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.

Most data stacks are excellent at answering: “What happened?” Very few are good at remembering: “Why did we decide this?” That’s a massive blind spot. Every meaningful decision creates context: • assumptions • confidence level • alternatives considered• time pressure And then… it disappears. High-maturity data systems include decision memory. Here’s what that looks like: 1️⃣ Decision Logging Not just outcomes, but: • what signals triggered action • what thresholds were crossed • who (or what) made the call 2️⃣ Assumption Tracking Every decision is tied to assumptions. When assumptions change, the system flags it. 3️⃣ Outcome Attribution Did the decision Help ?Hurt? Have no effect? Most teams track results but not causality. 4️⃣ Feedback into Models Signals that consistently mislead get down-weighted. Reliable ones gain influence. This turns hindsight into learning. 5️⃣ Retrieval at Decision Time When a similar situation appears, the system surfaces: • past decisions • outcomes • lessons This is institutional memory — automated. Data alchemy isn’t about storing facts. It’s about remembering judgment. The future belongs to systems that don’t just analyze the past, but learn from their own decisions.

Being data-driven isn’t about reacting to numbers. It’s about deciding in advance: • which signals matter • which decisions they inform • and which ones you’ll ignore Most dashboards fail because they show everything. The strongest teams I’ve worked with do the opposite: They reduce data until only decision-critical signals remain. AI makes it easier to compute. It doesn’t make it easier to choose. That part is still human. Good data systems don’t answer more questions. They answer the right ones, consistently. Something I’ve been thinking about recently.

Everyone’s obsessed with making AI predict outcomes — revenue, churn, demand, sentiment. But the true leap forward isn’t in prediction…it’s in perception. AI is learning to see reality as it shifts. It notices when your customers’ tone changes, when your product’s positioning starts slipping, when data stops behaving normally. That’s not forecasting —that’s awareness. The next generation of systems won’t just answer questions —they’ll sense when the right question needs asking. That’s the moment when AI becomes more than analytics becomes adaptive intuition. And the data leaders who design for perception — not just prediction —will build the most resilient companies of the decade.

just a few weeks ago, this project was nothing more than an idea.Today, it has become my first true professional experience in automation, deployed for a real estate client.And honestly, what a journey. 🎯 The challenge: fully automate Facebook publishing from Google Sheets The goal seemed simple at first: 🟦 The client fills a Google Sheet 🟦 Texts + images are retrieved 🟦 Posts are automatically published on Facebook But behind this simplicity lies real engineering work:Facebook API, image processing, error handling, trigger logic, testing, optimization a genuine first professional project. 🔧 From draft to production 100% functional today I built everything locally, on a modest machine, dealing with real-world constraints: ⚡ Power outages 📶 Unstable internet 💻 Limited resources A reality that no tutorial really shows, especially in Africa, but it’s no excuse not to deliver an effective, robust, and adaptable solution in any environment. Despite all that, the workflow now works from A to Z at the client’s site: ✔️ Google Sheets → n8n ✔️ Automated posting validated ✔️ Active monitoring by me The client immediately saw the value.The result? They already asked me to take it further:"I want a cloud version now more stable and accessible anywhere." A true sign of trust. 💡 What this first assignment taught me In just a few days (less than 7), I learned to: - Manage a full project from start to finish - Deliver a professional solution that actually works - Explain technical concepts simply - Build a reliable workflow despite constraints - Maintain a production system for a real client This first experience confirmed one thing:Automation is not just a technical skill it’s mostly human understanding, adaptability, and rigor. 🖥️ And then there was TeamViewer A little personal touch:I remember my high school years everyone talked about TeamViewer, but I had never really used it.It was just “a software I knew by name.” Fast forward several years, and this same tool allowed me to: