According to a new press release, “Neo4jⓇ, the leader in graph technology, announced the availability of Neo4j for Graph Data Science, the first data science environment built to harness the predictive power of relationships for enterprise deployments. The unpredictability of the current economic climate underscores the need for organizations to get more value out of existing datasets, continually improve predictive accuracy and meet rapidly changing business requirements. Neo4j for Graph Data Science helps data scientists leverage highly predictive, yet largely underutilized relationships and network structures to answer unwieldy problems. Examples include user disambiguation across multiple platforms and contact points, identifying early interventions for complicated patient journeys and predicting fraud through sequences of seemingly innocuous behavior.”

The release continues, “Neo4j for Graph Data Science combines a native graph analytics workspace and graph database with scalable graph algorithms and graph visualization for a reliable, easy-to-use experience. This framework enables data scientists to confidently operationalize better analytics and machine learning models that infer behavior based on connected data and network structures. Alicia Frame, Lead Product Manager and Data Scientist at Neo4j, explained why Neo4j for Graph Data Science is the most expeditious way to generate better predictions. ‘A common misconception in data science is that more data increases accuracy and reduces false positives,’ explained Frame. ‘In reality, many data science models overlook the most predictive elements within data – the connections and structures that lie within. Neo4j for Graph Data Science was conceived for this purpose – to improve the predictive accuracy of machine learning, or answer previously unanswerable analytics questions, using the relationships inherent within existing data’.”

Read more at PR Newswire.

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