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What is a semantic layer? Enterprise AI & analytics guide

Datavid — Thu, 16 Jul 2026 08:00:00 GMT

Quick answer: A semantic layer is an abstraction that sits between your raw data sources and the tools that consume them (dashboards, analytics platforms, AI systems), translating technical data structures into shared business concepts, governed definitions, and traceable relationships. It helps every team, tool, and AI agent in your organization work from the same governed definitions.

What Is an Ontology? How It Reduces AI Hallucinations

Datavid — Tue, 14 Jul 2026 08:00:00 GMT

Quick answer:

An ontology is a formal model of entities, their properties and how they relate, readable by humans and machines alike. For CDOs deploying enterprise AI, it is what turns documents into verified knowledge: retrieval becomes accurate, answers explainable and AI hallucination risk is reduced because answers are grounded in governed, traceable knowledge.

DAM data strategy: fixing the data behind your assets | Datavid

tim.padilla@datavid.com (Tim Padilla) — Thu, 09 Jul 2026 08:15:01 GMT

A digital asset, on its own, is almost worthless. A logo, a product shot, a campaign video. None of it means much until you know what it represents, where it ran, who it was built for, and whether it actually moved the needle.

That context is the value. And here is the uncomfortable truth: I have watched play out across dozens of organizations, most digital asset management (DAM) systems manage the asset beautifully and barely manage the context at all.

That gap is rarely the platform's fault. It comes down to the quality and structure of the data feeding the DAM, and whether that data is shared across the rest of the business or trapped in a single tool.

I want to walk through both sides of that problem, because they are usually treated separately when they are really the same issue: a DAM is only as good as the data around it, upstream and downstream. Fixing that is what a real DAM data strategy is about.

How to Build AI Ready Data: A 6-Stage Framework for Enterprises

Datavid — Tue, 07 Jul 2026 08:00:01 GMT

Quick answer:

AI-ready data is data prepared for a defined AI use case, with the required quality, governance, context, accessibility, and traceability. For knowledge-intensive enterprise AI, this often includes semantic enrichment across structured and unstructured sources.

AI knowledge management: what to automate vs. keep human

Datavid — Thu, 02 Jul 2026 08:00:01 GMT

Quick answer:

AI knowledge management helps CDOs decide which enterprise knowledge tasks to automate, which to keep human-led, and which need human oversight. Done well, it turns fragmented knowledge into governed, trusted inputs that improve AI accuracy, transparency, and decision-making at scale.

What is knowledge management? A guide for the AI era

Datavid — Tue, 30 Jun 2026 08:00:01 GMT

Quick answer: Knowledge management is the systematic practice of capturing, organizing, sharing and applying what an organization knows so people can act on it. In 2026, it has become the foundation whether enterprise AI produces grounded, usable answers or creates additional verification work. The work is no longer about storing documents. It is about connecting knowledge so humans and AI can use it together.

GraphRAG for AI-powered products: Lessons from biobank data

alexandru.mortan@datavid.com (Alexandru Mortan) — Thu, 25 Jun 2026 08:00:00 GMT

Quick answer:

GraphRAG for AI-powered products helps teams move beyond simple Q&A features by grounding AI in connected, governed data. In life sciences, this means research users can ask questions across fragmented biobank environments and receive consistent, reusable workflows instead of one-off answers that depend on manual scripting, duplicated logic, or disconnected metadata.

Why publishers need earlier manuscript risk signals

Elaine.Ellerton@datavid.com (Elaine Ellerton) — Tue, 23 Jun 2026 08:00:00 GMT

Quick answer:

Publishers need earlier manuscript risk signals because integrity concerns are becoming much harder to spot. By connecting risk signals across multiple elements within a manuscript, editorial teams can quickly identify higher-risk submissions sooner, while keeping human judgement at the centre of every decision.

Neurosymbolic AI: How Knowledge Graphs Enable Explainable AI

Datavid — Thu, 11 Jun 2026 08:00:00 GMT

Quick Answer: Neural AI systems are powerful at finding patterns, but pattern recognition alone is not enough for regulated enterprise AI.