Caitlin Smallwood

Doing some thinking on projects for EnrollHere, Inc. as I help them build new AI capabilities on top of Databricks, and I keep coming back to one thing: A trusted data platform is one of the strongest moats a software company can build.

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Every data team knows the whiplash of a foundational initiative. You spend a year lobbying for a beautiful foundational layer. You finally get it greenlit for the next fiscal. You build it for six months. Mid-year, the CEO asks if you can pivot to “this other urgent business thing.” You can’t explain why you can’t. The pivot gets prioritized. Development stops. Entropy takes your half-built foundational layer to a better place beyond this world 🪦 Data teams: please don’t let this happen to Retrieval. We're all about to get that 2026 approval for foundational knowledge management. Please do things differently and protect your beautiful knowledge graph, ontology, semantic layer investments. How: 🌟 North Star → Get crystal clear on what you’re ultimately building. If you haven't written a white paper, you’re not clear. 🏎️ Fast Start → Pick a small, high-ROI use case that forces you one step toward the North Star. Ship only that - not the whole platform! Go fast. Measure value. 📣 Loud Part → Communicate success like your life depends on it. To your team. To your boss. To your CEO. Over and over. Beat your CEO to the punch - they should know about the ROI before they get those mid-year ideas. Repeat for the next use case. Shipping should get easier as foundation becomes sturdier. At Salesforce, our first use case was lead routing. It made millions. We’re not done - but now our focus is on shipping, not fighting entropy. h/t Miklós Molnár for talking about the power of fast high ROI use case

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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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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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We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

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I met one AI development agency founder, and they said our 50% RAG apps failed in production last year. Because teams don’t know how to handle: chunking + embeddings + vector DB + reranking + hallucinations + evaluation + monitoring. So I'm sharing a RAG Playbook that shows exactly how to build production-ready RAG systems, with practical examples. ✅ What you’ll learn from this playbook This playbook helps you go from: “RAG demo is working” → “RAG system is reliable in production” You’ll learn how to: - reduce hallucinations using prompting techniques - use advanced chunking methods (not random splits) - select the right embedding model for your use case - choose the best vector database (based on real needs) - add reranking to improve relevance - follow clear steps to build an enterprise-grade RAG system - run evaluation before production using key scenarios - monitor and optimize performance after deployment - track RAG quality using 4 powerful metrics If you’re building a RAG-based feature for your product, this playbook will save you weeks of trial and error. ✅ Save this post (you’ll need it later) 🔁 Repost for your team/network ➕ Follow Shubham Vora to learn more on AI agents.

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There are very few things that excite me more than applying my experience in new ways. The Master of Analytics program in NC State University allows me to do just that, applying lessons I've learned in intelligence research to data science. While there are endless topics in #machine_learning I am eager to write about, one topic that felt especially close to heart was how language is shaping our decision-making. In "Why Lingo Matters in Analytics", I chose to tackle one specific aspect of this: how our framing of the data must be as accurate and formal as our actual data. Our framing must be accurate and formal, because all throughout history people were hearing and reading ideas via *words*, not numbers; and thus we evolved to make decisions based on *words* and not based on numbers. When a data analyst/scientist makes a recommendation to a stakeholder, the actionable part is always in the words, never in the numbers. The numbers are there only to show rigor and make our claims verifiable. To quantify. To answer the "how much", but never the "what" or "why". The true benefit of AI is allowing us to reach conclusions much faster (and make funny animal videos faster too, I guess). But to reach these conclusions safely, and make the right decisions, we need the right framing to judge the numbers by. Have a fun read!

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