Data Integrity Solutions & Services
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The document discusses the importance of proactive and detective data integrity solutions for communication service providers (CSPs) in ensuring alignment between network infrastructures and operational support systems. By implementing a data integrity maturity model, a UK CSP was able to identify and resolve data inconsistencies, significantly improving service provisioning and customer experience while reducing revenue leakage. Overall, the approach led to substantial cost savings and enhanced operational efficiency.
![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](https://image.slidesharecdn.com/dataintegritysolutionsservices-120831034516-phpapp01/85/Data-Integrity-Solutions-Services-9-320.jpg)
Data Integrity Solutions & Services
- 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 2 | Infosys – Case Study
- 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. 4 | Infosys – Case Study
- 5. DI Prevention Strategy Figure 6: DI Prevention Strategy DI Detection Strategy DI detection strategy includes both detection and correction methods for DI fallouts. Infosys – Case Study | 5
- 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. 6 | Infosys – Case Study
- 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