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<\/div>\n\t\t\tBig data analytics trends in the telecom sector<\/h2>\n
According to Valuates Reports<\/a>, 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.<\/p>\n 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:<\/p>\n 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 solutions for telecom<\/p>\n It\u2019s no secret that telecoms are facing withering revenues<\/a> 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.<\/p>\n To add to the problem, competition in the telecommunications market has never been more vicious. Now throw the ramifications of COVID-19<\/a> into the mix and it looks like telcos have a pretty challenging situation on their hands.<\/p>\n Source: PwC<\/a><\/em><\/p>\n 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. Discover how Intellias helped a top German telecom company enhance data management<\/a><\/p>\n <\/div> \n <\/div><\/p>\n 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.<\/p>\n 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<\/a>.<\/p>\n 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.<\/p>\n 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.<\/p>\n But don\u2019t fret just yet \u2014 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.<\/p>\n Let\u2019s 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.<\/p>\n Source: BusinessWire<\/a><\/em><\/p>\n Here are just a few things that big data analytics can do to make a positive impact in the telecom industry:<\/p>\n 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.<\/p>\n Precise, timely, and comprehensive big data analysis<\/a> 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.<\/p>\n Engaging new customers takes a lot of time and effort, and so does retaining customers. Companies have to analyze customers\u2019 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\u2019 feelings regarding services they receive, immediately address satisfaction issues, and prevent churn.<\/p>\n 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<\/a> (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. AI and ML development services<\/p>\n 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.<\/p>\n 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.<\/p>\n 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\u2019 requirements.<\/p>\n 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<\/a> platform that offers users valuable location-based insights<\/a> as a fleet management system<\/a> and helps them optimize their business operations for better accuracy and higher ROI.<\/p>\n 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.<\/p>\n 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.<\/p>\n This telecommunications giant invests millions of dollars and writes millions of lines of code in the development of AI-based network technologies<\/a>. The AT&T Chief Data Office is hard at work applying the latest concepts<\/a> from the world of big data and AI to the company\u2019s software and hardware infrastructure in preparation for the wide adoption of 5G<\/a>.<\/p>\n The company\u2019s 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.<\/p>\n 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<\/a> to customers interested in added value in a number of areas:<\/p>\n 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.<\/p>\n 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\u2019s 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.<\/p>\n 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.<\/p>\n\n
\nBig data challenges faced in telecom<\/h2>\n
Core telecom business under pressure<\/b><\/h3>\n
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\nData heterogeneity<\/h3>\n
Disparate and siloed data sources<\/h3>\n
Extra-large data arrays<\/h3>\n
Data processing complexity<\/h3>\n
Benefits of exploring big data in the telecom domain<\/b>
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Use cases of big data in the telecom industry<\/h2>\n
Customer satisfaction<\/h3>\n
Customer churn prevention<\/h3>\n
Risk mitigation<\/h3>\n
\nNetwork optimization<\/h3>\n
Partnerships and monetization<\/h3>\n
Product development<\/h3>\n
Compelling big data case studies in telecom<\/h2>\n
Vodafone<\/h3>\n
AT&T<\/h3>\n
Deutsche Telecom<\/h3>\n
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Bottom line<\/h2>\n
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