A Comparative Study Of Revenue Assurance Vs. Data Warehousing.pdf

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A Comparative Study of Revenue Assurance vs. Data Warehousing

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White Paper A Comparative Study of Revenue Assurance vs. Data Warehousing

The telecom industry has a very dynamic landscape, which is constantly evolving at a furious pace. As a natural result of a margin driven revenue model, operators are constantly upgrading the services which are available to the end-customer. The result is that the core telecom network has become exponentially more complex, with various different systems interfacing with each other. The amount of underlying data that the analyst needs to crunch, to ensure operational and system efficiencies, has burgeoned beyond human capabilities. This paper analyzes the key differentiators between employing a Data Warehousing application as opposed to a Revenue Assurance solution, to ease the analyst’s workload as well as being an effective tool towards identification, investigation, representation and correction of revenue leakages.

Differences between Data Warehousing and Revenue Assurance To understand the differences between Data Warehousing solution and Revenue Assurance solutions, it is important to clearly define the two. Data Warehousing - The process of transforming data into information and making it available to the user in a timely enough manner to assist the user make business decisions Revenue Assurance Data quality and process improvement methods that improve profit, revenues and cash flow without influencing demand. Holistic revenue assurance optimizes business processes, the usage of existing assets, and ensures the integrity of data. From the functional definitions, it is clear that there exists a large gap between data warehousing and revenue assurance. Data warehousing (DW) is predominantly limited to the extraction, transformation and loading (ETL) of data, to be made available to a user in a timely manner with business intelligence metrics. In the case of revenue assurance (RA), ETL is merely the first step. There are business intelligence metrics built into the system at every stage of the Revenue Assurance workflow. In Revenue Assurance, it is critical to be able to not only identify issues in the underlying network, but it is equally important to be able to: Qualify the leakage Quantify the leakage Activate Corrective measures to address the leakage Setup controls to ensure non-recurrence of issues that lead to the leakage The complexity of the revenue assurance process begins to unfold when we are able to identify the cross-linkages and inter-play between not only downstream/upstream data flows, but seemingly disparate systems as well. As an example, CDRs being rejected at the billing platform might not be an issue of the billing system. It might be because the subscriber to be billed is not setup in the billing platform whereas he has been activated on the network. For holistic revenue assurance, it is important to undertake a complete study of the operator’s network topology and map it to his/her defined business flow. This includes a complete analysis of all call scenarios within the network with an exhaustive analysis of service delivery options and various business rule validations at each network element.

In Revenue Assurance, it is critical to be able to not only identify issues in the underlying network, but it is equally important to be able to: qualify the leakage, quantify the leakage, and activate corrective measures to address the leakage, setup controls to ensure non recurrence of issues that lead to the leakage 02

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White Paper A Comparative Study of Revenue Assurance vs. Data Warehousing

Approaches to Data Warehousing Dimensional Approach: The transaction data is stored under pre-defined “Dimensions”. The dimensions constitute the reference information that converts the data into facts or information. For example, a call can be broken into headings such as Record Type, Subscriber Type, input to Call Completion ratios etc. The disadvantage of such an approach is: a) The underlying data will be loaded into the database from multiple operational systems. As a result, maintenance of Data Integrity becomes a very complicated task b) If there are changes to the business flow, it would be required to make corresponding changes to the pre-defined dimensions. This process is quite complex and requires significant time and effort. This has to be re-done over and over again, for any business flow changes in the future. Normalized Approach: The transactional data, as it enters the data store, is stored following the Codd Normalization rule to an extent. The issue for the end users here is that: a) Cross-referencing data from different sources into meaningful information becomes a cumbersome task. b) It becomes difficult to mine the data without an in-depth understanding of both the sources of data as well as the structure of the data warehouse.

The optimal approach to Revenue Assurance The recommended approach to effective revenue assurance is a continual process of Monitor – Analyze – Correct

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White Paper A Comparative Study of Revenue Assurance vs. Data Warehousing

From the above diagram, we can effectively state that Revenue Assurance is an ongoing, intensive activity which requires well-defined processes. This allows the Revenue Assurance department to effectively analyze and translate large amounts of data into meaningful indicators of revenue leakage as well as revenue optimization opportunities. Towards this end, the analyst should be empowered to activate automated monitoring mechanisms. Based on prior analysis of the underlying network, these mechanisms would effectively highlight or alert the possibility of revenue leakage in any business stream. The automation process efficiently and accurately processes the immense amounts of data, which need to be analyzed. Since new services/products are introduced frequently, it requires a highly flexible system which can monitor the underlying data on the analyst’s terms. Over and above this, any system that addresses revenue leakages should be capable of recording the occurrence and capturing the information related to the rectification of the same. This would eventually become an automated check or key performance indicator to prevent the recurrence of the issue within the network.

From the diagram, we can effectively state that Revenue Assurance is an ongoing, intensive activity which requires well defined processes.

This allows the Revenue Assurance department to effectively analyze and translate large amounts of data into meaningful indicators of revenue leakage as well as revenue optimization opportunities.

For effective Revenue Assurance, the system must be capable of performing the following activities: 1) Check Integrity of underlying data File Sequence Checks Record Sequence Checks Inter-record generation time gap File Integrity (Size of files/Number of records per _le) Corrupted Records/Corrupted Files Key Field Validations (Duration field populations, start and end time capture, trunk seizure time versus captured duration, subscriber lookup validations, CDR cloning checks etc.) Missing Data checks Data Aggregation Duplicate CDR Checks Duplicate File Creation File statistics

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White Paper A Comparative Study of Revenue Assurance vs. Data Warehousing

Many of the above functionalities require customized solutions which cater directly to the requirement. For Revenue Assurance, it is necessary to validate the Data entering the system before any reconciliation is performed. This constitutes a few of the critical check points at the first level. 2) Business rule validations Replicating and validating filtration rules from core network (e.g. at Mediation) Tracking network usage to revenue realization (e.g. information from SS7 probes right down to the Billing Platform) Ability for analysts to mirror/edit business rules for revenue maximization analysis on the fly Creating verifiable audit trails with linkages to raw files from core network Ability to cross-reference usage data (e.g. CDRs) with subscription and core tables (e.g. Subscriber Information, Tariff Plan, Route Tables etc.) for effective and conclusive analysis Transference checks based on any profile field, or even non-profile fields, through the system. (e.g. Minutes of Usage per trunk as captured at network can be profiled, versus partner billing. Alternatively, the analysts might decide to track a “control set” of subscribers based on Mobile Directory Number from Network to Mediation to Billing) Ability to interface inherently with a test call generator. This is a popular method for telecom operators to test for CDRs not being generated, accurate rating being performed, misidentification of services etc. Re-rating analysis – For effective revenue assurance, the operator might choose to re-rate (separate from the billing platform) to identify flaws in rate plans which might have crept into the rating tables. For this purpose, it becomes necessary to be able to reconfigure a set of rate plans (called the “Control set” for rate plans), and re-rate the records within another system. This can then be compared to the output to the rating as applied by the billing system. Ability to check for misaligned subscribers – As the usage data streams are billed based on the subscriber setup on the system, it is critical for the operator to be able to track subscriber information across multiple systems (e.g. HLR to Provisioning to Billing). In this way the operator is able to identify clear mismatches, e.g. Subscriber setup on network but not configured on billing or Subscriber with access to all services on network but only being billed for one or two services at billing etc. 3) Effective and Extensive Workflows In Revenue Assurance, it is critical to not only identify areas of leakage, but to also have an effective knowledge/case capture module. This would help the Revenue Assurance department to identify, assign, track and report on all leakages discovered in the core network. The Workflow module should have the ability to communicate leakages cross multiple departments (e.g. Revenue Assurance team, Network team, Mediation team, Finance team, marketing team etc.) For efficient use, the workflow module should have accessible linkages to the underlying data. These would be the network raw files that would constitute “Proof of Leakage”. This module would need to be able to assign a cost to the identified leakages which would help the Revenue Assurance department to quantify savings/identified leakages. An integrated dashboarding and reporting tool ensures that the analyst has a 360 degree view of his/her area of interest, whereby the productivity of the analyst would increase manifold.

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White Paper A Comparative Study of Revenue Assurance vs. Data Warehousing

As discussed in the above points, since Revenue Assurance is a function in itself, the system should be able to track the efficiency of the concerned Revenue Assurance team. Not only does the system need to be very flexible to accommodate audits as per the requirements of an analyst, it should also be able to capture the findings and translate it across multiple teams as an actionable work-item. Furthermore, the module needs to be able to help managers have clear visibility of problem areas without having to perform deep-dives. From the perspective of an Revenue Assurance department, it is very important to be able to assign costs to leakages so that the savings of the Revenue Assurance team can be quantified.

Advantages of an Revenue Assurance solution over Data Warehousing There are several benefits that can be achieved through a revenue assurance solution that are not realized with data warehousing. A big advantage that Revenue Assurance has over the Data Warehousing is that the atomicity of the data is maintained exactly as it is present in the real world, i.e. XDRs are stored as XDRs. All necessary profiling/dimensioning is then done on top of this data. This is very helpful for the analyst as it does not introduce undue complexity into the data mining approach to be followed. To the analyst, the structure is transparent and seems to be an exact mirror of the underlying core network. The gambit of a Revenue Assurance system far outpaces the capability of a mere data warehousing application. In the case of more complex requirements, (e.g. calculation of prepaid credit depletion with Closing balance calculation vs. Subscriber usage), the data warehousing application would be ill-equipped to handle such online near-real time calculations. The primary function of the data warehouse would be to derive business intelligence based on the underlying data, but unlike the Revenue Assurance system, it would not be able to effect changes and data transformations required, (e.g. re-rating of billed records), which are required from the perspective of a Revenue Assurance department.

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White Paper A Comparative Study of Revenue Assurance vs. Data Warehousing

Functional comparison of Data Warehouses and Revenue Assurance Solutions Function

Data Warehouse

Revenue Assurance System

ETL Capability Comprehensive Record Integrity Comprehensive File Integrity Service Usage Check Re -rating capability Workow Management Business Intelligence Analytic s Quantication of leakages Leakage Correction Data cross - referencing Usage Reconciliation * Settlement Reconciliation * Subscription Reconciliation * Business Rule Validation Unauthorized Usage checks Activation Checks Sy stemic Flexibility

* Note: Most data warehousing applications provide a first level of reconciliation. But as the scope of this document includes end-to-end revenue assurance, in the comparison column, comprehensive reconciliation capabilities are referred to, including but not limited to, exception programming, KPI monitoring, business filtration and segregation, transference integrity etc.

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About Subex Subex Ltd. is a leading telecom analytics solutions provider, enabling a digital future for global telcos. Founded in 1992, Subex has spent over 25 years in enabling 3/4th of the largest 50 CSPs globally achieve competitive advantage. By leveraging data which is gathered across networks, customers, and systems coupled with its domain knowledge and the capabilities of its core solutions, Subex helps CSPs to drive new business models, enhance customer experience and optimise enterprises. Subex leverages its award-winning analytics solutions in areas such as Revenue Assurance, Fraud Management, Asset Assurance and Partner Management, and complements them through its newer solutions such as IoT Security. Subex also offers scalable Managed Services and Business Consulting services. Subex has more than 300 installations across 90+ countries.

Pipeline Innovation Award 2017 for Innovation in Managed Services and Security & Assurance category

Global Telecoms Business Innovation Award 2017,2016, 2015, 2014, 2012, 2011

Awarded Global Carriers Awards 2015

Winner of Telecoms Award 2015 - 'Advances in B/OSS' category

Awarded Best Global Product/Services

Received Global Market Share Leaderin Financial Assurance Award 2012

BSS&OSS Latin America Best Fraud Prevention Project Award 2016

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