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The client is a leading global financial services company with operations around the world. It provides services in three main areas: retail banking, wholesale banking, and brokerage & wealth management. Their retail banking services offer consumers branch office facilities and online banking. The wholesale banking focuses on home mortgage and commercial loans. The brokerage and wealth management services offer trading, retirement, and security investment functions.


One of the key goals for the client is to maximize their customers’ lifetime value by providing personalized services and timely offerings such as:

  • Attracting new customers through competitively positioned products that are easy to acquire
  • Retaining existing customers by offering products that intuitively suit their lifestyle and consumer habits
  • Identifying and marketing additional products that will benefit the customer and offer them superior products at the right time
  • Offering all of these products through multiple channels
  • Distinguishing and targeting suspects from prospects and the prospects from hot targets
  • Segmenting, distributing and intelligently marketing to any touch-point group
  • Personalizing customer service without encroaching on their privacy

The client wants to achieve these business goals by gathering data from diverse sources including multiple touch-points from customers and prospects and combining this with transactional data.


Xoriant was chosen due to its prior experience with similar banking clients, banking domain knowledge as well as for its deep expertise in various server technologies.

Xoriant’s first task was to understand the technical challenges involved in gathering data from diverse sources and customer touch-points. Some of these challenges were:

Web Data

  • Disparate data sources
  • No comprehensive metadata
  • Homegrown ETL (Extract, Transform, Load database functions) processes
  • Lack of data retention policy
  • No consolidation of data in a timely manner
  • The current server being unable to process data loads with
    acceptable time frames

Navigational Data

  • Logging is not active on all the production servers
  • Navigational data analysis becomes difficult on the secure site as the URL’s are encoded
  • Partner site data is not easily available
  • Inability to uniquely identify a customer (missing SSN) across the enterprise’s numerous (Systems of Record) SOR’s
  • Inability to uniquely identify a customer (missing SSN) across the enterprise’s numerous (Systems of Record) SOR’s
  • Inability to create a view of the all the customer accounts across various business units

Xoriant’s primary responsibility was to gather data from disparate sources and metadata formats, transform the data so it can be offered to different business units so they canprovide their customers competitive products in a timely manner.



Xoriant used the following technologies and platforms in this project:

  • DEC Alpha Unix, Mainframe, Sun Solaris, Windows NT, NCR-Teradata
  • JDBC, Oracle
  • Seibel (CRM)


Xoriant architected and built an Enterprise Customer Profile data warehouse to gather data from various applications (ex: Intuit, MARS, eGain, VIEW), touch-points (web, email, call centers) and flat files. Furthermore Xoriant built business rules to display data to the needs of various business units.

This entailed the following work:

  • Created several adapters to transform data from various data sources and metadata formats such as Intuit, MARS, eGain, VIEW, Flat files, etc. Creation of these adapters involved changes to business process flows as well as to the output formats of these apps
  • Designed these adapters to scale to future applications
  • Implemented extensive performance and reliability testing
  • Created a central repository to view all contact information from the MARS and eGain applications
  • Developed a Business Rules Engine implementing business rules involved in the interfaces between the applications integrated into the Enterprise Customer Profile data warehouse
  • Performed data cleansing, normalization,and migration across different applications
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