GiaPhu Dao

In her latest tutorial, Senior Manager of Information Design, Alyssa Huff, shares 10 heuristics, or rules of thumb, for improving dashboard design. Read now: https://pfdata.co/4ayeUZe Learn more from Alyssa: https://pfdata.co/4ppGKvp #informationdesign #visualanalytics #datavisualization

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Something I keep hearing from clients: "we built a data agent and it just didn't work." a16z just published a clear explanation of why. The problem isn't the LLM. It's that the agent has no business context - no understanding of what "revenue" means in your org, which table maps to which metric, which definitions have changed since the last data model rebuild. You're asking it to answer your CFO's questions using a schema it can't decode. The fix isn't more compute. It's a context layer that maps your business logic onto your data warehouse - built with AI but refined by the people who actually know what the data means. If your data agents are underdelivering, this is probably why. https://lnkd.in/gBVVxJT8 #AgenticAI #DataStrategy #EnterpriseAI #AIGovernance

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AI is only as powerful as the data behind it. A new Futuriom report, sponsored by Vultr, "Data Management in the Age of AI," explores how unified, high-quality data pipelines unlock #AI performance and efficiency. https://lnkd.in/gkch7yHE #DataManagement #CloudInfrastructure

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A plea for data literacy education! 🧑‍🏫 In this interview, Dr. Joe Perez explains why companies can no longer avoid investing in their employees’ data skills and outlines how to approach this effectively. 👉 https://lnkd.in/eEAxPpJM #tgoa #thegroupofanalysts #knowledgeworkers #dataliteracy

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One thing I keep seeing in enterprise AI rollouts: the highest-risk data usually isn’t in tables it’s in the messy stuff. Tickets, call transcripts, notes, emails… the place where PII shows up casually. That’s why I liked seeing Snowflake move in two practical directions recently: A clearer path to structuring unstructured data with Cortex AI Functions, so teams can extract consistent signals without building a one-off pipeline every time. AI_REDACT reaching GA automated PII detection + redaction for unstructured text, built into the platform. From an AI & Security Platform lens, this is the kind of “boring guardrail” that actually enables speed: you reduce privacy risk upfront, and you make cross-team alignment (engineering / data / security) much less painful. If you’re working with unstructured data today, where do you enforce redaction at ingestion, at query-time, or right before model access?

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Your Tableau license isn't your BI strategy. I've watched organizations spend six figures on visualization tools while the data feeding them was incomplete, unmapped, and ungoverned. Beautiful dashboards. Confident mistakes. Real BI leadership starts three layers deeper: → ETL pipeline integrity → Automated data quality monitoring → Governance frameworks with accountability built in → Source validation before anything hits the reporting layer I've built these systems at Wells Fargo, First Horizon, and BNY Mellon. The pattern is always the same — organizations that invest in the foundation make better decisions. The ones that skip it just make faster ones. Fix the pipeline before you polish the dashboard. Are you investing in your data foundation or just your front end? 👇

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Last week, I spoke at the Data Behind the Scenes conference #DataBS It's a conference run by Randy Au for data practitioners who are tired of polished case studies that skip over the actual work. Instead you get honest takes on project realities: the messy bits, the trial and error, the things that go wrong. I spoke about the 10 red flags that your multi-market research data went sideways. We've all had that moment, staring at survey results that look technically correct but feel fundamentally wrong, then having to explain why three countries' worth of data is unusable. Links to my talk, as well as the full playlist, are in the comments

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