Data Governance Must-Reads for Christmas Break

One of the most difficult questions in #MLOps is determining the root cause of a performance drop. Is it the model or the data? Alon Lanyado teaches a powerful technique: Inverse Probability Weighting.

On of my most viewed article in 2025 is "Data Catalogs 2.0 - Get Value from Metadata". Data Catalog is in a strong discussion. On the one side there is a need for Metadata, Data Governance and getting your data right. On the other side, Data Catalogs seems to be to complex and a good user experience is hard to reach. On which side of the Data Catalog do you see yourself? ▶️ https://lnkd.in/enznyHBU #DataCatalog #DataGovernance #DataOwner #DataSteward

AI-Ready Data vs. Analytics-Ready Data Identifiers of "ready," data consumption patterns, maturity paths, and possibilities of "upgrading" from Analytics-ready states to AI-ready states https://lnkd.in/gfVTmDiQ

AI-Ready Data vs. Analytics-Ready Data Identifiers of "ready," data consumption patterns, maturity paths, and possibilities of "upgrading" from Analytics-ready states to AI-ready states https://lnkd.in/g5MkG7y6

BMC is in the mix for DBTA’s 2026 big data predictions. Our CTO, Ram Chakravarti, explains why “digital twins” for data will play a major role in how enterprises manage complexity and stay resilient in the years ahead. Read the article:

Most analytics mistakes don’t come from bad SQL. They come from trusting the wrong data. Here’s a practical Data Validation & Trust framework to run before sending insights to stakeholders: ➡️ Confirm lineage (where it came from + what changed upstream) ➡️Check schema + duplicates (keys, types, exploding rows) ➡️Verify freshness + volume (is today’s data complete and on time?) ➡️Run quick anomaly + business sanity checks (does it match reality?) ➡️Document assumptions and reuse a simple checklist In short: trust first, then analysis. If you want to learn how to validate data like this in real projects, explore our Data Science & AI program. #DataAnalytics #SQL #DataQuality #BusinessIntelligence #Analytics

Great 2026 predictions blog by Principal Analyst, Stephen Catanzano. "2026 will not be defined by a single breakthrough or architectural change. It will be defined by a shift in mindset. Data management stops being treated as supporting infrastructure and starts being recognized as the system that enables intelligence itself." https://lnkd.in/eGvJSifD

The real value of a semantic layer isn’t abstraction — it’s accountability. In practice, most teams introduce a semantic layer to simplify queries, but stop short of treating it as a governed contract between data and decisions. When metrics aren’t versioned, tested, and owned at this layer, you haven’t reduced complexity — you’ve just moved it upstream. From building production analytics systems, I’ve seen trust improve only when the semantic layer becomes the single enforcement point for metric definitions, not a convenience layer for BI tools. A semantic layer without governance is just another modeling artifact. Treated correctly, it’s the backbone of decision-ready analytics. https://lnkd.in/gupic-qt

The future of data science will be augmented (but not automated). Modern data science systems are already complex, opinionated, and battle-tested. The real shift is not replacing them, but extending them in the right places. For example: 🔹 Hybrid systems are layered, not rebuilt → Your ETL, models, experiments, and monitoring pipelines stay exactly where they are. Agents sit alongside them, adding reach without breaking structure. 🔹 Agents are scoped companions, not general brains → One agent validates joins and schema drift. Another drafts baseline models. Another scans long-tail segments in monitoring. Each has a narrow job and clear boundaries. 🔹 Human judgement remains the control plane → Agents surface patterns, recall context, and simulate scenarios. Data scientists decide what matters, what is causal, and what action is taken. —— 💬 Where in your current workflow would an agent extend your reach without taking away control? 👇 Subscribe and read the full post to see concrete examples of hybrid systems in experimentation, monitoring, explainability, and decision-making.

Stop building data governance on weak reasoning and assumptions. 🧠⚠️ “We need to be data-driven.” “We need to be AI-ready.” “We need to digitize.” All good ambitions but they’re too vague to govern. Because none of them answer the basic question: What pain are we solving... and what decision is blocked today? When intent is unclear, governance turns into motion: more metadata, more rules, more ownership … often applied to the wrong data. ❌ 🥇 Clarity first. Then governance. 👉 Follow for more... thinking. Argue if you disagree 💬 Repost if you don’t 🔁 Or be nuts and do both 😉