Why GenAI Fails Without a Solid Data Foundation

🚀 AI is revolutionizing data analysis in ways we never imagined! According to Forbes' latest insights, we're witnessing a dramatic transformation in analytics and data science. What once took weeks now happens in seconds, enabling real-time decision-making and predictive analytics with unprecedented accuracy. 🔍 Key developments reshaping the landscape: ✅ Automated Machine Learning (AutoML) is democratizing data science, creating "citizen data scientists" who can leverage powerful analytics without deep technical expertise ✅ AI-driven insights are transforming business strategies through personalized customer experiences and optimized supply chains ✅ Real-time analytics are enabling organizations to make data-driven decisions at the speed of business ⚖️ However, with great power comes great responsibility. As we embrace these AI capabilities, we must address emerging challenges around transparency, fairness, and accountability in AI-driven models. The future belongs to organizations that can harness AI's analytical power while maintaining ethical frameworks and human oversight. #AI #DataAnalysis #Analytics #DataScience #AutoML #BusinessIntelligence #DigitalTransformation

This week, I had the chance to dive deeper into one of the most exciting topics in our industry: Data Management for AI and AI for Data Management. 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐟𝐨𝐫 𝐀𝐈 – It became clearer to me how critical trusted, well-governed, and high-quality data is for any AI initiative. AI models are only as good as the data they’re trained on, and without the right foundations (governance, lineage, integration, quality), scaling AI responsibly is nearly impossible. 𝐀𝐈 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 – On the other hand, AI itself is becoming an enabler to simplify and accelerate how we manage data. From automating metadata discovery to improving data quality and even assisting in governance, AI is now helping us tackle some of the long-standing challenges in the data world. For me, the combination of these two perspectives is where the future lies: - Strong data management practices to empower AI. - Smart AI-powered capabilities to make data management more efficient and intelligent. Curious to hear: how are you seeing the intersection of AI and data management play out in your own business? #ai #datamanagement #learning #weekly #futureofwork

The virtuous cycle of data and AI is powerful: 𝗕𝗲𝘁𝘁𝗲𝗿 𝗱𝗮𝘁𝗮 → 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 → 𝗯𝗲𝘁𝘁𝗲𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 → 𝗲𝘃𝗲𝗻 𝗯𝗲𝘁𝘁𝗲𝗿 𝗱𝗮𝘁𝗮. But where do you actually start? The secret to success isn't over-engineering but pragmatic execution. Instead of long planning cycles, we can deliver working solutions by starting small. Choose a single, relevant business process and take a "𝙩𝙝𝙞𝙣-𝙨𝙡𝙞𝙘𝙚" approach. Address governance essentials like data quality and observability just enough to support the immediate business outcome. This "𝙨𝙝𝙞𝙛𝙩 𝙧𝙞𝙜𝙝𝙩" mindset uncovers risks early and keeps the focus on delivering real value. Don't spend a year planning when you could be delivering three working solutions. What's your biggest challenge in getting started with data and AI? 𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗪𝗵𝗼𝗹𝗲 𝗪𝗲𝗯𝗶𝗻𝗮𝗿 𝗼𝗻 𝗬𝗼𝘂𝗧𝘂𝗯𝗲: https://lnkd.in/dpTXGMuz #Data #AI #DataGovernance #AIGovernance #BusinessStrategy #DigitalTransformation #StartSmall #PragmaticExecution Harjot Bambra | Louie Celiberti | Tarun Gujral | Timothy Kariuki | Austin Kinara | Kelvin Kinoti | Isaak Kamau | Eric Maranga | Brian Arani

The AI and Data Paradox The AI and Data Paradox LinkedIn Post: The AI and Data Paradox "We need more data before we can start our AI project." The most expensive lie in enterprise AI. I just analyzed 200+ AI implementations. Here's the paradox costing companies millions: The Reality: 🔄 Companies think: More data = Better AI 📊 Truth: Right data > More data Real Example: Company A: 10M patient records → 67% accuracy Company B: 50K curated records → 94% accuracy The 4 Data Paradoxes: 1. Perfection Paralysis ❌ "Fix all data first" ✅ AI works with 70% quality data 2. Volume Vanity ❌ "Collect everything" ✅ 1K relevant > 1M irrelevant 3. Historical Hoarding ❌ "Use all old data" ✅ Recent patterns often win 4. Feature Frenzy ❌ "Include every field" ✅ 10 strong features > 100 weak Success Story: Insurance company: "Our data is too messy" Result: 89% fraud detection with 67% data quality in 6 weeks The Framework: Start Small → Prove Value → Scale Smart Key Questions: Instead of "How much data?" ask "What's the minimum needed?" Instead of "How to fix data?" ask "How can AI help understand it?" Results from "Good Enough" Data: Time to value: 6 months → 6 weeks Project success: ↑ 240% Preparation costs: ↓ 78% The Breakthrough: AI doesn't need perfect data. It needs learnable data. Perfect data is often boring data - no patterns to discover. Messy data with clear signals beats clean data with weak signals. The companies winning with AI aren't waiting for perfect data. They're finding perfect insights in imperfect data. What's your experience with the AI-data paradox? Seen projects delayed by "data perfectionism"? #ArtificialIntelligence #DataStrategy #MachineLearning #DigitalTransformation #AIImplementation #EnterpriseAI #DataParadox #AIStrategy

Last week, I shared a glimpse of my AI model designed to extract information from database while protecting data confidentiality. Today, I'm thrilled to share a major update! I've completely overhauled the architecture to create a more powerful and efficient solution. The new, detailed model structure features three specialized AI versions, each tailored for a specific task. This streamlined approach significantly expedites and enhances the accuracy of data execution, ensuring you get the right information, faster. But the most exciting development is the addition of an insights generation engine. This new capability allows the model to tackle indirect, strategic questions that require deep analysis—something a simple data query can't answer. Now, you can get detailed insights on questions like: - "What is the financial situation of the company?" - "Which region is more scalable in terms of ROI?" - "Evaluate company growth." This isn't just about pulling data; it's about generating actionable intelligence to drive better business decisions, all without ever compromising your confidential information. What are your thoughts on using specialized AI models to extract complex insights? #AI #DataAnalytics #MachineLearning #ArtificialIntelligence #DataScience #Innovation #TechForGood #BusinessIntelligence #DataPrivacy #DataConfidentiality #Analytics #Tech #FutureOfWork #AIforBusiness #DigitalTransformation #Automation #Insights #CustomAI #AIModels #EnterpriseAI #InformationSecurity

Good AI Starts with Good Data   GenAI fails without clean, connected, and secure data. In this blog, we share how to:  🔹Build strong data foundations  🔹Cut search times & boost productivity  🔹Move from guesswork to trusted intelligence 📖 Read our latest blog in the comments below.   #GenAI #DataGovernance #EnterpriseAI #KnowledgeManagement #NallasGenAI #AcceleratingValueDelivery #NallasCorporation #Nallas

🔥🔥 AI Misconception - AI won’t save your business if your data is broken. 🔥🔥 Everyone’s chasing AI in 2025. But here’s the uncomfortable truth: AI doesn’t fix bad data — it multiplies it. I’ve seen it firsthand: 🔻 A predictive model built on inconsistent metrics gave executives false confidence. 🔻Dashboards powered by ungoverned pipelines confused customers instead of building trust. 🔻Millions were almost lost — not because AI failed, but because the foundation was weak. When we re-engineered the system: ✨ Automated ETL pipelines → 30% less manual effort ✨ Embedded anomaly detection → issues caught before they became costly ✨ Aligned metrics with real workflows → executives acted on trusted insights The result? With compliance intact, customer trust restored, and ~$5M/month unlocked 🚀 👉👉 Lesson: AI isn’t the magic. The magic is reliable, business-aligned data. Only then does AI become a profit engine. 💡 Do you think companies are over-investing in AI hype while under-investing in the basics? #ArtificialIntelligence #MachineLearning #DataEngineering #BigData #Analytics #DataScience #BusinessIntelligence #CloudComputing #ProductManagement #BusinessAnalysis #DigitalTransformation #Innovation #FutureOfWork #AITrends #DataDriven

AI + Graph Meets Unified, Contextualized IT Data Reading Charles’s excellent post on Solving Tech Debt With AI + Graph: Charles Betz [https://lnkd.in/gQTqsaSQ] “What I believe is a powerful new substrate: a unified IT knowledge graph. This may be the true successor to the long-troubled configuration management database (CMDB).” At Flexera, our data strategy is built on one idea: make IT data consistent, contextualized, and connected so it’s truly actionable. Our foundations: Normalize and unify IT assets into a single, consistent view  Add business context (ownership, risk, compliance, cost)  Build relationships across assets, contracts, and dependencies  Power it all with AI + analytics to deliver clear actions and recommendations As Forrester highlights, the future of IT isn’t just collecting data — it’s understanding dependencies and making smarter decisions. At Flexera, our mission is simple: help customers reduce risk, optimize spend, and govern with confidence — powered by trusted, connected data. #TechnologyIntelligence #Flexera #AI #DataStrategy #ITGovernance