Today's market is drifting from Network centric to customer centric where focus is primarily on Customer Experience

1. Preventive and Detective Data IntegritySolutions
Abstract
Today’s market is drifting from Network centric to Customer centric where
focus is primarily on Customer experience.
Communication Service Providers (CSPs) invest heavily in their network
infrastructures and their Operations and Business Support Systems (OSS/
BSSs). However, the actual associations between the network and supporting
OSSs/BSSs are either not fully automated or reconciled. This leads to
significant system, process and design affecting data integrity problems.
Without proactive data integrity management, OSS/BSS systems speedily
grow out of sync with one another and with the actual telecom network.
Such issue with synchronization not only makes revenue assurance difficult
but also drags down the efficiency levels of mission-critical processes. It
delays and derails service provisioning, modifications and troubleshooting
and drives the need for- manual clearance (of data fall outs), creation of
reconciliation jobs and raising change request for system, process and design
corrections. This case study discusses how Proactive and Detective Data
Integrity Solutions helps to prevent and gradually eliminate the causes that
lead to Data integrity issue to a substantial extent.
Oct 2010

2. Summary
A large Communication Service Provider (CSP) in United Kingdom realized that their core business was being hampered by
the lack of data integrity across OSS/BSS stack. Tracking trend of metrics like Right First Time (RFT), Delivered on Promised
Date (DoPD) etc isn’t of much use if the underlying data has been compromised. If data is unreliable, anyone having a vested
interest in the enterprise will question its credibility. Hence, it is crucial to promote data integrity prevention and detection
strategies which, in turn, will help in maximizing the Return on Investment (ROI).
The client required both preventive and detective data integrity management solution for its provisioning platform.
To assure improved services, better customer experience, increased ROI as well as minimum revenue leakage, Infosys
provided a robust solution by introducing Data Integrity (DI) maturity matrix model from prevention to launch of any
product across service provisioning stack. This initially started with determining causes for DI issues and gradually
progressed towards preventing them.
Business Problem
Client embarked on a business transformation program to move customers from old stack to a strategic stack in order to meet
regulatory guidelines and alignment to ‘Solution Oriented Architecture (SOA)’. This process encompassed various systems
across multiple platforms where inconsistencies were observed in the data. This inconsistent data was resulting in operational
delays, revenue leakage and poor customer experience; thus, affecting the organization’s brand image. Data integrity (DI)
issues had affected both systems as well as business and had become triggers for implementing DI measure.
Figure 1: Data Integrity Measure
Causes for lack of Data Integrity (DI):
Root causes for the lack of Data Integrity are illustrated in the following diagram:
Key issues that act as a trigger for implementation of DI maturity matrix include:
• Customer complaints
• Loss in revenue
• Impact on Right-first-time provisioning
• Trouble to Resolve (T2R) issues
• Customer Authentication issues
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3. L2C
Migration from legacy to strategic systems
Data iintegriity Causes
Data ntegr ty Causes
Changing to new technology / vendors
Developing and deploying new services
Operational issues and outages
Architectural issues or System design flaws
Distributed data model or data duplication
Jeopardy management processes and procedures
Advisor error/confusion
Figure 2: Data Integrity Causes
Solution inception
The solution planned by Infosys was in line with eTOM, especially service fulfillment vertical of the framework. It is driven
by Telecom Management Forum (TMF) approach of components which places emphasis on integrating system, process,
Process
Systems Integration Information
Products
Figure 3: DI-TMF approach
information and products through use of common modeling work or common objects.
Following processes were referred to while designing this solution:
• Business Process Framework (Business Management)
• FAB (Fulfillment Assurance Billing) end to end process flows- primarily service fulfillment process flow instance and
• Operational Processes like Customer care, Sales, Order Handling: Jeopardy Management, Service configuration and
problem management processes.
The ‘DI Solution’
Infosys was engaged by the client at the initial stage i.e. during requirement gathering phase of Software Development Life
Cycle (SDLC). DI champions drive the DI initiatives at Process, System and Design level. They are in-sync with each other
throughout the product lifecycle i.e. from product launch to in-life support.
Infosys – Case Study | 3

4. Figure 4: DI from Prevention to launch
*P&P= Process and Procedures; *RCA=Root Cause Analysis
By supporting creation of appropriate DI maturity model, Infosys enabled the client to specify minimum DI requirements
vital for launch of any product. This model was then used to identify potential systems, processes, design and metric
issues resulting in DI fallouts. End to end process flows for ‘FAB’, Customer Care, Sales, Order handling, Problem handling
Processes and in-Business process framework were used to derive this model.
Figure 5: DI Maturity Index
All the activities followed under DI were developed in reference to Customer Relationship Management (CRM), Service
Management and Processes (SM&O) and Supplier/Partner Relationship management(S/PRM) especially in Fulfillment area
and partly in Assurance and SIP vertical.
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5. DI Prevention Strategy
Figure 6: DI Prevention Strategy
DI Detection Strategy
DI detection strategy includes both detection and correction methods for DI fallouts.
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6. Figure 7: DI Detection Strategy
Results
Introduction of these formal processes to measure and control data integrity ensured that data assets were in control and
created value to the customer, business, service as well as product. Client gained benefits in 3 areas specifically– Business,
Operations and Program.
Business benefit
• Return on Investment (ROI) with inclusion of Prevention strategy.
• On average, the client was able to save approximately 147,000 GBP per year by introducing DI prevention
activity during the design phase.
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7. Figure 8: ROI with inclusion of Prevention Strategy
• Tool Automation
• Optimzed OPEX - Saving of approximately 101,000 GBP per year by automating one activity that requires Data
Integrity clearance so that DI fallouts can be corrected.
Figure 9: Tool Automation- OPEX
Operations
• Reduced defect seepage leading to DI issues - 10% decrease in DI issues reported because of conducting operation
process reviews.
Infosys – Case Study | 7

8. Figure 10: Defect seepage Before/After Process assurance
• Optimized operation cost by designing DI proposed solutions – Client, on average, saved 12,000 GBP per month
saved on DI clearance activities by designing solutions proposed by DI.
Figure 11: Cost savings on DI proposed Design solutions
8 | Infosys – Case Study

9. Program Benefit
The Root Cause Analysis work done by the –’ team has resulted in reduced data inconsistencies (77.9% over a period of 9
months) which resulted in a reduction of revenue leakage by 5.5mn GBP per annum.
Measure Pre-improvement Post Improvement
Number of issues causing Revenue
29464 6488
Leakage per month [A]
Customer Base 2046112 2046112
Average Revenue Per Customer [B] 20 GBP 20 GBP
Revenue Leakage Per Month [C] =
589280 GBP 129760 GBP
[A]X[B]
Reduction in Revenue Leakage Per
459520 GBP
Month [D]
Reduction per year[DX12] 5514240 GBP
Reduction in Revenue Leakage X2 = 5.5M GBP
Table 1: Program benefit- Reduction in Revenue Leakage