Data integrity means data is complete, updated and contextually accurate to the model at hand. To maintain integrity, data must be collected and stored in an ethical, compliant way and must have a well-defined structure where all characteristics are correct, verifiable and consistent.
Data integrity focuses on the health and maintenance of digital information throughout its lifecycle. Data integrity can be viewed as either a state, meaning that the data set is valid, or as a process, which describes the measures taken to ensure data set accuracy. Additionally, data integrity can be applied to database management as well through one of four categories: entity integrity, referential integrity, domain integrity and user-defined integrity.
Why Is Data Integrity Important?
Data integrity is crucial to ensuring the validity, recoverability, traceability, connectivity, reusability and maintainability of data.
Data is one of the largest driving factors in decision-making for organizations of all sizes. To create the insights that guide these decisions, raw data must be transformed and organized through a set of processes that make it easier to identify relationships in the data. Data integrity ensures the data remains accurate and uncompromised throughout this process. Poor data integrity can lead to incorrect business decisions and a distrust in the data-driven decision-making process, potentially harming a company’s future.
Lack of data integrity may also have legal ramifications if data is not collected and stored in a compliant manner, as outlined by international and national laws such as the General Data Protection Regulation (GDPR) and the U.S. Privacy Act.
Data can become compromised in a variety of ways:
- Human error like unintended alterations
- Errors in transferring
- Malware or hacker interference
- Disk crashes
- Bugs and physical device damage
- Illegal data collection
A thorough data integrity process is crucial and should include lock-tight data security measures, regular data backups and automated duplications, as well as the utilization of input validation, access control and encryption.
What Are the Different Types of Data Integrity?
Physical integrity and logical integrity are the primary types of data integrity.
Physical Integrity
Physical integrity is the overall protection of the wholeness of a data set as it is stored and retrieved. Anything that impedes the ability to retrieve this data, such as power disruption, malicious disruption, storage erosion and a slew of additional issues may cause a lack of physical integrity.
Many companies outsource their data storage to cloud providers like AWS to manage the physical integrity of the data. This is particularly useful for small companies that benefit from offloading data storage to spend more time focusing on their business.
Logical Integrity
Logical integrity allows data to remain unchanged as it is utilized in a relational database. Maintaining logical integrity helps protect against human error and malicious intervention as well, but does so in different ways than physical integrity depending on its form.
Databases use four variations of logical integrity:
- Entity integrity involves creating primary keys to identify data as distinct entities and ensure that no data is listed more than once or is null. This allows data to be linked to and enables its usage in a variety of ways.
- Referential integrity refers to the processes used to store and access data uniformly, allowing rules to be embedded into a database’s structure regarding the use of foreign keys. This creates a consistent and meaningful combination of data sets across the database. Critically, referential integrity allows various tables to be combined within a relational database, facilitating uniform insertion and deletion practices.
- Domain integrity refers to processes that ensure accuracy in each piece of data included in a domain, or a set of acceptable values that a column may contain.
- User-defined integrity provides constraints created by the user to ensure data follows rules that entity, referential and domain integrity do not enforce.
Frequently Asked Questions
What is data integrity?
Data integrity refers to practices that ensure data remains accurate, complete and consistent throughout its lifecycle. It involves keeping data compliant with relevant laws and implementing measures to keep data secure.
Why is data integrity important?
By making sure data is complete and accurate, data integrity allows organizations to quickly identify patterns and make informed decisions. In addition, data integrity helps businesses account for issues like human error and malicious actors.
What are the main types of data integrity?
The two main types of data integrity are physical integrity — which protects data while it’s stored and retrieved — and logical integrity — which ensures data is accurate in relational databases.
