While much of the world slowed down in the past year, the adoption of connected technologies did not. During the global pandemic, almost 75% of enterprise IoT users increased the rollout and adoption of their IoT projects as they recognized the benefit of remote management and secure connectivity of devices, using data to improve insights and enable new ways of doing business and improve services.
Putting IoT data to work
Technological evolution is a constant in the enterprise. What is not as guaranteed is realizing ROI from the deployment of new technologies. However, a path to business value for IoT and industrial IoT (IIoT) has emerged - running analytics that incorporates IoT and other types of organizational data. This combination enables businesses to connect areas of the organization through data and bring new insights into relief.
While most IoT implementations to date have focused on the important work of monitoring products or operations, we anticipate an increased focus on prediction as collected data is put to more strategic use.
AI and IoT: a winning combination
AI has enabled many advances in automation and analytics, including IoT applications. However, IoT is also driving enterprise use of AI. The IoT Business Index by the Economist Intelligence Unit found that not only is AI viewed as vital to realizing the value of IoT data, over a quarter of respondents also said that IoT data is pivotal to their current planned use of AI.
Many different industries are seeing the benefits of combining the two technologies. Adoption is particularly high in transportation and logistics, as interactive maps and smart route applications communicate with smart cars connecting via a central hub.
It is an exciting area ripe for innovation, as showcased by AI-powered data analytics start-up, Tangerine. The company has created cutting-edge products that marry IoT and AI, producing analytics such as a smart camera that warns of driver drowsiness and distracted driving, preventing accidents, and boosting safety.
With organizations looking to increase their data-driven decision-making to navigate a changing market and user behavior landscape while staying ahead of the competition, AI trained on IoT data will provide tremendous value in the years to come.
IoT security is more important than ever
With increased IoT adoption, as more smart devices are deployed and more data is collected, the attack surface grows and incentive increases for cyberattacks. One of the key steps involved in securing IoT networks is managing thousands of devices across networks and providers at scale. Having a consistent interface and standardized integration allows for easier device tracking, alerting the IT team as to when a device needs to be updated or is communicating with another device outside of its remit.
Companies require flexible solutions that enable them to quickly and securely develop, deploy, and manage IoT with devices built on a secure root of trust. The integration of defenses from hardware to software provides IoT security across the value chain from the chip to the cloud. Selection of the right device management platform offers security at every stage in the product lifecycle, from provisioning to decommissioning.
GSMA Intelligence found that 85% of enterprises have changed their security practices as a result of their IoT deployments. With over 60% of these respondents citing establishing a security-first strategy as a competitive differentiator, we can expect more companies to get on board. As competition increases in the manufacturing and industrial sectors as U.S. and U.K. companies struggle to match the lower costs of their counterparts in Asia, we can expect security to play an increasingly important role in gaining a competitive advantage.
Companies need to integrate their defenses from hardware to software to ensure IoT security through the value chain from chip to cloud. Selecting the right device management platform plays an important role in security at every stage in a device’s lifecycle, from provisioning to decommissioning.
IoT compute on the edge in 2021
Current progress suggests that this year edge computing will finally shift from proof-of-concept to full-scale deployments in multiple industries. As the processors used in modern IoT deployments get more advanced, applications and analysis can be performed at the edge rather than in the cloud, reducing latency issues, reducing bandwidth requirements, and enhancing security and privacy.
IoT deployments generating huge volumes of data in the traditional server-based model can be unreliable for applications needing real-time data. For example, factories operating heavy machinery or critical infrastructure such as water plants responding to sensor data often rely on the speed of data to mitigate risk. Edge computing will continue to see increased adoption as a result.
As edge computing becomes more mainstream (Gartner predicts 75% of all data will be processed at the edge by 2025), we’ll see the rise of multiple edge computing platforms. Many of these systems have proprietary APIs and runtimes, use different protocols and adhere to different certifications. Enterprises can benefit from an IoT platform with expertise in cloud and edge ecosystems, able to translate from IoT edge device, to edge gateway, to cloud, managing devices, connectivity, and operational security. Being able to bring all of these aspects together will not only further encourage increased adoption but provide companies with the level of control and ownership that they are seeking.
IoT is here to stay and, as the trends show, organizations will continue to innovate to gradually realize their true value. Organizations are beginning to explore how to achieve this value, whether it be through increased data collection and processing on the edge such as location-based services which have already proved valuable in the wake of COVID-19 to enhance customer service, integrating AI as an extension of IoT capability to helping increase efficiency, automation, and accuracy or security enhancements to gain a competitive advantage through the use of IoT. As companies focus on value, it continues to grow as it is integrated into foreign data sets, subjected to AI algorithms, and connected to cross-functional business processes.