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Pain Points

Achieving customer 360 with a data lake, data warehouse, or even a CRM or CDP that runs on traditional relational databases can present several challenges:

Data Silos and Complex Relationships

Today’s customers have numerous touchpoints to enterprises, with data scattered across multiple systems and departments. The multidimensional nature of customer relationships further complicates the data integration process. Relational databases are ill-equipped to effectively represent and query these intricate relationships, impeding the development of a cohesive customer view.

Slow Updates and Rigid Schemas

Customer data is generated at a high velocity. Relational databases struggle to keep up with the rapid pace of data updates, resulting in lags in data availability. Additionally, as customer data evolves, the underlying data schemas may need to be modified or extended to accommodate new data elements or changing business requirements. However, making schema modifications in relational databases can be so disruptive that affects ongoing operations.

Lack of Network Analytical Abilities

The real value of data hinges on extracting meaningful insights and understandings from it. Relational databases were primarily designed for data storage and retrieval, while analyzing complex relationships and patterns within networked data often requires writing extensive and complex SQL queries with multiple table-joins. This process can be longer than is practical.

customer36-graph-model

USE CASE

Harnessing Graph Power for Business Success

Harness the power of graphs to unveil intricate connections and unearth valuable insights. With features like real-time updates, seamless data integration, and high-performance deep-traversal capabilities, graphs empower businesses to provide exceptional customer experiences and make informed, data-driven decisions that drive success.

  • Consolidating diverse data sources
  • Agile integration with emerging business sectors
  • Powerful contextual queries
  • Seamless connection to Machine Learning models
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Significance

Harnessing the true potential of data is complex. Comprehensive customer data holds immense value for modern companies, vital for their survival and success, but only when the hidden relationships between customers and all touchpoints are uncovered to the maximum extent. Building a customer 360 view is a critical step towards achieving this objective, and graph is a strong force to make it happen in an accelerated fashion.

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