What Is Data Integrity?
Data Integrity Definition
Data integrity is a concept and process that ensures the accuracy, completeness, consistency, and validity of an organization’s data. By following the process, organizations not only ensure the integrity of the data but guarantee they have accurate and correct data in their database.
The importance of data integrity increases as data volumes continue to increase exponentially. Major organizations are becoming more reliant on data integration and the ability to accurately interpret information to predict consumer behavior, assess market activity, and mitigate potential data security risks. This is crucial to data mining, so data scientists can work with the right information.
Types of Data Integrity
Organizations can maintain data integrity through integrity constraints, which define the rules and procedures around actions like deletion, insertion, and update of information. The definition of data integrity can be enforced in both hierarchical and relational databases, such as enterprise resource planning (ERP), customer relationship management (CRM), and supply chain management (CRM) systems.
Organizations can achieve data integrity through the following:
Physical integrity means protecting the accuracy, correctness, and wholeness of data when it is stored and retrieved. This is typically compromised by issues like power outages, storage erosion, hackers targeting database functions, and natural disasters, which prevent accurate data storage and retrieval.
Logical integrity ensures that data remains unchanged while being used in different ways through relational databases. This approach also aims to protect data from hacking or human error issues but does so differently than physical integrity.
Logical integrity comes in four different formats:
Entity integrity is a feature of relation systems that store data within tables, which can be used and linked in various ways. It relies on primary keys and unique values being created to identify a piece of data. This ensures data cannot be listed multiple times, and fields in a table cannot be null.
Referential integrity is a series of processes that ensure data remains stored and used in a uniform manner. Database structures are embedded with rules that define how foreign keys are used, which ensures only appropriate data deletion, changes, and amendments can be made. This can prevent data duplication and guarantee data accuracy.
Domain integrity is a series of processes that guarantee the accuracy of pieces of data within a domain. A domain is classified by a set of values that a table’s columns are allowed to contain, along with constraints and measures that limit the amount, format, and type of data that can be entered.
User-defined integrity means that rules and constraints around data are created by users to align with their specific requirements. This is usually used when other integrity processes will not safeguard an organization’s data, allowing for the creation of rules that incorporate an organization’s data integrity measures.
Data Integrity vs. Data Quality
Data quality is a crucial piece of the data integrity puzzle. It enables organizations to meet their data standards and ensure information aligns with their requirements with a variety of processes that measure data age, accuracy, completeness, relevance, and reliability. Data quality goes a step further by implementing processes and rules that govern data entry, storage, and transformation.
Data Integrity vs. Data Security
Data security involves protecting data from unauthorized access and preventing data from being corrupted or stolen. Data integrity is typically a benefit of data security but only refers to data accuracy and validity rather than data protection.
Data Integrity and GDPR Compliance
Data integrity is a key process to helping organizations comply with data protection and privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR).
What Are Some Data Integrity Risks?
Key threats to organizations ensuring data integrity include:
Human error offers a major data integrity risk to organizations. This is often caused by users entering duplicate or incorrect data, deleting data, not following protocols, or making mistakes with procedures put in place to protect information.
Bugs and Viruses
If data is unable to transfer between database locations, it means there has been a transfer error. These occur when pieces of data are in the destination table but not the source table of a relational database.
Compromised hardware can result in device or server crashes and other computer failures and malfunctions. Consequently, data can be rendered incompletely or incorrectly, data access removed or limited, or data can become hard for users to work with.
How To Ensure Data Integrity?
Preventing the above issues and risks is reliant on preserving data integrity through processes such as:
Data entry must be validated and verified to ensure its accuracy. Validating input is important when data is provided by known and unknown sources, such as applications, end-users, and malicious users.
Remove Duplicate Data
It is important to ensure that sensitive data stored in secure databases cannot be duplicated onto publicly available documents, emails, folders, or spreadsheets. Removing duplicated data can help prevent unauthorized access to business-critical data or personally identifiable information (PII).
Back Up Data
Data backups are crucial to data security and integrity. Backing up data can prevent it from being permanently lost and should be done as frequently as possible. Data backups are especially important for organizations that suffer ransomware attacks, enabling them to restore recent versions of their databases and documents.
Applying appropriate access controls is also important to maintaining data integrity. This is reliant on implementing a least-privileged approach to data access, which ensures users are only able to access data, documents, folders, and servers that they need to do their job successfully. This limits the chances of hackers being able to impersonate users and prevents unauthorized access to data.
Always Keep an Audit Trail
In the event of a breach occurring, it is crucial that organizations are able to quickly discover the source of the event. An audit trail allows businesses to track what happened and how a breach occurred, and then find the source of the attack.
How Fortinet Can Help?
Organizations can secure databases with Fortinet through firewalls and security technologies. Businesses can build security into the core of their data center environments by deploying technologies with an integrated approach from Fortinet and Nuage Networks. These solutions harness groundbreaking technologies and networking expertise to secure data centers against evolving security threats, protect data center application integrity, and safeguard virtual machines and the underlying network fabric.
The Fortinet FortiGate VMX solution is purpose-built for VMware’s software-defined data center, which provides secure virtualized network traffic and visibility into the hypervisor level.
What is data integrity?
Data integrity is a concept and process that ensures the accuracy, completeness, consistency, and validity of an organization’s data.
Why is data integrity important?
The importance of data integrity increases as data volumes continue to increase exponentially. Major organizations are becoming more reliant on data integration and the ability to accurately interpret information to predict consumer behavior, assess market activity, and mitigate potential data security risks.
How do you secure data integrity?
Organizations can secure databases with Fortinet through firewalls and security technologies. Businesses can build security into the core of their data center environments by deploying technologies with an integrated approach from Fortinet.