So think of this as we're now shifting our mindset from thinking of data as a cost center to now data as a profit center, which is a significant shift for a lot of our clients. And in fact, I would say SAP was thinking about it the same way as well. And that's why we're seeing a lot of buzz around their latest product, SAP Business Data Cloud. We really think it could provide the recipe that brings a few key ingredients together to really make this happen. And to make it really easy, the way I'm going to look at the key ingredients is you're going to need the raw material, you're going to need some of the building blocks, you're going to need an engine that makes all of these things work, and then you're going to need the catalyst that will really pour the fuel into the fire.

In terms of the raw material, if we go step-by-step, think of this as the unified and harmonized data fabric that not only includes your S4 data, but also your LOB apps, data from sensors, non-SAP data. And obviously with BDC, SAP is introducing the idea of managed data products, so it makes it very easy from a data ingestion standpoint. And the zero copy concept that they have introduced to BDC really allows you to reduce the number of copies of that data. So it's really one trusted source that everybody can use. And you get that business context, meaning a customer in my SAP S4 is a customer in BDC and it's a customer in my AI model. Those are the raw materials that kind of make all of these things work. The building blocks, so it's really moving to shifting from a data as a service to a data as a product concept. It's a big game changer that will really accelerate value from a lot of data and analytics programs. So within BDC, SAP is going to provide a managed way to get your trusted data products where you can now spend most of your time driving insights, reasoning and taking action rather than building all these complicated pipelines and wrangling all that data.

And this gives them a product mindset behavior change as well, meaning because it's a product, there'll be consistent innovation, it's going to be managed differently, it'll have the transparency and the trust built into it. And that's really the definition of that building block of a data product. The third piece is the engine that makes it all work. I'm so glad we can now finally graduate from simply displaying data to actually taking actions and maybe even reasoning with it, or talking to it. And the engine really here is some of the advanced analytics and AI capabilities that are now part of BDC.

And the last piece, the catalyst is again, all the data in the AI use cases, they're not really helpful if you don't have that business semantics. And I think that's really where BDC's good value proposition here is: it actually understands the nuances and the context of an SAP end-to-end process. I mean, BDC will actually understand the difference between an SAP sales order and a delivery note and all the associated data structures and the implications of leveraging some of those data structures within SAP. So really as we move towards a modern data economy from a cost center and to a profit center type of a view. BDC is a welcome change to the modern data economy. All the possibilities are endless, but not all the ingredients are now coming together for us to actually be able to unlock some of those things for our clients.

00:18:40 Chip Kleinheksel: So Som, transition us a little bit then. You talk about the ingredients we need. Now, how do you execute and drive value on a data-driven strategy?

00:18:49  Som Suresh: I mean, to me, Chip, the question is really about the gap between strategy and execution. I'm always a big fan of Deloitte's value-centric approach when it comes to some of these things, and it's always three simple questions. The first one is you always want to start with the 'why'. Why are you undertaking an initiative or some kind of a data-driven strategy? Is it a cost optimization play? Is it a risk reduction or margin uplift kind of a play? So you have to really nail down the why first before you go deep dive into a crazy initiative. The second question we always want to start with is 'what'. What are the capabilities that will satisfy what I just talked about from the why perspective?

And it doesn't have to be something super shiny. It needs to be something meaningful and impactful that can actually add value and address the why. And the last piece of it is the 'how'. I mean, we have a variety of technologies that are out there. SAP has a plethora of tools. We have BDC in the mix. Databricks is in the mix. So really look at how, at the last piece in terms of how are you going to solve those capabilities, what technologies do we want to bring in to help address it? And maybe the fourth piece I would add is the 'who'. Who are you solving these things for and how am I going to make sure that I always keep the human element of change management and how am I going to bring all of that into some of the capabilities that I'm driving? So this is a simple framework that I think that moves this forward.

00:20:11 Chip Kleinheksel:  I like it. And maybe to summarize it, you can't be tech looking for a problem. You can't be a hammer looking for a nail. It's what is the business outcome you're trying to strive for. Satya, can you tell us a little bit about Skyworks' partnership with Deloitte and SAP? Walk us through the implementation and the results it yielded, if you don't mind.

00:20:30 Satya Jayadev: We've been engaged with Deloitte for the last three to four years. We started our S4 journey about two and a half years ago, and Deloitte came in and helped us do a phase zero. And again, this was part of our process where we wanted to make sure that we reduced our technical debt. We started to build a foundational transformation in terms of what we saw in the next few years. So it was very well planned, and I think going with Deloitte was very critical because of their experience in this space. And it turned out to be a really great experience.

We probably were one of those companies that went live on time and slightly under budget as well. Along the course of what we did, we also made sure that we were building a system that was more unified, that was well integrated with our other systems. The use of datasphere was becoming very critical for us, because we knew what that could do for us, especially with our data strategy and our AI strategy to follow that. So to me, the experience has been very enriching. It's been a huge learning for us in understanding how the market's been evolving. And I think Deloitte was able to give us a lot of updates on how companies were strategically using systems and data in their decision making process. So it was very helpful to understand that I always believe in people, process, technology in that order, and I think who we partner with is extremely critical and we were very lucky to have great partners in this journey.

00:21:57 Chip Kleinheksel: Yeah, you are clearly one of those who are leading the way. And what I love about it, from what I've heard is, it's not a static, “We did a project and we're done.” It's a continuous evolution. As technology evolves, you're embracing it and seeing how you can take advantage of it.

00:22:14 Satya Jayadev: Absolutely.

00:22:14 Chip Kleinheksel: Could you share with us a specific example, whether it's in engineering, manufacturing, customer, where you use the technology to work the data into shape and the insights it brought?

00:22:25 Satya Jayadev: So I'll give you the concept of how we are doing it more than the example of what we've done. So about 10 years ago, we were clearly measuring, and we found that we were spending 80% of the time gathering data, and only 20% of the time analyzing that data. And we wanted to flip that. And I think that's where the digital transformation era came in. And that's where we started to think about our systems, our process and things like that.

And now, in the advent of AI, we feel like we are spending a whole lot of time analyzing data, but a whole less time trying to make decisions. And now we want to flip that. So we want to now spend less time analyzing data through AI, through predictive analytics, through more AI driven insights and then use more time in making those decisions. So I think that's why this is a jump from the digital transformation era into the AI era, which is a very stark difference in the way that we see it.

And we are so glad that we are not looking at the table stakes of IT or the foundational IT with regards to managing our systems, ensuring that the digital thread is as seamless as possible. Our asset goes across the organization where it doesn't drop through areas where it's purely manual. So we are trying to make sure that that digital tapestry is as clear, as clean as possible. Now that we have got that, we are now trying to focus on data and AI on a more holistic basis.

So the focus is now shifting, which speaks about the maturity of the organization too, to say that the organization is now thinking towards the future, thinking about where the order winners are. And I think that to me is going to be extremely critical. We are now thinking about, can we do a touchless quarter and close? That's one example. Can we look at all the images that we get from our machines and see if we can detect problems faster? Can we proactively fix those ahead of time? Those are some of those projects that we continue to work on, and we want to make sure that that's where the effective use of AI comes into play. But that's only successful if the data is as solid as it can be, as clean as it can be.

So these are some things that we are still working on. The age of gathering data is pretty much gone. It's now as short as possible. The days of analyzing the data is also, kind of gone too, where AI helps you do that. It's all about making decisions and making sure that the authenticity of those decisions or the hygiene of those decisions gets better and better and better over time. And I think that's where Skyworks is focusing on with all the data that we've got.

00:24:58 Chip Kleinheksel: I love how you said it in a really simple framework, which was you went from gathering data, automating those activities to then analyzing data, now streamlining and automating those activities. So now you're spending your time actually taking what you get from the analysis-

00:25:14 Satya Jayadev: Making decisions.

00:25:14 Chip Kleinheksel:  ... to actually make the best decisions you can make.

Satya Jayadev: Correct.

Chip Kleinheksel: It's a great way to say it. Som, what about yourself? Do you have any specific examples, whether it's engineering, manufacturing, customer, where you're seeing the technology be used in insights to really drive value?

00:25:30  Som Suresh: Absolutely, Chip. I mean, there's a lot of use cases that are out there that our clients are doing, but given where we are from a market and an economy standpoint, there's a couple of patterns that really stand out. A lot of our clients are using data and technology to drive insights around one, margin uplift. What can I do to help get a better margin for my organization? The second area that's top of mind for them is what do I do and get a better cash flow? How do I make sure that I can have more of my cash on reserve? Because that's really a top-of-mind for a lot of our clients. And the third piece for some of our heavy industries is around what's the best way to utilize my asset and my inventory? So some of these use cases really are at the forefront, if you will, of where data and technology can really shine together.

00:26:14 Chip Kleinheksel:  Satya, how do strategic data use and predictive analytics help innovative and fast-paced companies like yours prepare for the future?

00:26:22 Satya Jayadev: I think the importance of data that we spoke about is extremely key when it comes to engineering design. It also is key when it comes to operational automation and things like that, because think about this. There are cycle time for products to get to a phase where it can be sent to mass production. It's pretty long today, and we are now trying to see, how can we shorten that? How can we shorten months to days and days to hours? So those things are going to be extremely important.

I think we know that if we can do that, then opportunities can pop up everywhere. Through the effective use of AI and data, we could have a huge impact on our top line growth of the organization. That's an area that we are focusing on all the time. And then now that we have designed this product, how do we operationalize that? How do we mass produce that? How do we reduce the time to mass produce it? How do we improve the quality of those products as we mass produce it?

All of those, if you see the root of all of that, it comes to how well do we have that data structure and how well can we apply machine learning and AI to that? And I keep talking about machine learning and AI because that's a game changer. We all know that we are all past the hype cycle. We are all past the point where we feel like there is true value in it, but how do we carefully, and how do we harness the potential in a way that is... We have to think from AI to ROI, because the age of experimentation is over. We've all done that and now it's all about how do we operationalize quickly? And I think that's where the secret sauce is, and I think that's where Skyworks is working hard in terms of trying to make sure that we get to that phase of our maturity process.

The most important thing that we see, Chip, is we've got a number of folks in the business now who are plugged into our data. A number of folks who have access to that data, who are now trying to build models, machine learning models. We have a great team within our own technology and manufacturing group that is harnessing that data. They're building models as we speak. And I think that to me in the next few months, to years, is going to be a game changer. I feel that that will give us a competitive edge. And as I mentioned at the beginning, it's an absolutely fantastic time for a transformational CIO, and an absolutely horrible time for an operational CIO.

So I think if you're a transformational CIO, you're going to be enjoying this time because you have the ability to kind of change gears. You have the ability to drive into a transformation of sorts for your organization, but if you're going to be an operational CIO sitting and waiting for very reactionary things to happen, it's not going to move the needle for you. So I think this is how we see it. This is how we see the evolution. Like I said, the focus is not on our systems. The focus is on our data and our AI at this point in time, and I think that is going to help us open a world of opportunities in terms of what we see.

00:29:16 Chip Kleinheksel: Yeah, and I do agree with you. Those who are transformational in this era are going to succeed. I said this earlier, you can't be static. It cannot be something where we're going to go do something over a period of time, have a technology capability, and we're going to set it and forget it and just use it. It doesn't work that way anymore. The technology evolves so fast and you have to have an architecture that allows you to change, and you have to have a mindset that is, is your organization willing to change with it. Both of you, thank you so much for the time here. I feel like our listeners won't be able to say that they have too much data and not enough insights after this fantastic discussion.

For a bit of fun, Satya, Som, if you could get perfect data to answer any question you wanted the answer to, what would the question be? Som, let's start with you.

00:30:05  Som Suresh: It's a pretty tough problem, and I don't think anyone's been able to predict it, even though I've been asking a lot of the LLMs to do it. So if I could have perfect data to answer any question, I would literally ask it, where should I invest my next $10,000?

00:30:18 Chip Kleinheksel: I like it. I like it. And Satya, how about yourself?

00:30:20 Satya Jayadev: Mine would be more along the lines of can my LLM give me a very personalized set of lifestyle interventions that would allow me or my family or everyone to live a very disease-free, pain-free life that would help reach until their natural lifespan with full vitality, full quality of life. If that LLM can give me, and that has to be very personalized, very customized, that would be fantastic.

00:30:48 Chip Kleinheksel: Health and wealth. I'll take both of your answers. I like it. Thank you so much to my guests, Som Suresh. Som, thank you.

00:30:55 Som Suresh: Thank you, Chip.

00:30:56 Chip Kleinheksel: And Satya Jayadev, Vice President and Chief Information Officer at Skyworks Solutions. Satya, thanks so much for joining us.

00:31:02 Satya Jayadev: Thank you.

00:31:02 Chip Kleinheksel: Thanks for showing us how data, innovative tools like BDC and strategy, converge in real time to keep companies ahead of the curve. And where the right partners become more resilient and competitive. From semiconductors to supply chains, the future isn't just being predicted, it's being modeled and built with every insight-driven decision.

00:32:21 Chip Kleinheksel: I'm Chip Kleinheksel, host of Resilient Edge, a Business Vitality podcast, paid and presented by Deloitte, and produced for Deloitte by BBC Storyworks Commercial Productions. Thanks so much for listening. We'd appreciate a review on your podcast app. Goodbye for now.