Turning unstructured data into a graph database is a deceptively complex problem. You need to extract entities, understand relationships, define schemas, and maintain data quality whilst managing costs and supporting new data sources. The challenge isn't choosing whether to use a graph database; it's choosing how to populate it. This guide walks through seven different… Continue reading A Complete Guide to Data Ingestion Approaches from Documents to a Graph Database
Unstructured and Unlinked Policy Documents ACME_CORP has policies scattered everywhere: PDF files in shared drives, API responses from regulatory systems, manually created requirement documents, compliance frameworks from vendors. Each source is different. Each format is inconsistent. And yet, your auditors want to know: Which rules actually prevent unauthorized access? Where do we have conflicting compliance… Continue reading Building Intent-Based Policy Graphs with Neo4j
Graph databases promise a natural fit for complex, relationship-heavy domains. But there's a successful implementation gap between a promising prototype and a production system that performs, scales, and survives organisational change. This article is based on my personal experience building three real-world graph ontologies in three different organisations. First was BT's network inventory (SRIMS), second… Continue reading Graph Ontologies Design from BT, Nature Metrics & NHS England