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A roadmap for CSPs that want to evolve from a more traditional BI approach to embrace and reap the benefits offered by Big Data.

Business Scenario

CSPs can have access to a wealth of information about their customers’ behaviours, preferences and movements but, often, aren’t able to take advantage of this strategic asset to extract business value.

Big Data promises to tackle this challenge although, the question remains:

How can CSPs leverage this rich, complex and, many times, unknown data?

How can it be used to gain valuable, unique insights about the business and customer experience?

We are witnessing the birth of a new Era, fuelled by the advances in mobile technology, convergence, IoT and cloud services, among others. This revolution is empowered by data analytics that is impacting the entire value chain from network optimization to tailored marketing campaigns using location-based and social networking technologies.

Big Data & Analytics Challenges

One of the most important differences between the two approaches to data exploration - traditional Business Intelligence (BI) and Big Data - is the sheer volume of Big Data processes.

The massive amount of data processed is closely related to the rich variety of sources it deals with. From network sensors, to Moment of Truth (MOT), and so many other sources, Big Data can attune and correlate different formats of structured and unstructured data, in contrast with traditional BI.

Also, velocity can be applied not only to the ability to quickly consolidate massive volumes of data from different sources but also to provide business insights in real or near real-time. Organisations used to have a delayed picture of the business due to time discrepancy between a report request and information receiving. Big Data changes that, having a direct impact on the operations and CSPs ability to quickly react to business threats or opportunities.

What is in it for CSPs?

Customer Experience

By deeply understanding customer’s behaviour patterns, CSPs are in a great position to offer personalised services and apply micro-segmentation campaigns. With customer experience analytics and covering the entire journey, CSPs can generate improved insights, making a difference on identifying the next-best-offer and effectively leverage up and cross sell offers;

Operational Excellency

To support the constant growth in mobile data, network investment is expected to continue increasing significantly. Big Data can support CSPs’ efforts in infrastructure expansion and, at the same time, control the current infrastructure by proactively helping to detect potential bottlenecks and outages through the correlation of network usage, subscriber density, along with traffic and location data;


Traffic data is a strategic asset that hasn’t been leveraged by CSPs to generate more revenue. For example, location-based data combined with anonymised user profiles, can be offered to retailers to help them understand traffic flows to offer dynamic promotions;

Risk Management

Identifying customer propensity to churn is not new, however Big Data provides a whole new level of predictive analytics by combining the analysis of many different factors, such as customer care interactions, social media sentiment analysis, quality of service and usage patterns.

Although the benefits are clear and there is a strong business case supporting the adoption of Big Data solutions, organisations are still concerned about the complex roadmap to undertake this uncharted terrain - How to mitigate the organisational impact while leveraging the current BI infrastructure and evolving it to a Big Data platform?

Furthermore, CSPs must not only consider which Big Data solutions best fit today’s needs but also how will they scale new data types, integrate disparate data sources and deliver value in a timely manner.

Our Approach

Celfocus Analytics

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Analytics provides new insights and tackles different business challenges, supported by a plethora of new tools and technologies, democratising access to information across the organisation, impacting the entire value chain.

Analytics covers the entire value chain and adds a layer of intelligence to the different building blocks, from network to the contact centre. With a significant track record in deploying analytic solutions in diverse areas such as network optimisation, customer experience enhancement, digital TV and IoT Q&S monitoring, among others, Celfocus provides an end-to-end support to CSPs’ Big Data initiatives.

Celfocus Analytics | Business Competences | Data | AI
Celfocus Analytics Competences

There isn’t a one-size-fits-all roadmap. Based on the business requirements and use cases, some solutions and technologies are more appropriate than others:

Data Visualization, for example, is best suited to extract knowledge from a visual representation of data through an interactive interface.

Big Data has the best fit when the goal is to process and store large data volumes that cannot be managed by a regular database.

Analytics has a transversal impact across domains, covering from network to business, operations, IT and management. Stakeholders and priorities are pivotal to having the right skill set and technical competences.

Celfocus Analytics Blueprint

graph | Celfocus Analytics | Business | Data | Analytics
Celfocus Analytics Blueprint Architecture

A challenge that CSPs face, when considering embracing Big Data, is how to evolve their current BI infrastructure and use it as a baseline, leveraging their current value and reaping the benefits that Big Data offers.

Celfocus’ framework envisions an architecture that not only protects the investment but also outlines the roadmap to help process high data volumes, from a myriad of different sources, and produce near real-time results, which positively impact the business.

The concept behind Celfocus’ blueprint architecture is encapsulated in a Data Hub - an aggregator of different technological layers that compose the Big Data architecture and where each layer has a different function and its implementation is incremental.

This approach empowers CSPs to automate different processes and proactively propose stronger value propositions, anticipating network halts or improving the overall customer experience.


Big Data Platform
Fast Data Platform
Business Analysts
Data Scientists
Feed/support automated systems
CAR Engine
Data Hub
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