Data Quality and Integrity Management for Telecom Operators
8 Pages Posted: 31 Mar 2015
Date Written: September 15, 2014
Telecom operators across the globe face the challenge of improving and maintaining data quality to reduce revenue leakage and process failures. On the basis of the latest predictions on gross spend from the Telecommunications Industry Association, which is set to amount to a staggering USD 215,000,000,000 of lost business in 2014 alone. In any business, a 215 billion loss is scandalous – especially if its essentially down to the administration of back office databases. 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. Telecom industry as such, struggles to maintain its data quality due to sheer complexity of the systems and functions involved and volume of data to be managed. The problem gets accentuated with stiff competition, frequent induction of offers by service providers, regulatory institutions in different countries for telecom industry, advancement of technology (such as number portability, IPTV, 3G services, etc.) 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 the spurt of COTS and free ware, DQ & DI is getting most hit, forcing operators to look for solutions. 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 1) leverage the experience gained in this domain 2) bring automation 3) build reusable and easily deployable components and 4) Ensure reduced costs of DQ & DI engagements. Clearly, there is significant business potential in this space.
Keywords: Data Quality Management, Data Integrity, KPI, Revenue Leakage Prevention, Enhancing Profitability and Customer Satisfaction
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