Tech mahindra
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Data Quality Management – Emerging Trends

Posted by: Asheesh Dongre On June 09, 2014 03:27 PM

Telecom operators across the globe face challenge to improve and maintain data quality to reduce revenue leakage and process failures. As per TMF, worldwide, telecom operators lose approximately 4% of revenue due to poor data quality.

In current circumstances, where Average Revenue Per User (ARPU), is one of the most important Key Performance Indicator (KPI) for any Telco, it becomes vital to curb revenue losses while retaining customer. In these days, where ARPU is monitored to the extent of one paisa/cent/pence, no telecom operator could afford to lose its share of money due to poor data quality.

Data quality and data integrity (DQ & DI) management market landscape is fast evolving. With spurt of COTS and free ware, DQ & DI is getting most hit, forcing operators to look for solution. There are plenty of DQ tools available in the market, but there isn’t any framework available which can ensure 100% data integrity across the system landscape. Telecom operators expect from DQ & DI solution providers that they will:

  • bring automation
  • have built reusable and easily deployable components
  • and ensure to reduce cost of DQ & DI engagements

For any operator, it is advisable to curb data quality errors at the inception itself. It should enforce rigorous checks to ensure that it data quality is maintained at source system. With almost all the engagements I did with various operators, one fact remains true that enhancing data quality at master system ensures that half the battle is won! Afterwards, a robust data governance framework will ensure that the data integrity is kept intact

With the arrival of Big data and social media, as per NASSCOM, data complexity will continue to increase in future and less than 10% of organizations are currently equipped to manage these unstructured data sources effectively. The diversity of data sources, its integrity across systems and its usage across business reporting, presents a technological challenge in capturing, storing and analyzing information between seemingly unrelated, large and complex data sources. Considering the sheer size of data that an organization has to deal with, it becomes imperative to plan for a robust and scalable framework for data quality management that could manage data across its life journey.

Our latest alliance with Informatica is a step in precisely this direction.

Tags: Thought Leadership, Data Management Services
 
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