The adoption of 5G technology is rapidly expanding, with projections indicating a significant connection increase over the next few years. Singapore currently falls within the top 5 countries with the best 5G availability in the world. Having more than 1,300 outdoor and 400 indoor and underground 5G sites making their average download speed one of the fastest in the world. However, to fully realise the potential of 5G, telecommunication companies must overcome challenges related to cost of infrastructure and urban topology such as line-of-sight obstruction, signal attenuation from high-rise buildings, limited space, and high-density areas. Once these hurdles are overcome, 5G networks offer high data rates and low latency, enabling real-time data transfer and opening new possibilities for businesses and consumers.
In this ubiquitous connectivity era, businesses must successfully bring 5G services to market. 5G provides increased network capacity, throughput, and responsiveness, allowing for the development of innovative business models that were previously impractical. One such advancement is network slicing, where a single physical network can be divided into multiple layers using virtualised architecture. This granularity enables service providers to customise connectivity for individual applications and users.
Real-time analytics plays a crucial role in the 5G revolution. To deliver a quality customer experience, it is necessary to understand how customers use and adapt to 5G services. This requires analysing massive volumes of data generated by 5G networks. As competition among network providers intensifies with the growth of 5G, real-time analytics will determine who can deliver the best customer experience based on data-driven insights.
While optimising the customer experience is essential, additional technical challenges must be considered in the era of 5G and real-time analytics:
1. The inevitable multi-and hybrid cloud future: Communications service providers have been slower to adopt cloud computing than their customers. However, with the prevalence of multi-cloud operations, the cloud has become an integral part of 5G technologies. Analytics becomes even more critical as data and app connectivity rely on containers.
2. Upgrading backhaul capacity: Backhaul networks, responsible for connecting the core to the essential functions of the telecom network, require upgrades to handle the increased data traffic and low latency demands of 5G. Data analytics can help identify areas that require prioritised upgrades, streamlining the process.
3. Handling network slicing complexity: Network slicing, which supports different business models and consumer needs, presents unique challenges. Real-time analytics is necessary to monitor the network’s performance and meet customer expectations.
4. Security and privacy concerns: With increased connected devices and data traffic, 5G networks provide more attack surfaces for cyber threats. Real-time analytics can detect and address intrusions and vulnerabilities in the network as they occur, ensuring user data remains protected.
5. Accessing disparate data sources: In the 5G era, analytics teams can no longer afford the time it takes to transfer data to a central location for analysis. An analytics platform capable of reaching and analysing data wherever it resides becomes crucial for seamless collaboration and efficiency.
As Karl Whitelock stated in a recent brief from IDC “it takes a mountain of data to deliver a quality customer experience in the 5G era” and this seems to be the case for businesses that don’t have robust data analytics capabilities to manage the complexities of 5G networks effectively. In the highly connected and urbanised environment of Singapore, telecommunication companies must harness sophisticated data analytics tools to efficiently manage the vast volumes of data flowing through 5G networks to keep up with societal demands and company growth.
By integrating real-time analytics, telecommunication providers can make strategic-based decisions based on all available data rather than down-sampled data sets, enabling providers to achieve insights and predictions faster and more accurately than their competitors.
This article is written by Stephen McNulty, SVP, Asia Pacific at OpenText
The insight is published as part of UPTECH MEDIA’s thought leadership piece, written within its repository of contributor articles.
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