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.

I'm really sorry to hear that Dave has decided to shut down the Skool, although the reasons are comprehensible. Is there any chance to preserve the teaching material, the classes, videos, links? Thanks for all the efforts!

I started this community over two years ago with a simple goal: to create a free place to learn, share knowledge, and help people get started with Python and data. For a long time, that worked well. Many of you showed up with exactly that mindset, and for that I am genuinely grateful. Unfortunately, the reality today is very different. Over time, the community has been taken over by spam, scams, and people trying to sell to or profit from others. At this point, most of the activity no longer has anything to do with learning or knowledge sharing. Keeping a free community like this healthy would require constant, active moderation. Since Skool does not have an API, that would mean someone on my team manually clicking through spam messages all day. That is not something I want anyone spending their time on. To be fully transparent, I have let this community run on autopilot for the past year. That is not fair to the people who are here with good intentions. Because of that, I have decided to shut down Data Alchemy. Going forward, all of our communities will be private and actively moderated. That is the only way to create a high quality, safe environment where people can actually learn and connect without dealing with scammers or noise. This does not mean the end of learning or staying in touch. I will continue to be active on YouTube, especially in the community feed and comment sections. Those are the best places to keep learning, ask questions, and stay connected without having to worry about spam or people trying to take advantage of you. And in case you have not seen it yet, I recently updated a full Python introduction course on YouTube. It covers everything I wanted to share in this free community, and more, in a single five hour video. Thank you to everyone who showed up, shared knowledge, asked good questions, and contributed in a positive way. I truly appreciate the time and energy you put into this community.

🚀 Have you noticed what's happening with Microsoft 365? Word, Excel, and PowerPoint are no longer just tools. They're becoming AI agents that understand your work context. Imagine: ✓ An agent analyzing your Excel data and suggesting insights ✓ Word automatically structuring reports in your style ✓ PowerPoint adapting presentations to your audience We're moving from passive tools to intelligent coworkers. What do you think of this evolution? 💬 Are you already using Copilot at work? For more content follow me here : SIKATI Yves Joseph | LinkedIn #MicrosoftCopilot #AI #Productivity #Innovation #Microsoft365

Everyone wants real-time data. Very few ask when a decision actually matters. That mismatch destroys value. Here’s the hard truth: Not every decision benefits from speed. High-maturity data systems are built around decision timing, not just freshness. Here’s how they work: 1️⃣ Decision Windows Each decision has a window: – seconds (fraud detection) – minutes (traffic routing) – hours (pricing) – days (strategy) If you don’t define the window, real-time data just adds noise. 2️⃣ Signal Readiness Levels Signals mature over time. Early signals are weak but fast . Late signals are accurate but slow. Good systems combine both. 3️⃣ Action Thresholds Decisions don’t trigger on data. They trigger on confidence crossing a threshold. This prevents overreacting to fluctuations. 4️⃣ Deferred Intelligence Some insights are more valuable after events complete. These feed long-term learning, not immediate action. Mixing these with real-time alerts causes chaos. 5️⃣ Timing Feedback Loops After each decision, the system learns: – was this too early? – too late? – just in time? Over time, timing becomes optimized automatically. Data alchemy isn’t about faster dashboards. It's about acting at the right moment. Speed without timing is just panic at scale.