Data management terminology can easily blend together into an intimidating wall of...
Ash Srivastava - June 30, 2026
Data management terminology can easily blend together into an intimidating wall of corporate jargon.
The next time someone tries to overcomplicate these layers to sound sophisticated, use this simple blueprint to cut through the noise.
🔹 Metadata: Data about data (the who, what, and when of your file)—giving you the raw, individual building blocks.
🔹 Semantics: The definitions and meaning ensuring everyone uses the same language to turn raw data into a shared vocabulary.
🔹 Taxonomy: A simple, top-down family tree used to categorize things—taking that vocabulary and organizing it into a clean hierarchy.
🔹 Ontology: A complex map showing how completely different things relate to one another—breaking out of a rigid tree structure to connect entities across an entire ecosystem.
🔹 Knowledge Graph: An ontology filled in with real, live data points—anchoring the structural framework to live reality so AI can perform complex, multi-hop reasoning.
🔹 Context: The real-time, situational wrapper that tells an AI exactly what is happening right now.
đź’ˇ In short:
• Metadata and Semantics define the data.
• Taxonomies and Ontologies structure the relationships.
• Knowledge Graphs make it real.
• Context makes it live.
đź’ľ Save this for your next alignment meeting.
♻️ Repost so a leader in your network sees this simplified framework.
đź”” Follow Ash Srivastava for insights on Data and AI Governance.
Inspiration: Article by Sanjeev Mohan
First shared on LinkedIn.