User Needs in the Telco sector

This section provides insights concerning specific Big Data benefits in the telecom sector. Benefits have been mapped to eTOM processes (eTOM). eTOM is specific standard framework for business processes design and deployment for telecom. eTOM stands for enhanced Telecom Operations Map. It is a guidebook built on TM Forum Telecom Operations Map (TOM). Currently, eTOM is the most widely used and accepted standard for business processes in the telecommunications industry. The eTOM model describes the full scope of business processes required by a service provider and defines key elements and how they interact. Among its advantages we can mention that it establishes a common vocabulary for both business and functional processes. The Framework enables to map the business processes into a language that all parts of an organisation can understand, thus supporting a business-driven approach to manage enterprise processes.

The benefits of Big Data for telecom can be empowered by combining data of different planes (for example, customer data with mediation data, etc.). These benefits are obviously not quantified individually and the impact of Big Data in the telecom sector is hard to estimate. According to existing surveys, the majority of senior telco executives do not understand yet the potential that Big Data presents, which makes it difficult to setup a strategy (European Communications Maganize, 2013). However, the benefits within the telecom sector include not only revenue for the sector itself but also the social impact it can bring, for example, in number of jobs it is expected to generate. According to Gartner (Gartner Press Release, 2012), 4.4 million IT jobs will be required to support Big Data by 2015.

Referencing the eTOM process again, the first analysis seems to point out that data produced in the Operations area can help the Strategy, Infrastructure and Product (SIP) domain. For a telecom operator, this means increasing the portfolio and value by leveraging the data produced by customers in the network and in the social media, as represented in the next picture.

Figure 18: Big Data and eTOM (eTOM)

The combination of all these benefits within different eTOM domains can be summarised as the achievement of the operational excellence for telecom operators. Nowadays1 operational excellence can be understood as providing true customer value through highly reliable products and services based on exceptionally good performance (Meissner, 2011).

It is challenging to manage reliability, recovery, change, service, collection, performance as well as customer experience and relationship. But the results can meet operator’s requirements for higher efficiency and improved cost management.

1

Operational excellence has had different interpretations along the telecom history. Before ICT (1900- 1970), the focus was set on standardization as a means to bring quality and thus reach operational excellence. After ICT (1970-2007), the key aspect was to be able to control the great variety and fragmentation. Now, with the explosion of mobile services, devices and the internet, operational excellence should be reinterpreted as “how to offer the highest value with excellent assets”.

5.3.2.1

Market, Product and Customer

The main benefits of Big Data-driven analytics can be summarized as follows:

 The ability to profile and segment customers based on socioeconomic characteristics can allow operators to market to different segments based on their preferences, enhancing customer satisfaction levels and reducing churn.

 Online social network analysis enables telcos to monitor consumer sentiments towards their operators, react to trends as they develop, as well as to identify influential individuals within communities for direct marketing.

 Building predictive models for customer behaviour and purchase patterns facilitates the accurate appraisal of each customer’s lifetime value, making possible it possible to focus on acquiring and retaining profitable clients. Price & Product mix optimisation, predictive churn management analytics, cross-selling, and location-based marketing are some examples.

 Dynamic analysis of market demand responses to price/product changes can facilitate optimal pricing and stocking decisions, reducing revenues lost through customer defections.

 Customer care and sales point efficiency can be met by optimising service time to customers, improving average speed of answer and also employee morale with more consistent procedures.

 Get a better control over different points of sales activities.

 Minimise IT involvement by automating processes in product and advanced analytics phases.

5.3.2.2

Service

Service configuration and activation processes can also be enhanced by Big Data:

 Enhance Service Orders delivery - Correct and complete service orders can be done faster. The process of activation on several service platforms can be optimised.

 Report service provisioning – Accurate monitoring of the status of service orders across different service platforms and network elements. Synchronisation of service status over different systems.

 Speed up the process of service activation by automatic fulfilment of service parameters depending on available data and closing the service order when activation is completed.  Ensure service provisioning activities are assigned, managed and tracked efficiently.  Identification of services that are no longer required by customers.

 Optimisation of mediation process and usage ticket production (efficiency in duplicate elimination, correction of usage data records on the fly).

5.3.2.3

Resource (network)

All the data on customer usage trends is going up and down the network. The analysis of that data can turn it into usable information.

Network analytics also enables tighter control over expenses. The analysis of end-to-end traffic patterns may reveal inefficiencies and extra costs derived from underutilised lines or inefficient use (calls that are routed off the network and back again may imply unnecessary phone charges).

Big Data brings the capacity to predict and optimise networks investment requirements, enabling the possibility to, e.g., optimally locate point-to-point routing demands from the traffic forecast, predicting network resource exhaustion in a timely manner or even identifying potential problems by gathering information from social media (e.g. many similar tweets may reveal network issues).

Advanced network analytics, with the ability to examine micro events, may significantly shorten resolution time even for the most complex technical issue anywhere in network. By searching for incidences in these systems, operators can identify hidden problems and correct them before they affect users and become extremely expensive to fix. For example, before incorrect bills are produced and sent to customers and consequent complaints arise.

Big Data can provide a Next Generation Network overview, i.e. unify different networks with different resources under the same operational framework, which can be very useful for network management.

5.3.2.4

Suppliers

Supply chains are complex systems, producing much data from various sources. Telecom players using analytics to forecast demand changes can anticipate their supply in order to mitigate revenues lost through stock-outs.

By analysing stock utilisation and geospatial data on deliveries, operators can automate replenishment decisions to reduce lead times, thereby minimising costly delays and process interruptions. Businesses can also use this data to monitor performance and control their suppliers.

Optimal inventory levels may be computed, through analytics accounting for product lifecycles, lead times, location attributes and forecasted demand levels. The sharing of Big Data with upstream and downstream units in the supply chain, or vertical data agglomeration, can guide operators seeking to avoid inefficiencies arising from incomplete information, helping to achieve demand-driven supply and just-in-time (JIT) delivery processes.

In the telecom sector, suppliers are many: points of sale, banks, SIM card and mobile manufacturers, IT providers, etc.

5.3.3 Stakeholders: Roles and Interest

The study of Big Data for Telecom requires covering the whole telecom business landscape. There are different stakeholders taking part in OSS/BSS information systems producing data that needs to be stored, retrieved and correlated in order to make more business.

In Telecom, there is a specific standard framework for business processes design and deployment for telecom (eTOM), the most suitable means to classify roles is to lean on it for our research. The enhanced Telecom Operations Map –eTOM– is a guidebook built on TM Forum Telecom Operations Map (TOM). Currently, eTOM is the most widely used and accepted standard for business processes in the telecommunications industry. The eTOM model describes the full scope of business processes required by a service provider and defines key elements and how they interact. Among its advantages we can mention that it establishes a common vocabulary for both business and functional processes. The Framework enables to map the business processes into a language that all parts of an organisation can understand, thus supporting a business-driven approach to manage enterprise processes. This seems to be a right approach as far as Big Data analysis for the telecom sector is concerned because eTOM provides a reference framework in which we can categorise all business activities at all levels of the telecom industry.

The first step is to focus on Level 0, where the highest sight is achieved. The following diagram places some stakeholders using the eTOM level 0 framework.

Figure 19: eTOM-based identification of players

In this picture, no distinction is made between Strategy, Infrastructure and Product and operations. This is something to be analysed in more detail at level 1 with some of the stakeholders represented in this picture.

Relevant actors in the telecom sector are listed below:

 Telecom operators

 Virtual Telecom operators

 System Integrators

 Network equipment vendors

 Device manufacturers

 Marketing 2.0 companies

 Regulatory bodies responsible for establishing the legal framework

 European Commission:

o DG Informatics (DIGIT): According to its mission statement its goal is to enable the Commission to make effective and efficient use of Information and Communication Technologies in order to achieve its organisational and political objectives

o DG Communications Networks, Content and Technology (CNECT): According to its mission statement this DG helps to harness information & communications technologies in order to create jobs and generate economic growth; to provide better goods and services for all; and to build on the

greater empowerment which digital technologies can bring in order to create a better world, now and for future generations.

Besides, the eTOM Business Process Model can be complemented with SID (Shared Information/Data), which provides an information/data reference model and a common information/data vocabulary from a business entity perspective.

Figure 20: eTOM SID model

This tool can be used to technically classify the data during the requirements elicitation phase.

5.4. Industrial Background

This section is divided into 3 sections, in order to most conveniently describe the similarities and unique features of the Telco and Media sectors. Section 2.3.1 covers the most important common features, section 2.3.2 deals with Media and Entertainment and section 2.3.3 deals with Telco. At the level of basic network infrastructure and increased data volumes, the sectors are identical, indeed, without broadband, wireless and cloud capabilities etc., modern digital media simply wouldn’t exist. That said, consumer and B2B media have distinctive characteristics, notably around content delivery and consumption across multiple devices and channels.

5.4.1 Common aspects of Telco, Media and Entertainment