Clean, healthy data can be a major competitive advantage, especially for businesses that invest the appropriate time and resources into their data management strategies. In the age of Big Data, organizations that harness data effectively and promote data integrity can make better data-driven decisions, improve data quality and reduce the risk of data loss or corruption.

In this guide, we’ll discuss how data integrity works and why it’s important for the overall health of your business. In addition, we’ll talk about what can be done to maintain high data integrity standards.

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What is data integrity?

At its most basic level, data integrity is the accuracy and consistency of data across its entire life cycle, from when it is captured and stored to when it is processed, analyzed and used.

Data integrity management means ensuring data is complete and accurate, free from errors or anomalies that could compromise data quality.

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Data that has been accurately and consistently recorded and stored will retain its integrity, while data that has been distorted or corrupted cannot be trusted or relied upon for business use.

Data integrity characteristics

To implement effective data management strategies, you must understand the characteristics of data integrity:

• Accuracy: Inaccurate data leads to incorrect analysis and decisions; therefore, businesses should ensure data is accurate, meaning it is error-free and correctly represents the real-world scenario or event it is supposed to depict.
• Consistency: Consistent data does not change erratically. It remains the same across all instances and over time unless it is intentionally updated or modified.
• Completeness: Complete data has all the necessary parts and information needed to lead to correct conclusions and support decision-making processes.
• Reliability: Reliable data can be trusted for its accuracy and consistency. It is data that users can depend on when making important decisions.
• Timeliness: Data that is not available in time for decision-making processes can be as detrimental as inaccurate or incomplete data.
• Validity: Valid data adheres to the set formats and values defined during the data design phase, allowing it to be used for specified purposes.

Why is data integrity important?

Data integrity is fundamental in regulated industries, where data must be accurate, complete and verifiable at all times. Poor data integrity can cause enterprises to lose money, positive public and industrial reputations and valuable production time.

Imagine you are a project manager who is running clinical trials for a drug that will be a game changer in the fight against cancer. You have conducted human trials over the past five years and are convinced you’re ready to move into production.

SEE: Data quality in healthcare: Current problems and possible solutions (TechRepublic)

In this example, any issues with data integrity could affect the success of your clinical trials and the ultimate product, which will likely cost your company millions of dollars and impact the public perception of your drug. This is just one example from the pharmaceutical industry, but this issue cuts across many sectors and data types.

Risks associated with data integrity

Data integrity is a complex and multifaceted issue. Data professionals must be vigilant about the various risks that can compromise data integrity and quality. These include the following:

Human error

In data management, human error is a major risk factor for data integrity. Human errors can occur when data is incorrectly input, processed or analyzed. In some industries, like finance or customer service, the reliance on multiple data sources can also lead to data integrity issues as staff members draw data from differing or siloed data sources.

Misconfigurations and security errors

If data is not configured correctly — for example, incorrect user permissions were set — it may be more vulnerable to cybercriminals or data breaches. Likewise, if data is not appropriately secured with encryption and access controls, it can be compromised by unauthorized individuals or programs.

Compromised hardware

Hardware can fail. Data can be accidentally deleted or overwritten. It can also be corrupted during data transfer and storage and may be unintentionally accessed or overwritten by other data users. If your organization is considering a migration to the cloud, assess the data quality and integrity on these legacy systems before making the shift.

Unintended transfer errors

When data is migrated between different data systems, data may be accidentally lost or corrupted during the transfer process. This situation can be a significant data integrity risk, especially if data is shared between different teams or sources.

Malware, insider threats and cyberattacks

Data integrity can also be compromised by malware or viruses that corrupt data. It’s important to have protections in place against malicious insiders seeking to steal data and cyberattacks that target data repositories or data infrastructure.

Managing data integrity through data governance

To mitigate many data integrity risks, data managers should implement a robust data governance strategy that includes data integrity checks at every stage. This process may involve:

SEE: Learn more about data governance best practices with TechRepublic Premium’s data governance checklist.

Types of data integrity

To effectively maintain data integrity, you must understand the two main types of data integrity that exist: physical integrity and logical integrity.

Physical integrity

Ensuring data integrity through physical means is essential for data processing and retrieval to function as intended. While software-based safeguards provide a critical layer of defense, you must also protect data via physical measures to ensure it remains unaltered and complete, even during an outage or other destructive event.

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Natural disasters, power outages, cyberattacks, human error and storage degradation can jeopardize data’s physical integrity. Therefore, organizations must recognize the importance of incorporating software and physical security measures to guarantee data accuracy and completeness over long periods.

Logical integrity

In a relational database, logical integrity ensures that data remains unchanged. This helps keep data safe from human error and malicious attacks.

There are four types of logical integrity that work together to ensure data is consistent and reliable:

• Entity integrity: Defines each entity’s primary key, making sure each record in a table has a unique identifier.
• Referential integrity: Ensures records in related tables are linked correctly.
• Domain integrity: Enforces rules about what types of data can be entered into specific fields within an Excel spreadsheet’s database table or column.
• User-defined integrity: Allows users to create custom rules for their databases, including limiting certain characters or words from being used in passwords.

Understanding the differences: Data integrity, data security and data quality

These three terms are often used interchangeably, yet they have distinct meanings and implications in data management. It is important to have a good grasp of the differences in order to optimize your data strategy.

Data integrity vs. data security

Though interconnected, data integrity and data security serve different purposes. As already outlined, data integrity refers to the accuracy, consistency and reliability of data throughout its life cycle. It ensures data remains unaltered and trustworthy from the point of creation to the point of use.

In contrast, data security focuses on protecting data from unauthorized access, breaches or cyberattacks. It involves implementing measures such as encryption, firewalls, access controls and other security protocols to prevent data from being compromised or stolen.

Data integrity vs. data quality

While closely related to data integrity, data quality is broader in scope. It encompasses the overall condition of data, including its accuracy, consistency, completeness, relevance and timeliness. Data integrity is a component of data quality, focusing on the accuracy and consistency of data. Data quality also considers other factors, such as the relevance of the data to the task at hand, the timeliness of the data, and whether the data is complete and comprehensive.

In essence, data integrity is a critical aspect of data quality, but not all high-quality data will necessarily have high integrity if it is not relevant or timely. For example, data could be perfectly accurate and consistent (high integrity) but not relevant to the business decision at hand (low quality).

Read next: Explore our list of the top data quality tools.