
Data integrity is an important aspect to the designing, implementation, and use of any distributed system and is absolutely crucial to the management of information, especially sensitive data. Any information system should be designed, programmed, monitored, and operated in a manner that ensures the confidentiality and integrity of data. The current issue revolving around electronic data security has resulted in a plethora of new management techniques and standards. However, these procedures are not always quality inspection enough to guarantee the integrity and safety of a computer system.
The term data integrity refers to the quality of the actual data set itself and the procedures followed to maintain that integrity. It is closely related to the security of that data set as well as the integrity of the network that supports it. Distributed systems are particularly at risk when it comes to ensuring the integrity and consistency of data. Often, systems become outdated and this results in less accurate and reliable outcomes.
There are many components involved when it comes to data integrity, some of which are quite technical. For example, data integrity refers to the consistency and validity of the transmitted or received information, including confidentiality, authentication, and availability. Among other things, it refers to the uniformity of the application's response time to requests. This is critical for companies that receive or deliver sensitive data or those of high sensitivity interest to them. The constant threat of attack by a variety of cyber criminals, both foreign and domestic, makes data security and protection a top priority for companies of all sizes.
Data integrity also includes data quality and the process of correcting errors or inconsistencies in the specific context. Data integrity is often compared to the quality of service provided by telephone services. While telephone companies can't afford to have call center accidents, they do have to make sure that their agents can take the time to correctly and completely address each call and ensure that the callers' needs are being met.
Data integrity also includes data quality and the process of traceability. The aim of data quality and traceability is to preserve data integrity through the establishment of a system that ensures the consistency and validity of all inputs and that ensures that all outputs remain consistent with each other and with the rest of the system. Traceability also refers to the methodologies, policies, and procedures that ensure that the information input to a system can be traced from its origin and identified, analyzed, produced, or transmitted as needed. In other words, traceability ensures that the inputs stay consistent and the output stays consistent.
Both logical and physical integrity refers to the quality of a database management system. Logical integrity refers to the ability of a system to correctly and completely execute its functions; physical integrity refers to the capacity of a physical device or component to withstand the influence of exterior physical forces such as pressure, temperature, and rain. These two integrity levels must be balanced for a successful system. While prevention of corruption is the goal of database management, database integrity must also be considered in light of the system's impact on the organization as a whole. Click for more datails.
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