Karen Gibson

Most data platforms don’t collapse because of technology. They collapse because of what’s missing: governance. Here’s the pattern I keep seeing: → New data source? “Just hook it up.” → New metric request? “Hardcode it for now.” → Lineage questions? “We’ll document later.” → Ownership issue? “Ask around.” On the surface, everything looks fine. Dashboards load. Queries run. Leadership claps. But underneath, the wall is crooked. Every new brick makes it worse. And then it happens: → Two teams report two different revenue numbers. → An AI model fails silently because the input changed. → An auditor asks a simple question nobody can answer. That’s when the shortcuts show up on the balance sheet. Governance isn’t about slowing teams down. It’s about building a platform that doesn’t collapse the moment someone leans on it. So ask yourself: Are you building something that scales, or something that looks good until it breaks? Because “we’ll fix lineage later” is just another way of saying “we’ll pay for it ten times over.” ➤ Hit Follow John for daily, no-fluff insights on data, AI, and what actually works. 🔔 Tap the bell on my profile to get notified when I post. ♻️ Repost if you’ve ever had to clean up someone else’s “we’ll fix it later.”

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AI is moving faster than most teams can handle. Data Management and literacy is now a vital skill for ALL your teams This week inside CTRL//SHIFT AI, here’s what we unpack for our members: 🚨 $364B projected AI spend from Big Tech in 2025 🧠 The predicted 10,000+ jobs lost to AI in July globally, and where the new demand is growing 💸 $2B raised by Thinking Machines to push autonomous agent AI ⚔ Amazon vs Walmart: The AI supply chain battle is heating up 📈 Governments are mandating upskilling — and why soft skills now drive hiring But here’s the thing no one’s saying loud enough: 74% of AI initiatives fail. Not because of tech, but because of poor data management and understanding, lack of AI strategy, poor to no training for their teams and no human context. That’s why we built CTRL//SHIFT: A space for founders, operators, professionals, and teams to not just “learn AI”… But to build confidence, clarity, and systems that work, without burning out. ✨ Data Fundamentals ✨ Strategy ✨ Mindset ✨ Automation ✨ Real human support 👀 Want in? Join the free tier of CTRL//SHIFT AI now and get weekly drops like this, real-time insights, and a smarter way to stay ahead of the shift. https://lnkd.in/e4PMb3BP

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Why so many data driven decisions crash and burn… even when the stats looked solid? It’s because too many people throw around Type I and Type II errors like they’re just buzzwords. Type I is cheaper, Type II is more expensive. That’s cute, but without a Loss Function that actually assigns real numbers to those costs, you’re just storytelling. In real statistical decision theory, you quantify exactly what each type of mistake costs you in dollars, time, reputation, or risk and you model it before you touch the data. Without that, your decision-making is just gambling in a lab coat. And here’s the kicker, everyone loves to talk about significance levels, but they quietly ignore Sample Size and Statistical Power. That’s like bragging about your Ferrari’s paint job without checking if the engine starts. Power determines how likely you are to detect the effect you care about, and without enough sample size, your data driven decision is basically a coin flip dressed up in p-values. If you’re not talking about Power alongside Type I and Type II errors, you’re selling half the story and the half you’re hiding is the one that kills your credibility. In the engineering world, the cost of an error isn’t abstract. Launch a bridge design with a Type I error? You could lose lives. Roll out a faulty circuit design with a Type II error? You might miss the chance to fix a flaw before it burns down a plant. In medicine, a Type I error could approve a useless or harmful drug, while a Type II error could bury a life saving treatment. In tech, people love to pretend everything’s reversible just roll back the update, but by the time you do, your customers are gone, your competitors are ahead, and your brand is bleeding. The stakes are always real, even when they’re just metrics on a dashboard. So here’s my challenge to you, stop treating statistical decision theory like it’s just a fancier way to run A/B tests. Start mapping your loss function in actual numbers. Start modeling your power and sample size before you celebrate your statistical significance. And next time you see someone hand wave through a Type I/Type II discussion without the math to back it up… ask them this: Are you making decisions based on evidence, or just running a well dressed coin toss? #statistics #datascience #researcher

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