Data Analytics in Telecommunications

Your essential guide to data analytics in telecommunications: how data is used, the impact of big data, the challenges and benefits, future technologies, and much more.

Jay Gupta
Jay GuptaGlobal Head of Telco Solutions
Apr 4th, 2024
15 min read

Telcos are up against an increasingly competitive market as their traditional revenue streams, such as connectivity, infrastructure, and voice-centric services, are commoditized. With so much change coming from the Internet of Things (IoT), digital convergence, environmental social and governance (ESG) issues, and the emergence of artificial intelligence (AI), telcos need to adjust their strategy to respond not only to a highly competitive market, but also economic pressures and supply challenges.

Data is the lifeblood that provides telecom organizations across the globe with the information they need to achieve a customer-centric competitive advantage. However, with up to 5% of EBITDA growth being missed due to the lack of good-quality data, now is the time to meet market challenges head-on with trusted data.

To make better decisions that move the business forward, telcos need a complete view of their data scattered across OSS, BSS and other systems, and the ability to extract insights from it.

Data analytics is one of these all-important areas. But what does it mean for telcos? In this useful guide, we will assess the importance, challenges, and impact of big data, and much more.

How is data analytics used in the telecommunications industry? 

In the telecommunications industry, data analytics has an important strategic and operational purpose.  From a strategic standpoint, it shapes critical decisions, influencing long-term business strategies and market competitiveness. Operationally, it is instrumental in enhancing network and service performance, underpinning revenue protection by adhering to stringent service-level agreements (SLAs), and maintaining the coveted five 9s (99.999%) uptime.

This balance between strategy and retention is vital. SLAs are not only operational targets; they are also key to customer acquisition and retention. They ensure high-speed and reliable services for retail customers and the highest uptime for business clients. Not meeting performance and quality targets impacts customer satisfaction and can also lead to significant penalties.

However, as the telecom industry pivots toward new digital operating models and customer expectations evolve, operators recognize the need to transcend performance and quality benchmarks. Data analytics must extend beyond the confines of SLA compliance, billing accuracy, and service performance metrics. It is increasingly about empowering every facet of the organization with the insights and analytical rigor necessary to inform decisions in an increasingly complex market environment. This broader application of data analytics is essential for telecom operators to remain competitive, compliant, and responsive in a rapidly changing world. 

The importance of data analytics in the telecommunications industry

Single customer view and supplier views

Contains integrating and interpreting diverse datasets, resulting in a more accurate and holistic view of customers and suppliers. This leads to better customer segmentation, more effective supply chain management, and improved customer relationship management.

Identifying cross-sell and upsell opportunities

Entails analyzing buying patterns, preferences, and customer journeys. This allows companies to anticipate customer needs and suggest relevant products or services, thereby increasing sales and enhancing customer experience.

Visibility of supply chain risk

By processing large volumes of up-to-date data, including external factors like market trends and geopolitical events, companies can stay on top of disruptions and red flags. This allows them to take preemptive actions to mitigate risks in the supply chain.

Visibility of customer and supplier compliance

Analyzes data related to activities that must adhere to compliance and code of conduct and flags anomalies and patterns indicative of noncompliance. This includes assessing compliance risks and ensuring adherence to standards and ethical practices.

Discovering customer prospects

Involves analyzing product data, market trends, and business networks to identify potential new customers. This leads to more effective sales targeting and customer outreach strategies.

Net zero and carbon accounting

Accurately tracks and analyzes emissions ratings and activities with environmental impact. This enables businesses to make data-driven decisions in their journey toward sustainability by choosing the right suppliers, meeting regulatory requirements, and enhancing ESG reporting.

Monitoring company code of conduct

Covers investigating employee and stakeholder activities for compliance with the company’s code of conduct. This helps maintain corporate integrity and ethical standards.

Fraud and corruption investigation

Proactively identifies and analyzes trends and anomalies indicative of fraudulent activities to effectively detect and prevent fraud and corruption. This allows organizations to safeguard their assets and reputation.

What is the impact of big data in telecommunications?

Big data has fundamentally transformed decision-making processes in many organizations, including telcos. Its essence lies in harnessing extensive datasets to extract valuable insights, enabling more informed decisions through an enhanced understanding of various options. Advanced analytics, incorporating AI and machine learning, further unlocks this potential by identifying patterns and trends that traditional methods and human analysis alone might miss.

The telecommunications industry recognizes big data as a key factor in driving innovation, overcoming challenges, and enhancing resilience to both expected and unexpected disruptions. This is increasingly relevant in a fast-paced world where breaking news can have an instantaneous impact.

Telecom operators are progressing at different stages in their big data journey, with the application of big data often concentrated in specific areas of the organization. A notable example is its role in driving IoT innovations to build strategic, revenue-generating solutions.

The advent of 5G networks, the growth of the IoT, and the surge in big data volume are catalyzing a shift toward using AI to help telecom operators evolve. More recently, advancements in Large Language Models (LLMs) and generative AI applications have spurred the industry’s commitment to harnessing big data, particularly to drive productivity gains across the organization.

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What is descriptive analytics in the telecommunications industry?

Descriptive analytics helps telcos understand and capitalize on trends, demonstrating the growing importance of big data analytics in the telecommunications industry. Trends from the last few years indicate the significance of big data analytics in the telecommunications industry. Important industry associations recognize this. The TM Forum, for example, published a data analytics guidebook, offering its members best practices, tools, and application program interfaces (APIs) to streamline big data projects.

Telecom operators possess extensive data on critical entities like customers, suppliers, business partners, and employees, often stored within isolated functional applications. For example, an enterprise resource planning (ERP) system helps manage resources like the supply chain, which is a form of descriptive analytics. This type of analytics focuses on tracking activities, comparing current metrics with historical data, and deriving trends and insights from that known data. The output guides procurement teams to make inventory adjustments based on past seasonal demand patterns or evolving market trends.

However, the scope of descriptive analytics is not without limitations. In areas like supply chain management, this process might overlook issues requiring additional context. For instance, it may miss signals of insider fraud, inflated prices, or security threats that could impact future dealings with a supplier. The inclusion of analytics from more diverse data sources could augment descriptive analytical tools by identifying such anomalies, providing a more comprehensive and nuanced understanding of operational challenges.

What is predictive analytics in the telecommunications industry?

Predictive analytics helps the telecommunications industry navigate the complexity of future customer behaviors and needs, which are more intricate than ever before.

While descriptive analytics provide insights into past performance, they fall short in forecasting the future. Predictive analytics steps in here, extending beyond historical data analysis to forecast future outcomes. This empowers business users to make more informed decisions. For instance, predictive analytics can enhance average revenue per user (ARPU) by identifying customers likely to purchase new products or upgrade services and by predicting churn probabilities.

However, predicting customer behavior and needs is increasingly complex. Todays customers are more discerning and strategic in their purchases, and their journeys are fragmented across multiple channels, both online and in physical locations like stores or trade shows. This results in more complex datasets, where spotting patterns is challenging due to the multitude of records and data points often stored in disparate systems and in various formats. 

Moreover, the last decade has seen a surge in software as a service (SaaS) application adoption, a notable example is in digital marketing. Keeping pace with numerous transformative applications has been a significant investment for organizations. Accurate predictions and effective decision-making now demand a heightened focus on data quality. Capturing the correct context and ensuring data accuracy for customers, suppliers, business partners, and other entities is essential to derive meaningful predictions to inform business decisions.

What are the types of data used for telcos?

The telecommunications industry has consistently been at the forefront of analyzing network-level data. This includes traffic patterns, usage data, predictions of network failures, effective management of traffic loads, and oversight of upgrades. Developing analytics around the network has long been a fundamental practice in the industry and remains its operational cornerstone. However, with the increasing shift toward software-driven technology and the widespread adoption of cloud applications, there is a growing need to adapt. The goal is to extend data analytics with the same level of rigor into other domains, ensuring a comprehensive approach to intelligence across all areas of operation.

Expanding analytics capabilities also needs the integration of internal datasets with external ones to gain a more holistic view of relevant entities. For instance, procurement teams more than ever need to onboard suppliers rapidly to meet technology and customer demands while simultaneously adhering to stringent standards in the corporate code of conduct and governance. This includes areas like cybersecurity, sanctions, carbon emissions, and other risk factors that could have financial and reputational consequences if inadequately addressed. By incorporating external datasets such as corporate registries, watchlists, and cybersecurity ratings, telecom operators can enhance their risk assessment and streamline decision-making processes, ensuring more robust and responsible business operations.

Challenges in implementing data analytics for telcos

Data quality, inconsistencies, and preparation

The foundation of any data analytics process is effective data management. Challenges in accuracy and de-duplication are particularly evident when source quality is poor. Traditionally, data management solutions have required conforming to a fixed data schema, which is an extremely time-consuming process when working with data in different formats and of varying quality.

Traditional data management systems also obstruct scalability due to their processing limitations and lack of third-party data enrichment. Consequently, the lengthy process of deriving value from data often leads to cumbersome and ineffective analytics.

In contrast, context-based data management using entity resolution accelerates value realization and reduces risks through phased implementation. This shift is key to enabling more sophisticated and impactful data analytics. Now the aforementioned data quality challenges are addressed by enabling data matching regardless of source quality. Modern platforms take a schema-agnostic approach and allow for source-independent data ingestion. Instead of requiring high-quality data at the source, newer methods assess data quality in context for a more effective approach to data integrity.

    Diverse, disparate, and siloed data sources

    In the dynamic landscape of the telecommunications industry, one of the paramount challenges in data analytics is the intricate web of diverse, disparate, and siloed data sources. Telecommunication providers generate a vast array of data from various touchpoints, including customer interactions, network performance, and operational processes The challenge arises from the heterogeneous nature of this data, often stored in different formats, structures, and locations. Siloed databases and systems further complicate the integration and analysis of these datasets, hindering the comprehensive understanding of the business ecosystem.

    To harness the full potential of data analytics, telcos must grapple with the formidable task of harmonizing these disparate sources, breaking down information silos, and implementing robust data integration strategies. Overcoming this challenge is essential for unlocking valuable insights that can drive strategic decision-making, enhance customer experiences, and optimize operational efficiency in the fast-paced and competitive telco landscape.

      Data privacy and security

      Telecom providers deal with vast amounts of sensitive customer information, making them attractive targets for cyber threats. Striking the right equilibrium between extracting valuable insights from data and safeguarding customer privacy is crucial, necessitating robust encryption protocols, secure data storage solutions, and adherence to stringent regulatory frameworks to ensure the integrity and confidentiality of the information being analyzed.

        Skill and talent gap

        As the demand for sophisticated data-driven insights rises, there is a scarcity of professionals equipped with the requisite expertise in data science, machine learning, and advanced analytics. Outsourcing certain aspects of data analytics can supplement internal capabilities, enabling telcos to expedite their digital transformation and innovation efforts without struggling with the challenges of an immediate shortage of in-house talent.

          What are the future technologies for analyzing data in telecommunications?

          Future technologies underpinning data analysis will pave the way for smarter, more automated, and integrated systems. Central to this evolution is AI, providing essential tools to interpret the massive data volumes telecom operators generate. With telcos expected to spend at least $200 million annually on their cloud journey in the next three to five years, the quality and accessibility of data in cloud environments become increasingly vital. AIs effectiveness depends more than ever on the quality of the data it processes. Suboptimal data quality leads to inaccurate insights and misinformed decisions, negatively impacting business performance. Therefore, the adoption of AI and advanced analytics must be complemented by robust data quality technologies, ensuring data is clean, consistent, and reliable.

          The data fabric emerges as a key framework for data quality. It brings a greater level of agility, efficiency, and intelligence to data management, particularly relevant as telcos invest heavily in cloud infrastructure. It adds context by unifying internal and external data records. For telecom operators, this translates to clearer, more integrated insights, enabling more informed business decisions in a rapidly changing industry. The data fabric's ability to support, enhance, and partly automate decision-making will be crucial. It will influence productivity and aid employees in various domains such as compliance, customer experience, supply chain, risk, operations, and finance. Moreover, the technology driving this level of decision-making will focus on delivering insights that are interconnected, contextual, and continuous.

          What is the future of telecommunications data analytics?

          The future of data analytics in telecommunications lies in a sophisticated blend of human and machine intelligence, creating a decision-making ecosystem that fully contextualizes data. As the telecommunications industry navigates an era of rapid digital transformation and the increasing impact of global events on businesses, the industry is poised to adopt analytics technologies that will integrate external factors like geopolitics, weather, and regulatory changes into everyday decision-making processes. This approach will enrich organizational data analytics systems, enabling them to assimilate a wider range of influences effectively and quickly.

          One notable recent event is the impact of generative AI on the supply chains for critical computing hardware. To navigate these challenges, some organizations are working hard to protect relationships with suppliers to secure access to new GPUs and gain a competitive edge. However, this situation is not without risks, as evidenced by the increasing prevalence of counterfeit and inferior hardware resulting from these shortages. This scenario is a real-life example of the imperative for data analytics in telecommunications to be attuned to emerging trends and external data to mitigate the risks.

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