Big data is the fuel that can (and will) drive the entire telecom industry towards higher revenues and better customer service. The majority of telco execs are now awakening to the fact that big data in telecom that has mostly been discarded or left unused has, in fact, immense financial potential.
With the right approach to data science and big data analysis, telecommunication companies can dramatically improve their services and make their subscribers happier — and without the need to make major investments in upgrading the hardware infrastructure.
Let’s take a closer look at the big data telecom landscape and review a few relevant use cases. Here’s what we’ll talk about:
- Data-related challenges faced by telecom operators
- The benefits of exploring the big data telecom domain
- Big data use cases in telecom
Data-related challenges faced by telecom operators
It’s no secret that telecoms are facing withering revenues from their traditional services, voice calls (including roaming), and text messages. As a matter of fact, the only kind of service that telecoms can financially rely on these days is selling data traffic in different shapes and forms.
To add to the problem, competition in the telecommunications market has never been more vicious. Now throw the ramifications of COVID-19 into the mix — and it looks like they’ve got a pretty challenging situation on their hands.
Core telecom business under pressure
These circumstances are worrying not just on the business side of things. From the technical perspective, initiatives related to big data in telecom have a lot to do with data management challenges in general.
In the majority of big data telecom use cases, collected data is not uniform. Back in the day, it could consist almost exclusively of well-structured call detail records (CDRs), making it easier to analyze datasets and run reports. Today, telcos have to analyze multiple data points and work with data in various formats: log entries, plain text files, binary objects, streams, and so forth.
Disparate and siloed data sources
The sheer scale of operations of any given telco almost guarantees that data is not stored centrally. Companies that have not yet migrated everything to the cloud are likely to still be using siloed, non-synchronized local storages. Unifying them into a single data pipeline requires a team of skilled data professionals with a solid data engineering strategy.
Extra-large arrays of data
What used to sound like a gargantuan amount of data some 10–15 years ago would not raise an eyebrow today. Data consumption by users and big data generation by corresponding software systems are steadily growing year-to-year. The number of data sources is going up as well. All this makes it increasingly difficult to capture, process, and store big data for telecoms.
Data processing complexity
Back in the day when big data analytics in the telecom industry was restricted to running SQL queries against well-structured sets of tabular data, the task of mining for business insights was somewhat easier. Today, with data flows coming in multiple formats and requiring an individual ETL approach in every case, telecommunication companies are facing a variety of technical challenges.
But don’t fret just yet — the industry has every chance of beating these challenges. With so many opportunities arising from telecom big data analytics, telcos are now considering every opportunity to retain customers and boost revenues by offering new value-added services. Find out how we assisted a German provider of commercial VoIP services in building a full-fledged cloud communications platform
Find out how we assisted a German provider of commercial VoIP services in building a full-fledged cloud communications platform
Let’s take a better look at the benefits of properly managing data and using specialized telecom software to draw actionable insights and improve decision-making in the big data telecom world.
The benefits of exploring the big data telecom domain
Here are just a few things that big data analytics in the telecom industry can do to make a positive impact:
- Help telcos get a clearer vision of potential new products
- Effectively prevent traffic fraud
- Design and implement value-based network capacity adjustment strategies
- Substantially improve the customer experience
- Resist subscriber churn
- Constantly monitor network capacity and react to demand fluctuations faster and with more precision
- Reduce the cost of field service trips while keeping customer satisfaction levels high
Achieving these goals takes time and substantial efforts at many levels — from business stakeholders and technical leaders to multiple IT specialists working in such areas as big data, data engineering, machine learning, and artificial intelligence.
The wide adoption of telecom big data analytics will primarily contribute to the following areas.
Precise, timely, and comprehensive big data analysis powered by specialized AI tools will enable telecoms to create more personalized service offers thanks to proper customer segmentation, as well as customer behavior and experience research. It also empowers telecoms to streamline their service portfolio, design and implement new features, and provide the best customer support possible. Interested to learn how we helped build a cutting-edge big data analysis platform for a Fortune 500 company that enabled our client to redefine customer behavior?
Interested to learn how we helped build a cutting-edge big data analysis platform for a Fortune 500 company that enabled our client to redefine customer behavior?
From identifying security breaches (something that over 60% of enterprises find hard to do without AI) and SIM-card fraud to detecting early signs of customer churn based on changing usage patterns, AI tools working on top of layers of accumulated and constantly updated relevant big data help telcos live up to high security standards and retain their customer base.
When combined, network telemetry, CDRs, data statistics, and equipment alerts enable CSPs to set up and maintain effective network self-diagnostics and self-configuration tools. Based on various issue occurrence scenarios ingested by a neural network, such tools can identify potential threats early on and dynamically reroute traffic to healthy nodes.
Partnerships and monetization
In addition to being valuable in and of itself, big data collected by telcos can be sold to or shared with third parties interested in monetizing it. For instance, insurance companies, marketing agencies, banks, or other financial institutions may be interested in learning more about the behavior of a particular cohort of users, which will help them optimize their service offerings.
Now that we know what the areas for improvement are, let’s consider a few big data telecom use cases.
Big data use cases in telecom
We mentioned the use of big data and data analytics for providing third parties with powerful business intelligence tools. Vodafone did exactly that with the launch of their own Vodafone Analytics platform that offers users valuable location-based insights as a fleet management system and helps them optimize their business operations for better accuracy and a higher ROI.
The service uses a combination of big data accumulated by the company and visualization tools from Citilogik and Carto to enable business users to access a convenient map-based visual browser of the activity logs of millions of Vodafone subscribers. Businesses interested in getting detailed data for their marketing campaigns can do so without investing considerable amounts of money into their own research.
This is just one of the many ways in which Vodafone leverages the power of big data, but it demonstrates how big data can be used not for subscribers or the company itself, but for other businesses as well.
This telecommunications giant invests millions of dollars and builds millions of lines of code in the development of AI-based network technologies and the corresponding community. The AT&T Chief Data Office is hard at work applying the latest concepts from the world of big data and AI to the company’s software and hardware infrastructure in preparation for the wide adoption of 5G.
The primary interest of the company is in the development of advanced AI-enabled tools fueled by big data coming from various sources, edge computing solutions for next-gen IoT devices, and intelligent software-defined networking (SDN) solutions for automatic network configuration, troubleshooting, and management.
The fifth-largest CSP in the world, DT has been using big data for years and is now offering a wide range of big data products and services to customers interested in added value in a number of areas:
- Mobile advertising
- Soccer analytics
- Smart parking
- Traffic management
- Smart fleet management
In addition to providing ready solutions, the company also provides secure hosting and data transfer services to customers seeking a reliable partner in the telecom domain.
Big data and the technologies it relies on, such as AI and ML, are the driving force behind progress in the telecommunications industry. We have reviewed only a few big data use cases in telecom, but it’s evident that major players are already using big data technologies to continuously improve customer service, network resilience, data transfer speed, and accessibility of their services on a global scale.
In the future, we will be seeing more examples of big data applications that will not only allow telecoms to thrive in an increasingly digitized world but also change the way we use telecommunication services every day.
Contact our experts to learn more about securing a competitive advantage and unlocking new sources of revenue by leveraging the power of big data.