Machine Economy Trends Transforming the Telecommunications Sector

Over the last decade, the telecommunications sector underwent a great shift: going digital. 

Those who succeeded were able to adopt new, digital technologies to modernize their operations, improve customer experiences, and make themselves more efficient. Now that these technologies are becoming increasingly affordable and mainstream, we’re on the verge of the next great telecommunications shift; getting smart. 

Advancements in network infrastructure, computing power, and 5G technologies have given rise to a new era of telecommunications powered by AI, machine learning, IoT, and other "smart" technologies in what's being referred to as the "Machine Economy". 

These smart technologies are already permeating all aspects of the telecommunications sector, making it easier to monitor operations, identify problems, optimize strategies, and pinpoint efficiencies. By leveraging this wave of new technologies to their advantage, companies can stay ahead of the competition for years to come.  

3 Machine Economy trends that will rapidly transform the telecommunications industry 

Telecommunications companies continue to operate complex networks made up of physical servers, switches, routers, firewalls, lines, towers and other infrastructure. As the underlying technologies that power these systems continue to advance at an exponential rate, their capabilities will become increasingly advanced and intelligent. 

This transformation will be made possible by these 3 primary technology trends: 

1. AI and Machine Learning

Machine learning is a branch of AI that provides systems the ability to automatically learn and improve from experience without explicit programming. AI and machine learning allow machines to do things like recognize images, translate languages, and predict customer behavior. 

Telecommunications companies are investing heavily in machine learning to turn their existing data into new revenue opportunities. For example, telecommunications companies could use machine learning algorithms to predict where call volumes are highest and then add more capacity at those locations. In this way, telecommunications companies can avoid costly network expansion projects that involve significant lead times and upfront expenses.   

Telecommunications companies are also using AI and machine learning for customer service. AI provides telecommunications companies with the ability to automatically route customer calls or chats from a company website to a human agent. Machine learning algorithms determine the best way to interact with customers based on previous conversations and adjust their behavior accordingly. AI in the form of "chatbots" can even learn to automatically respond to commonly asked questions in an effort to reduce workload on human agents.  

Machine Learning can also be used for predictive maintenance. By analyzing data from telecommunications networks, machine learning models can predict when a hardware component is likely to fail and flag it for preventative replacement or maintenance.  

Fraud detection is another area where telecommunications companies are using AI to improve existing processes. By using complex algorithms, telecommunications companies can detect nefarious use of their networks in real-time and take action.   

2. Internet of Things (IoT)

IoT refers to the network of physical devices, vehicles, and other items embedded with electronics, software, sensors and connectivity which enables these objects to connect and exchange data.   

The telecommunications sector is an ideal candidate for IoT due to its broad use of connected devices and high demand for connectivity. In fact, telecommunications companies are leading adopters of IoT technology in both consumer and business applications.  

Telecommunications companies are already connected to many devices, such as cell phones, laptops, tablets, and wearable technology. When telecommunications companies add connectivity to other items in the home or office, they can enable new applications that enhance customer experiences and create new revenue opportunities.  

For example, telecommunications companies can provide customers with IoT cameras that detect when a child arrives home from school and send an alert to the parent's smartphone. The telecommunications company could also offer customers connected smoke detectors with built-in 911 connectivity to give them peace of mind in the event of an emergency.   

Telecommunications companies can also integrate connectivity into products, such as thermostats and appliances, to offer customers new services. For example, telecommunications companies could provide energy management recommendations that help customers cut their electricity bills by controlling air conditioning or lights.  

Telecommunications companies are also using IoT devices for maintenance purposes to improve network performance. For example, telecommunications companies can use sensors to monitor network equipment and infrastructure, such as cellular routers and base stations, from a central location. In the event of an outage, telecommunications companies can use this data to have personnel arrive at the scene faster which can minimize downtime for customers.  

3. Robotics

Robotics is the integration of artificial intelligence, machine learning, software, sensors, actuators, and other systems to create autonomous machines.  

Telecommunications companies could use robots to automate jobs that are currently done by humans. Robots have shown potential in telecommunications, particularly in maintenance tasks. For instance, robots can be used to inspect telecommunications equipment that is difficult or dangerous for humans to access, such as cell towers or underground telecommunications cables.  

These robots are equipped with cameras that send live video back to operators who then assess the conditions of these assets based on what they see. This provides telecommunications companies with much more information than physical inspections can provide, which is important as telecommunications companies face increasing pressure to modernize their infrastructures.   

In addition, IoT sensors can gather data from telecommunications networks and then use robotics to take actions based on the information collected. Using data collected by these sensors, telecommunications companies can take proactive steps to avoid damage to their networks in the face of severe weather events or other natural disasters.   

Data is the lifeblood of the Machine Economy 

It's clear that the telecommunications sector has a lot to gain from these technologies. However, none of this is possible without data.    

In the Machine Economy, data will continue to increase in both volume and value, but many data teams are already struggling to keep up. 

Data volumes are exploding, but expectations for how fast data should be curated, prepared, and delivered for analysis and AI/machine learning haven’t changed.   

Traditionally, the data preparation process has relied on a highly-complex stack of tools, a growing list of data sources and systems, and months spent hand-coding each piece together to form fragile data “pipelines”.  

Then came data management “platforms” that promised to reduce complexity by combining everything into a single, unified, end-to-end solution. In reality, these platforms impose strict controls and lock you into a proprietary ecosystem that won’t allow you to truly own, store, or move your own data.  

It’s clear that the old ways of doing data management simply cannot meet the needs of modern data teams in the Machine Economy.  

Data professionals are in desperate need of a faster, smarter, more flexible way to build and manage their data estates.  

Meet TimeXtender, the low-code data estate builder.  

TimeXtender empowers you to build a modern data estate 10x faster with a simple, drag-and-drop solution for data ingestion and preparation.  

Code and documentation are generated automatically, which reduces build costs by 70%, frees data teams from manual, repetitive tasks, and empowers BI and analytics experts to easily create their own data products.  

TimeXtender seamlessly overlays your data infrastructure, connects to 250+ data sources, and integrates all the powerful data preparation capabilities you need into a low-code, agile, future-proof solution.  

We do this for one simple reason: because time matters.  

TimeXtender empowers everyone on your team:  

• Business leaders get fast access to reliable data, with 70% lower build costs and 80% lower maintenance costs.  
• Data teams get freedom from manual, repetitive tasks, and have more time to focus on higher-impact analytics projects.  
• BI and analytics users get a code-free experience for creating their own data products – no more bottlenecks.  

By making the complex simple, and automating all that can be automated, our goal is to free up millions of human hours that can be used to execute on what matters most and change the world.  

This is what winning looks like in the Machine Economy. 

Learn How to Become Data Empowered with TimeXtender   

Book a demo to learn how we can help you build a modern data estate 10x faster, become data empowered, and win in the Machine Economy.