Jarosław Diatczyk’s Post

✔ Companies unifying data see 379% ROI over three years ✔ Embedding analytics drives 85% higher sales growth and 25% greater margins The solution? A single, governed, AI-ready data foundation. Microsoft Fabric unifies data engineering, analytics, BI, governance, and AI. At DataArt, we see it as the strategic foundation to unlock the real value of enterprise data. 👉 Read the full article by Constantin Taivan, Solutions Architect: https://lnkd.in/dVeqK4yC #MicrosoftPartner #MicrosoftSolutionsPartne #AzureCloud #MicrosoftFabric #datatransformation #AI #MicrosoftAICloudPartnerProgram

There’s no shortage of AI hype right now, but smart organisations know it’s not just about the AI itself, it’s about the data behind it. 🔍 Enter Microsoft Fabric, a unified, Software as a Service (SaaS) data platform that simplifies ingestion, governance, analytics and more. Whether you're dealing with siloed systems or chasing better insights, Fabric brings everything together so your AI and BI projects have the solid foundation they need to succeed. In our latest blog, we dive into why Fabric is more than a new name in Microsoft’s catalogue. It's a strategic shift that’s helping customers modernise their data platforms, reduce complexity, and unlock real impact, like improved customer experience, sustainability tracking, and whole-of-government data reuse. Want to explore what this could look like for your organisation? Check out our Microsoft Fabric Ideation Workshop, the perfect way to start mapping out your data future. 👉 Read the blog: https://bit.ly/4mK9I7U #MicrosoftFabric

Private Preview - Databricks One on Azure Simplifies AI and Data Development. What if you could manage data engineering, analytics, and AI development on one platform? Databricks One in Azure Databricks is creating waves. It merges advanced tools for data science and AI with simplified workflows and enterprise-grade governance. Now, you can focus on innovation instead of juggling tools. Check out what it offers: 💡 Unified workflows to align data teams seamlessly 🔒 Top-tier governance for improved compliance and security ⚡ High-performance capabilities to meet enterprise demands 🤝 Collaborative tools to drive teamwork and accelerate insights This is more than a platform; it's a shift in data strategy. Simplification without compromise. Businesses leveraging integrated platforms like these often see faster deployment times and reduced operational burdens. Are you prepared to streamline your data approach? Explore its potential today: https://lnkd.in/d--dK6Vr

Agentic AI will reshape enterprise operations by 2027. Key points: 50% of enterprises using generative AI will pilot autonomous agents in the next three years 33% of enterprise applications will embed autonomous capabilities by 2028 40%+ of projects risk cancellation due to unclear ROI and weak governance Evidence shows real productivity gains—55% faster coding with GitHub Copilot, 14–34% efficiency improvements in customer support, and 40% higher quality in knowledge work tasks with GPT-4. The challenge is execution. Without disciplined governance and a clear value path, these projects fail. Full breakdown here: https://lnkd.in/eSapeMkS

Most churn is silent—tickets go quiet, then a cancellation lands. This post outlines CSAT Guardians: AI agents on Microsoft Power Platform that scan sentiment, wait time, SLA drift, and reopen patterns to flag risk early and trigger the right action—nudge, swarm, goodwill credit, callback, or knowledge push. The architecture pairs Dynamics 365 and Dataverse as the system of record with Power Automate plus Azure AI Language/Azure OpenAI for analysis and orchestration, all with human-in-the-loop guardrails. Expected impact: faster first response, fewer SLA breaches, higher CSAT/CES, and lower cost per case. A snapshot shows silent churn down 18%, CSAT +9 points, and a 42% faster FRT after 90 days. The post also offers a 30/60/90 blueprint, governance steps (PII redaction, approvals, audit logs), and KPIs to track. Explore the reference architecture and playbooks: https://lnkd.in/dzJ48NH6

Recently, I’ve been working with #AWS infrastructure and exploring the power of AWS Bedrock for enterprise AI adoption. When your data lives in multiple formats across the enterprise, #Bedrock makes it easier to bring structure and intelligence together. By setting up a knowledge base with structured data, you can sync your data warehouse and start querying tables in plain human language. This means analytics teams and creators can prototype faster. For anyone building AI-powered solutions on enterprise data, Bedrock is proving to be a strong choice—scalable, secure, and ready to integrate with existing AWS workflows. It’s been exciting to see how these tools accelerate not just analytics, but collaboration across teams. Does it mean I can remove all data worries ! No. But this knowledge base become source to your agents to start using it more effectively in AI solution. #AWS #AWSBedrock #AI #GenerativeAI #CloudComputing #DataAnalytics #AIAdoption #EnterpriseAI #MachineLearning #Innovation #DigitalTransformation

⚡ From Data Lakes to Smarter Agents Microsoft’s updates to OneLake in Fabric might sound like infrastructure news, but for AI agents, it’s a big deal. Highlights: 🔗 Mirroring for Oracle + Google BigQuery. 📊 OneLake Table API (preview) for secure table discovery. 🕵️♂️ Integration with Azure AI Search to build context-aware agents. Why it matters: Agentic AI thrives when it has access to timely, structured, and context-rich data. These updates mean less friction in connecting agents with real-world enterprise data — and more potential for multi-agent collaboration at scale. 👉 Imagine DeepSeekMoE models plugged into Fabric pipelines. What kinds of agent workflows would you build first?

3 Steps to Data-Driven Transformation (On a Lean Budget) Tight budgets shouldn’t stall transformation. Focus on three high-impact moves: 1. Consolidate your data. Unify sources on a lakehouse or Microsoft Fabric. Kill silos. Standardize governance. Unlock clean, reusable datasets. 2. Optimize cloud spend. Right-size compute. Reduce tool sprawl. Reinvest savings into growth. FinOps = cost control that fuels innovation. 3. Accelerate with AI. Once the foundation is clean, AI delivers quick, measurable wins—recommendations, forecasting, automation. Why boutique integrators? Agility. Precision. Accountability. Firms like C2S deliver rapid ROI by cutting waste, shipping quick wins, and scaling what works. If savings are the key to funding AI, let’s architect a lean, unified platform that shows value in weeks—not quarters. Consolidate >> Optimize >> Accelerate #Lakehouse #Databricks #MicrosoftFabric #BudgetInnovation #FinOps #DataGovernance #AIForBusiness

🚀 Unify Your Data Universe with Microsoft Fabric 🌐 In today’s data-driven world, fragmentation is the enemy of insight. Data silos slow innovation, complicate governance, and hinder AI readiness. That’s why I’m excited about how Microsoft Fabric is transforming the way we unify, govern, and activate data across the enterprise. 🔗 OneLake is the backbone—enabling zero-copy, zero-ETL access to data from Oracle, Google BigQuery, Azure Blob, and more. With OneLake shortcuts and mirroring, Fabric simplifies integration and accelerates time-to-insight. 🧠 Copilot-powered intelligence and Purview governance ensure that data is not only accessible but secure and trusted—critical for AI-driven transformation. 📊 Whether you're building real-time dashboards, predictive models, or generative AI apps, Fabric provides a unified platform that scales with your ambitions. 💡 From retail to healthcare to finance, organizations are already seeing the impact: faster decisions, reduced costs, and smarter operations. Let’s stop managing data in silos and start unlocking its full potential. #MicrosoftFabric #OneLake #DataGovernance #AIReady #Azure #Copilot #DataPlatform #UnifiedAnalytics

Do you know what’s funny? Most companies today strive to be data-driven, or even AI-driven. The single most important factor in making that happen? Bridging the gap between data professionals and business stakeholders. By far. All the modern data stack stuff, cloud, discovery, self-service, AI fabric, Mesh, are sooo much less important. Can anyone seriously claim that buying a shiny new data platform or orchestration tool matters more than actual collaboration between business and data teams? If that’s the case, it’s kind of bizarre that 90% of the data community spends its time arguing about which platform or tool is “best.” Same with company spending: 90% goes into devs’ salaries and their tools, and 10% (if that) into collaboration processes and tools… which, paradoxically, is where 90% of the results would come from. Just sayin’… And on a completely different note… I had the chance to see some beautiful art by Monira Al Qadiri. It made me think: maybe data modeling is a bit of art too, isn’t it? 🙂