Is Master Data Management still relevant for unstructured data?

𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗱𝗲𝗮𝗱 - 𝗶𝘁 𝗷𝘂𝘀𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗸𝗻𝗼𝘄 𝗶𝘁 𝘆𝗲𝘁 Remember when data lakes were supposed to solve everything? Store all your data in one magical repository and analytics would flow like water downstream. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱: Your data lake became a data swamp. Studies show up to 80% of data lake initiatives fail to deliver expected business value. Poor governance turned your strategic investment into an expensive graveyard of unusable datasets. The fundamental problem isn't technology - it's centralization. When one team controls all data access, you create bottlenecks. When domain experts can't manage their own data products, quality suffers. When AI projects need clean, discoverable data, they find neither. 𝗧𝗵𝗲 𝗽𝗮𝘁𝗵 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝗶𝘀 𝗱𝗮𝘁𝗮 𝗺𝗲𝘀𝗵. Instead of centralizing everything, data mesh distributes ownership to domain teams. Each business unit becomes responsible for their own high-quality "data products" while maintaining universal standards for discoverability and compliance. Think of it this way: your data lake tried to be a central warehouse. Data mesh creates a network of specialized shops, each with expert owners who understand their customers' needs. 𝗙𝗼𝗿 𝗔𝗜 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻, 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴: • Domain teams ensure data quality because they own the outcomes • Self-service infrastructure accelerates innovation cycles • Built-in governance prevents compliance nightmares • Data becomes discoverable and trusted across the organization Before you embark on your next AI project, ask this critical question: 𝗜𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗲𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗼𝗿 𝗯𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀? Most enterprises discover their biggest AI blocker isn't algorithms or compute power - it's data that doesn't work when you need it to. The companies winning with AI aren't just buying better tools. They're fixing their foundations first. #datamesh, #datamaturity, #aireadiness Parallaxis

🚀 New Blog Alert on Medium 🚀 “Data Governance Simplified: A Beginner’s Guide for 2025” In today’s data-driven world, organizations struggle with data silos, poor quality, and compliance challenges. Without a strong data governance framework, scaling AI and analytics becomes nearly impossible. At Segmetriq Analytics LLP, we break down the essentials of Data Governance in simple terms: ✅ Metadata Management ✅ Data Quality Rules ✅ Data Lineage ✅ Business Glossary ✅ AI & Agentic AI Integration 👉 Read the full beginner-friendly guide here: 🔗 - https://lnkd.in/dRYq4quB 📊 Because in 2025, businesses that govern data well will lead the digital race. #DataGovernance #DataStrategy #AIinBusiness #AgenticAI #DigitalTransformation #DataQuality #MetadataManagement #SegmetriqAnalytics

💡 Data Governance is no longer optional — it’s the backbone of data-driven decisions. I recently wrote a blog on “Data Governance Simplified”, where I break down: ✔ Metadata Management ✔ Data Quality Rules ✔ Lineage & Glossary ✔ AI-driven Governance 👉 Here’s the link: https://lnkd.in/dARSY4ic Curious to know — in your organizations, which area do you find most challenging right now? #DataGovernance #DataQuality #CDO

🔍 Issue 12 — From Data to Knowledge: The Power of Semantic Layers 📬 This post is part of the “Data Management in a Nutshell: Insight Series”—short, high-impact takeaways from the full Data Management in a Nutshell newsletter, now followed by over 6,260 professionals worldwide. This review is the result of my impressions and key insights gathered during the #DGIQ and #EDW2025 conference hosted by DATAVERSITY. 📩 Subscribe to get the full version delivered to your inbox: https://lnkd.in/dmZHyAm2. 📊 A semantic layer is crucial for transforming data into actionable business insights. It abstracts data from multiple sources—structured, unstructured, and semi-structured—into a unified, business-aligned view. By bridging the gap between complex data environments and end users, it enhances accessibility, context, and meaning for both humans and machines. 🧩 Semantic layers shift data management toward knowledge-centric architectures. They connect raw data to business context using metadata, taxonomies, ontologies, and knowledge graphs. This evolution supports reasoning, inference, and richer queries—essential for advanced analytics, automation, and generative AI. 🏗️ The effectiveness of a semantic layer depends on a well-structured architecture. High-performing implementations integrate metadata across diverse sources, enrich it with ontologies, and deliver it via APIs, dashboards, or AI systems. Tools such as graph databases, cataloging platforms, and orchestration engines ensure semantic consistency and accessibility. 🎯 Not all data use cases require a semantic layer. Its value is highest when organizations need to unify fragmented data, integrate structured and unstructured content, or deliver contextualized insights for AI and analytics. It is less relevant in flat, transactional environments with rigid schemas. 🤖 Next-generation semantic layers are evolving toward intelligent automation. AI agents are increasingly used to build ontologies, automate profiling, and enhance reasoning. These emerging layers allow AI not only to consume knowledge but also to manage and optimize data systems—bridging legacy environments with modern, AI-enabled operations. 📘 Want more practical insights like this? Follow the Data Management in a Nutshell: Insight Series for weekly posts. #data #datamodeling #semantic

🌐 Semantic layers are quickly becoming the foundation for turning raw data into knowledge. From bridging fragmented sources to powering intelligent automation, they are reshaping how enterprises prepare for AI-enabled operations. 📖 Explore Issue 12 of the Data Management in a Nutshell: Insight Series: https://lnkd.in/esX5hhyu

🌐 Semantic layers are quickly becoming the foundation for turning raw data into knowledge. From bridging fragmented sources to powering intelligent automation, they are reshaping how enterprises prepare for AI-enabled operations. 📖 Explore Issue 12 of the Data Management in a Nutshell: Insight Series: https://lnkd.in/esX5hhyu

🔎 Let’s clear up the confusion around warehouses, lakes, and lakehouses. A real data warehouse isn’t just a fast database. It’s defined by: ✔️ Subject orientation (built around business concepts) ✔️ Integration (consistent keys and definitions) ✔️ Historic data persistence (true history, not overwrite) That’s the foundation for enterprise data integrity. Without it, AI and analytics run on shifting sand. Over time, the lines blurred. Analytical databases were called “warehouses.” Then came data lakes. Then lakehouses. All powerful technologies — but let’s not mistake them for the discipline of a true warehouse. 👉 A lakehouse on its own is not subject-oriented, integrated, historized persistence. The missing link? Data Vault modeling. By making your integration layer subject-oriented, deduped, and historized, you give the lakehouse the persistence and trustworthiness of a true warehouse. With this, AI and analytics can finally rely on it without compromise. 💡 The takeaway: Any modern platform can become reliable — but only when paired with a modeling approach like Data Vault. That’s when you unlock a real foundation for analytics and AI. At Sudar.io, we make adding Data Vault effortless.

Data Governance isn’t a bureaucracy. It’s the decision factory of your entire supply chain. If planning and execution feel slow, noisy or political, it’s usually a data problem disguised as a process problem. What it really is; * A system of ownership + rules that produces reliable inputs (master & transactional data) for faster, auditable decisions. * The backbone of Digital Transformation and AI—no single source of truth, no scalable analytics or advanced algorithms. * The first step to visibility & performance management - for dashboards to give you insight and direction, and OKRs to actually work. Where companies stumble; ✔️ IT alone is responsible (--need to elaborate here --)) ✔️ Ownerless data fields (lead time, MOQ, service targets) → fingerpointing, firefighting. ✔️ Excel creep—shadow copies beat the golden record. ✔️ Metric theater— “more is better” syndrome with awful mistakes on measuring the wrong thing, e.g., accuracy focus instead of bias and error to drive OTIF and inventory investment. ✔️ No change discipline for new SKUs, promos, supplier shifts. The NexusLogIQ angle; * Playbook: Field-by-field rules, ownership, validations, and workflows—lean, practical, auditable. * RACI-first design so decisions don’t stall; responsibility is explicit, not implied. * Analytics-ready data for planning models & AI (traceable features, less drift, better service/cash outcomes). * Quality gates at ingestion: Completeness, Validity, Consistency (then Uniqueness, Timeliness). * Visibility that matters: dashboards tied to WAPE, bias, σ(FE), OTIF, Turns/DoS, write-offs. 🎯 Result: fewer stockouts/expedites, cleaner S&OP decisions, lower working capital—without heroics. If you want a 2-week Data Governance Health Check including an action plan, let’s talk. 📩 info@nexuslogiq.com | 🌐 www.nexuslogiq.com #SupplyChain #DataGovernance #SOP #IBP #Planning #Analytics #AI #MasterData #NexusLogIQ ÖMER BAKKALBAŞI #CPG #Retail #Manufacturing

🔍 From Warehouses to Lakehouses… and now “Data Mesh”? For years, we’ve debated Data Warehouses vs. Data Lakes vs. Lakehouses. But the next evolution is already here: Data Mesh. ⚡ What’s different? Instead of centralizing all data in one giant system, Data Mesh promotes: Domain-oriented ownership (teams own their data as products) Decentralized architecture (no single bottleneck) Self-serve data platforms (engineers + analysts can move faster) Federated governance (standardization without slowing innovation) 💡 Why it matters: Warehouses are great for BI Lakehouses are powerful for AI/ML But Data Mesh is about scaling people + processes, not just storage. 👉 In short: Warehouse = What happened? Lakehouse = What’s happening + What’s next? Mesh = Who owns it + How it scales across the org #DataEngineering #DataMesh #Lakehouse #Analytics #AI #FutureOfData

🚀 From Data to Decisions: The Power of Data Warehousing In today’s fast-paced business environment, decision-making depends on more than just access to data — it depends on how well we structure, store, and analyze it. That’s where Data Warehousing comes in. As Bill Inmon, the “father of data warehousing,” defined back in 1990 — a data warehouse is subject-oriented, integrated, time-variant, and non-volatile. Simply put, it provides a single source of truth that empowers executives, analysts, and managers to: 🔹 Access consolidated historical data for long-term insights 🔹 Perform advanced OLAP analysis (slice, dice, drill-down, pivot) 🔹 Drive customer, financial, and operational analysis with confidence 🔹 Support AI and machine learning models with clean, structured data For banks, financial services, and corporates, this isn’t just a technical function — it’s a strategic enabler. By separating operational systems (OLTP) from analytical systems (OLAP), organizations can run the business while also analyzing it — without compromise. 💡 The future? Data Warehousing is evolving into AI-powered ecosystems, where metadata, automation, and governance ensure accuracy, scalability, and trust in every insight. 👉 How is your organization leveraging its Data Warehouse? Is it just a storage solution, or is it truly shaping strategic decisions? #DataWarehousing #AI #Analytics #BankingTransformation #Strategy #DigitalTransformation