Duodata Blog

• How to solve semantic layers in Databricks - Databricks metric views are a good start for AI self-service analytics. Why the semantic layer still has to sit across platforms and in the hands of the business.
• Why a data catalog isn’t enough for AI to talk to your data - Five things AI needs to talk to your data platforms — and that a data catalog does not deliver.
• The Great Spreadsheet Wars: Why Your Modern Data Stack Still Needs a Translator - In this post, Shawn Johnson explains why analytics problems are really semantic problems. Different teams define the same metrics differently, dashboards drift, and trust erodes. He shows how a business-approved metrics layer brings shared meaning back to data, reduces governance friction, and gives AI the context it needs to produce reliable answers. The takeaway: speed without shared meaning is expensive chaos. Fix the context, not the pipes.
• Creating a New Category: The Hardened Conceptual Metrics Layer - Metric chaos isn’t a dashboard problem; it’s a meaning problem. Learn why semantic drift happens, why AI amplifies it, and how a hardened conceptual metrics layer creates business-approved metrics everyone trusts.