Why Is Data Integrity Important | 4 mins read

Why is Data Integrity Important? And How to Minimize Risk

why is data integrity important and how to minimize risk
Chloe Henderson

By Chloe Henderson

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.

What is Data Integrity?

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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.

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The Importance of Data Integrity

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.

Types of Data Integrity

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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
Entity integrity relies on primary keys, or specific values that represent pieces of information, to ensure data is not duplicated or absent in any fields. This model is commonly found in relational systems that store data in tables.

  • Referential Integrity
Referential integrity is a series of processes that ensure information is stored and retrieved in an organized fashion. This model uses set database principles to ensure only verified data alterations, additions, and deletions are made. Some rules also include limitations that prevent duplications, guaranteeing accuracy.

  • Domain Integrity
Domain integrity refers to the collection of procedures that maintain data accuracy and validity within a domain. A domain is a set of verified values accepted in an information field to limit data amount and formatting.

  • User-Defined Integrity
User-defined integrity uses standards set by the user to filter and regulate data. Unlike the other models, user-defined integrity can be personalized to fit the specific values of businesses.

Tips to Reduce Data Integrity Risk

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To further mitigate data integrity risk, companies should consider the following tips-

  • Integrate Data and Systems
Businesses with several disconnected systems are more likely to handle inconsistent data due to disjointed processes. Modern integration solutions seamlessly link established systems to streamline real-time metrics, ensuring reliable and accurate data.

  • Utilize Access Control
By enabling data access control, businesses can program verified users and employees into systems. This prevention measure ensures that information is secure and intact.

  • Implement Backup Processes
Utilizing backups, such as recovery databases or warehouses, ensures data is secure and recoverable in the case of system malfunction. Backups maintain integrity by regenerating data back to its original form.

  • Leverage Audit Trails
With automated audits, data entries are time-stamped with the date, time, and user information of anyone who altered, added, or deleted figures within the system. These records create a trail that is useful in the event where developers need to trace an error back to its origin.

  • Enable Password Protection
In addition to access control, companies can enable passwords to prevent unauthorized users from compromising data integrity. However, the IT department must disable the password storage tool on computers to avoid security hacks.

  • Implement Security Measures
With firewalls and encryption tools, companies can ensure data integrity by actively deferring cyber-attacks.


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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