Big data for telecom is the fuel that can (and will) drive the entire industry toward higher revenues and better customer service. The majority of telco executives are now awakening to the fact that the big data in their industry, which has until now mostly been discarded or left unused, actually has immense financial potential.
With the right approach to data science and business data analysis, telecommunications companies can dramatically improve their services and their subscribers’ experience — without major investments to upgrade hardware infrastructure.
Big data analytics trends in the telecom sector
According to Valuates Reports, the global market of big data analytics for telecom is expected to increase from $198.08 billion in 2020 to $684.12 billion by 2030, growing at a CAGR of 13.5% during the forecast period. Right now, the global population produce more data in two days than throughout decades of human history by simply browsing the internet. Imagine the amount of data coming from smart homes and cars, on-demand videos, streaming apps, gaming, and other entertainment and educational applications we use daily.
Telecom companies have to be able to collect this massive amount of data from different sources, analyze it, and distribute the insights to disparate databases. That is what drives the growth of the big data market. To cope with all the changes, companies have to stay alert to the newest trends in big data analytics in the telecom industry:
- Data as a service (DaaS), using cloud technology to provide on-demand access for users and applications
- Smarter artificial intelligence (AI), enabling better learning algorithms with a shorter time to market
- Predictive analytics to examine modern data and historical events to predict possible future hazards and happenings
- Edge computing as a way to process massive amounts of data and consume less bandwidth
- Hybrid clouds providing needed flexibility and more data deployment options by moving processes between private and public clouds
The aggressive growth of technologies will continue producing even higher volumes of data, which telecom companies must process smartly to stay ahead of their competitors.
Big data challenges faced in telecom
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 telcos have a pretty challenging situation on their hands.
Core telecom business under pressure
Source: PwC
These circumstances are worrying not just on the business side of things. From the technical perspective, initiatives related to big data in telecommunication have much to do with general data management challenges.
Data heterogeneity
In most big data telecom use cases, collected data is not uniform. Back in the day, data could consist almost exclusively of well-structured call detail records (CDRs), making it easy 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, etc.
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 will likely be using siloed, non-synchronized local storage. Unifying all of this data into a single data pipeline requires a team of skilled data professionals with a solid data engineering strategy.
Extra-large data arrays
What used to sound like an immense amount of data some 10 to 15 years ago would not raise an eyebrow today. Data consumption by users and data generation by corresponding software systems are steadily growing year to year. The number of data sources is growing as well. All this makes it increasingly challenging to capture, process, and store big data for telecom operators.
Data processing complexity
Back in the day, when big data analytics in the telecom industry was limited to running SQL queries against well-structured sets of tabular data, the task of mining for business insights was somewhat easier. Today, with data coming in multiple formats and requiring an individual ETL approach in every case, telecommunications companies face various 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.
Let’s take a look at the benefits of properly managing data and using specialized telecom software to draw actionable insights and improve decision-making in the telecom world.
Benefits of exploring big data in the telecom domain
Source: BusinessWire
Here are just a few things that big data analytics can do to make a positive impact in the telecom industry:
- Help telcos get a clear vision of potential new products
- Effectively prevent traffic fraud
- Improve targeted marketing and services for customers
- Design and implement value-based network capacity adjustment strategies
- Substantially improve the customer experience and 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 high
Achieving these goals takes time and substantial effort 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.
Use cases of big data in the telecom industry
Customer satisfaction
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 portfolios, design and implement new features, and provide the best customer support possible.
Customer churn prevention
Engaging new customers takes a lot of time and effort, and so does retaining customers. Companies have to analyze customers’ behaviors and take corresponding actions to prevent customer churn. Big data enables platforms where customer data from transactions and real-time communication streams are brought together. Telecom data analytics use cases give insights into this data, help disclose customers’ feelings regarding services they receive, immediately address satisfaction issues, and prevent churn.
Risk mitigation
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. For example, these tools can help with identifying security breaches (over 60% of enterprises find it hard to work without AI) and SIM card fraud to detect early signs of customer churn based on changing usage patterns.
Network optimization
When combined, network telemetry, CDRs, data statistics, and equipment alerts enable communications service providers (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 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, and other financial institutions may be interested in the behavior of a particular cohort of users, which will help them optimize their service offerings, and, thus, boost big data monetization in telecoms.
Product development
Developing a product is a complex process requiring proper control and management. Big data analytics use cases in telecom companies can greatly assist with data-driven product development, internal feedback, and marketing intelligence. Apart from that, smart data solutions help ensure high product performance and conformity to customers’ requirements.
Compelling big data case studies in telecom
Vodafone
We mentioned using big data and data analytics to provide third parties with powerful business intelligence tools. Vodafone did exactly that with the launch of their 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 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 in 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 by CSPs not for subscribers or the company itself but for other businesses as well.
AT&T
This telecommunications giant invests millions of dollars and writes millions of lines of code in the development of AI-based network technologies. 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 company’s primary interest is developing advanced AI-enabled tools fueled by big data from various sources, edge computing solutions for next-gen IoT devices, and intelligent software-defined networking (SDN) solutions for automatic network configuration and troubleshooting.
Deutsche Telecom
The fifth largest CSP in the world, Deutsche Telecom 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 provides secure hosting and data transfer services to customers seeking a reliable partner in the telecom domain.
Bottom line
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 this industry, but it’s evident that major players are already using big data technologies to continuously improve customer service, network resilience, data transfer speeds, and accessibility of services on a global scale.
In the future, we will see more use of data analytics in telecommunications. That will not only allow telecoms to thrive in an increasingly digitized world but will also change how we use telecommunication services daily.
Contact our experts to learn more about securing a competitive advantage and unlocking new sources of revenue by leveraging the power of big data.
