At present, major enterprises pay special attention to the application of cloud computing technology, and cloud-based solutions also provide great value for enterprises. The ability of the cloud to handle big data is bringing more benefits to enterprises, which is well illustrated in the cloud solutions used in the supply chain.

In this solution, the method of data collection and sharing has always been revolutionary. In the past, enterprises had to deal with the supply chain composed of thousands of suppliers and verify every supplier who accessed the enterprise ERP system through EDI. Using EDI, it is necessary to repeatedly test the matching of API between each supplier and enterprise until all data transmission and security authorization between suppliers and enterprises are realized. At this point, suppliers will be allowed to enter the enterprise's ERP system. But this process is laborious and repetitive, and it does consume IT resources.

Then came the cloud solution of supply chain. The solution prequalifies thousands of suppliers and manufacturers around the world to access the confidential network, instead of sequentially and repeatedly examining the qualifications of suppliers one by one as before, and the cloud provider is responsible for the data pool enjoyed by * * *. The * * * shared data pool includes not only transaction documents, but also transportation and loading documents, orders, technical specifications and charts of products, and other documents that are crucial to the production and transportation of goods and the provision of services to the market. The end result is a database containing big data and small data in the cloud. If you have the correct security permissions, anyone who is allowed to enter this network can access these data at will.

Few enterprises would think of connecting every product manufacturer and supplier to a central network through a database, but enterprises have seen these results in their business processes. Now, it takes only a few hours to add a new supplier to the cloud network, while it used to take several months for EDI certification. Because each participant uses the same database in the cloud, the communication in the cloud is less chaotic. The network of cloud manufacturers and suppliers also enables many different companies to exchange standards and big data securely.

The method adopted by the cloud is to assign a name to each part of big data so that everyone can access it; Provide a business rule for each trading partner in this cloud network. These rules allow each partner to assign security permissions and permissions to individuals in other organizations with which they exchange information.

Although enterprises have taken meaningful steps to implement this cloud solution to deal with external business process problems that their internal systems cannot solve, enterprises should now pay close attention to what tasks the cloud has accomplished, apply these "lessons learned" to their internal systems and how to deal with big data. Let's see what these lessons are:

A: Take a more "democratic" approach to data, whether it is big data or small data.

The central database in the cloud works very well because it contains big data and small data closely related to specific business functions. Enterprise data marts should be constructed in the same way.

B: For the security of big data, use an authorization method that the business department can control.

Transferring security authorization management to the final business department can create flexibility in communication. However, in order to maintain the safety standards of enterprises, this issue should be seriously considered. At the beginning of this process, it is best to seek advice from external safety compliance experts.

C: Pursuing "Single Version"

Whether you are dealing with structured, semi-structured or unstructured data, the more information you can integrate into a set of facts, figures and charts that everyone in the enterprise can use, the more likely you are to avoid the confusion caused by different data released by different systems. When you build "data marts" of big data, you have an excellent opportunity to standardize the data input of these marts and start "doing it right".