{"id":13178,"date":"2026-04-15T10:43:27","date_gmt":"2026-04-15T14:43:27","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/?p=13178"},"modified":"2026-04-15T10:43:29","modified_gmt":"2026-04-15T14:43:29","slug":"how-data-is-the-new-oil-transcript","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/how-data-is-the-new-oil-transcript\/","title":{"rendered":"How data is the new oil \u2013 Transcript"},"content":{"rendered":"\n<p>In this episode of\u00a0<em>The Industry Forward Podcast<\/em>, John Nixon and William Hahn discuss why data is the new oil, how to mine insights from data lakes and the future of multidisciplinary AI for engineering applications.<\/p>\n\n\n<div class=\"embed-megaphone\">\n<iframe loading=\"lazy\" frameborder=\"0\" height=\"200\" scrolling=\"no\" src=\"https:\/\/playlist.megaphone.fm?e=TLFIE9851190071\" width=\"100%\"><\/iframe>\n<\/div><!-- Megaphone -->\n\n\n<p><strong>John Nixon:<\/strong> Hello and welcome to today&#8217;s episode of the Industry Forward Podcast, where we explore the key trends, transformative technologies and real-world innovations that are reshaping fields from consumer-packaged goods (CPG), life sciences, energy and beyond. I&#8217;m your host, John Nixon, global vice president of Process Industries at Siemens Digital Industry Software.<\/p>\n\n\n\n<p>Today, we&#8217;re continuing a series in which we will explore the impact of digitalization on the process industries. Our topic of the day will be how data is the new oil. My guest is Bill Hahn, director of Solutions Consulting at Siemens Digital Industry Software. Welcome, Bill.<\/p>\n\n\n\n<p><strong>William Hahn: <\/strong>Thanks, John.<\/p>\n\n\n\n<p><strong>John Nixon:<\/strong> Now, \u201cdata is the new oil.\u201d Why do we say that as a species?<\/p>\n\n\n\n<p>Well, oil is always directly tied to GDP (gross domestic product), the wealth of a nation. And so, Oil, like any resource that is absolutely imperative to the power, the energy, the wealth, the economy of a nation, is incredibly important. [This is] because oil provides power, power opens-up industries, industries develop wealth and opportunity for all of us to be employed, innovative, entrepreneurial. It is the lifeblood to the springboard of innovation.<\/p>\n\n\n\n<p>When you think about data, and we hearken it to oil, data itself has now become a resource. Bill, <a href=\"https:\/\/blogs.sw.siemens.com\/thought-leadership\/ontologies-how-to-make-sense-of-the-data-explosions-in-process-industries-transcript\/?_thumbnail_id=13025\" target=\"_blank\" rel=\"noreferrer noopener\">when we talked earlier around the importance of data<\/a>, with assets, or with the processes that assets within a system now support, the beauty is that data opens up opportunities for innovation, for entrepreneurial zeal, if you will. So, for me, it&#8217;s very easy to say oil is a resource, which will eventually be eclipsed because we&#8217;ll have new resources for power. But at the heart, the reason why it became so instrumental in moving our species forward was because of the innovations it allowed us as a species to unleash.<\/p>\n\n\n\n<p>Data does the same thing. <a href=\"https:\/\/blogs.sw.siemens.com\/thought-leadership\/ontologies-how-to-make-sense-of-the-data-explosions-in-process-industries-transcript\/?_thumbnail_id=13025\" target=\"_blank\" rel=\"noreferrer noopener\">Data, when it&#8217;s within context, and it has, as we had discussed earlier, ontologies and relationships to other data<\/a>, you bring that together. And in my mind, what that does is data becomes this enormous resource. We often say data lakes, just as we say oil reservoirs. So, we&#8217;re creating these virtual reservoirs of data. But as long as you retain its context, it has real value to unleash new innovations. Just as oil does with power and then power subsequently providing innovations in industry.<\/p>\n\n\n\n<p>Do you see data in that same kind of resource manner? And do you see it as akin to what we like to think of as commodities? Is it a commodity or is it a bit more?<\/p>\n\n\n\n<p><strong>William Hahn: <\/strong>Yeah, there&#8217;s so many layers to that, John. You know, for one, we can look at how oil is everywhere, right? Not just powering the lights that are on in this room right now, or the heat, or air conditioning and everything else that&#8217;s running on electricity today. It&#8217;s in our plastics, right? It&#8217;s in the laptop I&#8217;m using, and the headset that I have on, in my phone, right? Oil is everywhere. And it&#8217;s a necessity to run everything that we interact with on a day-to-day basis.<\/p>\n\n\n\n<p>And in the same way, data is the same. right? Behind everything is data. Again, I can go through those same examples. The laptop that I&#8217;m using, right? How much data went into the engineering and manufacturing of the laptop I&#8217;m using, or my headset, or my cell phone, right? How much data went into getting the electricity to my house, right? From in terms of grid monitoring and making sure there&#8217;s enough capacity versus demand, and using data from smart meters to understand demand from each residential household and from all of the industrial complexes in the nearby area, right? Data is literally driving everything just like oil is, kind of behind the scenes, helping all the pieces move.<\/p>\n\n\n\n<p>And then, in a much more simple terms, \u2026 I could say, \u201canybody in the business, oil makes us a whole lot of money, and so does data. Right?\u201d <strong>Laughs<\/strong><\/p>\n\n\n\n<p><strong>John Nixon: <\/strong>Bill, I want to tease out a little bit more. You [allude to] productivity. I&#8217;ll tell you what comes to my mind, and then I want to go a little bit further with you.<\/p>\n\n\n\n<p>When you look at, let&#8217;s say biomass, meaning wood, and then you look at coal, and then you look at oil. And we could keep the analogy going, but we&#8217;ll stop at oil for just a moment. With each one of those cases, biomass, you know, when we were in the caves and we were just coming out as a species onto the plains. And then you look at how we discovered coal. We kept discovering units of measure, greater energy density per mass as we were moving forward, which drove greater productivity for us as a species.<\/p>\n\n\n\n<p>And when you mention productivity, I also think about the fact that, interestingly enough, when you look at data centers now, it used to be they were measured in one fashion: how much computational power they could produce over square footage. Now they look at it in terms of the amount of power consumption over that same square footage. So, energy and computational power are linked quite directly now. So, you&#8217;ll hear data centers will demand X gigawatts in order to be able to run, or it&#8217;s how many gigawatts, or how many watts per whatever the measure of square inch or square foot, or even over the acres that these data centers are now occupying.<\/p>\n\n\n\n<p>So, for me, productivity. <strong>Pause.<\/strong> Productivity as a species was related to energy density and it still is. And now that energy density has kind of transferred over to data centers and has been inexorably linked to data and its value.<\/p>\n\n\n\n<p><strong>William Hahn: <\/strong>Yeah, I like where you&#8217;re going, John, with the energy density. I mean, we look at, dollar per barrel of oil in terms of how we&#8217;re making money in the industry. Right, with the energy density of oil?<\/p>\n\n\n\n<p>Same thing with data. I think we can make an analogy there and call it data density, right? You know, go back years, let&#8217;s just look at an engineering example, right? We used to have paper drawings we had to make by hand. And that helped us in manufacturing, right? To make sure we make the right thing the first time. And then we moved into, 2D CAD systems and 3D CAD systems, and now simulation systems and manufacturing simulation. The more dense the data got, the greater our return for new product introduction, right? So, the faster we could engineer and the more efficient we could manufacture.<\/p>\n\n\n\n<p>So, I think there&#8217;s something to be said, like you said, with biomass and then, the density of oil and the dollar that that&#8217;s worth: to a data density and what&#8217;s the dollar value of the amount of data that I have and what I can do with it. And then you add AI, and you mentioned data centers, right? The ability to create really dense areas of data that we can now analyze extremely rapidly with AI. I think it&#8217;s a dollar per barrel of oil analogy, right? The dollar you&#8217;re going to get out of that data is going to get exponentially increased. And that dollar is going to show itself in streamlining engineering, streamlining manufacturing, reducing cost, reducing time to market for customers to get their products out to market by increasing this data density.<\/p>\n\n\n\n<p><strong>John Nixon: <\/strong>When you mention data and contextualization (read good quality, dense data). For me, it&#8217;s with contextualization, you gain insights, right? And so, these insights that you have around your processes, they can save you a tremendous amount of money. So, a lot of times when we say \u201cdata is the new oil,\u201d what we&#8217;re talking about are the insights that data provides that allows you to have the decision support that you need.<\/p>\n\n\n\n<p>You just mentioned things like synthetic data (read simulations). You&#8217;re creating synthetic data around a process which closely mimics the real world because it has physics-enabled guardrails as part of that solution. And it will provide you [with] insights around, perhaps, efficiencies. You know, I often use the term forensic. I have an issue. Maybe I&#8217;ve got crack propagation, or I&#8217;ve got holidays (flaws, holes and discontinuities) on coatings. So, I forensically need to have traceable data and have that within contextualization. So, I have the insight as, why am I having this issue? And then I need to look forward in a prescriptive manner. What do I need to do to prevent this in the future? Or what can I do to improve future designs?<\/p>\n\n\n\n<p><strong>Changes topic.<\/strong><\/p>\n\n\n\n<p>Bill, earlier we talked about the importance of data. We&#8217;ve talked about AI. I want to probe a little further into that relationship. I&#8217;ve read this before and I often say it myself, \u201cIf you don&#8217;t have a good data strategy, you don&#8217;t have an AI strategy.\u201d When I look at data for an organization, I think about the path of maturity they need to have to lead to the good results of AI. You need to be able to configure your enterprise and how you&#8217;re creating, curating, storing and reusing data.<\/p>\n\n\n\n<p>There&#8217;s the connectivity that needs to exist. You refer to it as ontologies, and I&#8217;ll ask you to go a little further into, for example, RapidMiner (Rapidminer\u00ae portfolio software) and knowledge graphs (William Hahn: sure). And then with that in place, how does that then not only liberate us to use AI effectively, but do so and feel confident? How do we drive better confidence around that AI? There&#8217;s a link between data and AI. I&#8217;d like for you to take us down a little bit more technically. What does that mean?<\/p>\n\n\n\n<p><strong>William Hahn: <\/strong>Yeah, and I think from a Siemens perspective, we&#8217;re looking at this as a multi-tier, or multi-level, type of strategy or approach. At the bottom foundational level, right? That&#8217;s where your foundation models are, right? So, you&#8217;ve got all the models available: OpenAI<sup>TM<\/sup>, Claude<sup>TM<\/sup>, et cetera. These models are available for public consumption.<\/p>\n\n\n\n<p>We&#8217;re also, from a Siemens perspective, supplementing that because what are those models good at? Text, images, video, voice, right? And they&#8217;re really tuned for how the majority of public uses ChatGPT today: which is creating funny memes and graphics, right? <strong>Laughs. <\/strong>Honestly, that&#8217;s really what they&#8217;re tuned for. But not so much for engineering and manufacturing.<\/p>\n\n\n\n<p>So, Siemens, not just being a software company, but being one of the largest global manufacturers, we\u2019re taking all of our manufacturing knowledge and building our own industrial foundation models. So, at a foundational level, we&#8217;ll be able to supplement the public models with Siemens models that are focused on engineering and manufacturing use cases.<\/p>\n\n\n\n<p>Then if we move up a level, up to the next tier, if you will, we have our enterprise applications. PLM, like Teamcenter\u00ae software, MES, like Opcenter\u2122 software, Polarion\u2122 portfolio, Simcenter\u2122 software, Designcenter\u2122 software, right? All of the platform applications, that single source of truth we talked about that are throughout a lot of our customers today. Each one of those applications is going to have, I&#8217;d say, isolated AI use cases where they&#8217;re going to have their own co-pilots, right? I&#8217;m going to have a BOM (bill of materials) co-pilot in Teamcenter. I&#8217;m going to have an execution operations co-pilot in Opcenter. And they&#8217;re going to be tuned to do very specific things in their areas of expertise. And those co-pilots might talk to each other, but they&#8217;re really tuned specifically for that application. And then throughout the ecosystem, even outside of Siemens, right, you&#8217;ve got your ERP systems, your CRM systems, and all the major players. They&#8217;re going to have co-pilots, right, that do what they do very well.<\/p>\n\n\n\n<p>But what happens when I have a question that spans one, two, three, four, or five of these enterprise applications, and I need to know the context across all of these? This is where we move to the next layer, or tier, of our strategy, which is what we&#8217;ll call a data fabric. We talked about that ontology or creating that fabric of data that connects the relationships across all these enterprise applications. For that, Siemens has made some strategic acquisitions. You mentioned RapidMiner that came from the acquisition of Altair\u00ae. [Rapidminer\u00ae Graph Studio\u2122 software\u200b] is a part of RapidMiner, that provides a graph database, an ontology that can connect and create that web of information across all these enterprise applications that we can now have, call it a multidisciplinary co-pilot, right, a multidisciplinary AI agent that looks at the ontology, that looks at the data fabric of all the interconnected data and can answer those more complex questions that you&#8217;re not going to be able to answer at just the level of a single enterprise application.<\/p>\n\n\n\n<p><strong>John Nixon: <\/strong>Bill, as you talk about this, you mentioned single source of truth. What I find interesting when talking to customers and potential customers in the market: a single source of truth to an engineer might be very different than what a single source of truth might be to someone on the plant floor. So, you have these varying, quote\/unquote, single sources of truth. What you describe when you talk about multi-discipline, let&#8217;s say multi-persona, support with AI is what I would call. <strong>Trails off.<\/strong> Well, actually, I don&#8217;t call it, I would quote <a href=\"https:\/\/www.linkedin.com\/in\/jennifer-petrosky\/\" target=\"_blank\" rel=\"noreferrer noopener\">Jennifer Petrosky, one of our colleagues here at Siemens<\/a>, she once said, \u201cwell, what&#8217;s emerging, John, is an authoritative source of truth.\u201d And so, everybody has their world that they&#8217;re working in, [and] the AI that&#8217;s supporting them there. Thanks to ontologies, RapidMiner, the ability to have, if you will, a database of relationships that exist, that you&#8217;ve defined, these ontologies that you&#8217;ve created, these relationships between all these different single sources of truth in those worlds, you now have aggregated together and created an authoritative source of truth.<\/p>\n\n\n\n<p><strong>William Hahn: <\/strong>A hundred percent, you hit the nail on the head, John. I think, [when] we look at engineering, Teamcenter is that authoritative source of truth. You look at research now with our acquisition of Dotmatics\u00ae, where the Luma\u00ae platform will be the scientific authoritative source of truth in research. And then you look on the shop floor where Opcenter is the authoritative source of truth. And then you get this combined source of truth, right? You were saying the authoritative, combined source of truth at the data fabric level that really brings all these relationships together. So, you can get not just the context of your discipline, but this multidisciplinary system of systems level \u2014 using an MBSE (model-based systems engineering) term there \u2014 level answer when I start leveraging AI to really get insights out of the data.<\/p>\n\n\n\n<p><strong>John Nixon: <\/strong>Bill, thanks as always for a great conversation. I really do appreciate you making time.<\/p>\n\n\n\n<p><strong>William Hahn: <\/strong>Thanks, John. It was good talking with you today. Hope to talk to you soon.<\/p>\n\n\n\n<p><strong>John Nixon: <\/strong>I enjoyed the metaphor of \u201cdata as the new oil,\u201d but really moving beyond that metaphor, and actually talking about the value of data.<\/p>\n\n\n\n<p>Well, that&#8217;s all the time we have for this podcast. We talked about data. We talked about how it&#8217;s plentiful. It&#8217;s very volumetric. And the importance, though, is to make sure it&#8217;s contextualized, right? We&#8217;ve got ontologies, we&#8217;ve got relationships. We understand the value of that data. And like energy density, you know, data, it\u2019s density is driving productivity, it&#8217;s driving innovation, economic value. And we spent time really digging into the critical link between data and AI. I mean, without a data strategy, you&#8217;re not going to have an AI strategy.<\/p>\n\n\n\n<p>So, I want to thank our audience for joining us today. I hope this episode helped you process your process industry knowledge. So, please join us next time and we&#8217;ll continue to explore technologies and ideas that are shaping the future of industry.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">John Nixon &#8211; Global Vice President of Process Industries at Siemens Digital Industry Software<\/h2>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"200\" height=\"200\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2026\/03\/John_Nixon.jpg\" alt=\"\" class=\"wp-image-13032\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2026\/03\/John_Nixon.jpg 200w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2026\/03\/John_Nixon-150x150.jpg 150w\" sizes=\"auto, (max-width: 200px) 100vw, 200px\" \/><\/figure><\/div>\n\n\n<p>As Group Vice President for Process Industries at Siemens, John leads a global team that helps process industries leverage digital solutions that enhance efficiency, accelerate innovation and achieve sustainability goals.<\/p>\n\n\n\n<p>John has over three decades of experience in strategy, operations and technology deployment for energy, chemicals, life sciences and CPG. He is well versed in the operational and business pressures of industry, including regulatory demands, decarbonization, talent gaps and the push for innovation.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.linkedin.com\/in\/johnnixon\/\" target=\"_blank\" rel=\"noreferrer noopener\">Connect with John on LinkedIn<\/a><\/p>\n\n\n\n<div style=\"height:31px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">William Hahn, Director of Solutions Consulting at Siemens Digital Industry Software<\/h2>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"200\" height=\"200\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2026\/03\/William_Hahn.jpg\" alt=\"William Hahn\" class=\"wp-image-13030\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2026\/03\/William_Hahn.jpg 200w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2026\/03\/William_Hahn-150x150.jpg 150w\" sizes=\"auto, (max-width: 200px) 100vw, 200px\" \/><\/figure><\/div>\n\n\n<p>As Director of Solutions Consulting at Siemens, Bill leads the digital transformation initiatives for large enterprise clients across life sciences, CPG, energy, chemical and infrastructure industries.<\/p>\n\n\n\n<p>Bill has over 18 years of experience in digital strategy development, systems engineering, and business process optimization. He specializes in end-to-end software development, and implementations, that aligning technology solutions with business objectives like risk management, compliance and operational resilience.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.linkedin.com\/in\/william-hahn-iii\/\" target=\"_blank\" rel=\"noreferrer noopener\">Connect with Bill on LinkedIn<\/a><\/p>\n\n\n\n<p>All trademarks are property of their respective owners.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;Data is the new oil&#8221; is conditionally true. See how to mine insights from data lakes and the future of multidisciplinary AI for engineering applications.<\/p>\n","protected":false},"author":118754,"featured_media":13179,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1],"tags":[13706,13872,2],"industry":[],"product":[],"coauthors":[13868],"class_list":["post-13178","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-ai-2","tag-data-is-the-new-oil","tag-digitalization"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2026\/04\/Topsides-4-0-noFloorStructure_original.jpg","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/13178","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/users\/118754"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/comments?post=13178"}],"version-history":[{"count":4,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/13178\/revisions"}],"predecessor-version":[{"id":13208,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/13178\/revisions\/13208"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media\/13179"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media?parent=13178"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/categories?post=13178"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/tags?post=13178"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/industry?post=13178"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/product?post=13178"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/coauthors?post=13178"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}