Keeping data safe and intact ensures data-driven functions, such as reporting, conducting analyses, and producing actionable insights, can be performed accurately and efficiently. Data integrity guarantees that internal information within management systems is valid and secure, whether it is stored in the database or transferred across systems.
However, there are models of data integrity that use various processes to ensure that the information meets specific standards. It is crucial for businesses to understand the types of data integrity and how to maintain these standards in order to optimize data management.
As a state, data integrity refers to the accuracy and validity of information over its entire lifecycle. Businesses cannot use low-quality data because inaccurate information would generate erroneous reports, analyses, and insights. Compromised data can also raise security hazards for enterprise systems, increasing the risk of software viruses. This makes maintaining integrity essential and the primary goal of many data security systems.
As a process, data integrity is the procedure in which information runs through to ensure reliability and accuracy. In order to maintain integrity, established data management and integration solutions run a series of quality checks, validation procedures, and compliance rules on transferred data. These processes use a programmed set of business principles to filter out inaccurate and insecure information.
However, companies should be aware of the common factors that often compromise data warehouses, including-
- Human Errors - Data entered manually significantly increases the risk of errors, deletion, and duplicate data. Physical inputs also tend to fail protocols and mandatory compliance rules, disrupting processes, and yielding negative results.
- Transfer Errors - Without a proper data integration strategy, information is subject to security breaches. Additional errors can also occur when transferred data is present in the target source but no longer exists in its source system, creating a disjointed database.
- Bugs and Viruses - Compromised hardware due to hacks or malfunctioning firewalls, can invade data warehouses, corrupting the data stored in hard drives.
By ensuring quality and accurate information, data integrity optimizes data-driven operations and decision-making processes. Management could not monitor performance levels for processes such as marketing, fulfillment, shipping, and sales without adequate data quality.
When collecting big data, internal information is collaborated with external data to reveal patterns and trends that would otherwise be overlooked. However, unless the information is secure and correct, businesses cannot accurately generate actionable insights, projections, and analytics.
Data integrity also enables users to search and trace information origins to the source system. This allows businesses to keep a paper trail, reducing the time it takes to conduct system audits. This element also guarantees that data can be restored in an emergency situation, increasing system reliability.
There are several types of data integrity, each method entailing multiple processes to protect and regulate information. The standard data integrity variations include-
1. Physical Integrity
Physical integrity protects the completeness and accuracy of the information stored in a database and retrieved upon user request. Incidents such as a power outage, system hack, human error, and outdated software can ruin physical integrity. Once this has been compromised, IT managers and programmers cannot obtain the original data.
2. Logical Integrity
Logical integrity keeps data consistent as it moves through different processes within a database. It also protects the information from security breaches and human error. Logical integrity is more complicated than physical integrity and has four different models-
- Entity Integrity
- Referential Integrity
- Domain Integrity
- User-Defined Integrity
To further mitigate data integrity risk, companies should consider the following tips-
- Integrate Data and Systems
- Utilize Access Control
- Implement Backup Processes
- Leverage Audit Trails
- Enable Password Protection
- Implement Security Measures
Data protection is essential for ensuring that internal information is accurate, secure, and traceable. Without integrity, business data cannot be used to make successful data-driven decisions to improve performance and operations.
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