An efficient Enterprise Data Warehouse (EDW) powered by Data Intelligence may seem unattainable for many corporations. However, with the right people, technology, and a partner like Slalom, any company can achieve data modernization. Embracing this transformation can unlock new insights and drive better decision-making across the organization. #Slalom #DataManagement #DataIntelligence

😓 Most enterprises today deal with fragmented data systems: warehouses for structured analytics, lakes for big data, specialist ML platforms, separate governance tools. That spells complexity, slow time-to-insight and increased risk. The Lakehouse paradigm changes that. By unifying data types, analytics workloads, and governance under one roof, it reduces silos and accelerates innovation. Discover how we guide organizations through this transformation. https://lnkd.in/gHG-FxFT Sign up for a call with us today: https://lnkd.in/gDf2YTQN #TransformXperience #DigitalTransformation

In data modeling, striking the right balance between simplicity and functionality is key to delivering value. When should you simplify your data model, and when should you add more features? Here are some guiding principles: Simplify Your Data Model When: ✔️ Complexity starts to hinder understanding, usability, or performance. ✔️ Redundant or irrelevant data bloats the model with little to no business value. ✔️ You need to enhance maintainability and scalability, reducing technical debt. ✔️ The model’s core purpose gets lost in excessive detail, slowing down analysis. Add Features to Your Data Model When: 🚀 New business processes or regulatory requirements demand richer data representation. 💡 You need to unlock advanced analytics, AI, or automation capabilities. 🔄 The model must evolve iteratively to support future growth and emerging use cases. 🎯 Greater data granularity or relationships improve decision-making accuracy. The goal is to keep data models practical and effective constantly evolving balance to fit real business needs. Simplify to clear the fog, add features to empower insights. Finding this balance leads to better data-driven decisions and a more agile enterprise. #DataModeling #DataManagement #Analytics #BusinessIntelligence #DataScience #TechTips #QASPL

The discussions following my recent post “Do We Still Need Traditional Data Modeling Tools?” have been some of the most insightful I’ve seen in a long time. From experienced practitioners to modern tool builders, the consensus is clear: data modeling is more important than ever — but the way we do it must evolve. Leaders like Serge Gershkovich (SqlDBM) and Johannes Hovi (Ellie.ai) shared great perspectives — showing that the future isn’t about abandoning tools, but about making them open, automated, and collaborative. The visual medium will always matter — but it now needs to be powered by metadata, integration, and AI-assisted automation so that our models stay alive, consistent, and connected to real delivery. Thanks to everyone who contributed — Werner, Robert, Anke, Joerie, Harmen, Guido, Steve, Thierry, Reeves, and others — for making this such a rich conversation. The fact that both vendors and practitioners are now aligned on this evolution is a sign that we’re moving toward a shared goal: 👉 Modeling as a living system — not a static diagram. https://lnkd.in/e_vDkPt2

🔥 WHY YOUR DATA PIPELINE IS BROKEN AND HOW DATAOPS CAN FIX IT Is your data pipeline facing issues? In the digital age, data is a valuable asset for every business. However, many organizations are confronted with significant challenges in collecting, processing, and distributing data. Inaccurate or untimely data can slow down your business, leading to failures in making informed decisions. Transform these shortcomings into opportunities with DataOps—a cutting-edge methodology designed to enhance the speed, reliability, and efficiency of your data flow. DataOps not only enables a continuous and robust data stream but also fosters collaboration between teams, from engineers to end users. Explore the detailed article to gain a deeper understanding of how DataOps can revive your data pipeline and ensure your business remains strong in an increasingly competitive landscape. Read more at: https://lnkd.in/gu76u9F9 MagicFlow | TechData.AI

Why #DataEngineering Is the Secret Ingredient to Smarter Decisions. Most businesses don’t fail because they lack data.  They fail because their data isn’t structured, integrated, or trusted. That’s where Dataloop’s Data Engineering comes in. It helps organizations:  ✅ Automate data collection from multiple systems.  ✅ Clean and standardize data for accuracy.  ✅ Build data warehouses and pipelines.  ✅ Enable self-service analytics and fast decision-making. The result?  👉 Better decisions, faster.  👉 A single source of truth.  👉 Less time fixing data — more time acting on it. Data engineering doesn’t just move data — it moves businesses forward. #Automation #DataManagement #Analytics #DataStrategy #DataDriven #DataAnalytics #DataHub #Dataloop #DataloopGlobal

𝐐𝐮𝐞𝐫𝐲 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐞𝐯𝐞𝐫𝐲 𝐃𝐁 𝐚𝐠𝐞𝐧𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰 🚀 In a data-first economy, query optimization is reshaping how organizations derive value from data assets. As datasets expand and workloads diversify, efficient query planning becomes a strategic differentiator that speeds decision-making and lowers operational costs. Across the industry, teams are experimenting with learned cost models, adaptive indexing, and dynamic plan selection. Some organizations report latency reductions of 20–70% on complex analytics, and compute costs drop 15–40% due to smarter plan pruning. Others cite shorter ETL windows, faster dashboards, and improved SLA adherence for real-time data. Observability dashboards tracking 95th percentile latency, cache efficiency, and plan stability guide ongoing tuning. What trends or metrics are you watching in query optimization? #ArtificialIntelligence #MachineLearning #GenerativeAI #AIAgents #MindzKonnected

From data to decision: where traceability creates real value. Capturing data is good but turning it into intelligent decisions… that’s a different league. The difference between a useful system and a bureaucratic one lies here: • Critical data available in real time • Visibility of the entire process not just records • What gets measured, gets understood. And what gets understood… improves. Data nobody sees = missed opportunity. Poorly organized data = wrong decision. An automated, well-structured data = a competitive advantage. At TRAZiT, we design traceability that informs, not that gets in the way.

In today’s data-driven world, traditional system reports only scratch the surface. Businesses need deeper, AI-powered insights to truly understand what’s happening beneath the numbers. Our latest blog explores how AI-powered data analysis goes beyond routine reporting, helping organizations detect patterns, uncover loopholes, and make smarter, faster decisions. Read more: https://lnkd.in/dMQffhk7 #AIPoweredAnalytics #DataIntelligence #BusinessGrowth #DigitalTransformation #ProteusTechnologies #insightdb #databasequery