OSEMN: A timeless framework for data analytics

🚀 Exciting times for Big Data especially with the accelerated focus on Gen AI! Given that Generative AI (Gen AI) is heavily dependent on robust data engineering practices, which form the foundation for its accuracy, success and effectiveness. The way organizations collect, process, and leverage data continues to evolve rapidly, bringing both new opportunities and challenges. 🔍 Key trends and news shaping the big data landscape this year: ✔ AI & Machine Learning: Nearly 65% of organizations are now adopting AI-driven analytics, leading to smarter forecasting and automation across industries. ✔ Edge Computing & IoT: With the global IoT network surpassing 30 billion devices, real-time analytics at the edge is driving smarter cities, healthcare, and manufacturing. ✔ Cloud Data Platforms: Migration to the cloud is accelerating, enabling scalable infrastructure, seamless integration of structured/unstructured data, and faster insights. ✔ Open Source & Data Democratization: Tools like Apache Spark and open data initiatives are making big data analytics accessible to more teams and fostering innovation. ✔ Data Ethics & Security: Enhanced cybersecurity, real-time anomaly detection, and global regulatory compliance (GDPR, DPDP) are top priorities to keep data assets secure. 🌐 As data volumes climb toward 182 zettabytes this year, these advances are pivotal for organizations committed to data-driven decision-making and digital transformation. #BigData #DataTrends #AI #EdgeComputing #CloudData #DataSecurity #Innovation #DataEngineering #ApacheSpark 

From Data to Decisions: The 5 Key Steps of Data Analysis Data is everywhere and but insights only emerge when we follow a structured process. Figure 1: Steps of Data Analysis reminds us how to turn raw numbers into real impact: 🔹 1. Define the Problem → Every analysis starts with clarity. What challenge are we solving? What decision do we want to improve? 🔹 2. Collect the Data → Relevant, quality inputs matter. From sales records to customer interactions, the right data sets the foundation. 🔹 3. Clean & Prepare → Raw data is messy. Removing errors, duplicates, and gaps ensures accuracy and reliability. 🔹 4. Analyze → This is where the magic happens and using techniques like statistical models, machine learning, and visualization to uncover hidden patterns. 🔹 5. Interpret & Act → Insights only matter when they drive action. Translate results into strategies that improve operations, decisions, and outcomes. 📊 These steps aren’t just a cycle and they’re a mindset for making smarter, evidence-based decisions in business, research, and beyond. As AI, IoT, and cloud technologies evolve, these principles remain the core of data-driven transformation. The real advantage? Organizations that can move seamlessly from data collection to action will lead in tomorrow’s competitive landscape. #Analytics #DataScience #AI #DecisionMaking #Innovation #FutureOfWork

AI in Supply Chain: Revolutionizing Efficiency Over a Decade With over 10 years in AI, I’ve watched it reshape supply chain management from reactive to proactive. In the early days, supply chains relied on manual forecasting with limited accuracy. Now, AI-driven demand forecasting models achieve up to 85% accuracy, minimizing overstock and shortages. Reinforcement learning optimizes logistics routes, cutting transportation costs by 20%. Digital twins, powered by AI, simulate supply chain scenarios in real-time, enhancing resilience. The rise of AI-integrated IoT ensures end-to-end visibility, from warehouse to delivery. As sustainability becomes critical, AI is paving the way for greener supply chains. How has AI transformed your supply chain operations? Read more about AI in supply chains: MIT Sloan - AI in Supply Chain Management #MultimodalAI #HealthcareAI #InsuranceTech #AIinHealthcare #DataIntegration #PersonalizedMedicine #AIforInsurance #DigitalHealth #HealthTech #TechInInsurance #AIApplications #FutureOfHealthcare #InnovationInInsurance #DataScience #MachineLearning #AI #ArtificialIntelligence #DigitalTransformation #TechTrends #ML #DeepLearning #Automation #AIInBusiness #DataScience

𝐌𝐨𝐨𝐫𝐞'𝐬 𝐋𝐚𝐰 𝐜𝐚𝐧 𝐛𝐞 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐞𝐝 𝐛𝐞𝐲𝐨𝐧𝐝 𝐭𝐫𝐚𝐧𝐬𝐢𝐬𝐭𝐨𝐫𝐬 - 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐚𝐬 𝐰𝐞𝐥𝐥. We're living in an era of exponential change on two fronts: 𝐃𝐚𝐭𝐚 𝐃𝐞𝐦𝐚𝐧𝐝: Driven by the rapid evolution from Predictive AI to Gen AI and now AI Agents, coupled with new business models and regulatory pressures. 𝐃𝐚𝐭𝐚 𝐒𝐮𝐩𝐩𝐥𝐲: Fueled by the explosion in the Volume, Variety, and Velocity of data from sources like IoT and real-time systems. This creates a critical question for every business leader: 𝘐𝘴 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯'𝘴 𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘵𝘰 𝘱𝘳𝘰𝘤𝘦𝘴𝘴, 𝘮𝘢𝘯𝘢𝘨𝘦, 𝘢𝘯𝘥 𝘥𝘦𝘳𝘪𝘷𝘦 𝘷𝘢𝘭𝘶𝘦 𝘧𝘳𝘰𝘮 𝘥𝘢𝘵𝘢 𝘥𝘰𝘶𝘣𝘭𝘪𝘯𝘨 𝘦𝘷𝘦𝘳𝘺 18 𝘮𝘰𝘯𝘵𝘩𝘴 𝘵𝘰 𝘬𝘦𝘦𝘱 𝘱𝘢𝘤𝘦? If the answer is no, you're not just standing still - you're falling behind. This relentless pressure is precisely why existing approaches to managing data are failing. To handle this reality, you need to make a 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘢𝘭 𝘤𝘩𝘢𝘯𝘨𝘦 - 𝘮𝘢𝘬𝘦 𝘥𝘢𝘵𝘢 𝘢 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘤 𝘱𝘪𝘭𝘭𝘢𝘳 𝘢𝘯𝘥 𝘭𝘦𝘷𝘦𝘳𝘢𝘨𝘦 𝘢 𝘴𝘺𝘮𝘣𝘪𝘰𝘵𝘪𝘤 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘱𝘦𝘰𝘱𝘭𝘦, 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴, 𝘢𝘯𝘥 𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘱𝘪𝘭𝘭𝘢𝘳𝘴. In "Data as the Fourth Pillar," my co-author Siddharth Rajagopal and I provide the frameworks for building this model, with a case study by Ruediger Eck of AUDI AG. The pre-order link is in the comments! 👇 Taylor & Francis Group #DataAsTheFourthPillar #MooresLaw #DataStrategy #AI #DigitalTransformation #Leadership #Innovation #BookLaunch

The AI Industry Playbook https://lnkd.in/g4XapA55 It’s a massive Notion database with 200+ detailed solutions for real-world problems in industries like: 1. Energy 2. Agriculture 3. Art & Design 4. Sports & Fitness 5. Space Exploration 6. Education & Research 7. Real Estate & Construction 8. Retail, E-commerce & Goods 9. Telecommunication & Technology 10. Manufacturing & Industrial Automation 11. Automotive, Mobility, Transportation & Logistic 12. Environmental, Weather, Sustainability & Earth Science 13. Media, Entertainment, Gaming & Publishing 14. Finance, Stock, Investment & Insurance 15. Technology, Innovation & Engineering 16. Consumer Goods and Lifestyle 17. Government & Public Sector 18. Healthcare & Life Sciences 19. Security & Surveillance 20. Hospitality & Tourism 21. Electronics & IOT Access it here: https://lnkd.in/g4XapA55

𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗠𝗮𝗿𝗸𝗲𝘁: 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗼 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘿𝙤𝙬𝙣𝙡𝙤𝙖𝙙 𝙁𝙧𝙚𝙚 𝙋𝘿𝙁 𝘽𝙧𝙤𝙘𝙝𝙪𝙧𝙚: https://lnkd.in/dXjg9dNe 𝗗𝗮𝘁𝗮 𝗮𝘀 𝗔𝘀𝘀𝗲𝘁 – In the digital economy, data is the new currency. The rise of the data extraction market is enabling organizations to unlock hidden insights, automate processes, and gain a competitive edge. 𝗠𝗮𝗿𝗸𝗲𝘁 𝗠𝗼𝗺𝗲𝗻𝘁𝘂𝗺 – With exponential growth in unstructured data from web, social media, IoT, and enterprise systems, the demand for advanced extraction tools is soaring globally. 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 & 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 – From market intelligence and compliance monitoring to AI training data and financial analytics, data extraction is fueling smarter, faster decision-making. 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 – Companies investing in automation, cloud-based solutions, and ethical AI-driven extraction will lead the next wave of digital transformation. #DataExtraction #BigData #AI #DataAnalytics #Automation #DigitalTransformation #MarketGrowth #DataDriven #BusinessIntelligence #FutureOfWork

AI-Driven Supply Chain Orchestration: Building the Cognitive Core The supply chain is no longer just about moving goods—it’s about orchestrating intelligence across every node, every process, and every decision. We’ve entered an era where connected systems, predictive insights, and autonomous responses are reshaping the very DNA of supply chain operations. What once ran on static forecasts and siloed tools is now evolving into self-learning, adaptive ecosystems powered by AI. ✔️ Connectivity – Breaking down data silos to create a real-time nervous system across suppliers, plants, logistics, and customers. ✔️ Predictive Intelligence – Using AI and graph-based models to foresee disruptions before they ripple across the value chain. ✔️ Autonomous Orchestration – Moving from human-dependent interventions to AI-driven responses that optimize production, balance inventories, and reroute logistics—automatically. The result? Cognitive supply chains that don’t just react but anticipate, simulate, and self-correct—turning uncertainty into opportunity. Business leaders today aren’t just asking “How can we make our supply chain more efficient?”—they’re asking “How can we make it more intelligent, adaptive, and future-proof?” The answer lies in building the cognitive core: a layer of AI-powered orchestration that fuses IoT signals, enterprise data, and market intelligence into living decision networks. This isn’t tomorrow’s vision—it’s today’s imperative. Organizations that embed intelligence into their supply chains now will set the benchmark for resilience, agility, and growth in the decade ahead. 🔹 #SupplyChainInnovation #CognitiveSupplyChain #AIinSupplyChain #IntelligentEnterprises #DigitalTransformation #SupplyChainResilience #FutureOfWork #GraphAI #PredictiveAnalytics #SmartManufacturing

🚀 The AI Tsunami is Transforming Data Science! (5 Trends You Can't Ignore) The future is now! The data science landscape is shifting *fast*, and 2025 is shaping up to be a pivotal year. Here are 5 trends I'm watching closely: 1. 🤖 Agentic AI: The Rise of Autonomous Co-Workers. Forget chatbots. Think AI *agents* managing complex workflows end-to-end. Collaborative AI networks are already transforming operations. #AgenticAI #AIAgents 2. 📊 Augmented Analytics: Democratizing Data. AI is leveling the playing field! Anyone can now leverage advanced analytics for faster, data-driven decisions. Think streamlined insight, empowered teams. #AugmentedAnalytics #DataDemocracy 3. ⚡️ Edge + IoT: Real-Time Revolution. Billions of IoT devices feeding real-time data! Edge computing delivers lightning-fast insights *at the source*. Imagine instant optimization in retail, manufacturing, and more. #EdgeComputing #IoT #RealTimeData 4. 🔧 MLOps Evolved: AI for AI. MLOps platforms are getting smarter, using AI to optimize the entire model lifecycle. Data scientists can finally focus on creativity & strategic impact! #MLOps #AIforAI 5. 🔍 Open-Source Reasoning: The Next Frontier. Open-source AI models like DeepCogito v2 are challenging proprietary solutions, offering transparency and customization crucial for enterprise adoption. #OpenSourceAI #ResponsibleAI The demand for data science talent is soaring! It's not just about analysis anymore—it's about building intelligent, autonomous systems. Which trend is most transformative in your view? Let's discuss! 👇 #DataScience #ArtificialIntelligence #MachineLearning #AI2025 #TechTrends #Innovation #Analytics #DataAnalytics

#BOI published an interesting article about "The future of AI-driven insights - From understanding consumers to enabling intelligence" (link in comments). A few reflections that stood out to me if we apply this to digital (IoT) products: 1. Personalization as perceived value Users feel “seen & listened to” when the surroundings adapt to them, based on their interactions. It’s not just functionality, it’s about that next level of personalization where the product’s system learns from your personal usage and preferences, possibly even adapting the interface accordingly, creating an exclusive experience. The additional value of that sense of uniqueness often outweighs the actual technological complexity behind it, making it at least worthwhile to investigate. I believe in the future, AI will be an integral part of UX. 2. Dynamic contextual information “Understanding who someone is right now” sounds extremely powerful, but I wonder if there’s appetite for the level of data-sharing this would require. Continuous emotional/event tracking feels like a red line for many users. Data can be used for good or bad. Just like technology isn’t inherently good or bad, it depends how you use it. The risk of sharing data is that it could contain information, not directly clear/sensible to the human eye, but extrapolatable by AI, that could be taken advantage of. I’ll elaborate this with an example in the comments. I do believe some contextual insights can be used for the right use case, especially when they rely on non-intrusive or less personal data. Perhaps in the (far) future, the world collectively has decided how to ethically go about this. For now that zone seems gray, knowing Belgium is currently undecided about the new EU legislation proposal about mandating the scanning of all private communications. 3. Orchestrated intelligence & closed-loop engines To differentiate your product or service, embedded intelligence in the backbone is a must. Architecting systems that continuously learn and accelerate decision-making, without manual reconfiguration, will be key for the next generation of intelligent products. For IoT products, that requires both software and hardware development expertise. Not an easy task, as they require different development methodologies. 💡 I’m curious how others building digital platforms and innovative software see this: → Where do you draw the line between personalization and privacy? → How do you think orchestrated intelligence should be embedded into product architectures today? Let’s discuss in the comments or send me a DM!

Most companies think Vision AI ends with detection. Detection without context doesn’t mean much. The real power comes when you connect the dots. That’s where a Vision AI Aggregator changes the game. Pull together: Raw inputs — cameras, IoT sensors, logs, historical video Context layers — temporal drift, spatial stitching, multi-modal fusion Reasoning engines — edge intelligence, cloud memory, policy guardrails , all converging in one hub that feeds: Automations (slow a spindle, pause a crane zone, replenish stock) Ops Systems (MES, CMMS, EMR — with real evidence, not alerts in silos) People & Alerts (actionable tickets, dashboards, nudges in the flow of work) And the impact is very real: Manufacturing — stop a defective batch before it leaves the line Construction — predict near-miss risks by combining video with weather & shift data Retail — replenish shelves before sales dip Healthcare — flag anomaly drift across months of imaging, not just a single scan This is Vision AI as an operating system for the enterprise , measurable against MTTR, defect escape, p95 latency, safety incidents, and throughput. And here’s the intriguing part: It’s not about bigger models. It’s about smarter relationships between detections. Question for you: If you were designing a Vision AI stack today, would you bet on edge reasoning for instant action or cloud context for deeper foresight? #VisionAI #EdgeAI #AITransformation #ComputerVision #ThoughtLeadership