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.