How Modern Data Science Teams Create and Share Actionable Insights: A Manager’s Guide to Graph Data Science
To manage a successful data science team, it is important to produce the best and most reliable predictions for decision makers. A modern data science approach, using graphs, offers a unique way to analyze data to create fast, actionable insights.
Adopting a graph data science engine helps you operationalize analytics and machine learning to get to the strongest predictive models, among other advantages that we will explore.
Read this guide to discover:
- How graph data science uniquely finds and connects data relationship patterns, leveraging relationships in analytics and machine learning for better predictive data.
- Benefits that scale across departments and industries to show how graphs significantly improve anomaly and fraud detection, supply chain optimization, recommendation engines, and more.
- The unique power of “pretuned” ready-to-use graph algorithms that can identify specific nodes of a graph and analyze relationships across the connected data you are targeting.
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