The impact of 5G will be broad and deep—in a variety of industries. Here’s how CIOs need to start planning to gain competitive edge.
Since the advent of cloud computing, we’ve been waiting for the next big thing in enterprise IT. 5G networking may just be it.
5G, the next generation of wireless networking connectivity, may look like a faster version of the networks we have now. It is—but it’s more than that. The quantitative differences in 5G compared with existing technologies will enable qualitative changes that bring an array of opportunities and challenges to CIOs. “5G will potentially have an energizing and catalytic effect on a whole array of technology and services in IT,” 451 Research indicates. “Few will be left unaffected.”
Four areas where CIOs should plan for this impact are the following:
5G network architecture will eventually deliver the capacity to transmit 1,000 times more data than we do today. And it will be done in one-fifth the time at speeds ranging from 10 to 100 times faster than current network technologies. Such dramatic improvements in the technology will benefit existing applications and services but also provide a key capability for designers and developers to bring new kinds of services to the market.
Let’s look at the impact of 5G on various aspects of the enterprise and in several industries.
As 5G network architecture is deployed and made more publicly available, more data will be generated, collected and available for analysis. Existing applications that depend on network performance will see significant speed improvements. A two-hour movie, for example, can be downloaded in less than four seconds with 5G. That is impressive, but a corollary impact is even more important: faster networks with higher throughput create more opportunity to collect data, which in turn drives new kinds of services. As CIOs have come to see the benefits of 5G—low latency, faster network speeds and improved network performance for data-intensive tasks—company CIOs will increasingly want to know, “What new services can we build on 5G?” These kinds of services show the range of possibility for the impact of 5G on industries:
● In the real estate industry, homebuyers could use virtual reality technology to virtually browse properties of interest inside and out, without leaving their location.
● Manufacturers will use predictive models to plan maintenance and identify anomalies or other breakdowns in machine data, possibly preventing a future failure.
● Advertising and marketing may combine customers’ personal profiles with real-time location data to target ads based on the businesses in close proximity to particular individuals.
● New ways of delivering lower-cost healthcare could include telemedicine, medical consultations and diagnoses with remote medical professionals.
The Internet of Things (IoT) is becoming more central to the enterprise IT landscape, particularly in industrial scenarios: 86% of manufacturing, transportation, and oil and gas organizations are adopting IoT technologies, and 84% believe these tools are very and extremely effective, according to an Institute of Electrical and Electronics Engineers (IEEE) survey. Industrial IoT demand has been driven by increasing automation, a focus on worker safety and overall efficiency as well as the increasing use of smart sensors.
The quantitative differences in 5G compared with existing technologies will enable qualitative changes that bring an array of opportunities and challenges to CIOs.
Using 5G network architecture to collect detailed data from virtually every phase of an enterprise’s industrial operations can provide a treasure trove of valuable information and insights. Unfortunately, many companies haven’t taken advantage of data. A New Vantage Partners study found that 69% of organizations surveyed do not have a data-driven culture, and 53% do not treat data as an asset.
These findings are echoed by other data. “The failure of some high-profile digital transformations has led company leaders to be wary of transformational initiatives,” wrote Randy Bean and Thomas H. Davenport in Harvard Business Review. They note that data-driven business cultures require a long-term initiative, which is not always compatible with short-term financial goals. Without the adoption of data-driven decision making, much of the value of an increasingly instrumented operation will be lost.
More data will drive the development of better tools and techniques for using that data. Artificial intelligence, especially machine learning, will identify valuable information in an expansive set of data.
A construction company may deploy video monitors to collect data about the context of accidents. There may be enough humans on staff to analyze video around the time of a known accident, but it’s unlikely a company has sufficient employees to view all the monitoring data collected from all locations. AI can sift through hours of video to identify relevant footage more quickly.
AI can also detect poor safety practices or defective equipment. Identifying these kinds of problems before an accident will improve worker safety, prevent project delays and contain costs.
As companies turn to AI to become more efficient, preempt accidents and provide other data insights, new challenges emerge, including how to institutionalize AI in business processes. Companies may need to retrain workers, and they certainly need to rethink business operations.
Another challenge: Expanding the footprint of data collection has implications for security and privacy that should not be overlooked.
Before deploying an array of instruments and sensors connected to your network, consider the security and privacy implications. As more devices are connected—by some estimates, there will be 31 billion by 2020 and 75 billion by 2025--means a large attack surface for malicious actors. As with other devices, IoT and edge devices need to ensure the confidentiality of data, the integrity of data and the availability of its services.
Maintaining confidentiality is critical for protecting private information, such as personal health and financial data. One way to prevent the disclosure of private information is to schedule its destruction rather than store it without end. It may be more efficient and secure to keep information derived from data, such as the number of customers entering a store through a particular entrance without storing facial imagery.
Devices should be authenticated before accepting data from or sending commands to a remote device. Attackers may try to imitate or spoof a legitimate device in an effort to gain a foothold on the network. Once accepted as legitimate, a device could corrupt or otherwise manipulate data sent to edge, cloud, or on-premises analysis and storage systems.
Maintaining availability can be difficult when a network suffers a distributed denial of service attack (DDoS). The first step in mitigating a DDoS attack is detecting it. Limiting the rate at which an account can send data as well as dropping or shedding data that exceeds that limit is a common technique in mitigating denial of service attacks. Authenticating client devices and limiting incoming traffic to only known and trusted addresses also reduce the impact of attacks.
5G networking provides high-capacity connectivity that not only meets the needs of existing networked services but also provides the foundation for building entirely new services. This points the way toward the true impact of 5G over the next several years.
The impact of 5G technology shouldn’t be underestimated. And while it may take time for its infrastructure to be built out, its impact is already emerging in fields such as healthcare, manufacturing and more.
To exploit the potential of 5G technology, enterprises should prepare for larger data volumes, greater instrumentation, increasing use of AI, and the need to apply security and privacy controls at scale. As the speed, performance and lower latency of 5G technology is combined with technologies, such as AI and edge computing, it will likely lead to new services that generate revenue, promote efficiency and reduce costs.
Dan Sullivan is a software architect specializing in streaming analytics, machine learning and cloud computing. Sullivan is the author of NoSQL for Mere Mortals and several LinkedIn Learning courses on databases, data science and machine learning.