Most of us can’t live without our mobile devices. Whether we’re using a smart phone to connect with social media or a tablet to watch videos or shop online, we’re digitally connected. That’s not about to change either. Within the next five years, more than 5.5 billion consumers will be clicking and swiping away on mobile devices. Each user, on average, will consume 30GB of data each month. You don’t have to do the math to know this will require that a huge amount of data traffic be moved across telco networks.
Clearly, as the number of users, devices, data and traffic increase, telco service providers (SPs) are playing an increasingly pivotal role in enabling the digital society. SPs are, however, facing disruptive forces that are changing the landscape and presenting new challenges and opportunities.
Disruptive forces changing telecommunications industry
For years, both fixed and mobile SPs have followed a technology-driven, one-size-fits-all approach. The fundamental strategy was to increase speed and bandwidth everywhere possible. But today, it’s not about just bandwidth and speed. The soaring demand for data among both consumers and businesses, SPs are faced with evolving technology, industry standards and regulations, and business models present SPs with new challenges as well as new opportunities:
Technology. SPs are embracing fifth generation mobile networks (5G) that will provide more capacity and a higher density of mobile broadband. They are also moving to architectures based on Software Defined Networking (SDN) and Network Function Virtualization (NFV) combined with hybrid operating models.
Industry standards and regulations. Although the 5G standards are still a few years away from mass deployment, various spectrum frameworks, and roaming and termination rules are already being adopted. Other significant regulatory challenges include net neutrality and data protection.
Business models. SPs are shifting focus to improving their ability to handle many connections concurrently within the same cell while maintaining low latency. Other business drivers include zero rating data plans and location-based mobile advertising.
Mind the analytics and action gap
Though consumers and businesses generate boatloads of digital data, SPs have typically taken limited advantage of it, mainly using historic data for analytics. Since telecom networks are by definition real-time with a high availability rate, most SPs have focused instead on the data that can drive operational decisions for availability, redundancy and scale.
SPs have a tremendous breadth and depth of real-time data available across their businesses, most of it waiting to be leveraged. Data and analytics have been mostly about usage of historic data for predicting strategic insights but less about decisions in the moment of subscriber engagement:
Network infrastructure. Data is tracked for network speed, latency type, signaling and location information, faults and outages, and more. Most of the data here are used by SPs to make daily operational decisions on routing, traffic management, and access.
Usage. Voice call information, data and SMS usage, application behavior and use, device types in the network are just some of the usage data available to SPs. Typically SPs have utilized this data to predict future usage patterns based on historic analysis.
Customer. SPs capture subscriber information such as age, income, preferences, billing and payment history, but have taken limited advantage of this data set in real-time customer interactions as well as social networks and apps.
Billing. Call data records (CDR), event data records (EDR), tariff plans, usage, adoption, pricing and marketing data are among the types of data that reside in SP billing systems. While SPs have leveraged data from billing systems to further proactive marketing and targeting of customers, most of this happens offline rather than real-time and in decision silos.
Support. Network issues, support calls, orders, fraud and anomalies are among the data tracked in SP support systems. SPs, however, do not use support data to analyze customer loss. Doing so in real-time could lead to greater insight and drive customer retention actions in real-time.
Real-time decisioning is transformative for telecommunication services providers
Historically, transactional systems and analytics systems have been deployed and maintained in separate silos. However, major advances in data analytics, artificial intelligence, machine learning, and database architectures are now enabling the convergence of transactional and analytical systems. What this means is analytics can now be integrated with transactional processes to foster real-time decision making. “Transactional analytics,” as this is known, can enable SPs to achieve breakthrough cost savings and capital intensity while maintaining or even increasing their scale.
With transactional analytics, SPs can achieve transformation across three strategic areas:
Networks. SPs can dynamically manage and optimize network resources to deliver the best user experience at the lowest cost.
Customer-centricity. With real-time decisioning, SPs can provide personalized products and services, more engaging customer experiences and reduce churn. For more about how transactional analytics transforms the customer experience, see this post.
Digital innovation. Hyper-personalization based on real-time customer insights and usage will help drive new revenue models for SPs.
Transactional analytics requires a new architecture
For telco digitalization strategies to be successful, the analytics process must be tightly integrated with transaction processing systems. Prevailing relational and NoSQL databases with caching, as well as RAM-based, in-memory databases often fail, particularly when unanticipated peak loads occur. Without warning, response times can become unacceptable, errors can occur, systems can crash, and data can get lost.
Modern telco transactional analytics systems need to ability to:
Continuously ingest, correlate and analyze structured and unstructured data;
Analyze data in real time, make decisions and take action instantaneously – during the course of the transaction;
Move data and decision points to the edge of the network for faster, better and lower cost execution;
Scale to millions of transactions across millions of subscribers.
The Hybrid Transactional/Analytical Processing (HTAP) architecture, according to Gartner, is best suited for these applications. By removing the latency associated with moving data from operational databases to data warehouses and data marts for analytical processing, HTAP enables real-time analytics and situation awareness on live transaction data.
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