Data Model for Teradata Unified Data Architecture
Teradata reports how a
Global Bank reduced Churn by analysing Big Data, (scroll down to see the Report).
This Architecture combines 'Big Data' with conventional data and appears on the Teradata Web Site.
A Global Bank Reduces Churn
A large global bank was struggling with reducing churn in profitable customer segments.
A key challenge was integrating customer interaction data across multiple channels from numerous silo’ed repositories.
The size of the data – billions of records per month - also made the analysis of this information a very complex exercise.
Leveraging the powerful analytic capability of the Teradata Unified Data Architecture, this leading financial institution
built an enterprise view of all customer interactions and identified the most frequent paths to account closure across all
As a result, the bank expects to reduce churn among profitable customers by 5%, simply by identifying and removing
events that were causing a high number of account closures.
Technology leveraged includes:
A Teradata Enterprise Data Warehouse for historical customer transaction, profile and product information
Teradata Aster to analyze and discover patterns through nPath analysis Hadoop for loading, storing and
refining data and optimizing storage costs
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