Florent Silve

AstroBee generates ontologies...but what is an ontology, and if they're important, why don't you already have one? I've written a short piece on Kimball star schemas, ontologies, and how each relate to Bill Inmon's original approach to data warehousing in the early 90s. Dig in to find an overview of a fascinating technological debate and the re-emergence of an approach to data modeling that's been sleeping for 20 years.

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T-1 week to #ReGovern2025!  We've been talking to hundreds of data leaders this year, and one thing's clear: there is no playbook for the AI era.The best data teams aren't waiting for one either. They're writing it- 12 months ahead of everyone else. And next week, they're showing up to share what they've learned. Join the conversation: https://lnkd.in/ep6eTTWw

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So excited to share that Goodeye Labs is now backed by Hamel Husain, the leading expert who teaches the industry how to evaluate AI for reliable outcomes! Our AI evaluation tool Truesight is now generally available as well. Get Truesight here: gettruesight.com Most AI evaluation tools measure generic metrics like accuracy, helpfulness, coherence. These can all look green while your users are frustrated. Generic metrics can't possibly measure what's important for YOUR specific use case and application. Truesight takes a different approach. We let the person who knows what "good" means drive the AI evaluation process, and make this evaluation available to the rest of their team to measure and improve the underlying AI product. For example, for a lesson plan generator AI, a teacher can create evaluations grounded in their expertise on their specific pedagogical approach and learning outcomes instead of relying on generic metrics like "is this good teaching material" that are guaranteed to miss the mark. Most people who use Truesight can get reliable evaluations that measure their specific use case within minutes. Feel free to reach out to me via DM if you want to chat about your evaluation challenges and how we can help.

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**Databricks Secures a Staggering $100B Valuation on $4B ARR** Databricks' impressive rise to a $100 billion valuation, following a recent $1 billion funding round, demonstrates the robust demand for cloud data platforms. As we observe this growth, it prompts a reflection on the scalability of data solutions in the current market. At ElevenX Capital, we believe that investments in companies like Databricks highlight the importance of harnessing data for innovation. How are you strategizing your portfolio in the evolving landscape of data technology? #investing #innovation #venturecapital #entrepreneurship

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Just-in-Time UI: At Aligned Labs we frequently share reports about the data collection, fine-tuning and eval work that we do with clients.  They often need different views or refined analysis, which is why we built Whiz-bang Boom! Our new tool takes a CSV or JSON file and generates a shareable, interactive report. You can easily modify the report or focus the analysis with a simple prompt, and it creates a new revision. You can organize reports into spaces and customize those spaces with a simple prompt. Try it out. https://wizbangboom.com #businessIntelligence #AI

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Recently got asked about our data modeling approach - first piece of advice isn't novel to those in the space: read The Data Warehouse Toolkit by Ralph Kimball & Margy Ross! Beyond that, here's our quick framework for handling complex datasets: **1. Large source systems with 50+ tables** Map the business process first. At Uber, we had thousands of tables - but only 20-30 powered 80% of decisions. Start with core tables, add complexity only where needed. **2. Schema changes** • Version control everything in dbt or similar modeling framework tool • Test in staging / development environments first • Document breaking changes religiously **3. New business requirements** Ask three questions before adding "just one more column": - Does this fit our existing model? - Who else needs this in 6 months? - What's the maintenance cost? Building clean data models isn't about perfection - it's about building something that evolves without requiring yearly rebuilds. What's your biggest data modeling challenge? #DataEngineering #Analytics

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Today the Pulse team published a deep dive on why a single “accuracy” score doesn’t tell you if a document extraction system will survive in production. The goal here is to lay out an introductory but still rigorous evaluation methodology - we have an exciting open-source benchmark building on this research coming out very soon. Let’s do the math: take 1,000 pages, each with 200 data elements. A model that’s 98% “accurate” on paper still produces 4,000 incorrect values. Now make some of those: 1/ Broken reading order that scrambles multi-column layouts 2/ Tables with shifted columns or missing headers 3/ Cross-page context lost entirely That’s enough to silently corrupt an entire dataset without throwing a single error. We’ve processed hundreds of millions of pages and built a multi-axis evaluation framework to measure what actually matters: reading order validation, region-level ANLS, reading order accuracy, TEDS for table structure, and continuity checks across page boundaries. The result? Fewer silent data corruptions, more predictable performance, and pipelines that keep working on the next million documents you haven’t seen yet. Full technical write up in the comments!

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⚠️ Founders - consider the below reasons to not use Forward Deployed Engineers (FDEs) to acquire your *first* customers. This signals (1) lack of PMF; (2) miscalculation b/w development and sales timeframes; and (3) mischaracterization of Palantir Technologies timing when they deployed their FDE model. 💡 At the early-stage, it's crucial to demonstrate ability to repeatedly acquire customers with limited resources - capital, team, & time - that supports the narrative of 'more funding will amplify my traction.' Bespoke solutions for one company demonstrates zero repeatability. These custom solutions will not carry over to other customers within the same cohort. 💡 AI accelerates engineering but sales, system integration, and stakeholder buy-ins are perpetually slow. This imbalance results in concentrating all your engineering resources into one company, ignoring other opportunities, and still not going live on an accelerated timeline. 💡 Palantir didn't start using FDEs until year 3 when they got their first *gov't* customer, not their first customer. Gotham was already built and had strong PMF. Gotham allowed Palantir to take GTM risk by using FDEs b/c if it didn't work, Gotham was humming in the background. TLDR: At the early-stage, repeatable customer acquisition is the holy grail. Don't let AI deceive you that it can accelerate human behavior like it has with development cycles. It C.A.N.N.O.T.

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