Although technology enthusiasts welcome the arrival of the Industrial Internet of Things, 73% of companies still do not implement any industrial IoT innovation. While industry giants are rapidly adopting digital transformation, SMBs are addressing the real challenges and issues that hinder industrial IoT applications.
Integration with traditional technologies
The Industrial Internet of Things generates a lot of data, which puts high demands on the underlying infrastructure. However, most mature enterprises still use traditional software and hardware solutions to process structured data, while IoT devices quickly generate large amounts of unstructured data. In addition, these data need to be processed and analyzed in real time, while legacy systems are tailored to completely different methods.
Enterprise cloud computing solutions can provide a reasonable alternative, but only to a certain extent, because many business-specific applications are developed on traditional infrastructure and cannot work in a cloud computing environment.
In addition to integrating legacy devices with the Industrial Internet of Things, another challenge is network traffic. The traffic generated by all third-party devices will have a huge impact on the network. Of course, the network technology itself will also change.
One thing is clear: in today's business environment, production suspensions lead to huge revenue losses for companies, and the lack of integration with legacy systems is a serious problem, thus hindering the adoption of industrial Internet of Things.
In order to make full use of the industrial Internet of Things, enterprises must carry out comprehensive or partial software and hardware transformation, which is a costly task. In the traditional notion, meeting the IoT challenge requires the purchase of separate storage, computing, and network capacity, which requires unprecedented costs.
To date, most organizations have adopted a hybrid cloud model—keeping part of the process to the cloud platform while preserving key operations in the on-premises data center. Industry giants like Google and AWS are providing IoT platforms, but there is no denying that enterprise cloud computing costs are quite high.
This is a big problem. Obviously, traditional security systems (such as firewall anti-virus applications) are difficult to deal with security threats in the Internet of Things era. However, some experts have eliminated these concerns. They claim that today's technology has evolved to meet enterprise-level security needs.
Manageability and control issues
These problems are very serious. Unlike enterprise-level system failures, most consumer-grade IoT devices are barely able to cope with emergencies. However, if an accident occurs at a manufacturing plant, refinery or mine, the consequences may be more serious, resulting in potentially dangerous and life-threatening conditions. Before the industrial IoT system has an impeccable performance record, companies will be wary of large-scale adoption of the Industrial Internet of Things.
The connectivity of the Industrial Internet of Things is another challenge. Traditional centralized infrastructure models are sure to be phased out due to the large number of devices connected to the network. Many experts want to use distributed cloud models to implement edge computing. Technology giants like Dell are now rolling out edge computing gateway devices that will act as IoT hubs and handle mission-critical operations, while core cloud networks will remain available for data storage and analytics.
In a market economy, device manufacturers and developers offer a wide range of tools, sensors, and transport protocols that may or may not be compatible with one another. Simply put, sensors manufactured by Company A may not work with Company B's data processing platform. For early adopters of the Industrial Internet of Things, this could lead to vendor lock-in. Until now, some countries are trying to introduce and adopt uniform standards, but this situation is still far from perfect.
In many ways, the lack of relevant talent is a core issue. Although self-learning artificial intelligence is still in the development stage, the implementation of industrial Internet of Things requires the help of human resources experts, and these experts are few. Companies must persuade existing technology professionals with relevant expertise in data science, network technology, and machine learning to continue to serve, and these professionals are also required to provide higher pay.
In this case, companies often use domestic and overseas outsourcing measures to get help from IoT talent and develop custom enterprise solutions.