Hi everyone, welcome to our webcast sponsored by Salesforce. I'm Roboan, global content director with Foundry, and I'll be your moderator today. I'm joined by a great panel of speakers who will discuss how CIOs can leverage data and analytics platforms to increase value across the business. Let's meet our guests. First is Mark Kazinski, SVP of Finance and Technology with Nissan Americas. Hello Mark, please tell us a bit about your role.
Sure, I'm Mark Kazinski. I'm with Nissan. I'm the CFO as well as the head of IT for Nissan Americas. So, I'm in a unique position in terms of understanding the information technology needs of the organization, as well as being responsible for the financial activities of the organization. It gives me a unique perspective maybe that others don't see all the time.
Interesting to hear more from that perspective as we get into the discussion. Thanks Mark. Next is Martin Kastner, a principal with KPMG who heads up the firm's analytics and AI practice. Hi Martin, what can you tell us about what you do at KPMG?
Thanks Rob. I'm Martin Kastner, a partner with KPMG. I wear multiple hats. I lead the analytics and AI team in the US. I specifically focus on the industrial manufacturing industry and our clients in that industry. I like to bring the technology and all the great opportunities that we have to offer, and the technology offers these days, to really solve specific industry problems. We'll probably get more into that discussion, but I'm very excited to be here and I think there's great opportunities leveraging AI in the industry.
Thanks for joining us, Martin. We're also joined by Vandana Gangar, SVP of Data Cloud at Salesforce. Welcome Vand, what can you tell us about your role at Salesforce?
Hello everyone, I'm Vandana, part of Salesforce product team focused on Data Cloud, with emphasis on building the customer 360, as in the cross cloud and the ecosystem around the Data Cloud, which is going to help enable our customers in their digital transformation journey. So, looking forward to learning from what Mark and Martin have to say from their expertise.
Great, so let's jump in with some context. What are some of the challenges and pressures that CIOs are facing as they look to empower data-driven decision making more broadly at their organizations?
Martin, we'll start with you on that.
Yeah, I think there's technology always offers great opportunities, but I think we also see a shift in consumer expectations and a shift in the industry. A lot of the automotive OEMs and larger industrial manufacturing industry are going through a transition where they're shifting from just being great engineering companies to really understanding the ultimate customer and how the customers are using their products. So, they're becoming more of an engineering company that also needs to embrace the consumer industry as well as the high-tech industry. There's lots of different changes happening in the industry, and I think client expectations are driving that as well. From our perspective, we like to bring the industrial and functional knowledge into the analytics and help our clients think through how they need to change their operating model and how they need to be operating a little bit differently. It's a very exciting time right now, I think.
Exciting and a bit terrifying all at the same time, I would imagine. Vandana, what would you add there in terms of some of the challenges that you're seeing organizations face around their data and analytics strategies?
There are three ways that we have seen customers use their data strategy to enable. First and foremost, as Martin called out, it is to do with improving the productivity of people. How do they automate it, how do they personalize it, and how do they take it to the next level? The second aspect is where the customers are pushing it, which is improving the customer experience. This is where the end-to-end journey from marketing to sales to customer commerce to services, providing personalized experiences that truly differentiate one vendor from another. The third aspect, which is becoming even more important in today's frame because technology has made it available, is thinking of newer business models. We have seen several of our customers with Data Cloud who have totally taken their business models to the next level in terms of creating newer revenue streams.
And enabling what would have been a cost. Center into a profit center or creating a next generation of customer experience. For which customers are more willing to pay for. So that is how I would see the three aspects of where customers are taking it as an upside to leverage the data strategy that they have. Great, thank you. Mark, how does what Martin and Vanden have described compare with your own data-driven journey at Nissan Americas? Yeah, I mean, I think that you know, like any journey, um, you know, technology is something where you know, it's a I always say it's going to be a marathon, it's not a sprint, and and it's a journey that everyone's been on for a while. So it's a continuous journey. I think AI and some of the new developments and new technologies coming on board are going to maybe accelerate the pace of technology adoption. Um, certainly we have within any organization, I think you have early adopters to that technology, and I think the key is, um, you know, to kind of embrace that journey and and the organization embraced that journey. I think one of the things we're seeing today is that you know data governance, data structure, the data is more critical and I think that there's an understanding of that within the organization. You know Martin talked about different revenue streams, certainly in automotive, you know we see more emphasis on other revenue streams outside of just selling the vehicle, selling subscription services. Um, you know there's mobility, autonomous drive, there's a whole host of new technologies coming in that give more value to the customer but uh, data is at the core of it and trying to unlock that value for the customer is going to be key. Um, but again I think that uh, from my perspective, you know it's how do you kind of progress that journey and accelerate that journey from uh, from you know because I don't think it's going to be a choice. I think every organization is going to be on this journey, it's how quickly and uh, the organizations move and embrace some of the technology that's going to be available. Great. As I understand it Mark, you've created a chief data office uh, to enable and unlock some of this business value that you described. Can you talk about that a little bit? And I understand as well that it reports into the business side of the house. So talk about that kind of, that kind of connection between it and business as it relates to the chief data office. The key for the data office was kind of enabling three uh, three pillars: data governance, business transformation, and data and analytics. Um, you know, clearly the data office isn't, it's not about application development, it's not about necessarily even automation, it's about providing um, the toolkits to the business themselves so that we can have what we call data athletes within the business to make better business decisions. Um, to enable them to look at information differently and then unlock things that may have been uh, hidden before. So, you know, what are those key business insights that we can unlock using some of the uh, the techniques that the data office will allow us to utilize? But I think it's key to have the business really thinking about what are their priorities in terms of business needs and utilizing the data office as an enabler to do that. But it's key to have all the businesses really engaged with the transformation of how we use data within all the functions. Interesting. Um, you had mentioned AI before, uh, Martin curious to hear kind of what you're hearing from your clients, what you're learning about how AI and particularly generative AI are impacting data-driven strategies. So, when we think of uh, um, generative AI, we kind of think of it in three different high-level use cases. One is the consumer or employee productivity gains, where you just, you know, use it to improve writing, summarize a text, or something where you just put something very specific into um, like a chat GPT or some large language model. Um, the other thing is that we will expect that all of our alliance partners, all software companies, are going to come out with Gen functionality embedded into their software, uh, and into their functionality. So, it will be part of all kind of software suites.
Where we, as KPMG, think about AI and where it's really transformational is when you think about large process transformation. So, in the past, we have been thinking about how do we automate the steps in a process or automate parts of a process. What's what GPT and AI is really allowing us to
Do is to think about how do I get from. The input of an of a process to the. Output of a process in the most. Efficient way, uh, and leverage automation. Um, as a first step, rather than trying to. Automate previously manual steps. And if. We think about automation first, then we. Start to think, where do we, uh, integrate. The subject matter experts into the. Process most effectively. So it's almost. Like a symbis of the subject matter. Experts working with the machine to, um. Together. And that's really what's. Driving a much, much higher level of. Automation of process and complete. Transformation in some instances, uh, so I. Think that's really exciting. And, um, I. Think we already starting to see a lot. Of benefits in productivity and accuracy. And risk reduction. Vand we know Salesforce has. Invested heavily in, in AI. What would you. Add there in terms of CIO should be. Thinking about AI in relation to their. Overall data strategy? I would just build on where. Martin and Mark were leading to which. Were very good points and I would add to. It, it's very important to define the. Limits, to define the project, to define. The scope, to define what exactly we are. Trying to do and also to define the. Right tools to make it happen because. You do not want to have a very. Complicated piece where you can just Jo. A time series old school data science. Model. Uh, related to it is the part of. Where people try to, uh, think that they. Have to be data scientist really to be. Doing and leveraging AI, uh, but that. Is where technology has changed and. Evolved significantly, uh, with the large. Language models of the prompt builders. And this is where Salesforce has. Invested quite a bit to get AI in the. Hands of people who really know how to. Do their business very well. Who may not. Be the data scientist. And this is where. I think Mark was going more in the hands. Of not the IT and the CIO function but. CDO function outside of it. Mark, anything. You can share about kind of how you're. Using or experimenting with AI or gen. And kind of what you're learning along. The way? Sure. As, uh, I think several of. The speakers have mentioned, Martin and. Vanduh, both have talked about not. Eating the elephant but taking small. Bites, right? And looking at pilots and. That's exactly what we're doing, right. So we're trying to utilize it in a variety. Of ways. Um, you know, across multiple. Functions. So if we talk about like the. TCS function within our organization or. The customer satisfaction team. Um, you. Know, we're trying to use Predictive. Analytics to to look at, um, you know when. A battery may fail, for example. So using. Connected data from the vehicle. Um, and. Then using Predictive Analytics to say. Okay, if these certain things happen. There's a high likelihood that battery. Will fail within the next, you know. Certain windows. So things of that nature. But we're also trying to utilize it, you. Know, from a sales perspective to look at. Things like sales forecasting or. Effectiveness of various incentive. Tactics. Uh, and other things. So I, I think. That's, you know, one of the things we're. Finding. And, and the key for us is being. Able to look at really granular data, you. Know, and when you know a lot of times. People are looking at averages. And so. Forth and, and by looking at you know. Trying to get the data at the at the. Most granular level, uh, we're uncovering. Insights that otherwise were hidden in. The averages. So, um, so that's, that's a. Key for us that, that we're looking at as. As well as trying to expand what other. Functions can we utilize this in? Uh, you. Know, because it has applications whether. It's HR or whether it's purchasing or. You know so, uh, trying to get those. Functions on board as well. Great. Um, we've. Touched on this as we've gone along but. I'm curious in terms of kind of the. Guidance you'd provide around how CIO. Should be tying all of these pieces. Together, vanand what key components do. They need to have in place to improve. Their data maturity. Start capturing. More business value, all the things we've. Talked about to this point. That's a great. Question, Rob. And um, this is foundational. And fundamental. Uh, in so many ways. And as Mark was alluding to a little bit. In terms of the use cases of that, the. First and foremost, people have to define. Is a specificity of the use case and I. Cannot say it enough. U because if you. Try to boil the ocean, you may not get. Anywhere anytime soon. So that is where. Define the use case whether it is in. Marketing where you want to stitch the. Data together to personalize the user. Experience across different assets that. The customers may have or you want to. Then connect that data and create it into. A digital sales and marketing function. Where you want to reach in a. Personalized manner to a broad of the. Customers that are going to help create. A new value for the firm, then think. About taking all of this in terms of the. Automated prospecting, liberating the.
Gen AI where machine can help you. Personalize and write that particular email that may have been too difficult to write if you have to write it a thousand times over. Then taking it to the next level in terms of enabling that very personalized sales experience and connecting it in terms of the customer flywheel for the commerce transaction. Taking it from the commerce transaction to that customer success, which is where Mark was going. We are increasingly seeing our customers use the data Cloud for the purposes of what may have started as a marketing investment into improving and taking the customer experience in the contact centers. This is to reduce the cost of calls and the time that people have to spend, as well as improve the quality of the conversation and create very happy satisfied customers. But that's not it, as Mark was referring to. These days we have lots of data that gets generated out of IoT sensors, which are all over the assets, the vehicles, and the manufacturing facilities. But that generates a lot of data, like really a lot of data to be processed. And that is where the technology that we are working on comes in, to help make meaningful sense out of it. So the machine is reading the aberration and abnormalities so as to trigger the action where it is needed, and a manual person does not have to sit in there to observe that. It also helps create the next generation of customer experience, as Mark was talking about the car battery. We are seeing a lot of auto manufacturers who are personalizing it to create that service appointment. But why just say that your car needs a new battery? Also, take it to the next level and say, "Can I help you schedule the appointment? Where can you take the car and how does it fit well in your calendar to take it to the dealership to change it and get you a better experience? So it's the whole gamut of it, the whole spectrum of it that people are trying to work on, and they are finding value in different aspects of it. But it comes down to where are you starting? The starting point is the most important one because I have seen some customers who try to do everything and they go nowhere, and then there is another set of customers who feel so jaw-dropped by looking at all the power of it that they just sit on the sidelines trying to think about where is it that they want to start or waiting for that perfect scenario to come in or that perfect data strategy to be aligned. The starting point for them should be now and right now in terms of defining that they should start. It's good to fail fast, to learn fast. Got it. Got it. Good. Thank you, Martin. Anything you'd add there in terms of practical guidance for getting started or continuing your journey in terms of a data maturity standpoint? Yeah. I think I'll echo what V and what Mark have said already. But I really wanted to emphasize that we're starting to see the convergence of business data ownership and IT as an enabler to really drive new insights. When we think about data, it's typically data that comes from not just a single source but multiple sources. So what you're going to produce, how are you going to better sell and better understand your customers might come from understanding your channels, your customers, your supply chain, your cost structure. So that shows you that there's lots of different data that needs to come together. The business needs to own that data and any work that you do needs to be business-led and value-driven. But it's really where the business needs to work across departments to bring things together. And then the integration of these data sources, that's the technology and all that's really driving new insights. Great. You've provided some great use cases and practical guidance for getting started or evolving your journey. How about some closing thoughts from each of you? Mark, we'll start with you. A lot of what Martin and Vanden mentioned resonates with me in terms of our journey. What I would say is a big piece of it is about getting the blocking tackling of data quality, data governance, and so forth set in the organization and the functions understanding their requirements. Another key element is identifying where you can have significant value and trying to take those pilot initiatives and growing off of those. Once you see success, other organizations and people get excited and want to join. It's going to be transformative in terms of how people work.
Having the change management associated with that and having the buy-in of the organization is going to be key as well. So, business transformation and change management is going to be a key element of this as well, and how to upskill individuals so that they understand the future. I think those are all key elements for me. All great points. Thank you, Mark.
Martin, your key takeaways please. Similar to what Mark was saying, technology and the capabilities are moving very fast. At the same time, adoption and expectations from employees and consumers are also happening. We need to rethink the combination of the human subject matter experts. The reskilling of people is going to be critical. Data ownership, data quality, and the granularity of data are also important. We need to combine humans, data, and technology to drive transformation. Specifically for us at KPMG, we need to understand our client's data, business, industry, and the problem we're trying to solve as well as the technology. It's a triangular relationship between the client, technology, and service providers who all need to work together to solve these complex issues.
Vand, your closing thoughts please. The first aspect is building on top of Martin and Mark's points, which is ensuring you have a vision of what you're trying to do. Do not start with technology and then think about what to do. Prioritize the business problem, the business priority, and the process you want to automate and improve. Understand the technology options and how Salesforce and data fit into the landscape. Culture eats strategy for breakfast, so bring your people along and make sure they understand that technology will make them more productive and provide a better experience. Upskill your workforce and have champions who can accelerate the adoption. Adoption is key for success. Thank you, Mark, Martin, and Vand.
