Analytic Partners vs Neo4j

Turning Data Deluge into Decision-Making Fuel

Analytic Partners' Preeti Croke shares lessons from industry events on how brands can transform vast amounts of data into actionable insights. The article emphasizes breaking down organizational silos, adopting a shared taxonomy, and not outsourcing human judgement to AI. It highlights the importance of balancing global consistency with local relevance, using examples like Guinness's 'Lovely Days' campaign. It also stresses that consumers are partners, not datapoints, as demonstrated by Oscar Mayer's Wienermobile activation. The core message is that while data and AI can organize and inform, the final decisions and human connections remain central.

• Analytic Partners' Preeti Croke authored the article.
• The article cites Allie Keith, CEO at The Excellence Office, comparing data to water.
• Guinness's 'Lovely Days' campaign adapted its strategy to local markets, resulting in a commercial bump.

Neo4j GraphAware Financial Crime Intelligence debuts for full-cycle detection, investigation & prevention

Neo4j has launched GraphAware Financial Crime Intelligence, a new solution for full-cycle detection, investigation, and prevention of financial crime. This follows the acquisition of GraphAware and the launch of intelligence analysis solutions.

Introducing Neo4j GraphAware Financial Crime Intelligence

Neo4j launches GraphAware Financial Crime Intelligence, a new solution for financial crime detection and investigation, and introduces Graph Analytics for self-managed deployments.

Benchmark of Five Managed Graph Databases

A reproducible benchmark comparing CognoDB Cloud, Neo4j AuraDB, Memgraph, FalkorDB and ArangoDB revealed major pitfalls in naive measurement: geographic placement of managed instances skewed raw latency numbers, server-reported execution time (via Bolt drivers) is required for fair engine-to-engine comparison, and CognoDB v0.9.11 exhibited a background-indexing behaviour that caused indexed lookups to return no results while an index was building, silently dropping relationship writes. Concurrency characteristics differed: cloud-hosted databases scaled with client concurrency due to network latency hiding server idle time, while local instances became CPU-bound and slowed. The author published the full harness and raw data on GitHub.

• Five graph databases were benchmarked: CognoDB Cloud, Neo4j AuraDB, Memgraph, FalkorDB and ArangoDB.
• Geographic placement of managed cloud instances (e.g., CognoDB in Google Cloud us-east4 and Neo4j AuraDB in Google's Singapore range) skewed raw wall-clock latency measurements.
• Using server-reported execution time via Bolt drivers (network excluded) was necessary to fairly compare engines in different regions.