by Siemens Digital Industries Software
Energy Transformation
Claim This Podcastby Siemens Digital Industries Software
14 episodes
Updated Bi-weekly
Accepts GuestsHas SponsorsLocation 🇩🇪
20
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality40
Social0
YouTube0
Engagement0
Podcast Overview
by Siemens Digital Industries Software
Language
🇺🇲
Publishing Since
3/9/2022
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20
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality40
Social0
YouTube0
Engagement0
7
Excellent Areas
1
Good Performance
11
Growth Opportunities
excellent
Episode Length
10 minutes
Performing excellently!
good
Show Experience
12 episodes over 3.0 years
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poor
Publishing Consistency
Every 92 days
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Recent Episodes
November 10, 2025
Harnessing the Power of Knowledge Graphs for AI - Part 2
In Part 2 of this series on Knowledge graphs, we discuss how these powerful tools are transforming industries, especially those grappling with vast amounts of complex data, like energy, chemicals, and infrastructure. The discussion kicks off by highlighting a common challenge for field engineers: the sheer difficulty of gathering comprehensive information about a single asset, like a flange connection. Historically, this has been a "laborious task," often involving sifting through mountains of paper manuals, retyping information, and manually linking disparate systems. This complexity has often slowed down digitalization in these industries compared to manufacturing. Bill Hahn points out the immense scale of data, particularly in areas like upstream oil and gas, where geological databases can contain billions of data nodes. This is where RapidMiner steps in, with knowledge graphs offering a revolutionary approach: Handling Massive Data: RapidMiner is designed to manage tens to hundreds of billions of data nodes, performing complex queries in seconds or minutes, not days. This speed is achieved through in-memory processing and optimized relationships. Virtual Integration: Instead of painstakingly migrating all data into a single, massive data lake, knowledge graphs create a "virtual web of relationships" across existing data sources. This means data stays where it is, but its connections are made intelligently in memory. Empowering AI: Ben Secondly explains that knowledge graphs provide the "dense, information-rich context" that AI agents and large language models (LLMs) crave. This ensures that AI responses are more accurate and reliable, reducing the "hallucinations" that can occur when LLMs guess relationships in uncontextualized data. It's like giving AI a super-smart librarian for all your company's information! A key clarification is that knowledge graphs don't replace existing system integrations. Instead they are complementary. Existing integration tools can be used to "surface" data into the knowledge graph's ontology, creating a higher-level data integration plane. This means companies can leverage their prior investments while gaining new, powerful capabilities. An instructive analogy can be used here: browsing a webpage. When you visit a website, your browser brings a version of that data into memory. You're not downloading the entire web server! Similarly, a knowledge graph pulls a view of data from systems like ERP or PLM into memory, allowing you to ask questions and run reports without migrating the entire database. You can "refresh" this view to get the latest information. Perhaps the most exciting takeaway is the promise of rapid time-to-value. Practically speaking, organizations can start seeing value from RapidMiner in weeks. This is a dramatic shift from the "months or years" timelines often associated with large-scale data projects. The flexibility of the ontology means you don't need new development cycles for every new question, accelerating insights and decision-making.
October 15, 2025
Harnessing the Power of Enterprise Knowledge Graphs for AI and Analytics
This episode features Siemens veterans John Nixon and Bill Hahn, along with Ben Szekely coming from the Altair family (now part of Siemens). They discuss how Altair's RapidMiner platform fits into Siemens' portfolio and the broader AI and data ecosystem. Key points: RapidMiner provides a low-code platform for machine learning and data analytics, helping a wide range of users deploy AI models quickly. RapidMiner also includes enterprise knowledge graph capabilities, which allow companies to integrate and contextualize diverse data sources without having to move all the data into a centralized platform. The knowledge graph creates a virtual "blanket" over a company's existing data systems, automatically mapping relationships between data points from different sources (e.g. linking maintenance records to asset performance data). This contextualized data provides a stronger foundation for AI models, which tend to perform better with rich, interconnected data versus just accessing a data lake. RapidMiner enables faster time-to-value compared to traditional approaches of manually integrating data or building a monolithic data warehouse. A key use case is in maintenance and operations, where RapidMiner can quickly pull together all relevant data about an asset (from manuals to sensor data) to empower field technicians and engineers. Overall, the discussion highlights how RapidMiner's capabilities around data integration, knowledge graphs, and low-code AI can help Siemens customers accelerate their digital transformation and get more value from their existing data and systems.
March 13, 2025
The Resurgence of Nuclear Energy - Part 2
In part 2 of our Nuclear Energy Series, our panel delves into the critical role of supply chain management, data orchestration, and digital twins in shaping the future of nuclear power. The conversation highlights how these innovations are not only enhancing the safety and reliability of nuclear operations but also unlocking new possibilities for optimization and predictive maintenance. Key highlights of the discussion include: Digitalization and data orchestration are crucial to enable a "nuclear renaissance" Digital twins and advanced analytics (AI, ML, physics-informed models) enable: Simulation of "what-if" scenarios to predict and prevent failures Informed, real-time predictions of asset degradation and maintenance needs New nuclear reactor designs are prioritizing maintainability and serviceability to address historical challenges Reduced order models and edge computing are empowering near real-time simulation and decision-making for operators Collaboration between nuclear operators, regulators, and technology providers is crucial to accelerate the adoption of these transformative digital capabilities
14 total episodes available
Deep-dive analytics for Energy Transformation
Frequently asked questions
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- What is Energy Transformation?
- How often does this podcast release new episodes?
This podcast updates bi-weekly.
- Where can I listen to this podcast?
This podcast is available on 8 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Yes, this podcast regularly features guests.
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