New technologies are changing the landscape of CSPs
Communication Service Providers (CSPs) are looking to take advantage of data-driven digital transformation and leading towards becoming the Digital Service Providers (DSPs). The rapid change in the Telecom Sector is sheerly defined by the new upcoming technologies like Internet of Things (IoT), Cloud Computing, Edge Computing and Network Function Virtualization (NFV). But the evolution of all these technologies is marked by the revolution called 5G.
5G is the next big thing for CSPs today as it offers them multiple opportunities to move beyond traditional revenue streams and open up new avenues for growth. Typically, 5G in comparison to 4G has the potential to offer speeds of between 1-10 gigs per second which is approximately 20x to 30x faster, 10X reduction in latency and 100X Increased traffic capacity.
With 5G coming into the picture, the single technology that will benefit the most and drive everything will be AI-based Data Analytics. With the evolution of 5G, Data Analytics will be the early adopters as it provides the same benefits which were earlier obstacles for it e.g. – data agility, variety of data, edge computing etc. This will be the foundation for new revenue streams, use cases and even the likelihood for different types of business models within the sector, changing the landscape of CSPs.
How to get the most out of Data?
Telcos have always been blessed with the most amount of data with diversified data sources compared to any other industry but unfortunately, they were not able to use it effectively. So, data-rich and insight poor has always been a story of CSPs but this is where AI and ML chips in to drive Intelligent Analytics.
To work on data and get the insights out for decision making, Analytics is a must to have a grip on: –
Analytics & its Ingredients – AI, Edge Computing and Cloud
With 5G and IoT exploding with floods of data, there will be 75 billion connected things and devices by 2025 (Source: Statista) and these devices will be potentially streaming data every second of the day.
But the million-dollar question is, how CSPs can use IoT data to capture, analyze and drive business insights and actions in real-time? There lies the value of streaming analytics, edge, and cloud and how businesses extract actionable insights out of this real-time data.
To capture all the ocean of IoT data, ingesting it, meta-tagging and storing it in the cloud for analysis in the end is not always a smart move. With the speed of 5G and reduced latency, many use cases can take advantage of edge computing and edge analytics to reduce bandwidth cost, time and improve performance e.g., IoT Video Camera Sensor Data, Cloud Gaming, Autonomous Driving etc
Benefits of Edge Analytics
- Faster, autonomous decision making since insights are identified at the data source, preventing latency
- Lower cost of central data storage and management since less data is stored centrally
- Lower cost of data transmission since less data is communicated to the central data warehouse
- Better security/privacy since the most granular data such as video footage is not stored or communicated
However, for edge analytics to work, we need the support of a data lake & cloud data centre to analyse, test & train machine learning models and store data. After polishing these machine learning models, they can then be pushed back to the edge, making them more adaptive to new and changing circumstances and ultimately intelligent. So practically a hybrid-analytics model is formed to use it as per requirement providing agility to generate insights. Meaning the best of both worlds with some intelligence at the edge for quick action and more at the data lake for predictive and cognitive analytics.
So, hybrid analytics will be appropriate for the following reasons: –
Context: Context allows you to add and integrate to the sensor IoT data with diverse enterprise and legacy data sources for richer context and understanding.
Comparison: Comparative analysis lets you compare the performance of data across different scenarios or timelines to gain deeper insights. For example – How did the performance of one machine compare to a similar one on the same factory floor? How does it compare to one in another location? How did the equipment performance parameters vary over time? etc.
Machine Learning: Huge volumes of data at the cloud/data lake is used (for better sample size) to train and refine models. Once these models are built, they can then be deployed back out to the edge. Meaning models are prepared at the cloud and Personas are derived at the device edge and collectively it is used as an intelligent edge for contextual offers.
Analytics & Reporting: Analytics & Reporting will take the form of interactive data exploration by CSP’s data scientists or data analysts.
Visualization: It is responsible for the creation and visual representation of data.
Key Features of Virtualization: –
- Horizontally and vertically scalable
- Simple Drag & Drop Interface
- Enhanced visualization using R scripting component that brings predictive insights into your business data
- Advanced Visualizations (Leaflets, Mito Plot, SDK Java Scripting)
Real-time Decision Making: The system gathers data in near real-time and takes decisions as per business rules configured by the user and impose the decision to multiple downstream systems such as customer care portal, self-care portal, CRM, PCC, BRM etc. This help CSPs to take decisions in real-time to improve customer experience by: –
- Offering Right Service at Right Time to ensure consumer’s continued association
- Monitoring QoS of High Revenue & Key Account Cell Site
This is exactly where STL’s Intellza marks its success in leading global Tier-1 CSPs by providing a complete packaged solution with a unified data management platform and hybrid analytics model that empowers CSPs in achieving meaningful actionable insights from complex data anywhere, from the edge to AI, in a compliant and secure fashion.
Advanced 5G Use Cases in Telecom
When it comes to applications of analytics, STL Intellza has exciting no. of use cases which are as follows: –
Some more emerging Edge Computing Use Cases are also thrilling and are as follows: –
- Autonomous Vehicles: Auto driving cars need to be able to learn things without having to connect back to the cloud to process data. Some functions like braking, turning and acceleration will likely always be managed by the computer systems in the cars themselves. But what if we could offload some of the secondary systems to the cloud? These include things like updating and accessing detailed navigation maps.
- Industrial Automation: Help create machines that sense, detect, learn things without having to be programmed. Edge computing could spark the next generation of robotic manufacturing. The future 5G service could play a vital role in what’s called “Industry 4.0 – Digital Manufacturing”. The anticipated low-latency wireless connections could eliminate traditional wired connections to robotic assemblers. Updates come quicker. Products can get to market faster.
- Augmented Reality (AR) and Virtual Reality (VR): Creating entirely virtual worlds or overlaying digital images and graphics on top of the real world in a convincing way also requires a lot of processing power. Even when phones can deliver that horsepower, the trade-off is extremely short battery life.
Edge computing addresses those obstacles by moving the computation into the cloud in a way that feels seamless. It’s like having a wireless supercomputer follow you everywhere.
- Retail: Creating more immersive in-store environments with technologies like AR.
- Connected homes and offices: Complete tasks like turning on lights on command or changing the temperature. With edge computing, it will be possible for them to happen in near real-time.
- Predictive Maintenance: Help detect machines that are in danger of breaking and find the right fix before they do.
- Video monitoring: Handles data at the edge rather than sending it to the cloud.
- Software-defined networking: Requires local processing to find the best route to send data at each point of the journey.
- Blockchain: It requires a decentralized computing model. To execute Blockchain, we need to process ledgers locally and house them locally. Each node in a blockchain is a compute unit, so blockchain isn’t a centralized ledger, it’s a distributed ledger. Therefore, it’s edge.
Interested in more, visit our page https://www.stl.tech/network-software-products/data-intelligence/ and connect with us on our social media handles to help fulfil your data needs.