Industry 4.0, also known as the Fourth Industrial Revolution, uses information technology to promote industrial change, with the ultimate goal of achieving the interconnection of everything and industrial intelligence. Compared to the past three industrial revolutions, the salient feature of Industry 4.0 is the integration and utilization of data. In a series of continuous processes of data collection, transmission, storage and processing, data is an important thread running through manufacturing, organizational management, logistics and distribution, and end-use applications. Therefore, in this industrial transformation, data is upgraded to become a key production factor and strategic resource, which is the biggest difference between Industry 4.0 and previous industrial revolutions in terms of factor utilization.
In fact, data has been a factor of production since Industry 3.0 (industrial informatization). But after upgrading to Industry 4.0, the weight of data as a factor has increased significantly again, as the circulation of data is a prerequisite for the interconnection of all things. In this process, the scale of data collection and collation has reached unprecedented heights. Typical application scenarios under Industry 4.0, such as artificial intelligence, 5G, big data and smart driving, for example, cannot be separated from the collection and processing of large amounts of data.
In contrast to the last few industrial revolutions, data as a factor of production is characterized by virtualization and the free flow of data. In today’s highly developed world of the Internet and globalization, this has contributed to more efficient and responsive organizational management. However, many aspects of this new phenomenon are still uncharacterized, for example, the ownership of data rights is still controversial and the trustworthiness of data is difficult to determine, especially in industrial chains with end customers (e.g. smart driving cars).
1. Data rights disputes
Despite the current wave of Web 3.0, a huge crusade for the ownership of data on the Internet that has involved many organizations and individuals, the ownership of Web 3.0 data is clear: whoever produces the data owns it, and Internet platforms have no right to collect and use it. Whereas in the past online platforms collected and used user data because they were in a dominant position, the time has come for the return of rights and there is now a consensus among many parties that there is no disagreement about this division of rights.
However, there are still disputes over the ownership of end-user data under the Industry 4.0 system. In the case of smart driving, for example, the driving data is generated by the user when driving the car, but it is the car company that receives, stores, and processes the data. Some professionals, therefore, believe that the ownership of the data should belong to the smart car company and that users should simply ask the company for the data when they need it.
However, there are contradictions in this division of rights, specifically when the user needs to call up data. As driving data is closely related to the vehicle’s operating conditions, it is an important reference when the vehicle is involved in an accident and needs to be repaired and maintained, but when the user needs to call up the data, the data generated by their own vehicle is not readily available to them at which greatly reduces the experience of using it.
Therefore it makes sense to attribute the data to either party, to the company because the data is part of the production factors of the company and needs to be processed and analyzed by the company, or to the user because the data is generated by the user’s vehicle and is important information for the proper functioning of the vehicle. It is, therefore, possible to imagine that the driving data could be jointly owned by the company and the user, i.e. that both parties are data rights holders.
2. Trusted data
In addition to the ownership of data rights, the credibility of the data is also a matter worth discussing in depth. The data generated by smart driving is massive, with statistics showing that about 8G of data is generated per second and 10TB of data is generated every day. Such a huge amount of data is simply too much for the average user to keep and has to be kept by companies, but this also adds a barrier to data credibility.
In the case of the Tesla car accident, after the person had requested access to the driving data, it took several days for Tesla to release the data, which not only delayed access but also made it difficult to determine the authenticity of the data and even the person had questioned the completeness of the data.
This is a legitimate challenge, as only complete and original unaltered data is of value when the user needs to recall data for accident determination or maintenance purposes. But is the data sent back to the user by the company complete? Has it been tampered with? This is a question of data security that cannot be guaranteed. The German DE magazine explains that “security” has two meanings: “On the one hand, the data one obtains must be trustworthy, and on the other hand they must be secure against theft or damage from outside, e.g. hacking, espionage.”
It can be determined that in the Industry 4.0 end-user scenario, represented by smart driving, it is difficult to have the user keep the data themselves, as the amount of data is too large for the average user to carry. It is also not so trustable the data being kept by the company, as no one can guarantee the integrity of the data or whether it has been tampered with.
3. Possible solutions
How do you ensure the trustworthiness of data in an Industry 4.0 application scenario? Since it does not make sense for either the company or the end user to hold the data, bringing in a trusted third party to hold the data is an optimal solution.
Technology has evolved to the point where society’s understanding of what can be trusted has been turned upside down. In the past, people only trusted official or large institutions, but today, when large institutions themselves have a crisis of trust (Tesla itself is a large institution, but why is the data it provides questioned by users?), decentralized technology based on blockchain has become the best solution for keeping trusted data.
For example, if MEMO decentralized storage is chosen as a trusted platform and MEMO is connected to a smart driving system, the driving data generated by the user will be automatically uploaded to the MEMO storage system. The private key can be held by the user only, and the data can be retrieved by the user when needed without any other third-party consent or approval. The user can then grant the company access to the data, allowing them to continue to use it as a factor of production. As companies only have access to the data, the authenticity and integrity of the data can be guaranteed and only such trusted data will have application value throughout the chain. Blockchain-based smart contracts and their range of decentralized verification and cloud storage technologies guarantee the security, reliability, and immutability of data, which are the foundations of trusted data.
The convergence of Industry 4.0 has blurred the boundaries of traditional industries, interconnecting the digital and physical worlds, the underlying manufacturers and end users, and data flowing and interacting in many chains. Blockchain applications were just beginning to emerge when Germany introduced the concept of ‘Industry 4.0’ some ten years ago, but the need to shape trusted data has led to the convergence of Industry 4.0 usage scenarios and blockchain technology. The decentralized storage platform, represented by MEMO, acts as a trusted third party in this scenario, shaping a trusted world with trusted data, which is inevitable for the development of a digital society under the Industry 4.0 system.
