About the Client

The subsidiary of a financial institution developed a Data Vault automation tool implemented as a Wherescape solution that handles data. Though, with that increase in data the customer needed an optimization of the data treatment.

Problem Statement

The provider wanted a model that fit the individual requirements in a more efficient way to support the parent company. 

The solution wasn’t able to support this use case due to multiple reasons:


  • The client uses a standardized model for several data sources
  • Increasing the amount of data led to crucial performance and scalability issues  
  • Workflow wasn’t able to handle huge data files and complex data sources which led to slower processes and  higher costs

The Challenge

The client wants to have a reworked Wherescape solution to handle a huge amount of data to accelerate business processes

  • Due to a certain lack of knowledge, the current solution didn’t fit the customers needs. The model wasn’t efficient enough
  • A deep technical knowledge/expertise in this area was needed since the current solution was a standardized implementation which didn’t fit the individual requirements of the customer

The Solution

  • Rework of the current Wherescape Workflow for incoming data sources
  • Integration of a single-source logical model
  • Breakdown of a huge model which leads to less errors since the dependencies were split to get a 1:1 relationship (1 data source to 1 model)
  • Technical split of CSV data files → was directly integrated into the Wherescape workflow.

Tangible Results for the Client

With the new workflow, the developers were able to:

  • React faster and more flexibly to incoming requirements of the parent company 
  • Get a reduction of development time through faster computation of data
  • Have a less error prone model which leads to reduction of time and costs

Technologies used

  • Wherescape 3D
  • Wherescape Red
  • Python
  • Data Vault 2.0

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