By Ocient Staff
The introduction of the smartphone almost 20 years ago completely redefined data demands for communications service provider (CSP) networks — adding SMS, MMS, a high-res camera, internet browsing, the proliferation of mobile apps, and more. But the exponential data growth of the last two decades is only accelerating: A recent report from the CTIA shows Americans’ wireless data usage jumped up 36% from 2022 to 2023 — the biggest year-over-year increase ever — surpassing 100 trillion megabytes of wireless data usage in the US alone. Americans are now sending more than 2 trillion texts and logging over 2 trillion voice call minutes every year — that’s over 6 billion texts and voice minutes every day.
This accelerating data growth is overwhelming CSPs’ conventional strategy of retrofitting legacy data infrastructure to keep up with data ingestion and retrieval demands. We talked about the growing challenges around basic security, compliance and “keep the lights on” operational demands of large scale data management in a recent blog.
But leading CSPs recognize that the goal isn’t to keep pace with today’s demands. Rather, it’s to create a competitive advantage for tomorrow.
By harnessing the wealth of network, geospatial, and IoT data streaming off the network every second of every day, CSPs have the opportunity to transform how they deliver their services — driving better network performance, delivering more personalized service experiences, and leveraging data to develop new revenue streams, from innovative new products to profitable new partnerships.
But, to execute on these use cases and capture the business value within their data, CSPs need to build out the infrastructure to handle data ingestion, retrieval, and analytics at full speed and scale — and do so in a consolidated and cost-effective manner.
Four next-gen applications of data analytics for CSPs
1) Improving service quality and enhancing customer experience
The first order of business for CSPs is ensuring that end user experience on the network meets or exceeds expectations. Quality of service has long been the No. 1 reason people leave their CSPs. But today, tolerance for poor service quality is lower than ever — customers jump ship after one or two bad experiences.
CSPs can harness network data to get ahead of bad experiences by monitoring both quantitative and qualitative sources of data on customer experience. Quantitative sources include monitoring and analyzing call experience, looking for non-connections, dropped calls, etc.; looking at internet speeds and identifying throttling issues; and monitoring broader network access issues to recognize early signs of bandwidth issues or outages.
That data can be cross-referenced with qualitative customer experience data. This includes data from the contact center on customers calling (or emailing, chatting, etc.) to report issues, outages, etc., as well as data signals on website, social, mobile app, or other channels showing customers looking for information or assistance to resolve issues, or other signs of frustrating experiences.
These real-time analytics applications give CSPs a much more real-time look at what’s happening across the network, in terms of the experiences that customers are having right now.
By feeding call records, internet usage patterns, and other forms of metadata into advanced analytics, AI, and machine learning tools, telcos can surface actionable insights about service performance issues, so they can be rapidly responsive in resolving issues. This kind of responsive service can not only curb negative customer sentiment around network issues — it can often turn a network issue into a net-positive experience for the customer by impressing them with intelligent, proactive service.
2) Proactive network analysis and planning
Immediately seeing and rapidly remediating problems when they do occur is critical. But much of the same network data can be leveraged to take a more proactive and strategic approach to planning and optimizing the network for future growth.
However, today’s highly complex networks — comprised of multiple spectrum bands and a variety of third-party technologies — can make network monitoring and planning activities exceedingly difficult.
The emergence of advanced data processing and analytics finally gives CSPs the toolset to harness all their diversity of network data at speed and scale. These advanced analytics engines can effectively and instantly bring together data signals across all customers, all devices, all nodes of the network, all data flowing across the network (CDR, ICR, 5G data, etc.), as well as other data streams, like equipment/back plane information, available bandwidth/by location, real estate information, competitor footprint/prospect customer base info, etc.
This new level of integrated network analytics allows CSPs to design the optimal solution or path forward for some of the outputs. Moreover, these broad and deep network insights enable CSPs to take a more data-driven approach to prioritizing issues or improvements based on their potential impact or business value. Organizations can strategically invest in building up their networks in ways that will drive the highest (or most immediate) ROI.
Next-gen predictive analytics and machine learning tools will also prove extremely valuable for growth planning. CSPs can more confidently forecast or predict future network demands to shore up or build out their networks.
3) Developing innovative, highly personalized services
CSPs have a of behavioral insights buried within their network data. All those IP internet records, call records, SMS messages, etc. reveal important insights about customer preferences. Being careful to follow all appropriate legal mandates and opt-in regulations, CSPs can connect this data with demographics to create cohorts or groupings of similar types of users.
Broad and deep analyses of this data can deliver better understanding of individual preferences and behaviors. This will allow CSPs to deliver hyper-personalized services, more targeted and effective marketing and promotions, and ultimately drive customer satisfaction and loyalty.
For instance, telcos can use AI to recommend data plans or content bundles based on a customer’s usage patterns or offer real-time assistance through AI-powered chatbots.
4) Using data to create new revenue streams
The behavioral insights within network data hold promise that extends far beyond the figurative walls of the CSP. This is valuable information that is already in high demand across all sectors of our increasingly connected, data-driven economy.
For example, thought leaders are envisioning telco data as the fuel for a new kind of “smart city,” where IoT and AI technologies come together to enhance various aspects of city management and services to create cities that are more efficient, responsive, and sustainable.
This is just one way that CSP network and system data can be packaged and productized to create new revenue streams. Telcos are also developing privacy-safe models for providing data to third parties, so partner organizations can leverage deep and broad insights to drive their own innovation.
For example, CSPs can use data analytics to offer targeted audience insights to advertisers, provide the foundational data for new market research, and harness geospatial data to help partners drive location-based services.
These innovative data analytics applications can create new revenue streams to boost profitability, support further investment in innovation, and give telcos a competitive edge in an increasingly crowded market.
Building future-ready data infrastructure for next-gen analytics
As CSPs look to capitalize on the above opportunities, their legacy tech stacks are the limiting factor. Traditional data warehouses fall short, unable to keep pace with rapid data growth and complex analytic demands.
To harness the full potential of this data, organizations need a future-ready data infrastructure designed to handle extreme data volumes, deliver real-time analytics, and provide advanced capabilities such as machine learning and geospatial analysis.
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- Consolidated data types and sources: CSPs need the ability to ingest data from multiple sources and formats at scale, enabling comprehensive, high-resolution visibility into complex network environments. This consolidation allows for more efficient data management and supports richer insights across various data streams.
- Optimized performance on advanced analytics: To handle growing data volumes and complexity, CSPs need to step up to a higher level of storage and processing technologies, such as NVMe-based solutions. These new kinds of technologies can execute sophisticated queries and analytics on large datasets without latency, ensuring that insights can be generated quickly, even during peak loads.
- Support for integrated machine learning: Although it’s clear that ML can provide many valuable opportunities to use data in new and innovative ways, building ML into an existing architecture can be prohibitively difficult and expensive. Embedding machine learning capabilities directly into the analytics platform helps CSPs train and deploy models on large volumes of data without extensive data movement. This integration accelerates the development of actionable insights and improves decision-making processes.
- Purpose-built geospatial analytics tools: The limitations of many of today’s spatial analytics platforms keep organizations from being able to analyze more than a fraction of the geospatial data at their disposal. Utilizing purpose-built geospatial analytics allows CSPs to enrich their data models with location-based insights, enhancing capabilities like network optimization, location-based services, and targeted marketing. Supporting a wide range of geospatial data types and functions can unlock new business opportunities and improve service delivery.
- A simplified data analytics stack: It’s also important that CSPs mitigate the problem of tech bloat within their data analytics stack. CSPs should aim for platforms that combine SQL OLAP capabilities with real-time analytics, allowing for direct data ingestion from streaming services or source databases without the need for complex ETL processes. A streamlined data analytics stack can reduce operational overhead and improve efficiency.
Leveraging next-gen analytics infrastructure to meet compliance demands
As more CSPs build out the data infrastructure to support next-gen analytics applications, this infrastructure modernization will move from a strategy for competitive differentiation into a more urgent necessity for survival. But today, CSPs are facing another challenge which presents a very similar set of demands: lawful disclosure requests that are rapidly increasing in both volume and complexity, requiring CSPs to build out more advanced capabilities to retain and rapidly retrieve relevant data to meet these requests.
Fortunately, the advanced capabilities that enable CSPs to efficiently ingest, process, and analyze massive volumes of data for personalized services and innovative applications can also be applied to meet this lawful disclosure challenge. CSPs can leverage very similar strategies to streamline the retrieval and secure handling of data required for compliance, minimizing the operational burden and reducing response times. This integrated approach not only helps CSPs fulfill regulatory obligations efficiently but also ensures that compliance does not become a separate, costly, and cumbersome process.
How Ocient can help
Ocient is at the forefront of helping CSPs build future-ready data warehousing and processing infrastructures to enable next-gen analytics. Ocient’s unified and scalable solutions are purpose-built for these challenges, offering a comprehensive approach that helps CSPs unlock the value of their data, optimize their operations, and prepare for the next wave of digital transformation.
Our unique Compute Adjacent Storage Architecture™ (CASA) transforms, stores, and analyzes 20x to 50x more data using NVMe solid-state drives and ultra-fast networking, making it possible to continuously ingest terabits of data per second and generate timely, business-critical insights.
Moreover, CASA addresses the other key priority: cost, which often limits speed and scale. By reducing the amount of hardware and the number of cloud instances required to enable always-on compute-intensive workloads, Ocient customers typically see energy savings of 50-90% with CASA and the Ocient Hyperscale Data Warehouse™.
Our approach includes:
- Tailored solution engineering: From pilot to production, Ocient collaborates closely with your team to design custom schema, queries, and ETL workflows that meet your specific requirements. We streamline the entire process, from loading and concurrency testing to training, onboarding, and go-live support.
- Rapid deployment and ROI: Ocient’s data analytics solutions are fully operational within months, enabling faster time-to-market without adding resources. This means you can achieved time and focus on growing your business.
- Flexible management services: Ocient’s expert team can also handle the ongoing management of your data warehouse deployment, including software installation, upgrades, monitoring, and more, so you can focus on your core business priorities.
- No appliance lock-in: Enjoy the benefits of large scale analytics without vendor lock-in. Ocient’s architecture works seamlessly across on-premises data centers, public cloud providers like AWS and Google Cloud, and our own OcientCloud™.
- Predictable pricing: Ocient prices each system by the number of CPU cores or nodes rather than the amount of compute consumed, which lowers the costs associated with always-on data workloads.