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| Case Study |
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Data Quality Assessment for a leading
acoustic product manufacturer
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Through a comprehensive data quality assessment exercise, Patni helped a leading acoustic product
manufacturer create the foundation for its Unified Customer Repository initiative
The Client
The client is one of the leading manufacturers of acoustic products.
The Challenge
For ensuring the highest levels of customer satisfaction, the client had created
multiple delivery channels to give customers the choice of buying the product
from any channel they wanted. Customers had the option of buying directly
from the company's store, a third party retail store or even buy products
online. All these different delivery channels were powered by different
applications such as Siebel, SAP and home grown Web applications.
With customers being acquired through different delivery channels, and
information spread across different applications, the client did not have a
single view of the customer. In some instances, the same customer information
was captured through different applications leading to inconsistencies and
duplication.
The fragmented nature of customer information posed the following
challenges:
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No centralized customer data ownership for control |
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Inability to support future product directions |
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Inability to detect customer fraud due to missing linkages between the
customer and order information |
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Inability to understand marketing preferences of each customer |
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Inconsistency in privacy options of customers. |
With the objective of enabling a single view of the customer, the client decided
to create a Unified Customer Repository (UCR). As the quality of data was an
extremely critical factor for building an effective UCR, Patni recommended a
comprehensive data quality assessment.
The solution
With extensive experience in similar data quality assessment exercises, Patni was invited
to advise and suggest best practices for ensuring accurate customer data. As a starting
step, the scope of the project was determined through the use of comprehensive
questionnaires by collaborating with customer representatives from different categories
of users. The inputs of this survey helped the customer get an understanding of all the key
attributes that could uniquely identify a customer.
Business definitions and business rules were defined for key customer elements, and voice
of the customer was captured to improve data quality. These elements were measured
with respect to parameters such as completeness of information, record duplication, data
reconciliation issues, unexpected entries and internal inconsistencies.
An applications compliance scorecard was built with standardized business
definitions and rules for critical data elements. Data sampling and data
profiling using Trillium Discovery was done for understanding the structure,
content and quality of data. Existing data was measured against this
scorecard and defects were analyzed to understand root causes.
Understanding the importance of people participation, Patni conducted
multiple interviews, educated the client on data quality processes, and
made presentations to key stakeholders across the organization to get the
required buy-in for the data quality assessment initiative.
Patni also assisted the client in evaluation and selection of the right data
quality product. The evaluation of the product was done using the Pugh
Matrix method. Vendors were asked to provide proof of concept of the data
quality assessment tool. Results of 90 test cases on sample files cleaned by
the vendors were checked with respect to different categories relevant to
the customer's environment. This included parameters such as data
profiling, data cleansing, level of standardization, architecture, level of
integration, performance, systems requirements, security, development and
maintenance and vendor viability.
The Technology
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Siebel |
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SAP |
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Oracle |
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Trillium Discovery. |
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The Benefits
Patni's solution helped the client understand the key steps that were
critical for improving data quality. Through this solution, the client laid
the foundation for its UCR initiative.
Some of the significant benefits include:
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Good understanding of the quality of data in key elements across
different applications |
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Identification of common data quality issues and implementation
plan to fix these issues |
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Standardized processes that helped in maintaining data integrity and
quality. |
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