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Data management

GraphRAG for life sciences: Why explainability is the missing piece

GraphRAG for life sciences pairs knowledge graphs with RAG to ground LLM in verified scientific evidence. See where it delivers value and how to pilot it.

by Datavid

Ai and genai

Why regulatory data architecture is the control challenge of 2026

Banking and finance firms struggle to explain AI outcomes. Learn why financial data management and AI governance matter in 2026.

by Clive Smith

Knowledge graphs and semantics

GraphRAG for pharma: How to ground LLM outputs in trusted enterprise knowledge

See how GraphRAG for Pharma grounds AI features in knowledge graphs, delivering consistent answers, traceable citations, and reliable user experiences.

by Datavid

Graphrag

Why enterprise AI is hitting a scale wall (and how GraphRAG fixes it)

Why enterprise AI fails to scale: fragmented data, weak governance, and lack of trust. See how GraphRAG enterprise AI addresses these challenges.

by Calin Ginga

News and events

Datavid is now part of C5i

Datavid joins C5i group, strengthening knowledge graph AI capabilities

by Lucia Maria Coppola

How Data Science Improves Healthcare Outcomes

Data intelligence

How Data Science in Healthcare Is Solving Some of Medicine’s Biggest Challenges

Learn how data science improves healthcare outcomes through predictive analytics, personalized treatment, and operational efficiency. See real-world applications and what it takes to build an AI-ready data foundation.

by Datavid

What Is a Data Governance Policy? Why It Matters

Data management

What Is a Data Governance Policy and Why It Matters?

Learn what a data governance policy includes, why it matters for regulated industries, and how to build one that supports compliance, data quality, and AI readiness.

by Datavid

How to Migrate from On-Premise to Cloud | Step-by-Step Guide

Data intelligence

How to Migrate from On-Premise to Cloud (Without Losing Data You Can't Afford to Lose)

A practical guide to migrating from on-premise to cloud. Covers strategies, step-by-step process, common pitfalls, and how to make your data AI-ready.

by Datavid