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.