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PII and PHI don’t stay in one place. As sensitive data moves across the lakehouse, transformations and reuse can weaken visibility into who can access what. Keep classification, masking and governance connected to the data.

We don't show p-values on the main experiment results screen in Eppo. (They're ever-so-slightly hidden in a more advanced "statistical details" tab) The "problem" with p-values, insofar as most businesses have plenty of non-statisticians running experiments, is that they have a very nuanced and precise definition. Academic researchers get the definition wrong all the time. (There are several quite embarrassing studies demonstrating this) And once you are even a little bit incorrect in your definition, assessing the p-value itself is a totally useless exercise at best. Sometimes it's downright harmful. If you want to evaluate which statistical concepts are worth explaining to your coworkers, imagine drawing a "precision-usefulness curve" for each - as Giorgio Martini once suggested to me. I.E., how much usefulness does this [concept/statistic] lose once your definition is no longer 100% correct? The p-value has a quite unforgiving one, in my experience. Confidence intervals, on the other hand, seem far kinder. Yes, they also have a nuanced definition that very few get right. (although they are maybe one step easier since we don't have to explain them being conditioned on the null) But even with a naive interpretation (usually a probablistic one that sounds more like a Bayesian credible interval - e.g. "there is a 95% chance the true value is in this range"), stakeholders can still walk away with reasonable intuition about what their experiment results suggest. Even a simple three-word intuition like "range of uncertainty" or "the experiment's precision" will likely guide sound decision-making. I've been taken with this as a simple little "precision-usefulness" mental model ever since Giorgio mentioned it - a good consideration for folks who care about making experimentation more accessible to teams throughout our orgs. (graphs are illustrative/made up by ChatGPT and not necessarily my exact opinions 😆)

Had a fantastic time last night speaking at the Berlin Experimentation Meetup - well over 100 experimentation pros joined us (several flew in from all around Europe!) and packed the room. A few highlights from my talk: 💡 Presented a framework that distills the job of an experimentation leader into 3 main objectives 💡 Made the case for platforms and processes that enable self-service for the end users in our orgs 💡 Shared about a dozen ideas from Eppo and beyond on how to accomplish this by giving users empowerment, guidance, and time back As I said multiple times over the course of the day, I'm not sure that there's a US city where we could get 130 registrants for an experimentation meetup, especially with only light promotion. There's a very real energy and enthusiasm for experimentation here in Berlin and I appreciate everyone in that room for helping lead the charge! It was such a pleasure getting to meet so many of you. Many thanks to Marcel Toben and Patrick Gunia, PhD for the invite and for what you've organized here, and to Nils Stotz on the photo credit and helping host me yesterday. ⏩ Want to hear some hot takes and practical ideas on scaling your experimentation program at your next event? I think this talk is crazy ready for an encore... DM me and let's make it happen :)

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You migrated to Iceberg. So why are your queries still slow?

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Our CTO Jim Myers got tired of checking dashboards for USDC activity every week. So he built this instead. It's a Flipspace automation that monitors USDC across seven chains. Parallel SQL queries pull metrics, an AI agent runs anomaly detection and flags what matters, and the whole thing lands in his inbox every Monday morning as a formatted briefing with recommended actions. The part worth watching: after any run finishes, you can have a conversation with it. Ask follow-ups, drill into specific chains, understand what's driving a spike. The agent has full context of every query result. Building these is drag-and-drop. Jim walks through the whole thing in under 4 minutes.