Obsolete Data Modeling Techniques: Why They Fail Today

Stop blaming AI when your data is a mess. Every week I see posts about "AI doesn't work" or "LLMs are overhyped." The real problem? Garbage data architecture. What I see in most companies: jane_finance_suggestion_v3.xls on someone's desktop Same document saved in 5 different folders with different names on 3 different platforms. No metadata, no version control, no approval chains Data spread across 7 disconnected systems APIs that barely talk to each other Then they wonder why their AI project failed. What actually works: 2024-Q3-budget-forecast-finance-dept-v1.2-approved.xlsx Structured metadata: department, document type, approval status, version Proper file hierarchy: /finance/budgets/2024/Q3/ Author, reviewer, approval chain documented Consistent naming conventions organization-wide Most companies need a good librarian more than they need AI. Fix your information architecture first. Organize your data properly. Implement decent search with metadata filters. You might discover you don't even need AI, just proper data management. But that's not sexy enough to sell as "digital transformation." Bottom line: AI amplifies what you already have. If you have chaos, you get amplified chaos. If you have structure, you get useful results. Stop throwing technology at organizational problems. #DataManagement #AI #DigitalTransformation #InformationArchitecture #LLM

📝 Can AI transform Documentation and Metadata Management? Absolutely. Traditional documentation — static wikis, manual lineage diagrams, scattered notes — can’t keep pace with the speed of modern data ecosystems. They’re tedious, error-prone, and often outdated the moment they’re published. But AI is rewriting the playbook. From auto-generating lineage graphs to inferring entity relationships and writing human-readable column descriptions, AI is turning documentation from a chore into a living, intelligent asset. Imagine documentation that isn’t static — but evolves with your pipelines. ✅ Automated lineage extraction directly from SQL, Spark, and orchestration code ✅ Intelligent entity and relationship detection for better discoverability ✅ NLP-powered column descriptions that improve clarity and self-service analytics ✅ Continuous metadata updates in sync with evolving schemas and jobs This isn’t just efficiency. It’s the foundation for trust, governance, and collaboration in modern data teams. 🔍 Explore how AI is reshaping metadata management from a bottleneck into a strategic enabler in the latest blog (Part 6 of the AI-Augmented Data Engineering Series): 👉 https://lnkd.in/gPcp6euG #VoicesOfNeurealm #AIinData #DataEngineering #MetadataManagement #Governance #Innovation #ThoughtLeadership #Documentation

Your AI strategy is only as strong as your data architecture. 🏗️ Before diving headfirst into AI and Machine Learning, we need to talk about the foundation. Many organizations are eager to harness the power of AI but overlook the single most critical element: a robust and scalable data architecture. You can have the most advanced ML models, but without a solid architectural foundation, you will face significant roadblocks. A well-designed architecture ensures: Scalability: It can handle growing data volumes and processing demands. 📈 Integration: It's built to seamlessly connect with and feed data to AI/ML platforms. 🔗 Data Quality & Governance: It provides clean, reliable, and secure data — the bedrock of any effective algorithm. ✅ Bottom line: Don't build your advanced analytics on a shaky foundation. A future-ready data architecture isn't just an IT requirement; it's the core enabler that unlocks the true business value of your AI integration. ✨ #DataArchitecture #AI #MachineLearning #DataStrategy #DigitalTransformation #BigData #BusinessIntelligence

According to statistics, 90% of #AI initiatives fail after the PoC stage. Recently, I read a discussion about why #Text2SQL with #LLM “doesn’t work.” The main reasons mentioned were: - lack of analytical thinking among users; - #SQL is just a tool, not real analytics; - LLM cannot understand which question really matters. This may seem true only at first glance. The real reason for these failures lies deeper - in the approach to building AI-first products. Most solutions are built in a very straightforward way: without understanding the data structure, relationships, preprocessing or postprocessing, and without proper architecture or context. In practice, if you give the model a database schema, relationships, domain description, and a well-designed pipeline, accuracy can go above 95%. And if you also let the agent ask the user for clarifications when needed, not a single end-customer has ever said that “it’s not usable.” Real intelligence appears where there is context, data, and system architecture. It’s a bit ironic that many technically strong people who easily work with LLM APIs (though not yet with #MCP or Tool Use) still design AI systems without thinking broadly enough - and those projects often fade away after the PoC stage.

How do you integrate AI LLMs into Data Governance? Well, a little over a year ago, I worked on a project where the goal was simple: reduce costs and improve governance in Snowflake. We approached it in two phases: 🔹 Phase 1 – Traditional Approach Used metadata tables to identify inactive users and disable them. Flagged stale datasets not queried in months and moved them to cheaper storage or purged them. Manual scripts + scheduled tasks got us a solid 20–30% cost reduction and tighter security. 🔹 Phase 2 – Early AI/LLM Adoption Leveraged Snowflake’s Cortex AI functions (AI_CLASSIFY, AI_COMPLETE, etc.) to analyze usage logs + object metadata. LLMs helped classify tables into active, archival, or purge candidates based on usage patterns and documentation. Built AI-driven alerts for inactive users, idle warehouses, and redundant datasets. This second layer brought additional 15–20% savings and far less manual review. Lesson learned: start with the basics, then layer in AI. The real magic comes when LLMs work alongside traditional governance—faster, smarter, but still compliant. I’m curious: how are you (or your teams) using AI or LLMs to improve data governance, cost efficiency, or platform observability? #DataGovernance #Snowflake #AI #CortexAI #GenAI #DataArchitecture #CostSavings

I realise my last post was a little "ranty" so I decided to do a TLDR version: AI is only as powerful as the data it learns from. Yet too many organisations leap into AI without laying the groundwork: a solid data architecture. Without it, you risk: - Siloed, inconsistent data - Biased or inaccurate models - Compliance headaches - Underwhelming ROI With it, you unlock: - Clean, governed, accessible data - Scalable AI solutions - Trustworthy insights - Real business impact Good data architecture isn’t just a technical concern—it’s a strategic necessity. If you're serious about exploiting data through AI, start with the foundation. Are you attempting an AI revolution? Do you have any experience to share? If you need experts to help with the foundations or building an AI strategy, please get in touch with Envitia. #DataArchitecture #ArtificialIntelligence #DataStrategy #DigitalTransformation #AIInnovation #DataGovernance #TechLeadership #SmartData #FutureOfWork #Envitia #LinkedInThoughtLeadership

🧠 𝗗𝗮𝘁𝗮 𝗧𝗿𝘂𝘁𝗵: 𝗪𝗵𝗮𝘁 𝗔𝗜 𝗦𝘁𝗶𝗹𝗹 𝗖𝗮𝗻’𝘁 𝗗𝗼 🧠 AI gets hyped as the answer to everything. In reality: AI can support, streamline, accelerate... but it can't replace the fundamentals. After 30 years in corporate, leading data & architecture, I’ve seen the same truths hold: 🔹 𝗗𝗲𝗳𝗶𝗻𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 – AI still depends on human clarity and context 🔹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 ≠ 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀– It’s warehousing, pipelines, architecture, BI, and more 🔹 𝗔𝗜 𝗰𝗮𝗻’𝘁 𝗰𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝘄𝗵𝗼𝗹𝗲 𝗰𝗵𝗮𝗶𝗻 – Parts, yes. The entire value chain, not yet 🔹 𝗡𝗲𝘃𝗲𝗿 𝘀𝗸𝗶𝗽 𝘁𝗵𝗲 𝗯𝗮𝘀𝗶𝗰𝘀 – The need for strong foundations only grows with AI 🔑 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗧𝗶𝗽: Get your data house in order, then let AI amplify the value. 📣 𝗟𝗲𝘁’𝘀 𝗸𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗼𝗻𝗲 𝗮𝗻𝗼𝘁𝗵𝗲𝗿: In practical data work, where do you see AI excel, and where does it still miss the mark? #NeverStopLearning • More on the talk, event details, and photos: https://lnkd.in/eJ4VB-Bf ✨ 𝘋𝘢𝘳𝘦 𝘵𝘰 𝘚𝘖𝘈𝘙. 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮 𝘺𝘰𝘶𝘳 𝘸𝘰𝘳𝘭𝘥.™ ✨ #AIRealityCheck #DataGovernance #AnalyticsLeadership #DataAI #DigitalStrategy #DataTruths #DareToSOAR

🍂 Fall Forward With Smarter RAG Architectures 🍂 As we step into Fall, it’s the perfect season to rethink how organizations harvest knowledge and turn data into actionable insight. At YaanAI, we leverage Retrieval-Augmented Generation (RAG) to empower smarter, faster, and more strategic decisions. Here’s a snapshot of classic and emerging RAG architectures shaping enterprise AI: Classic & Proven Approaches 1️⃣ Naive RAG – Vector-based retrieval for simple, effective Q&A. 2️⃣ Multi-Modal RAG – Integrates text, images, and audio for multi-format understanding. 3️⃣ HyDE RAG – Uses hypothetical answers to retrieve the most relevant documents. 4️⃣ Corrective RAG – Validates outputs against trusted sources to reduce hallucinations. 5️⃣ Knowledge Graph RAG – Converts documents into structured graphs for reasoning. 6️⃣ Hybrid RAG – Combines vector and graph retrieval for unstructured + structured data. 7️⃣ Multi-Step RAG – Breaks complex queries into sub-questions for layered reasoning. 8️⃣ Agentic RAG – AI agents plan, reason, and dynamically retrieve across sources. Emerging & Specialized Approaches 9️⃣ Federated RAG – Secure retrieval across distributed datasets without centralizing data. 🔟 Personalized RAG – Adapts to user profiles, preferences, and past interactions. 1️⃣1️⃣ Temporal / Time-Aware RAG – Captures recency and trends for better forecasts. 1️⃣2️⃣ Domain-Specialized RAG – Optimized embeddings for specialized industries. 1️⃣3️⃣ Contrastive RAG – Refines embeddings to distinguish highly similar content. 1️⃣4️⃣ Memory-Augmented RAG – Maintains long-term context for multi-turn interactions. 1️⃣5️⃣ Explainable RAG – Adds reasoning and provenance layers for transparency. 1️⃣6️⃣ Reinforcement Learning RAG (RL-RAG) – Continuously optimizes retrieval using feedback. 1️⃣7️⃣ Multi-Agent RAG – Multiple AI agents collaborate to retrieve, reason, and synthesize information. At YaanAI, we don’t just implement RAG—we strategically integrate it into enterprise workflows, enabling smarter supply chains, faster decision-making, and better outcomes across industries like Life Sciences, Healthcare, High-Tech, Manufacturing, and Consumer Goods. 📧 info@yaanai.us | 🌐 yaanai.us #AI #MachineLearning #CognitiveAI #RAG #GenerativeAI #KnowledgeGraph #DigitalTransformation #SupplyChainAI #EnterpriseIntelligence #Innovation #DataDriven

Knowledge Graphs are a powerful alternative to the traditional vector DB based RAG . However , from our experience there are particular use cases that shine with Knowledge Graphs and some for which KGs are not always the right tool. These are few cases where Knowledge Graphs are unsuitable ❌ Data is flat and transactional (e.g., simple rows in a database). A relational DB is faster and simpler. ❌ Relationships don’t matter. If you only need metrics, aggregations, or Insert/Select/Update/Delete operations, a graph adds overhead. ❌ Low data complexity. If your dataset is small and doesn’t evolve much, the cost of building/maintaining a graph outweighs benefits. ❌ Lack of governance. Poorly managed vocabularies, ontologies, or metadata will make the graph confusing rather than insightful. ❌ Performance is critical for heavy analytics. Graph traversal can be slower than optimized columnar or in-memory DBs for certain workloads. #DataEngineering #AI #DataArchitecture #KnowledgeManagement #GraphDatabases #DataScience #EnterpriseAI

Interesting academic paper on how data platforms and architectures should be redesigned to be #agentic-first (or agent-first). This requires fundamental changes to how data is served by data platforms including new query interfaces, new query processing techniques, and new agentic memory stores. Serving Our AI Overlords: Redesigning Data Systems for Agents https://lnkd.in/gHNiT_PE #AI #agentic