The complexity of a telecom business is very high, given operations (focusing on some of the most critical process areas) like: • Customer relationship management: a telecom operator with a complex product portfolio offered to a complex set of customer segments (residential & business) via a complex organisation of Customer touch points (CTPs), has to deal with the support of very complicated CRM operations on: customer inquiries, order management, sales management, billing & collections, problem & fault handling, QOS/SLA management. Handling different customer segments and channel segments can be a very challenging task. • Telecom network resource management: a telecom operator has to manage efficiently complex telecom network resources, in order to optimize the telecom service offered: improve the quality of service, reduce the time to order fulfillment, reduce the number of network infeasibilities. The complexity of various telecom networks owned by a large operator (e.g. fixed PSTN, mobile telephone, data networks (leased lines, frame relay, IP, ATM, MetroEthernet)) and the involved resource-facing business units (respective technical departments) • Partner relationships management: a telecom operator is exchanging information on network usage (CDRs) with other operators and may be reselling products of other telco providers. Order management and billing systems of both parties, need to be synchronized.
Major Telcos generally manage thousands of discrete business processes, to run their operations. To automate a subset of critical processes, they typically operate tens or hundreds of BSS/OSS software applications. Moreover, to achieve end-to-end process automation, application integration is required. Consequently, process automation complexity is high.
Poor data definition quality. Lack of effective documentation of the existing data structures, the meaning of entity types, entity type attribute fields. Legacy systems often have insufficient data definition of their structures, which is not sufficiently maintained as they evolve. Lack of data definition standards leads to a variable level of data definition quality, throughout the various OSS/BSS.
The lack of a corporate dictionary which facilitates communication within the Enterprise. This fact may lead to confusion in management documents, like headers in reports. The fact that many employees may know what is meant by a term, does not mean that this term can be unambiguously used by a broader group of people, in a complex telecom environment. Documentation on product descriptions, business processes or business rules is based on business terms which should be explicitly defined. Otherwise these management documents have limited usability (confined to a small part of the organization, dependent on those who drafted it, in order to explain it). Information silos at all levels. Standalone systems and databases which do not share information with other apps. (CRM, billing, NMS, order fulfillment, e-channel). Information architecture would involve loosely coupled or isolated databases, thus developing departmental ‘information silos’. These silos have many negative consequences: • Information on network resources resides at ‘NMS islands’ or is manually kept and is inaccessible by the telco service provisioning process and the supporting OSS • Information on service ‘installed-base’, is not available to the CRM system (e.g. needed for value building or fault management purposes) • Information on order fulfillment progress is not available to customer facing channels (e.g. to serve customer inquires on order fulfillment) • No billing or payments information is available to serve customer inquires at the customer facing channels (limited CRM - billing system integration) • the Customer interaction history would be fragmented in various isolated systems, serving specific Customer touch points (CTPs). CTP coordination is not facilitated. Customer holistic view is difficult to achieve. • the web channel may not be able to perform complete transactions with the Customer (limited integration with operational systems) • information consolidation for business intelligence purposes (e.g. customer loyalty building and retention, up-selling products, improvement of business process performance, efficient service management) is very difficult or infeasible.
Account or line-centric data structures. Traditionally, many large telco businesses, have focused narrowly on direct operational needs like order handling & invoicing, when designing their information architecture. This way they have developed ‘line-centric’ data structures. A real Customer could have more than one accounts linked to one or more ‘lines’. These account records were not linked in the customer database. In this case, more than one Customer records, would exist for the same real Customer. This data model would not reflect accurately the real Customer entity and its relationship to the telecom business. Any analysis on Customer data which are stored in a line-centric structure is problematic. For example, one might want to calculate a simple Customer value ranking, based on the last quarter invoiced amounts. However, this would rather be an account value ranking, than a Customer value ranking, since the analysis would probably not aggregate all accounts related to a real Customer. Business wise, it is erroneous to carry out Customer analysis on the account level, since this analysis may give an incomplete picture about a Customer.
Non standardized data values on certain entity type attributes (e.g. address fields, Customer IDs, names, incompatible operations codes), impede the process to match and link data of the same real entity, from different sources. This fact raises obstacles in the effort to consolidate information. Moreover the insertion of non standardized values at the entry point of the information value-chain, results in the diffusion of these non standardized values throughout the enterprise information value-chain. E.g. if a non standardized address is stored at the CRM system which manages incoming orders, this non standardized address is sent to the service provisioning system and the billing system, thus spreading low quality information throughout the IT enterprise architecture.
Incompatibility in data structures (product catalogue, address book). Incomplete data structures (reflecting poorly relationships between entities) lead to the capture of incomplete information. A product is not structured as an entity with all its components linked to it (e.g. the network components required to implement a telco service). Commonly, legacy service provisioning systems are not able to support the introduction of new complex telecom products.
Incomplete information in databases (e.g. poor customer info). Many optional fields perceived as holding unimportant information are left empty. This has been the case in environments where productivity goals undermine information quality goals. As soon as management realizes the importance of this additional information (e.g. Customer demographics), the organization starts enriching the information captured. Older data often have a higher degree of incomplete fields.
Monolithic systems would support various functionality categories (e.g. order capture and fulfilment workflow management, network inventory and activation functionality, trouble ticketing functionality) in a single system. However these systems are designed and built to support a specific set of telco products. Consequently, they cannot be used in order to introduce new telco products. Their information architecture is not expandable.
Telecom products supported semi-manually. The link between the service ID and the network resource IDs which enable it, is not available at the BSS/OSS level – it is only available at a standalone file at the network department (an information silo). The ability to efficiently trace the network components enabling this service is reduced with significant operational consequences.
Information quality culture. E.g. manual technical procedures lead to changes in the network which are not captured in the network inventory database. Steps fulfilled in a service fulfillment process are not captured in the service provisioning system. Information on resources freed is not entered in the network inventory.
‘Information float’: information available in print or other form, which is not stored in the appropriate reference database in order to be accessible by all business functions which need it. It is commonly experienced with manual or semi-manual processes. An example of info float is the delay in data entry of print forms, capturing customer interaction information.
A typical modern Telco Enterprise architecture is comprised of the following: • Business support systems (BSS): CRM system to handle sales, orders, customer inquires, faults, complaints • A Billing system(s) to handle billing operations • An ERP system to support financial and accounting operations • A Dunning system to support the dunning process • Operations support systems (OSS) to support network inventory management, service activation and workforce management. • A BSS/OSS integration layer, based on the SOA architecture principles • A data warehouse which is used for business intelligence
A standard information model
Telemanagement Forum’s (TMF) eTOM is a process framework which aims to structure & standardize the view of telecom operations. This industry standard can contribute to the adoption of a modular high quality information model. Moreover TMF has introduced a reference data model called SID, in order to provide a guideline on the integration of BSS/OSS platforms, which shall reduce integration complexity and cost. Commercial implementations of this reference data model exist in the niche market of telecom middleware solutions.
Though there are generally accepted principles towards a telecom Enterprise architecture, achieving high quality of information is a very challenging task in the complex telecom environment. It requires alignment of Commercial, Network and IT divisions towards a common goal of continuous improvement.
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