Innovation and Data Analytics

The idea of submerging computer servers in a liquid coolant to cut data center energy consumption by 70% is a breakthrough in sustainable tech innovation. Traditional cooling systems consume significant energy, but with non-conductive liquid coolants, it's possible to safely dissipate heat while keeping electrical circuits dry and operational. This method optimizes thermal management, capturing all the generated heat and drastically reducing the need for conventional fans and chillers. Sandia National Laboratories approach could set a new standard for energy efficiency in data centers, making them greener and more cost-effective. Florian Palatini ++

🚀 Now publicly available 🚀 The Data Innovation Toolkit! And Repository! (✍️ coauthored with Maria Claudia Bodino, Nathan da Silva Carvalho, Marcelo Cogo, and Arianna Dafne Fini Storchi, and commissioned by the Digital Innovation Lab (iLab) of DG DIGIT at the European Commission) 👉 Despite the growing awareness about the value of data to address societal issues, the excitement around AI, and the potential for transformative insights, many organizations struggle to translate data into actionable strategies and meaningful innovations. 🔹 How can those working in the public interest better leverage data for the public good? 🔹 What practical resources can help navigate data innovation challenges? To bridge these gaps, we developed a practical and easy-to-use toolkit designed to support decision makers and public leaders managing data-driven initiatives. 🛠️ What’s inside the first version of the Digital Innovation Toolkit (105 pages)? 👉A repository of educational materials and best practices from the public sector, academia, NGOs, and think tanks. 👉 Practical resources to enhance data innovation efforts, including: ✅Checklists to ensure key aspects of data initiatives are properly assessed. ✅Interactive exercises to engage teams and build essential data skills. ✅Canvas models for structured planning and brainstorming. ✅Workshop templates to facilitate collaboration, ideation, and problem-solving. 🔍 How was the toolkit developed? 📚 Repository: Curated literature review and a user-friendly interface for easy access. 🎤 Interviews & Workshops: Direct engagement with public sector professionals to refine relevance. 🚀 Minimum Viable Product (MVP): Iterative development of an initial set of tools. 🧪 Usability Tests & Pilots: Ensuring functionality and user-friendliness. This is just the beginning! We’re excited to continue refining and expanding this toolkit to support data innovation across public administrations. 🔗 Check it out and let us know your thoughts: 💻 Data Innovation Toolkit: https://lnkd.in/e68kqmZn 💻 Data Innovation Repository: https://lnkd.in/eU-vZqdC #DataInnovation #PublicSector #DigitalTransformation #OpenData #AIforGood #GovTech #DataForPublicGood

AI’s ability to unlock insights from unstructured data is a massive breakthrough for businesses. I have been beating this drum for a while now. But the real magic? It happens when you combine structured and unstructured data. Here’s why. AI made it possible to ask questions of structured data, like company records, contact records and deal status, and get answers back in natural language. That was a breakthrough. Now, it is possible to ask evergreen questions of unstructured data, like emails, calls, video conferences, transcripts of meetings, and get real-time insights, also in natural language. That is another breakthrough. An even bigger one. But businesses don’t just need breakthroughs. They need results. And to get them, they need insights from both structured and unstructured data—working together. Let’s make it real with an example. Picture a sales leader getting a live feed of every time a competitor is mentioned in sales calls. Even better? AI identifies the salesperson who’s best at handling those objections. That’s unstructured data in action to deliver insights. But there are deeper questions they want to answer, like: Is there a competitor we consistently lose to? Is a new competitor suddenly appearing in deals in specific regions? To answer those questions, they need structured data. They need to cross-check their list of competitors with closed-lost and closed-won reports and pipeline trends by region. Now, they don’t just see what’s happening—they know which competitors to worry about and what messaging works best against them. That’s not just a useful insight—it’s a game-changing one. A smart sales leader won’t stop at knowing which competitor is a threat. They’ll turn that insight into action—launching targeted email campaigns, updating sales playbooks, and creating competitive content. But here’s the catch: AI-powered insights are only valuable if they’re accurate, governed, and respects permissions. AI has opened up a world of new possibilities. The question then becomes: How can businesses turn those possibilities into results? It is by unifying structured and unstructured data with the right context and governance to drive faster action. That's the key to unlocking AI's potential to help businesses grow! And that gets us excited everyday!

❓ 𝗪𝗵𝘆 𝗱𝗼 𝘀𝗼 𝗺𝗮𝗻𝘆 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 𝗳𝗮𝗶𝗹 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲? Because they skip the fundamentals. Without trustworthy, well-governed, and discoverable data, even the best AI models struggle to deliver consistent value. That’s why every organisation needs a clear, structured framework. ❓ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗧𝗿𝗶𝗻𝗶𝘁𝘆™ 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸? It’s a three-layer model designed to help organisations unlock the full value of their data and AI initiatives by building step-by-step capability: Foundational Layer Focus on data quality, governance, access, and compliance. Create trust. Semantic Layer Introduce shared understanding through metadata, ontologies, and knowledge graphs. Conversational Layer Enable everyone to interact with data using natural language and intelligent AI interfaces. ❓ 𝗪𝗵𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝗮𝗱𝗼𝗽𝘁 𝗶𝘁? ✅ It reduces duplication of effort by up to 40% ✅ Accelerates data product delivery by 3x ✅ Bridges the gap between technical teams and business users ✅ Enables true self-service, driven by trust and shared language ❓ 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗲𝗻𝗱 𝗴𝗼𝗮𝗹? A truly data-literate, AI-enabled organisation - where every person can find, understand, and use data effortlessly.

Need some energy for the new week?
 Get ready for high voltage. Innovation moves faster than the news cycle.
 These women build what the climate demands.
 Solar, fusion, carbon capture, real tools, real impact. Welcome to the energy revolution. 📌 Dr. Rose M. Mutiso The power strategist electrifying Africa’s energy future. PhD in nanotechnology, fluent in policy and physics. Co-founded Mawazo to train African women in STEM. Champions energy equity through African-led solutions. 📌 Dr. Esther Takeuchi The chemist who turned batteries into lifesavers. Holds 150+ patents in energy storage tech. Powers millions of implantable medical devices. Built lithium batteries that last—and save lives. 📌 Dr. Jodie Lutkenhaus The polymer pioneer building soft, smart power. Creates batteries from recyclable organic materials. Invents flexible energy for wearable tech. Designs storage systems that bend, not break. 📌 Dr. Sarah Kurtz The solar scientist who raised the efficiency ceiling. Developed cells that power both satellites and cities. Set global standards for solar performance. Made sunlight a stable, trusted energy source. 📌 Dr. Olga Malinkiewicz The physicist who printed power onto plastic. Invented perovskite “solar ink” for any surface. Made solar cheaper, lighter, and scalable. Turns windows and walls into power plants. 📌 Dr. Anne White The physicist decoding the chaos inside a star. Leads fusion research at MIT’s Plasma Science center. Maps turbulence inside reactors for stable energy. Builds the science behind limitless, clean power. 📌 Kirsty Gogan The advocate bringing nuclear back—with reason. Co-founded TerraPraxis to modernize nuclear solutions. Drives clean heat for heavy industry and grids. Pushes climate strategy beyond renewables alone. 📌 Dr. Betar Gallant The chemist trapping carbon with battery precision. Leads an MIT lab rethinking CO₂ as a resource. Designs materials that store energy and clean air. Builds new chemistries for a livable planet. 📌 Susan Petty The pioneer drilling for Earth's hidden power. Co-founded AltaRock to scale geothermal energy. Engineers tech to tap deep, constant heat. Builds baseload power beneath our feet. 📌 Katherine Hamilton The connector translating tech into law and markets. Co-founded 38 North to guide clean energy policy. Advises governments on grid, storage, and access. Builds bridges from labs to legislation. 📌 Audrey Zibelman The executive rewiring how nations use power. Modernized grids in New York and Australia. Now builds smart systems at X (Google’s moonshot lab). Designs grids that think, learn, and adapt. 📌 Nancy Pfund The VC who bet on green before it was hot. Early backer of Tesla, SolarCity, and Beyond Meat. Proved clean tech could win in the market. Funds founders building the climate economy. They build grids, not headlines. They power solutions, not slogans. Feeling energised already?

H&M just set a new standard for how fashion brands should approach AI. While many companies rush to replace humans with AI, H&M is taking a more thoughtful path with their new digital model "twins" initiative: The Swedish fashion giant announced they're creating AI replicas of 30 real models for marketing and social media, but with critical guardrails: - Models retain full rights over their digital replicas and how they're used - Compensation follows similar arrangements to current modeling contracts - All AI-generated content will include clear watermarks - They're partnering with Swedish tech firm Uncut for ethical development "We are curious to explore how to showcase our fashion in new creative ways – while staying true to our commitment to personal style," said Jörgen Andersson, H&M's chief creative officer. One model, Mathilda Gvarliani, summed it up perfectly: "She's like me, without the jet-lag." This stands in stark contrast to other brands' approaches. Remember when Levi's abruptly announced AI models for "increased diversity" in 2023, only to backtrack after backlash? Not everyone's celebrating. American influencer Morgan Riddle called the move "shameful," concerned about photographers, stylists, and production teams potentially losing work. Paul W Fleming from Equity union emphasized: models having control over their likeness and fair pay is "vital" but the "current landscape has little to no such protections." H&M's approach could become the blueprint for responsible AI adoption in fashion: - Preserving model agency and consent - Maintaining transparency with consumers - Creating clear boundaries around AI usage - Considering the ecosystem of creative professionals The fashion industry is at a crossroads. Will other brands follow H&M's lead in setting responsible standards, or will we see a race to the bottom? What's your take? Is H&M's approach the right balance of innovation and responsibility? How does this look? Would you like me to make any adjustments to better highlight H&M's approach? #ArtificialIntelligence #Photoshoot #Marketing

Energy Trading with Apache Kafka and Flink: Real Time Decisions in Action Real time data has transformed how the #energy sector operates. In energy trading, every second matters. Prices change fast. Supply and demand fluctuate. Weather patterns shift. #IoT sensors and smart meters constantly feed new information into trading systems. That is where #DataStreaming with #ApachaKafka and #ApacheFlink comes in. Together, they power the real time pipelines that make energy markets more transparent, responsive, and predictable. Leading companies such as #Uniper, #realto, and #Powerledger already rely on this architecture. Their results show how scalable, reliable, and event driven data streaming brings measurable business impact: • Faster decision making and improved risk management • Automated trading workflows and event driven alerts • Real time integration of IoT data from energy grids and sensors • Improved forecasting with fresh, contextual data Uniper uses Kafka and Flink to process millions of messages per day across trading, dispatch, and invoicing systems. Confluent Cloud provides the scalability and SLAs for mission critical workloads. Powerledger combines Kafka and #blockchain to enable peer to peer energy trading and renewable energy certificate tracking. re.alto connects smart meters, APIs, and #IIoT systems for solar and smart charging use cases. These examples show how Data Streaming creates the foundation for next generation #EnergyTrading systems, uniting financial and IoT data to deliver real time insights, flexibility, and compliance. In a world where milliseconds can mean millions, Apache Kafka and Apache Flink are not just technologies. They are strategic tools for modern energy companies. How is your organization preparing to handle the growing demand for real time data in trading and energy operations? #DataInMotion #IoT #StreamingAnalytics #EnergyInnovation #AI #EventDrivenArchitecture https://lnkd.in/eHAdJEcg

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

The most valuable dataset in capital and commodities markets isn’t in your data warehouse. ⮑ It’s what's in your chat. If you strip markets down to their core, they’re not defined by screens, matching engines, or protocols. They’re defined by communication - the ability of buyers and sellers to exchange information, intentions and ideas. Yet in today’s highly electronified environment, the richest form of communication remains the least used: the daily stream of chat between clients, sales and traders. Every day, hundreds of millions of words move across platforms like Bloomberg IB, Symphony, ICE Chat, LSEG Messenger, WhatsApp, and internal messaging systems like Teams and Slack. If you sit on a trading floor, you know that many of the earliest and most meaningful signals - interest, hesitation, sentiment, conviction - show up in conversation well before they appear in a price or print. But while we’ve digitised nearly every other part of the market, this conversational flow sits largely unstructured, uncaptured, and underutilised. It isn’t that firms don’t recognise its value - it’s that historically the technology simply didn’t exist to process messy, unstructured shorthand and jargon heavy, conversational dialogue at scale. That’s now changing. We’re entering a moment where unstructured chat data can be captured, analysed and understood with far greater accuracy than most people realise. Once you start seeing conversations as data - as well as workflow - the implications for market structure, liquidity discovery and trading strategy are significant. This shift may be far bigger than people expect...

AI has an insatiable appetite for energy. But, can AI help energy companies cook up a buffet? GE Vernova just acquired Alteia, the energy sectors first major acquisition to aimed at simultaneously powering the AI revolution and using AI to manage the resulting grid complexity. The acquisition will enable GE Vernova to, rather than building generic AI capabilities, develop visual intelligence specifically for energy infrastructure – enabling utilities to "see" their grids through AI-powered damage assessment, vegetation management, and asset inspection. Their GridOS® platform represents an AI-native approach to grid management, designed from the ground up for renewable energy integration rather than simply adding AI features to existing systems. GE Vernova's $9B commitment through 2028 represents one of the most aggressive AI investment strategies in the energy sector, far exceeding most competitors' disclosed AI-specific spending. This signals that leading energy companies view AI as fundamental infrastructure for future competitiveness, not just a technology add-on. Meanwhile, competitors across energy’s competitive landscape are taking their own approaches to AI. Siemens Energy leads with the most comprehensive strategy among traditional competitors, launching an industrial foundation model with Microsoft and pursuing workforce transformation (AI-powered learning for 250k+ employees), autonomous manufacturing (targeting 30% productivity gains), and AI-driven sales optimization. Schneider Electric, ABB, and Honeywell focus on partnerships and smaller acquisitions for IoT integration, predictive maintenance, and building automation. Notably, while some competitors have broader industrial AI portfolios, none match GE Vernova's strengthend, specific focus on AI for grid asset management; a critical differentiator as AI and visual data analysis become increasingly important for grid reliability. Every major energy company has embraced cloud partnerships (Microsoft Azure, AWS, NVIDIA) to support AI ambitions, but GE Vernova's sector-specific partnerships like its Chevron joint venture for AI data center power infrastructure demonstrate how companies are creating entirely new revenue streams. Traditional energy companies appear to be lagging in AI adoption, creating market share opportunities for AI-forward competitors. GE Vernova's is looking to win with a strategy of building proprietary AI capabilities through strategic acquisitions, rather than relying solely on partnerships. The companies that successfully integrate AI into their core operations – rather than treating it as an add-on – will likely capture disproportionate value as the energy sector digitizes.