Data life cycle management method

Abstract

The invention relates to the technical field of data management, and particularly to a data life cycle management method. The method comprises five processes of application development, data interfacemanagement, data storage, flow and authority control and security management, and can achieve the following beneficial effects that stripped data meets data integrity, and data needing to be consulted can be completely stored and completely presented; the data do not depend on a software environment and a language technology provided by a software manufacturer, and the content to be seen can be completely presented on any terminal computer; any software manufacturer can realize data stripping, take over the data stripped by other manufacturers and provide basic query and display functions; and in a system with relatively long running time, the light weight of the running system is realized by periodically stripping the data.

Description

technical field [0001] The invention relates to the technical field of data management, in particular to a data life cycle management method. Background technique [0002] Various systems are used in an enterprise, such as financial systems, office systems, business systems, sales systems, human resources systems, and so on. These systems are developed by different vendors, with different development platforms, development environments, and development languages. Even for the same system, the development platform, development environment, development language, and data structure used in different historical periods are not the same, which results in the continuous operation of various systems, the increase of system data, and the management cost of historical data. The continuous increase has become a burden in the construction of information technology. [0003] This kind of burden has brought many negative effects: First, the data cannot be separated from the support of ...

AI Technical Summary

Problems solved by technology

[0003] Such a burden has brought many negative effects: First, the data cannot be separated from the support of the original technology platform. To ensure the continuous use of the data, the maintenance of the original factory must be purchased, which increases the maintenance cost

Second, data operation also needs to rely on software, hardware and network environment, occupying the enterprise's network and hardware resource environment

At the same time, it is also necessary to purchase related maintenance services for the network and hardware, which increases maintenance costs.

Third, historical data generally runs for a long time, and most of the software and hardware used are old versions

Little or no technical support can be obtained, which increases the risk of system crash and data loss

Fourth, when the new system goes online, it is often necessary to take into account the data of the old system, which increases the development cost of the new system

Fifth, during the continuous operation of the system, due to the increase in the amount of data, the system slows down

System optimization and expansion are required, which increases the cost of related optimization and expansion projects

Sixth, when there are too many old systems, it is necessary to increase personnel within the enterprise to manage them, which increases the labor cost of the enterprise

Seventh, when major adjustments are made within the enterprise, the continuous operation of these old systems has to be considered

This again increases the company's management costs

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