Enterprise 5G: a catalyst for Big Data analytics

At a time when every competitive advantage matters, Asia-Pacific (APAC) businesses are under pressure to derive actionable insights from data. And they are increasingly turning to Big Data analytics to deliver.

Big Data analytics, in a nutshell, is the process of analysing Big Data to uncover information that can drive better-informed business decisions. It’s a tool perfectly suited for today’s increasingly data-driven business environment: enterprises worldwide will generate and collect 120 billion terabytes of data this year in various formats from a wide selection of sources.¹

How can you transform these unstructured masses of data into useful information that can refine and speed up business decisions and create better outcomes down the line?

For more and more businesses across the region, Big Data analytics technologies and techniques provide them with the capacity to analyse data sets and uncover hidden patterns, correlations, market trends, and customer preferences.

The outcomes vary per industry and application. Using Big Data analytics, retail operations may uncover more effective marketing strategies; financial firms might generate better fraud-prevention techniques; and manufacturing firms may find new ways to improve operational efficiency.

Big Data analytics is not a single technology or device: it encompasses various technologies like cloud computing, data mining, and predictive analytics. These individual components have one thing in common: they depend on 5G to enable key aspects of Big Data analytics.

For starters, 5G’s capacity for faster data transmission comes in handy where massive datasets need to be processed and analysed. 5G's high-speed connectivity ensures that data can be transmitted rapidly, enabling faster data processing and analysis.

5G’s low latency reduces delay in data transmission – a critical element for real-time analytics applications. This is particularly useful in areas like healthcare and public safety, where analytics-based insight needs to be delivered in real time, and managers need to make immediate decisions based on up-to-date information.

5G technology also supports edge computing: shifting computational power closer to the data source instead of transmitting all data to a centralised location for analysis. For instance, edge-based predictive analytics solutions can monitor a machine’s operating conditions and schedule future maintenance when needed. Abilities like these are powered by the Internet of Things (IoT), artificial intelligence (AI), and 5G working in concert.

Speaking of IoT: the high-density connectivity of 5G allows many IoT and sensor devices to connect simultaneously. This enables businesses to collect, analyse, and leverage data from IoT devices and sensors more efficiently.

In a manufacturing environment, for example, the high density and scalability of 5G can accommodate ever-increasing Big Data analytics demand without compromising the production line.

A single business might have tens or hundreds of terabytes of data to sift through, collected daily from IoT sensors, point-of-sale transactions, cloud storage, and even web traffic. These collections may be dispersed across different sources and systems, with information silos preventing them from being shared across departments.

This often results in incomplete or inconsistent data, which inevitably leads to inaccurate analysis and flawed insights. This is an APAC-wide problem: an InterSystems report found that 98% of the region’s financial services providers suffer from data and application silos, possibly contributing to the 87% who admitted to being frustrated from trying to use data to drive decision-making.⁴

To solve these problems, enterprises can implement robust data integration techniques and technologies to bring together siloed data sources, enabling a holistic view of the data for analysis. For example, when AETOS partnered with Singtel to launch its 5G Integrated Command Centre (ICC), they leveraged Singtel’s 5G Paragon platform and Multi-Access Edge Compute (MEC) capabilities to overcome operational silos.

To handle the large volumes of data, processing power, and storage needed for optimal analytics performance, businesses need to make expensive investments in the necessary technology infrastructure.

The high cost of a complete analytical solution or product may give some businesses pause; in some cases, previous significant upfront investments in technology may have yielded no tangible returns, holding leaders back from committing additional resources.

Business leaders need to know that they don’t have to go all-in when investing in Big Data analytics and its necessary infrastructure. They can start with smaller pilot projects or use cases – this allows lower-risk learning, experimentation, and eventual iteration, enabling the organisation to proceed stepwise with “small wins” as they demonstrate tangible results.

Singtel 5G Genie, for example, allows businesses to experiment on a small scale with 5G before scaling up their analytics projects, helping manage risks and build confidence among stakeholders.

APAC companies, as a rule, lack the deep bench of analytics talent needed to lead and implement analytics efforts.

A Gallup study reveals that 76% of APAC employers found it challenging to fill vacancies for jobs requiring digital skills – data professionals included.⁵

A Big Data analytics team requires individuals with expertise in data science, statistics, programming, and other related fields. Non-technical stakeholders within the organisation may not have the expertise to interpret results and translate them into actionable insights; the ongoing shortage of qualified, skilled professionals can make outside hiring expensive, especially as their small numbers make high turnover a near-certainty.

Alternatively, businesses can collaborate with outside partners and experts instead of hiring the whole team. They can leverage partnerships with external organisations to gain expertise, access specialised resources, and accelerate Big Data analytics initiatives.

Experts in the field can provide enterprises with guidance, implement best practices, and help overcome specific challenges, like integrating a 5G ecosystem with the analytics solution that can deliver the right business outcomes specific to their market.

They can even help provide an all-in-one orchestration platform like Singtel’s Paragon that combines 5G edge computing and cloud services. With Paragon, enterprises can tap Singtel’s network to access a robust ecosystem of 5G partner applications and deploy them across the edge on Singtel MEC and a public cloud of their choice.

The high speed, low latency, and increased capacity offered by 5G networks enable businesses to bridge the gap between their data and real-time insights, leading to faster and more accurate decision-making.

Overcoming obstacles to Big Data analytics and 5G adoption requires businesses to connect to the right technical expertise, allocate resources expertly, and be willing to adapt and evolve in a quick-changing, data-driven business landscape.

To help in this area, Singtel provides Singapore’s most advanced 5G connectivity through its 5G Standalone (SA) network and Paragon, a unified orchestration platform that combines the 5G network, MEC, and multiple clouds.

Let’s talk about how you can bridge 5G with your Big Data analytics aspirations, so you can gain the competitive edge you deserve.