Closing the Analytics Loop: How Graph Databases Complement your Data Warehouse?

Traditional data warehouses and analytic tools are useful for producing the baseline insights that allow organizations to work more intelligently. But consider the following questions: 

  • Which customers are most likely to defect based on the journey of those who have churned? Which customers are ready to buy more?
  • Why do certain combinations of drugs aid or abet the treatment? 
  • How can we identify and stop fraud in real-time by matching behavior with known fraudster patterns?

Graph databases build on the analytics foundation of data warehouses and data lakes and answer these questions with deeper insights into relationships.

Tony Baer, the Data Warehousing Guru, shares the best practices and real-world case studies for complementing your data warehouse with a graph database in this white paper. 

Download the White Paper

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“It’s huge data (terabytes) and finding influencers in that data, it’s not easy, but TigerGraph has scaled for us."

Vishnu Maddileti 

Director of Data Sciences and Analytics at Amgen

About TigerGraph

TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions. 


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