Currently, major enterprises are paying particular attention to the use of cloud computing technology, and cloud-based solutions are providing tremendous value to the enterprise, and the cloud's ability to handle big data is bringing even more benefits to the enterprise, as has been well illustrated in the cloud solution used for the supply chain.

In this solution, the way data is collected and ****enjoyed has been revolutionary. In the old days, organizations had to deal with a supply chain consisting of thousands of suppliers, validating each and every supplier that accessed the organization's ERP system through the EDI approach. With the EDI approach, it is necessary to repeatedly test the matching of APIs between each supplier and the enterprise, all the way to the realization of all data transfer and security authorization between the supplier and the enterprise. At this point, the supplier will be allowed to access the enterprise's ERP system. But this process is laborious and repetitive, and does consume IT resources.

In later years, a cloud solution for the supply chain emerged. Instead of sequentially and iteratively qualifying thousands of suppliers and manufacturers around the globe with access to a confidential network, this solution pre-qualifies suppliers one by one, as was previously the case, and the cloud provider takes care of the ****-enjoyed pool of data. This ****-accessible data pool includes not only transactional documents, but also shipping and loading documents, order forms, product specifications and diagrams, and other documents critical to the process of producing and transporting goods, as well as providing services to the market. The end result is a database in the cloud that contains both big and small data. With the right security permissions, everyone who is allowed onto this network can access this data at will.

Few organizations would think of connecting every product manufacturer and supplier to a central network with a single database, but organizations have seen these results in their business processes. Today, the process of adding a new supplier to the cloud network can be done in a few hours, whereas in the past, EDI certification took months. The confusion that arises from communication is less in the cloud because each participant uses the same database in the cloud. A network of cloud manufacturers and suppliers also enables many different companies to securely exchange standards and big data.

The cloud takes the approach of assigning each part of big data a name that everyone can access, and providing each trading partner in this cloud network with a business rule. These rules allow each partner to assign security licenses and permissions to individuals in other organizations with whom they exchange information.

While organizations have taken meaningful steps to implement such cloud solutions to address external business processes that their internal systems cannot, they should now also pay close attention to what the cloud has accomplished and apply these "lessons learned" to their own internal systems and how they handle big data. Here's a look at some of those lessons:

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

A centralized database in the cloud works very well because it contains both big and small data that's closely related to specific business functions. Enterprise data marts should be built with the same approach.

B: Use an authorization methodology for big data security that a business unit can control

Handing over the management of security authorizations to the ultimate business unit creates flexibility in communications. However, this should be carefully considered in order to maintain corporate security standards. At the beginning of the process, it is a good idea to enlist the advice of an external security compliance expert.

C: Going for a "single version"

Whether you're working with structured, semi-structured, or unstructured data, the more information you can consolidate into a single set of facts, figures, and charts that can be used by everyone across the enterprise, the more likely you are to avoid confusion caused by different systems publishing different data. data published by different systems, the more likely you are to avoid the confusion caused by different data published by different systems. As you build "data marts" of Big Data, there is a great opportunity to standardize the data fed into these marts and start "doing this right.