Ben Clinch

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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All of these graphs contain exactly the same information. But the last one is easier to process and remember. Just one week to go until the RSS conference where I'll be joining a panel chaired by Clare Griffiths to talk about #accessibility in #dataviz. This is a sneaky preview of the plot transformation I'll work through to show how optimising colours and typography can play a part in making graphs accessible, beyond (and including) making sure that the colours are colour-blind friendly. The talk signposts to a bunch of useful resources, which I'll share here in due course!

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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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The role of the data scientist is evolving from analyst to "𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁". This new reality is built on #agents, and for them to be truly effective, they need: 🛠️ 𝗧𝗼𝗼𝗹𝘀 that allow them to take action. 🧠 𝗠𝗲𝗺𝗼𝗿𝘆 so they can learn and recall. 🤖 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 so they can be trusted. We just launched a whole wave of innovations on Google Cloud to make this possible for everyone. We're focused on solving the really hard infrastructure problems so that you can focus on what you do best: architecting an enterprise that can think. Read more: https://lnkd.in/gJSmYQkW

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