In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Soren Marklund, Vice President of Global Services, Technology Consulting, and AI Data Strategy at Ericsson. They explore how Ericsson leverages modern data architectures to enhance customer interactions and drive business benefits. The discussion covers the importance of a unified data operating model, challenges faced with data silos, and the role of AI and machine learning in improving customer service.
In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Soren Marklund, Vice President of Global Services, Technology Consulting, and AI Data Strategy at Ericsson. They explore how Ericsson leverages modern data architectures to enhance customer interactions and drive business benefits. The discussion covers the importance of a unified data operating model, challenges faced with data silos, and the role of AI and machine learning in improving customer service.
[00:00:00] Producer: Hello and welcome to the Data Cloud Podcast. Today's episode features an interview with Soren Marklund, Vice President of Global Services Technology Consulting and AI Data Strategy at Ericsson, hosted by Dana Gardner, Principal Analyst at Interarbor Solutions. They explore how Ericsson leverages modern data architectures to enhance customer interactions and drive business benefits.
[00:00:26] Producer: The discussion covers the importance of a unified data operating model, challenges faced with data silos, and the role of AI and machine learning in improving customer service. So please enjoy this interview between Soren and Marklund and your host, Dana Gardner.
[00:00:41] Dana Gardner: Welcome to the Data Cloud Podcast, Soren.
[00:00:44] Dana Gardner: We're delighted to have you with us.
[00:00:46] Soren Marklund: Thank you for having me.
[00:00:44] Dana Gardner: You know, a few functions are more integral to an enterprise success than effective and efficient customer service and support. And over the past few decades, the means. Not just the ends of sophisticated customer services have proven beneficial to businesses in many ways by capturing all the information that it requires to deliver customer support.
[00:01:10] Dana Gardner: For example, companies coalesce and structure their essential knowledge and free it from silos by capturing and structuring all the feedback that they receive from what clients and customers tell them about how their products and services are working in use businesses. It gain priceless insights into making products and services better to maintain and to learn how to improve the next iterations.
[00:01:36] Dana Gardner: Clearly a focus on managing and exploiting all the data and information around the service and support function plays a, pays rather huge dividends. And that was true even before we applied machine learning and artificial intelligence tools into the mix. In today's discussion, we'll explore our multinational networking and telecommunications
[00:01:57] Dana Gardner: powerhouse, Ericsson, has employed modern data architectures to further innovate around customer interactions for multiple valuable business benefits. Soren, tell us why it's so important to get the data architecture right in order to pursue better customer service through greater use of ai.
[00:02:18] Soren Marklund: It is a very relevant question.
[00:02:20] Soren Marklund: And of course, what's something on our mind for quite a few years? I mean, we have, as a company wanted to be data driven in everything we do, whether it's in services, products, interaction with customers, and what have you, right? But the journey really started on how you drive intelligence. But we realized very quickly that the data fabric, the data foundation, and the data operating model is instrumental in making this happen.
[00:02:45] Soren Marklund: So we, we started a journey of, of really looking at data as an asset within the company. Prior to, to this, I would say that we, we dealt with data as input, output of different processes. We were, and we have been a very process focused company for many, many years When we started a data journey. Now here, we realized that the process itself is what driving information and efficiency, but the importance on data is equally important.
[00:03:13] Soren Marklund: So managing data and the life cycle of data and having a unified way of working with data is pivotal to make this happen for the journey that we are on. You could argue that we, we have been operating also in silos around data, right? And, and naturally that has meant that everyone within the company or even on the customer side, they do what they have in reach and in, in view of the data that they have, right?
[00:03:40] Soren Marklund: But the opportunity is really not, it's probably the biggest opportunity that I have ever seen happening in the industry right now. It's really breaking down these silos, really working in a data operating model, unifying it. And for Ericsson, being an international company, it's very important that we have a unified ways of working with data across the world.
[00:04:05] Dana Gardner: And of course, it's important for this data to be trusted to be valuable. It's the old adage of garbage in, garbage out. So you wanna have that high quality, trusted, secured data. So what has Ericsson done to pursue not only gathering as much data as possible, but adding a qualitative value and benefit as well?
[00:04:24] Soren Marklund: Yeah, so as we put the data operating model in place, you realize that we need to work in two dimensions. One is the digital capabilities that we need to embrace and, and innovate around, but the other, so say more horizontal aspect across the company, we need to have a data operating model. So we actually assign data managers
[00:04:45] Soren Marklund: to operate and, and take care of the assets as a data asset. So the two, the two dimensions, Dana, the, the horizontal being, the data operating model and the, the verticals being the digital capabilities, I. When you then look at that and say, how do you really trust the data? Because when we started the journey, you could argue we had many sources of truth.
[00:05:09] Soren Marklund: Whomever wanted to use data only trusted what they had in reach, right? So we collected a lot of data in multiple instances across the company. So now with the the data operating model in place, we are really harnessing common sources of truth so that we have one ownership and one drive to ensure that the data is for the quality it needs to have, ensure that it has the freshness that it needs to have, and also to make sure that we have the explainability, the metadata needed behind the data to really put it in the context of the user.
[00:05:47] Dana Gardner: Now, network devices are notorious for delivering reams of data. There's so much log data, for example. What were some of the challenges that you've been facing in order to not get overwhelmed, but find the jewels within all of that vast volume of data when it comes to examining your devices in the field and, and across entire networks?
[00:06:12] Soren Marklund: Yeah. To, to your point, I mean if you look at the mass of data that we have, it's easy to get trapped into a set of noise because there are so much, right? And you have to have the context of what, what you're trying to look for. So this is where, where the opportunity comes in for the service organization.
[00:06:30] Soren Marklund: This is where we have put a lot of emphasis within our units here in the company to really embrace and embed service knowledge into this process because that domain expertise allows us to be more focused on the data that we need to capture, but also to understand what is symptomatic in the data versus cause and effect in the data.
[00:06:53] Soren Marklund: So if we can really drive an understanding of the the, to say the true insights that we need to leverage to really understand. Whether it's performance of our products in the network, whether it's customer experience that our customers are having or, or even just looking at correlation points to try to understand what more could we innovate around the usage that our customers are having.
[00:07:18] Dana Gardner: Well, I'm really interested to hear more about how you've gone about this unified data structure and then what you can do with it when you've got it in place. But tell us first a little bit about yourself, about your role at Ericsson, your background and why taking on these sort of challenges are important and valuable to you in your career and as an individual.
[00:07:36] Soren Marklund: I have had the opportunity and the fortune to be working for a company that allowed me to work in many different roles over the years. So I have been in, in product side, I've been in sales, I've been in operations, and so all of the dimensions I've been able to see across the company on what really makes our customers experience really driven.
[00:07:58] Soren Marklund: So we, I have been very much focused with a passion around how do we transform to more customer centric data driven activities across the company. So the more the journey started around driving intelligence in everything we do, I realize that my passion is really around driving the strategies for the data and, and the intelligent transformation around everything we do.
[00:08:22] Soren Marklund: So I've taken a strong role over the the last two years to really do what I can do to further and accelerate the company and this uptake.
[00:08:30] Dana Gardner: And Soren, why do you think this is an exciting time in the evolution of technology to bring about the realization of your passion? Is this a unique inflection point in your thinking?
[00:08:44] Dana Gardner: Are there things that we can do now that we couldn't do before?
[00:08:47] Soren Marklund: Absolutely. I will say when you look back at the journey of, for example, the AI machine learning activities, it, it was very much driven by expertise and, and teams of expertise within the company because it required such deep data science expertise or data engineering expertise that it wasn't really accessible for for everyone, right.
[00:09:08] Soren Marklund: And, and we, we have been on that journey for, for many years and with the expertise we have developed some very smart and intelligent use cases around it. But the emergence of generative AI and now the agent AI and the multi-agent AI is really taking this to a much accelerated pace than, than we have had in the past.
[00:09:30] Soren Marklund: And I think this is really what's opening up the opportunity across the company because now we can work in, in natural language, we can work in no code scenarios. We can really data mine and explore the data and in many other ways than just relying on, on a, a top of the pyramid expertise to really guide us through.
[00:09:49] Soren Marklund: So this is really democratizing things within the company. I think that is really, we're gonna accelerate the path forward. When you look at the Gen AI models and the LLM models behind it, right, now, they're pre-trained, right? So, or at least to a great extent, pre-trained. So we need to just add our rag models and such to make it context aware into the what we are trying to do.
[00:10:13] Soren Marklund: But it's the playground. It's accelerating so much faster. Nowadays with advancements in technology than we used to have.
[00:10:22] Dana Gardner: Alright, well please, if you could describe Ericsson's federated operating model within, particularly within a hybrid cloud setup. How do you characterize and describe what's come to be the best and most up to date approach to this?
[00:10:38] Soren Marklund: Yeah, so as I mentioned, we, we have been working in silos and to great extent, you could argue that we still do many ways are working in silos, not only in silos on our side, between units and such, but also in silos between us and our customers. So the opportunity we really unifying a data architecture is really to embrace more collaboration across Ericson, across us, and, and our customers.
[00:11:05] Soren Marklund: So what we have tried to now implement and really driving forward is a more unified, federated operating model. And by that we mean that we can work in the context of different countries, different regions, and really work with our customers in that region. While at the same time, based on the sensitivity and the privacy and so on, we can harmonize the data
[00:11:27] Soren Marklund: on a global level to have the full insights of what's happening all across our customers and all the products and the behaviors of our products. And again, in, in this context, embedding the services knowledge is, is really done instrumental. So we are trying to converge between having a federated operating model and embedding the service knowledge into this process to drive, in the end, more insights.
[00:11:55] Soren Marklund: Having information at the fingertips in discussions and, and lifting the experience with our customers.
[00:12:01] Dana Gardner: And what have you looked for particularly in the providers, the vendors, the suppliers in the infrastructure behind this federated approach? What's important in order for you to get what you need to get this done?
[00:12:14] Soren Marklund: Yes. One of the fundamental aspects, I mean, in the past we used to say that we moved the data to the intelligent processing. That meant we were relying on having data transferred in, in many instances around the world to a home, so to say, where the applications were, were driven with. The beauty now with more, more federated model is that we can move the intelligence and the logic to the data.
[00:12:40] Soren Marklund: That allows us to innovate in much greater extent than we were in the past. And not only can we innovate more, but we can then engage in more partnerships. And this is probably the, the biggest, the unlocked opportunity for us as we move forward working with data in a partnership within the overall ecosystem
[00:13:01] Soren Marklund: between partners and us, but also partners on, on the customer side and the customers themselves. So having the federated operating model around the data architecture is really gonna open up a whole new span of innovation, we believe.
[00:13:17] Dana Gardner: And what do you, what would you consider some of the key technologies that are endowing you with this ability to break out of those silos and at the same time, manage, trusted, secure, comprehensive data and fabrics?
[00:13:32] Soren Marklund: So we, we know that the mission of just consolidating things to a common data warehouse or a common data storage or what have you, that is almost an impossible journey, right? So we, we realize we need to be in a hybrid mode across the company, but we also realize that we need to be able to take advantage of the data on a more unified way.
[00:13:55] Soren Marklund: So here is the data fabric really plays a, a critical role to make sure that we can work. With multiple databases across the company. And also interact with, with, with partners and, and customers on that context. Right. So I, I think as, as we look forward now, for example, the, the iceberg standards around how we work in a hybrid mode is, is really gonna make a difference we believe for us as we look at the harmonization of, of different architectures across the world.
[00:14:25] Soren Marklund: And then we will see what makes sense to consolidate the support of this journey. But the, the first step is really to make sure that we can operate in a more unified way. Doing the analytics, doing the insights, and really building the generative AI context above in a more unified way, because that is the, the real society innovation aspect we are looking for.
[00:14:47] Soren Marklund: The data aspect is more the enabler to, to make that reality in a more accelerated way.
[00:14:54] Dana Gardner: And of course doing this isn't just an academic activity. There's some huge benefits. So let's look at some of the takeaways from the business outcomes perspective, customer experience, and personalized services. For example, sort of how we started our discussion, how, how is what you've been doing around this unified data foundation and fabric contributed to the business benefits around the customer experience and, and services, for example?
[00:15:19] Soren Marklund: Yeah. So when you operate in a silo, you don't always have access to the the view of, of each vendor. So say in that context, the operators and the customers of whoever is operating on their side and we have been operating on our side.
[00:15:33] Soren Marklund: The beauty now with converging this too, is that we can be much more in a digital collaborative experience, and that is really the path forward to even accelerate from where we are today. So we see great opportunities in lifting the customer experience. When we have more unified data at hand, we can correlate data across the, our company, across the customers, and, and also with partners.
[00:15:59] Soren Marklund: The taking advantage of the marketplace, taking advantage of a plethora of different vendors capabilities, taking advantage of all of the reuse we can also have within the company to correlate. New insights that lift the customer experience. There is so much hidden within the data that in the past we have not been able to use.
[00:16:22] Dana Gardner: And I suppose the foundational description of productivity is doing more and better with less. And so is there a way of using this data to optimize networks or improve energy consumption and performance? How were you able to translate the data investment into solid returns when it comes to efficiency?
[00:16:43] Soren Marklund: Yeah. Very, very good, relevant question, right? And, and we, we are exploring this in, in all of the context. I mean, when we look at the energy deficiency, we have already a number of, of contracts with our customers around the world to, to work with, we'll say different power consumption schemas. There are low traffic times in the network, in different pockets of the network, and we can optimize the usage of energy in that context.
[00:17:08] Soren Marklund: But we also look at what, what might cause anomalies in consumption of energy in the network. And that is also an insight we can bring to our customers through, through data that we can gather and deliver insights back to our customers, that through explainability we can help the customers, we can guide the customer on what they can do, and certain things we can even drive in an autonomous way.
[00:17:32] Soren Marklund: So we are increasingly driving data as being a part of our journey to intent-based services and intent-based networks because that is ultimately how we reach the maturity and autonomous networks and autonomous services.
[00:17:47] Dana Gardner: Hmm. And another facet of better data analysis and and access is moving beyond extrapolation to true predictive analytics and being able to analyze for strategic advantage, and particularly against your customers who may not be doing this same diligence when it comes to data.
[00:18:07] Dana Gardner: So how are you able to get that intelligence on a predictive decision making benefit basis?
[00:18:15] Soren Marklund: Yeah, so this all relies on having our targeted information around the data, right? Because if we, if you take all the data, I mean, if you look at our, some of our global customers, I mean, the, the data volume are in, in petabytes every day, right?
[00:18:31] Soren Marklund: So. That is too much. If you argue, what can you do with that? It's really noise to begin with, right? And then we need to understand what do we look for to really correlate insights that will help the customer. So we, we try to lift more and more, as I say, experience KPIs in working with our customers to really make that reality possible.
[00:18:52] Soren Marklund: So we are trying to embed the service knowledge in capturing the right data at the right time to really make a difference in the end for the experience that our customers are having. And we have done this to, to some extent already. We, we applied a number of different activities and technologies in lifting anomaly understanding.
[00:19:14] Soren Marklund: Lifting a, a data insight perspective so that we can begin to understand more and more what is happening in our customer's network, and how can we correlate the information that we may have on the Ericsson side with the customer's data to drive more insights back to the customer.
[00:19:33] Dana Gardner: Yeah.
[00:19:34] Dana Gardner: It's fascinating how when you develop this language of data value, that when you extend that out to your supply chain or the ecosystem that you operate in or directly to your customers, that there's this mutual benefit and more sharing takes place, and therefore more opportunity to use different data sets in different ways.
[00:19:56] Dana Gardner: So do you see this data modernization and advantage as a way of binding organizations together? So the silos are not just internal, but you're breaking down external ones, cultural ones even as well.
[00:20:11] Soren Marklund: Yeah, exactly. And I, I used to say that you don't know what you don't know, right? Because it was what, when you have limited visibility to what you could do with the data, you really don't have a full perspective of what is within the data scope that I have, that I could know if.
[00:20:28] Soren Marklund: I could so say, decipher the right data points outta this, right? So, so we have been very focused on, on really trying to understand how can we lift certain aspects of the data for making an impact on, on the customer. And, and that data could be in many contexts, right? It could be efficiency, it could be experience, it could be innovation, it could be automation, and, and what, what have you, right?
[00:20:52] Soren Marklund: The data in the end is fundamental to all of these aspects. When you start this discussion, it's often, why do you want my data kind of discussion, right? So it is important of course, that this is a collaborative perspective 'cause there needs to be an impact, a benefit back, you know, otherwise collaboration does not happen, right?
[00:21:11] Soren Marklund: So we need to contribute, we need to embed our knowledge and expertise in, into the ways of working and, and our customers, they embed their knowledge and expertise and together we drive more insights from the data. And then we both benefit from evolving the advancements in, in all of our technologies that ultimately, again, helps our customers.
[00:21:34] Dana Gardner: Yeah, you know, we're starting to see actual productization of data and analytics themselves. So even though you might be a networking and telecommunications company where your bread and butter is hardware, software, networking devices, when you take advantage of the insights you can gain through those devices and relationships,
[00:21:54] Dana Gardner: you might be able to actually productize the analytics and sell that back and monetize that as, as a line item, as an actual, you know, profit and loss center, more emphasis on the profit. So are you starting to do that? Are you seeing that, where your data and analytics becomes yet another product set for Ericsson?
[00:22:12] Soren Marklund: Yeah, absolutely. I mean, we have seen this as kind of three, three steps on, on a ladder perspective, right? I mean, one, the first step is to, to really gain data insights from our products. What is the behavior, what is the configuration and, and, and how our products are really performing in the network. So that's on the, on the product level dimension.
[00:22:31] Soren Marklund: But then if you step up and say, okay, what is really happening on a network level, more an ecosystem or a network level, and then you begin to see what is happening network wide. You can enrich the data points from that perspective, that drives new insights, and then you can take it to the next level.
[00:22:50] Soren Marklund: Above that, look at the industry verticals that rides on the network. And then you have new data insights that you can enrich that with. So you, you build to say a incremental step of enrichments from the data to the network to the industry view. And that ultimately we will drive more insights. Because in the end, when you sit there in, in a vertical industry, you need to have an understanding of, well, how is, what is the behavior of the network and what is the behavior of the product?
[00:23:19] Soren Marklund: On the flip side, applying a product into the network and and driving the adoption of that also needs to have an understanding how does it really behave in the industry perspective, whether it's in manufacturing, whether it's in the automative industries, or public safety or whatever it might be. That full dimension is important.
[00:23:41] Dana Gardner: Yes. And having an advantage because of your perspective and doing the proper data modernization. Put you in a position to then create agents and, and constellations of agents that are trained in unique ways through that perspective. And so those agents become not just general purpose, but very specific to, to what you can do as a company.
[00:24:05] Dana Gardner: Are you, are you seeing that as an opportunity? And how far along on the agentic AI journey are you?
[00:24:12] Soren Marklund: This is probably the most exciting part of our, our journey now moving forward. Right, and and to your question, right? I mean, I, I think, or, or perspective, I think when you look at the adoption of, of gen AI or AI in general, right?
[00:24:24] Soren Marklund: I mean, the first question really comes, what is the precision and accuracy of this model, right? When do you begin to trust the model execution? When do you begin to trust that this is really an autonomous perspective, the relevant recommendations, actions, and what have you to take, right? And, and we, we have seen that this is a journey because many models, especially untrained models, they may not have more accuracy than, than maybe a 50, 60, 70% accuracy, accuracy rate, and that may not be enough.
[00:24:59] Soren Marklund: Really for a customer to take a decision on an action because especially for, for us working with our customers, taking, making a decision and taking an action of change into the network could have a huge effect. So, so we really want to make sure that we have the right precision and accuracy in, in the models that we recommend.
[00:25:19] Soren Marklund: So this why ability has been a key component in the ways of working from data and the services, again, can play a critical role there. So how do we lift explainability working with our customers? How do we drive trustworthiness in the models as we move this forward? And this is also why the agentic AI will play a significant role because we can use agentic AI to really streamline and improve the precision.
[00:25:47] Soren Marklund: Of different models because you can begin to have agent models interact with each other to improve the position before you ultimately make a change.
[00:25:57] Dana Gardner: Part of the way that we want people to understand this better is not just to talk about it abstractly, but to look at specific examples and use cases. So Sorin, do you have any specific examples on tactical or strategic benefits in some of these feedback loops that you've been describing?
[00:26:14] Dana Gardner: You know, maybe it's a chat bot family, for example, but are there some, you know, concrete ways that we can describe how this is working in practice?
[00:26:23] Soren Marklund: Yeah. I mean we, we have been in the chatbot business for, for many years, right? And, and I think we all maybe see chatbots as just a very initial starting phase on this journey.
[00:26:34] Soren Marklund: I think every one of us that have been working with, in our personal life, which with chatbots realize that it's not always working as well as we think it should work. Right. But when we now move this into the gen AI and, and more intelligent, I mean, it's surprising how smart the different processes can really become, right?
[00:26:53] Soren Marklund: So, so that, that is the journey we, we are now on and we have a number of, of pilots and proof of concepts already with our customers around the world. Ultimately, this drives the journey towards intent-based setups. What is the intent that the user wants to achieve, and how do we ensure that we have the right agents to ensure that that intent is being delivered?
[00:27:18] Soren Marklund: Having the right data and the right logic to deliver on that intent, but doing so in a way that drives explainability so that whomever is the user, whomever is the intended to say control tower around this can really understand. What is happening in the network? What is happening in different context of actions being taken?
[00:27:41] Soren Marklund: So it's not a, a, so say autonomous activity living in its own life, but it's really an augmented world where we, we as, as, as people around the processes are more resource managers in other ways, or managing agentic processes, ensuring they do the right thing and ensuring that the explainability is there to do the right actions based on what they know.
[00:28:04] Soren Marklund: And I should know, right. But we have, we, we, we started this journey a few years back around applying. Uh, generative AI and, and we have, we have seen this, or our ambition around this is to evolve it into three different dimensions. One is what we refer to as talk to data, and this is deep diving into data in whatever context that the user wants to do and, and leverage generative AI in that context.
[00:28:33] Soren Marklund: So talk to data is the first step. Then we talk about, talk to the digital twin. It is 'cause we want to also be able to simulate different scenarios using gen AI and, and agentic AI, and the data behind it, of course, right to, to really explore scenarios in a simulated mode and we call that then talk to digital twin.
[00:28:56] Soren Marklund: And then the, the ultimate view, which where we also have had a number of exploratory pilots already in place, is talk to the network. Because ultimately that's where we want to be. And this is, I would say, the biggest transformation that we are on together with our customers because we used to for, I would say the last 50 years or so, we have been very reactive in the way of working around our customers.
[00:29:21] Soren Marklund: We look at alarms, we look at configurations, and then we act and react accordingly. But now with the emergence of, of these technologies, we can begin to really explore in a natural language, explore what is the performance of, of certain products in the network, what anomaly might there be seen, and, and do so in a natural language form.
[00:29:39] Soren Marklund: And this is a huge transformation compared to having the expertise to really deep dive into the databases and the data context and you know, really filtering through the noise A across all we do, lifting that to a natural language interaction, empowered by adjunct AI aspects behind it.
[00:30:01] Soren Marklund: With the right data at the right time, of course, to make sure that you can bring the trustworthiness to the data.
[00:30:07] Dana Gardner: Well, it truly sounds like you've expanded the notion of customer service and support more into a relationship with your customer and the data that the data is almost like a third leg on the stool.
[00:30:20] Dana Gardner: There's you, your customer, and the data, and then the analytics benefits that you can enjoy. Working together. So what, what does the future portend, what do you see coming next in elevating this relationship that's brand new, that this really uncharted territory in terms of being able to have that relationship beyond just being reactive, as you say?
[00:30:44] Soren Marklund: Yeah, because in the past when, when they more, were more in the reactive mode, we were more in a holding pattern waiting for their customers and their products to, to break or fail or, or, or drift off in some way. And then we, we jumped in and began to reactively address those with our customers. Now, the journey that we have, uh, in front of us and to a great extent already embarked on is
[00:31:07] Soren Marklund: the data that we can gather, the data insights that our customers can share with us will allow us to understand the performance of our products in a much more proactive way. So we have started a, a practice around what review referred to as the customer success transformation. And this is probably also one of the, the key aspects of, of the journey ahead.
[00:31:31] Soren Marklund: When we have the data, when we have the intelligence, how do we really now ensure that we make the greatest impact possible on our customer's processes? That is the customer success transformation that we, we have embarked on. So that entails really for us to work with our customers, breaking down the silos, really understanding what is success from the customer's perspective, what is helpful for them, how could we help our customers in their processes, their models without force, without being intrusive to what they do and how they do things.
[00:32:07] Soren Marklund: But really understanding the data points that we can take advantage of and help guide our customers if they have certain customer success objectives. How do we contribute through more data-driven processes, more customer-centric processes to ensure. We can be a part of that journey and make the greatest impact that we can.
[00:32:28] Soren Marklund: In the past, I would argue that we, we have, we have developed a lot of intelligent capabilities over the years, but often we, we develop it and then we go to the customer, say, Will this apply to you? Look what we can do. This is the insights we can bring to you if you adopt this product or this service.
[00:32:47] Soren Marklund: Now with a more customer success transformation perspective, it's really looking at the other way around. What is the customer trying to achieve? What are the bottlenecks that are in front of them? What data are they not utilizing? What insights do we have? That can really bring about a difference for the customer in their journey.
[00:33:06] Soren Marklund: So this customer success transformation is really the overreaching umbrella over everything we're trying to do around the data and, and the intelligent activities.
[00:33:16] Dana Gardner: Wow. Well, you can't get more strategic than that. Helping your customers at the highest level to anticipate their future and then providing them what they need perhaps before they know it themselves.
[00:33:25] Dana Gardner: That's the ultimate business value I should. Well, great. Thank you so much. We've had our latest Data Cloud podcast guest here, Soren Marklund, the Vice President of Global Services Technology Consulting and AI Data Strategy at Ericsson. We so much appreciate you sharing your thoughts expertise and experience, Soren.
[00:33:47] Soren Marklund: Thank you for having me. Really enjoyed it.
[00:33:49] Dana Gardner: My pleasure.
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