Chandra K’s Post

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 & ML: Artificial Intelligence and Machine Learning in engineering – The Next Leap in Manufacturing In the past decade, Industrial Automation has transformed how we build, monitor, and maintain systems. Now, AI & Machine Learning (ML) are taking it to the next level. Imagine a plant where: > Machines predict failures before they happen. > Energy usage optimizes automatically based on real-time demand. > Production schedules self-adjust to maximize efficiency. We’re not talking about the future — these solutions are already being implemented in forward-looking industries. Why it matters: > AI enables predictive maintenance with higher accuracy. > ML algorithms identify patterns humans might miss. > Combined with IoT & automation, they reduce downtime, save costs, improve safety and many more. In my work, I’ve seen how data-driven insights + automation can revolutionize plant performance. The real game-changer is integrating AI/ML into everyday operations — making manufacturing smarter, safer, and more sustainable. What’s your view — will AI replace operators, or will it become the ultimate partner in production? 😊 #AI #MachineLearning #Industry40 #Automation #IoT #PredictiveMaintenance #ManufacturingInnovation

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 Next Big Shift in Artificial Intelligence: Edge AI Artificial Intelligence has powered digital transformation for years-but the real breakthrough isn’t happening in the cloud. It’s happening at the edge: directly on devices, machines, and sensors. This is Edge AI—and it’s redefining how industries operate. ✨ Why Edge AI is powerful Ultra-fast decisions: Real-time insights without cloud delays. Privacy by design: Sensitive data processed locally, lowering risk. Always-on reliability: Functions even without stable connectivity. Efficiency at scale: Reduces bandwidth use and cuts costs. 🌍 Industry impact already underway Healthcare: Wearables that detect health anomalies instantly. Manufacturing: Machines predicting failures before they occur. Retail: Personalized in-store experiences from on-device analytics. Transportation: Autonomous vehicles making split-second choices. 📊 Market momentum The global Edge AI market was valued at $20.78B in 2024 and is projected to reach $66.47B by 2030 with a CAGR of ~21.7% (Grand View Research). Other forecasts suggest growth to $143B by 2034 (Precedence Research). 💡 The bigger picture Cloud AI isn’t going away—it’s still critical for training complex models. But Edge AI complements it by bringing machine learning inference directly to the point of action. Together, they form a hybrid ecosystem that is smarter, faster, and more resilient. Edge AI isn’t just an emerging technology-it’s becoming the backbone of enterprise-grade AI adoption. #EdgeAI #ArtificialIntelligence #MachineLearning #DigitalTransformation #IoT #FutureOfWork #EnterpriseAI #AI

🤖 AI Development Trends: The Transition from Tool to Ecosystem Artificial intelligence has moved from the lab to the masses, from a "trial" for businesses to a "must." In 2025, several key trends in AI development are accelerating: 1️⃣ Multimodal AI: The integrated application of text, voice, image, and video will enable AI to move beyond just "talking" to "seeing," "hearing," and "understanding." 2️⃣ Industry-Specific Models: Smaller, more accurate models are emerging in fields such as healthcare, finance, and manufacturing, helping businesses implement applications more quickly. 3️⃣ AI + Automation: From code generation to process optimization, AI is gradually becoming the "second engine" of business operations. 4️⃣ Edge AI: AI no longer relies on the cloud; more computing will be performed locally on devices, empowering IoT, wearables, and smart devices. 5️⃣ Responsible AI: With stricter regulations, transparent, explainable, and fair AI systems will become a must-have for businesses. 🌍 AI is not just a technological trend; it's also a core force for organizational transformation and the reshaping of competitiveness. Whether a company can seize this wave of trends will determine its competitive position over the next 5–10 years. 👉 What changes has AI already brought to your industry? #AI #Artificial Intelligence #FutureTrends #EnterpriseStrategy #DigitalTransformation

In the fast-paced world of data, having a structured approach is everything. One framework that continues to stand the test of time is OSEMN (pronounced awesome). This is one of the first frameworks I learned in Data Analytics and honestly, it’s a game changer! Even today, with AI and automation everywhere, this simple process still keeps me grounded. Here’s what it’s all about: - Obtain – Gather data (from APIs, databases, sensors, you name it) - Scrub – Clean it up (because messy data = messy insights) - Explore – Look for patterns, trends, and “aha” moments - Model – Build predictions or segmentations that answer real questions - Interpret – Translate it all into something useful for decision-making What makes OSEMN so powerful? It’s not just about crunching numbers—it’s about ensuring data is reliable, actionable, and ethical. Relevance today: Data is exploding from IoT, social platforms, and AI-driven systems Businesses demand more than “what happened”—they need “what’s next” Scrubbing and interpreting help maintain trust, compliance, and clarity It’s versatile across industries: healthcare, finance, retail, and beyond. Efficacy: The OSEMN process remains effective because it’s simple, iterative, and bridges the gap between technical rigor and business value. It empowers organizations to unlock the true potential of their data while keeping impact at the center. In short, OSEMN isn’t just a framework—it’s a mindset for approaching data analytics with clarity, structure, and purpose. #DataAnalytics #OSEMN #AI #MachineLearning #BusinessInsights

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

🚀 Discover the latest trending topics in AI Agents for 2025! Key developments include: 1. **Specialized AI Agents**: Tailored for specific tasks like customer support and financial planning. 2. **Multimodal Capabilities**: Processing text, voice, and images for richer interactions. 3. **Autonomous & Proactive Agents**: Moving towards self-decision making and task execution. 4. **Ethical AI & Transparency**: Prioritizing fairness and clear explanations in AI-driven decisions. 5. **Integration with IoT**: Seamless control in smart environments. 6. **Open-Source AI Models**: Democratizing AI development. 7. **Human-AI Collaboration**: New roles emerging to bridge human and artificial intelligence. These trends highlight the dynamic evolution and growing integration of AI agents in business and daily life. #AI #AIAgents #ArtificialIntelligence #TechTrends #Innovation #FutureOfWork

Computer vision is a foundational element of a transformative Physical AI system. By far, no other modality can match the richness and depth of information provided by video footage. #ComputerVision #OperationalEfficiency This capability enables a shift from rigid automation to adaptive intelligence, with applications that are already driving measurable outcomes: - In manufacturing, vision systems detect defects instantly, improving quality control. - In automotive service, vision-driven solutions track service bay utilization to surface bottlenecks and increase throughput. - In asset tracking, computer vision and IoT transform legacy yards into intelligent ecosystems, eliminating manual searches. These applications show how Physical AI, powered by vision, can unlock new efficiencies and competitive advantages. https://lnkd.in/gHdm5pBF

🚨 AI Won’t Fix Your Supply Chain — Unless You Do This First Everywhere we look, companies are racing to add AI and generative AI into their operations. But here’s the truth: most won’t see real results. Why? Because digital enablement isn’t just about adding new technology. It’s about strategically integrating AI, machine learning, IoT, and automation into the way you market, sell, operate, and serve customers. When done right, digital enablement can: ✅ Boost customer loyalty through better service and personalization ✅ Increase revenue with smarter insights and faster decision-making ✅ Accelerate operations by streamlining workflows across the supply chain ✅ Improve resilience with real-time visibility and predictive capabilities When done wrong, it becomes a costly distraction — wasting time, money, and resources. Within the Tompkins Ventures network, we have technology experts and Business Partners who know how to translate digital tools into measurable results. From breaking down silos to optimizing supply chain performance, our focus is on practical solutions that create end-to-end value. 💬 I’d love to hear your perspective: How are you using AI, generative AI, or machine learning in your operations today? Are you seeing real value — or more hype than substance? #DigitalEnablement #AI #GenerativeAI #MachineLearning #SupplyChainInnovation #DigitalTransformation #TompkinsVentures #SCM #SupplyChainOptimization #BusinessGrowth #SupplyChainManagement