30 minutes instead of 3 hours. Yes, you read that right. I discovered Power BI Modelling MCP Server and it completely changed how I analyze data. Context: Analyzing the downloads data of my ebook "The AI & Data Alphabet 2025" ➤ Before: 2-3h manual cleaning + DAX measures creation ➤ After: 30 minutes flat What changed: ✓ Automated data cleaning ✓ Intelligently generated DAX measures ✓ Optimized relational model effortlessly Result? I can finally focus on : → Design → Visualization → The story my data tells No more time wasted on repetitive tasks. AI handles the technical work, I focus on insights. Need to analyze your Excel or Google Sheets data to save precious time? Comment "MCP" or contact me directly here:eufyves@gmail.com See the demo here : https://www.linkedin.com/posts/sikati-yves-joseph-039215330_powerbi-mcp-dataanalytics-activity-7405983959383293952-FufT?utm_source=share&utm_medium=member_desktop&rcm=ACoAAFNTSu0BMmAIuDsPU-opNQXNfJE12Ba-Vg4 and follow me on on my socila media availble here : https://bit.ly/m/yvesvirtuel #share #entrepreneur #AI #africa #education

I see this a lot in teams working with data + AI. Pipelines are busy. Events flowing. Running analysis and nurturing. Dashboards updating. Yet when a real decision needs to be made, people still ask: “Can someone look into this?” That’s a signal something’s off. A busy pipeline doesn’t mean a useful pipeline. Here’s the common issue, in simple terms: Most pipelines are built to move data, not to support decisions. They focus on: • ingesting everything • transforming everything • storing everything But they forget to ask one basic question early: 👉 What decision is this data supposed to help us make? When that’s unclear, pipelines become noisy. A healthier pipeline looks like this: Decision first Example: “Do we intervene when user churn risk increases?” Minimal signals Only ingest data that actually affects that decision(not everything you can track). Clear thresholds At what point should the system alert, act, or stay quiet? Simple output Not a dashboard. A recommendation, alert, or action. This is where AI actually helps —by filtering noise, summarizing context, and pointing to what matters now. Busy pipelines move data fast. Good pipelines move understanding fast. Data alchemy isn’t about making pipelines bigger. It's about making them calmer, clearer, and decision-ready.

When something breaks, teams usually blame: the dashboard, the pipeline, or the model. But most data problems start much earlier. They start with unclear thinking. If you don’t know what decision the data is meant to support, no amount of cleaning or modeling will save you. The best data teams work in this order: 1. Define the decision that matters 2. Identify the signal that influences it 3. Ignore everything else That’s the core of data alchemy. Not collecting everything . Not modeling everything. But reducing complexity until truth appears. AI makes computation cheap. Clarity is still expensive. And that’s why good judgment — not better tooling —remains the true edge in intelligent systems.

People talk about “finding insights” in their data as if it’s a treasure hunt. But real insight doesn’t just appear — it’s engineered. Every valuable data insight starts with a great question. - What are we really trying to understand? - What variable actually drives this outcome? - What pattern matters, and what’s noise? AI helps us see faster, but not think better — that’s still on us. The best data teams don’t wait for magic moments. They build systems that generate insight consistently. In the end, insight isn’t luck. It’s design, iteration, and interpretation — the real alchemy of intelligence.

There’s a quiet misconception in every data conversation right now: that AI is here to replace human decision-making. But in reality —AI is here to prove intuition right (or wrong) faster. Think about it 👇Every bold idea starts as a hunch. Before AI, testing that hunch took weeks or months. Now, you can simulate it in hours. That’s not replacement — that’s amplification. The smartest teams aren’t “data-driven". ”They’re intuition-driven and data-validated". That’s the new equilibrium: Humans generate insight. Ai verifies it at scale. It’s not man vs machine. It’s instinct + intelligence = speed