 Good afternoon, nerd fam, and welcome back to Barcelona. We're here at MWC Midway through day two with our wall-to-wall coverage here on theCUBE. My name is Savannah Peterson, joined by my brilliant colleagues, Dave Vellante and John Furrier. Thank you both for being here. Although I'm not going to lie, the attention is not going to be on you two, this particular panel, because we have a data queen in the house, ladies and gentlemen. Please welcome Kailen. Kailen, thank you so much for being here. You are the Global Chief Data Officer at Ericsson. What does that mean? Thank you. Thank you very much for having me. It's great to return to theCUBE. I think- Yes, almost 10 year veteran, right? I think my first appearance was nearly 10 years ago. And at that time, we were talking about the growing importance of the role of data and enterprise transformation, data and AI transformation, which is fantastic. So you've been a great partner with us for all this time. Great to be back in a new role, a new capacity. So last year I joined Ericsson as their first Global Chief Data Officer. So very excited to perhaps share a little more of what we're doing and what we're going after in that role. Well, please do. Yeah, go for it. First of all, global in the title. And that was something that when you were at IBM, Inderpal really emphasized. Wasn't just a sort of a narrow divisional role, but so it's in your title today. So describe a little bit more about that role. Exactly, right. And if you recall, when we did the first set of research, it was qualitative at the time, because there were just few data officers in industry. So we interviewed 2025 and about 2015. And we found that those who are investing in data and building out that foundational data capability, we're seeing incredible business benefit. So that spurred the launch of data office within IBM organization. So we drove about a billion dollars in business benefit over a six year period of time. We also did that extensive end to end process mapping in our key business areas. So we showed about 70% improvement in process time, cycle time, we were able to move our most sensitive, crown jewel data onto our trusted data and AI platform. And we were able to achieve some important compliance related initiatives, GDPR readiness at the time. So it was that similar thinking, I think around really investing in data foundation capabilities to then spur and support both de-risk and accelerate data and AI related initiatives. So, yeah. Foundation, word foundation, foundation models, AI coming together. I mean, it must be pretty intense right now in your view to see all the attention now globally on AI. Exactly right. And maybe bridging between those last two questions, that was the approach we took within the IBM enterprise was to really focus on scale and AI accelerator organization. So we had use cases and data governance and management, risk insights, supply chain. So I'm bringing that same thinking and perspective to Ericsson, which is four fronts of this industry, technology-wise, they've really invested and committed to making data a priority as evidenced by my new role in the elevated mandate. Our CEO himself interviewed me during the process and I really felt the commitment to data across the enterprise and the organization. And you asked an earlier question, how are we going about it? How are we tackling? So there had been some initial work done, which is fantastic to build a federated data model. And we have data governance occurring at the domain level. So we have about 30 data domains, supply, logistics, marketing, I think all of our functional areas as well as across our key business, MAs, BAs in the functions and identifying the AI use cases that we can really go after and show impact. And that's how we're tackling at a high level. Can you give us a quick- What's your reporting structure? Sorry. Sure. That's always interesting to me as we debate it, what's the right structure? There's no perfect structure, but- Absolutely. And as we know, CDO is an industry, right? About a third report to a CIO, a third to a CXO and then a third to some other kind of CEO type relationship. So we're quite fortunate. I report to our senior vice president and COO who reports directly to the CEO and he's responsible for driving digitization as well as simplification. So as we're attacking all the underlying data processes and figuring out ways to drive efficiency and improvement, we're able to deliver on those objectives. And then within the scope of our organization, our global operations team, we have our CIO, head of supply, sourcing, real estate, our global business services. We have several really critical business areas coming together under one umbrella to drive that improvement experience for our employees and for our customers, for the organization. You have an incredible bird's-eye view across multiple domains. And you mentioned that you've seen a lot of different examples of AI across those domains. Absolutely. Could you share some of those with us? Absolutely. And the big shift in thinking that we set out to achieve last year was from process to impact. So we were looking at improving data quality and data access from a process perspective and we really implemented a shift in thinking to start with the business problem. So we have a number of examples where within the marketing organization we've improved 200,000 plus marketing records. Within the logistics space, we've identified missing shipment dates and we've launched a transportation execution monitor capability so that our clients have better visibility into delivery of goods in a timely manner, the transparency is there. And so we're again tackling it from a domain perspective, encouraging robust data governance at the domain level as part of our federated structure. But then also looking at what can we do to centralize some of our core enterprise data so that all of our data teams can take advantage of that centralized enterprise data as it pertains to really most important maybe finance, HR, et cetera. Do you find that AI, I mean you've been in IoT, you've been working in this space for a while, you've been dealing with AI and data for a while but obviously the hype curve is really hitting a peak. Do you find that that's accelerating your ability to achieve some of your goals? Do you find that that's more buy-in from the company at large? Absolutely and there's, I would say within Ericsson, you know it's an incredibly intelligent, you know highly functioning engineering culture. So there's this drive to innovate and deliver this, you know improve seamless employee experience as well as you know the best platform and network connectivity for our customers. So high importance in terms of mandate from top down to your point and then we're using it, I think there's this growing recognition that there's data dependency for our AI efforts to scale so the accessibility, the quality, the access, you know those are the things we have to really tackle and go after in order for us to deliver on this promise a couple maybe specific examples, you know we're doing a lot of the AI related work in those areas I mentioned. Some others that we're exploring are, you know, within the augmented data management space where we're using metadata to really drive insights across our products and supply as well as some new capabilities that we're looking at. So I have a AI leader on my team that has a background in a series of patents and watermarking data for example. So how do you identify the source of data? How does that combat some of the biases that you see later on in the deployment of your AI models? So do you have that kind of traceability you know throughout the development and deployment phase? So we're both, you know, tackling it as I mentioned from a specific domain, pain point, business problem to solve as well as some of this really innovative, you know, new things that are coming. Have you seen any new issues emerge with AI's generative AI wave that's different that wasn't around a few years ago? I mean, we think governance certainly is important. That might change the equation, but that doesn't radically change too much, at least we think, but is there new areas that have emerged? Privacy's one, generative AI generates a lot of privacy issues. What, is there areas that you see coming into this next big wave? Absolutely, I would say quality and access. That's what we're seeing. Those are top priority outcomes we have to deliver against this year. You mentioned the partnership with privacy within the Erickson organization internally, we're partnering very closely with our chief legal officer and he has a scope of responsibilities that include privacy, compliance, regulatory and other, you know, many other commitments. And we're seeing that's a fantastic partnership because we can work together to figure out what the right data access policy should be internally to improve, you know, that employee experience. Assistant, you said that at IBM you generate $6 billion of value. Over six years. One billion over six years, yeah, still big number. But the theme here we're seeing in the industry here in telecom and other is just that there's net new business model opportunities coming out of getting that data harvested with generative AI specifically. How do you see that? What's your reaction to that? The net new creating new value. Absolutely, so what we're seeing is, you know, the 5G unprecedented in terms of adoption. However, if I look at the research, in my experience, it seems that the monetization opportunity is lagging behind and if you really explore underneath, it's the data availability, you know, the quality and the access. So that's what's asking us to ask these questions. What level of access threshold do we need? You know, a lot of organizations are moving from purely role-based where it's, you know, you have this role, you get this access to role-based. How is the data going to be used? Whether terms and conditions. How is data transfer and movement affecting this? Are we looking at, you know, governments, movements between different, you know, geographic bounds and government restriction? So maybe some of my past experience sort of operating in a highly regulated matrix, global organization is relevant as we navigate through the quality and access opportunities. Well, the evolution of the Chief Data Officer has been pretty fascinating because it really started within highly regulated industries and it was sort of a back office thing. Data quality, that's somebody else's problem. And then, really, when you were at the heart of it, it was, that was kind of boring but important. Then it became really important in the big data era and it was sort of the Wild West, everybody was doing their own thing with the dupe and CDO was asked, okay, can we bring this all together and try to find some ways to drive value through the organization. Now it's become full circle data quality. You can't have AI without data quality and it's become really, really important. So that has been sort of a fascinating progression. So you're in the heart of that now, actually the early days of that now. Do you find that you're spending more time on sort of internal processes, kind of like you did at IBM, or is it more externally focused, a combination? It's definitely a combination and I think at the heart of your question, there's a piece there around, we've been tackling some of these quality and access opportunities challenges for quite some time. So I think I've been in the industry almost nearly 20 years now and came at it, implementing these financial systems in public sector, federal government, highly regulated as you mentioned, and it was all around compliance drive. But we soon saw opportunity, can we deliver new products? Can we pursue a new business model opportunities, which we very much did. So to answer your question, I would say, we continue to focus on the data management core principles and making sure that we get the quality and access, right? But even more so, we're finding with our AI models, we have to have that piece there. And I'd say maybe two things that we're focusing on within Ericsson that I'm really excited about is one, really embedding ethical AI across a lot of our work. So trustworthy AI framework is something that we're pursuing strong partnership internally across our technology areas that really have expertise there. And then the second piece is data literacy. So I think we trained nearly 6,000 individuals within Ericsson last year in data literacy related modules. We have more than 500 a quarter that are upskilling in data and AI related areas. So I think we'll continue to see more and more of a need to educate, provide the right skills. And then I did an analysis recently within the team. We had a series of people that obtained really highly specialized AI capabilities, but because they did not projects to work on and apply their skills, potentially left the organization. So how do we retain our top data and AI practitioners and give them really challenging opportunities to work on? The research that we have shows that about 40% of the customers that we survey say that they're stealing from other budgets to fund their AI. Is that your experience? If so, where are you taking it from? We're going after, and having reported to a chief financial officer previously and having a strong relationship with finance within the organization, we're finding it's a collective effort. So everything that we do internally, I would say for the most part, is really intended to be a collaborative affair. So for example, within the data space, we launched a data and analytics platform last year with IT. We have a very strong CIO launch that platform. And we have several business areas that have come together to test the capabilities of that platform. Is that going to serve our needs for enterprise data, particularly for analytics capabilities? And it's maybe an example where several groups, business areas, we're co-sponsoring, we're putting both funding and our people's time together to make that successful. So everything we go after internally is collaborative and intended to represent the whole organization. One other example is we're standing up a data officer council where we have fantastic representation from all of our major business areas, market areas, as well as our group functions. And the intent there is, this is a strategic body for data-related decisions. So not just compliance, but also business outcomes focus. Is the, sorry, John, is the CDO office in that example that you gave about the collaboration and multiple sources of funding? Is the CDO office the adjudicator of that funding, or are you being pulled into some other sort of funding, you know, hurting the cat's mechanism? I've always been a big fan of a co-creation model where there's some centralized funding available to support because if you say this is a key decision for us at top level, nice to see it backed up with the funding. But you also want the business to contribute so that they really make sure to put their right resources against it and accelerate. So I like that model. And then the other piece is, as you go out and look at opportunities to decommission or consolidate sort of legacy systems, there's really an opportunity to reinvest that into some of the new AI initiatives. And having come from an organization with a large legacy background, hopefully some of that experience is helpful here too as we transform very rapidly toward, you know, the future. So you got the data council. Are you going to have an event? That's the question we want to know. Is there going to be an event to continue the, I mean- Why would you ask? Yeah, I bet you. I'm alive from this event. I mean, the momentum, obviously, Ericsson got a lot of interest in the data, a lot of data. And we've heard here at Telecom, and we've reported that everyone is saying that if you want to make money, you got to go to the device and work that network, that whole process. So I can imagine this community is going to grow. Your effort will expand. What's that ecosystem of your constituent stakeholders look like? Are they internal, external partners, got a council? Absolutely. And there's two approaches there. There's one, the internal decision-making body. And we certainly want representation across the business. And there's also the external community. You know, ultimately, we really want to improve our customer experience. Some other examples that we're going after specifically is we've identified some end-to-end customer journey flows. And maybe, for example, pricing. You know, from quote to delivery, there's opportunity to really automate and use AI in that experience. And ultimately, our customer benefits from a more seamless, transparent process. So exploring opportunities where tangible outcomes to our customers. And then you mentioned the external community. You know, that continues to be a passion of mine. Diversity, inclusion, really encouraging. In particular, we were talking before about... I was just going to take us there. Go ahead. Yeah, yeah, yeah. No, I can't help but think about it when you bring up ethical AI. And when we think about access and quality, yes, these words apply to data. They also apply to the teams that are structuring this data and putting in these guardrails. And I was so delighted to see your face walk up here and know that you were in a position of leadership as someone who looks like me. And know that there's diversity at Ericsson. You mentioned that it's a huge priority. Talk to me a little bit more about that. Absolutely. And I see it reflected in our highest level targets and metrics where we have gender diversity as a key target. And it's not just for gender diversity's sake. It's because we really think that building diverse teams leads to best outcomes. It does. And the data shows that, everything shows that. Exactly. What a great team right here. Yeah. And I think it's also other diversity and inclusion metrics. I mean, we look for people that have had a long tenure with the organization that understand the internal, you know, workings of the org. You combine that with some external talent that's joining us to bring maybe a Silicon Valley or other sort of emerging technology perspective. The gender piece ethnicity, we talk a lot about experience areas. So how do we take some of our legacy folks that know, you know, master data processes and have tooling experts and you pair them and combine them with some up and coming, you know, data scientists, AI ethicists. And then you really can not just build and deploy a model that's going to have the best sort of features and functions, but it's really going to be grounded and incorporated in the business process. And you see the true outcome. Because I'm shooting for, you know, the big business benefit metrics, process time, you know, all the things we know are really going to make an impact beyond just, you know, improving quality and access. I love that. Caitlin, that was a perfect answer. Last question for you before we wrap up this absolutely brilliant segment. What would be your advice to a young person watching this right now who is excited by what you just talked about to get into a career like yours? I love data. I come from a statistics background. You know, I really came at it from that way. I see there are many ways that data offices are successful. You know, some come from engineering, some statistics, some math. We see there's a real collaborative stakeholder engagement, everything we do is in collaboration, whether it's funding or data related, you know, decisions, because you've got to bring finance, IT, marketing, you've got to bring everyone on board. So I just, I really encourage our kind of up and coming practitioners to think of data career as something that's very rewarding and huge massive impacts for organizations. And it's going to be here to stay. Data is going to be a part of our lives for generations to come. It's not a hype curve tech in that regard. Caitlin, thank you so much. You're a radiant data queen. You've clearly earned the crown in this interview. John and Dave, always a pleasure to share with you. And thank all of you, our wonderful community, watching all around the world for our four days of coverage here from Barcelona, Spain at Mobile World Congress. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech coverage.