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 | Infosys – Case Study

SummaryA 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 ProblemClient 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.

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

Figure 1: Data Integrity Measure

Infosys – Case Study | 3

Solution inceptionThe 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,

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.

Figure 2: Data Integrity Causes

L2C

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Migration from legacy to strategic systems

Operational issues and outages

Developing and deploying new services

Architectural issues or System design flaws

Distributed data model or data duplication

Jeopardy management processes and procedures

Advisor error/confusion

Changing to new technology / vendors

Figure 3: DI-TMF approach

Systems

Products

Information

Process

Integration

4 | Infosys – Case Study

*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.

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.

Figure 4: DI from Prevention to launch

Figure 5: DI Maturity Index

Infosys – Case Study | 5

DI Prevention Strategy

DI Detection StrategyDI detection strategy includes both detection and correction methods for DI fallouts.

Figure 6: DI Prevention Strategy

6 | Infosys – Case Study

ResultsIntroduction 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.

Figure 7: DI Detection Strategy

Infosys – Case Study | 7

• 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.

Operations• Reduced defect seepage leading to DI issues - 10% decrease in DI issues reported because of conducting operation

process reviews.

Figure 8: ROI with inclusion of Prevention Strategy

Figure 9: Tool Automation- OPEX

8 | Infosys – Case Study

• 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 10: Defect seepage Before/After Process assurance

Figure 11: Cost savings on DI proposed Design solutions

Program BenefitThe 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 Leakage per month [A] 29464 6488

Customer Base 2046112 2046112

Average Revenue Per Customer [B] 20 GBP 20 GBP

Revenue Leakage Per Month [C] = [A]X[B] 589280 GBP 129760 GBP

Reduction in Revenue Leakage Per Month [D] 459520 GBP

Reduction per year[DX12] 5514240 GBP

Reduction in Revenue Leakage X2 = 5.5M GBP

Table 1: Program benefit- Reduction in Revenue Leakage