Frances O'Rafferty

Inference shall be a differentiating factor among the Data Scientists and an Important Element to focus in the Enterprise level data science and decision making. Even for any organisation that cares about the evidence that could answer the business question need to emphasise the Inference (simple statistical inference to causal inference), Statistical Inference is something helps in building better predictive models, This is the right time that “Noise in the Data Science Community is settling down”, and giving space for such dialogues and unique opportunities! A new practice of DS needs to be “Evidence and Inference” focused! Even if we want to understand the patterns of how adapted Gen AI systems helps in the business problems , how to perform evals of an Agent or a RAG solution - Inferential Mindset is somethting critical! ps - just thought of recording my thoughts and ideas as a video for better communication and engagement! 🙂‍↔️

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Every data governance program has one. The lone metadata evangelist. They carry the lineage dream like it’s a noble quest. They say things like “let’s just document the fields” as if that’s ever worked on its own. Meanwhile: → The business doesn’t know the tool exists → IT’s too busy rewriting pipelines → Product is shipping AI features without knowing the source → The only thing being updated is the org chart And still… they believe. In visibility. In documentation. In a world where someone might care what “Customer_ID” actually means. But here’s the truth: Metadata isn’t a tooling problem. It’s not even a documentation problem. It’s a relevance problem. If no one needs it to do their job better it won’t survive. So if you’re launching a governance effort, ask this first: → Who will use the metadata? → Why does it matter to them today? → What’s the lowest-effort way to deliver that value? If you don’t have good answers, you don’t have a metadata strategy. You have a desert. ✔ Follow 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 found this valuable.

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As a Data Architect, I spend a lot of time talking about data quality, but it’s not just a technical checkbox, it’s something we experience every day in real life. Think about it: - If the label on your medicine is printed incorrectly, the data quality failure could be dangerous. - If your GPS misplaces a road, that inaccurate data point can cause frustration (or worse, accidents). - Even something as simple as getting the wrong price at checkout is a reminder of what happens when data integrity is not maintained. In our systems, poor data quality leads to wrong decisions, compliance risks, and loss of trust. In real life, the stakes can be just as high. That’s why, when we design architectures and processes, quality must be built in, not checked at the end. ✅ Validations at entry points ✅ Automated checks throughout pipelines ✅ Governance policies that enforce consistency ✅ A culture where teams understand the impact of quality on outcomes Data is the foundation for AI, analytics, and decision-making. But if the foundation is weak, everything built on top of it becomes unreliable. So, whether in business or daily life, let’s remember: "good data is not a luxury – it’s a necessity." #DataArchitecture #DataQuality #Governance #TrustInData

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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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🚀 Day 55 of 100 Days of AI Automations Let me show you IBM’s new Granite-Docling — a true breakthrough in document understanding. 📌 The Problem: Traditional OCR can read text, but it often loses structure. ➡️ Tables break ➡️ Math gets messy ➡️ Context disappears This makes documents hard to use in AI workflows. 📌 The Solution: Granite-Docling, a compact 258M parameter model, preserves layouts, tables, equations, code, and even multilingual text. Using DocTags, it keeps structure intact—making documents ready for RAG and advanced AI workflows. 📌 Why It Matters: ✅ Open-source ✅ Efficient ✅ Enterprise-ready Granite-Docling doesn’t just extract text—it understands documents end-to-end. 🔗 Learn more in IBM’s official announcement: https://lnkd.in/djZuGaxG 🎥 Try the live demo on Hugging Face: https://lnkd.in/dPjaC5vN 💡 Curious about RAG? Build your first pipeline in minutes (no code): https://lnkd.in/dui86ZXA 🙏 Support me on YouTube and like, share & comment if this post helps! ✨ Special thanks to IBM for making this open-source and to Hugging Face for hosting the demo. #100DaysOfAI #AI #Automation #DocumentAI #GraniteDocling #RAG

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