Figure. 2. A multi-layer recursive explanatory structure model for supply chain data governance.
5.2. Data governance optimization structure hierarchy analysis
5.2.1. Data governance surface layer
The surface-layer indexes of data governance are the direct elements to evaluate the process of data governance, of which five indexes involve the governance subject, data, governance environment and governance technology of the governance system. They also indicate the comprehensiveness and rationality of the surface-layer governance system. Effective data use and metadata management are not only the basic elements to improve the efficiency of data governance optimization, they are also the indexes that can most easily show the governance results. The government department policy, the organization of enterprise data department and the competing environment of supply chain can directly influence the process of supply chain data governance optimization.
5.2.2. Data governance middle layer
The middle layer of data governance is supported by the root layer metrics, which act on the surface layer metrics, and the impact is indirectly penetrated into the supply chain data governance ecosystem. Data quality, as the core element of the middle layer of data governance, is an important yardstick to assess the results of data governance optimization. The supply chain requires high accuracy and timeliness of data, and each participant in the supply chain needs to make plans and risk decisions through real-time data in the processes of production, distribution, procurement and after-sales. The improvement of data quality not only increases the user’s consumer experience and trust, but also promotes the overall governance process of the supply chain.
Along with the root layer data operation technology to support the whole process of governance, the construction of information platform needs to rely on advanced technology development capability as support, and the sustainable development of industrial Internet also needs to integrate increasingly mature artificial intelligence, Internet of Things, cloud computing and other information technology(Shuangming et al, 2018). Since the orderly and safe flow of data is the premise of the intelligent construction and orderly operation of the platform, the development and construction of the information platform is positively influenced by the data sharing ability and data security, and acts on the collaborative development of each node enterprise in the supply chain and promotes the healthy competition in the supply chain market. Enterprises are driven by economic interests to enhance their core competitiveness by improving their data service and innovation capabilities, and then realize the personalized needs of those being served.
However, users, as users and governors of data, have their personal data needs met as a reflection of supply chain value realization. Stimulating the motivation of user data governance can promote the sustainable development of the governance system. As the leading force of supply chain data governance, the government is committed to the construction of a quality management system for the whole life cycle of data, and enhancing the government’s willingness for data governance is a prerequisite for promoting the overall planning and policy implementation of supply chain big data by government departments. The government can develop an effective data governance system through active governance policies, promote the transformation of data content into high-value streams, and provide a positive driving force for the sustainable operation of the governance system.
5.2.3. Data governance root cause layer
The root indexes of supply chain data governance are the decisive indexes of the optimization effect of supply chain data governance. The degree of data security risk, data technology operational support and data sharing capability have the greatest impact on the sustainability of the governance ecosystem.
It can be seen that the data operation technology supports the steady development of the whole governance system. Improving data operation technology from the root is more conducive to the intelligent construction of supply chain collaboration and industrial Internet. At the same time, advanced data technology can simplify the data service process and pursue the principle of cost and efficiency priority while continuously improving quality and maintaining data user satisfaction, so as to reshape the core competitiveness of each node enterprise in the supply chain from three aspects: cost reduction, efficiency increase and quality improvement. The development of cloud-based supply chain under the industrial Internet has also exposed problems such as data security crisis and obstructed data circulation in the process of achieving end-to-end digital transformation. Due to the trusted data interaction environment in the supply chain system, the open and transparent supply chain personal privacy data and false circulation data are exposed to users and supply chain node enterprises, which hinders the sustainable development of the supply chain data governance ecosystem. At the same time, the persistent problem of poor circulation data transmission efficiency throughout the supply chain also leads to the reduction of data validity and affects the convergence and sharing of data upstream and downstream of the supply chain.
Therefore, data technology support, data security and data sharing are not only the core issues of supply chain data governance that need to be solved urgently, but also the underlying governance logic that runs through the whole governance system.