Drew M.

🚪 "Push to Open" - A simple gate sign that perfectly captures the biggest challenge facing state Chief Data Officers today. Just met with Maryland's state CDOs to talk about a key question: We have unprecedented ability to link data across agencies—but just because we CAN doesn't mean we SHOULD. How do we assess this? Take this real example from our session: linking data for children with divorced parents across SNAP, child support, and welfare systems. High value for helping kids, but high risk with sensitive family data. The solution? Systematic frameworks, not gut decisions: ✅ The Five Safes (People, Projects, Data, Settings, Outputs) ✅ AI-ready governance (aligning across 3,000+ privacy laws) ✅ Dynamic risk management that says "yes" responsibly and ethically Governments will keep collecting data at an escalating pace. Success depends on building public trust while delivering real citizen value. What data "gates" are you pushing open? And more importantly—should you be? 🎥 Watch my 60-second take below 📖 Full framework and examples in comments Thanks to Stefaan Verhulst, PhD for the invitation to present alongside Lynn Overmann from the Beeck Center for Social Impact + Innovation, and to the Open Data Policy Lab, the Data Tank, and GovLab to join the session -- and to Data Foundation alum and MD #CDO Natalie Evans Harris for her leadership in MD. #DataGovernance #ChiefDataOfficer #StateGovernment #DataPrivacy #PublicTrust

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LLMs are everywhere and I believe one of the biggest challenges right now is aligning them to real-world utility Some thoughts from recent work: - Fine-tuning is great, but prompt engineering still wins for many lightweight use cases. - Evaluating with user-facing metrics (not just accuracy or perplexity) changes how you build. - Bias & fairness issues pop up even with apparently clean data. If you’re working with LLMs, what evaluation metrics do you prioritize, and why?

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The "Climate Tech" era as we knew it is over. Here comes "Climate Realism." The WSJ reports that VCs are pivoting away from traditional climate funds in 2026. But they aren't leaving the space but they’re just changing the playbook: - No more "Green Premium": If it’s not cheaper or faster than the status quo, it’s not getting funded. - Energy Security > Carbon Targets: Investors are prioritizing grid stability and domestic supply chains over pure emissions plays. - The "Retrofit" Era: Focus has shifted from replacing old industries to making them more efficient through tech. Is this "pivot" a necessary correction for the industry, or a dangerous slowdown in our race to net zero? Agree or disagree? Let’s discuss. 👇 #VC #ClimateTech #Energy #Innovation #BusinessTrends https://lnkd.in/gBFthdQn

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When two $100B gorillas connect, the junior partner has to suck its data throw a very thin straw. Not a good look for either side - Databricks should provide ADBC or ArrowFlight, while Palantir looks like an add-on that Databricks haven't gotten around to implementing yet. JDBC is not how to connect data between big data platforms. Arrow, with ADBC or ArrowFlight, gives columnar to columnar transfers without data serialization/deserialization and without columnar to row-oriented pivots. Hopsworks has had ArrowFlight support since 2023 - https://lnkd.in/dyAAcUSv Databricks and Palantir discuss their new integration in this video - https://lnkd.in/d_c_jSqD

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How do you showcase "invisible" impact? I spend a lot of time volunteering with non-profits on data analysis, infrastructure, and tailored engineering projects. I’m incredibly proud of the work, but because the data is private, I can't exactly post a GitHub repo or a case study. For those who review resumes or make hiring decisions: How much weight do you give to "unverifiable" volunteer projects? And what’s the best way to describe this work so it carries the same punch as a public portfolio? Would love to hear your thoughts in the comments! #DataScience #SoftwareEngineering #DataEthics #TechForGood

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Re-sharing this important work from Ainee Athar and our incredible Stanford Law & Policy Lab team. In essence, carbon removal solutions are maturing, but our governance systems aren’t keeping pace. This report outlines the urgent data challenges holding back high-integrity CDR and lifts up promising efforts from Cascade Climate, CarbonPlan, and Carbon Removal Standards Initiative that are working to build common standards and transparency. We call for: – Shared data infrastructure modeled on successful efforts in medicine and finance – Open-source platforms and cross-sector collaboration, especially with agriculture, waste, and land use – New forums for interdisciplinary MRV standard-setting and model validation   If we want carbon markets to have integrity, CDR isn’t optional; it’s essential. But we won’t get there without transparent, interoperable data systems.

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