12 Data Integrity Examples: Types, Industry Usage, and Risks
What Is Data Integrity?
Data integrity is concerned with the accuracy, consistency, and reliability of data stored in databases or other data storage systems. It is a vital aspect of data quality, which ensures that the information used for decision-making and analysis is accurate and trustworthy. To maintain data integrity, measures must be implemented to prevent corruption, loss, or unauthorized access to sensitive information.
The main types of data integrity are:
- Physical integrity: Protects data during storage, retrieval, and management from physical issues.
- Entity integrity: Ensures each row in a database table is uniquely identifiable.
- Referential integrity: Ensures consistency across data relationships in databases.
- Domain integrity: Ensures data entries fall within defined valid values and conditions.
- User-defined Integrity: Unique rules defined by users to meet specific business requirements.
To illustrate data integrity, we’ll show examples of the five types above, review real-life scenarios in which data integrity is crucial, and list several examples of risks to data integrity at organizations.
Types of Data Integrity with Examples
Let’s look at an example of each of the types of data integrity.
1. Example of Physical Integrity
Physical integrity involves protecting data from physical damage or corruption caused by hardware failures or environmental factors. For instance, using redundant storage systems like RAID (Redundant Array of Independent Disks) can help maintain physical integrity by distributing data across multiple disks to prevent loss due to disk failure.
2. Example of Entity Integrity
Entity integrity necessitates that each record in a table has its own unique identifier to avoid duplicates. By assigning a unique identifier, such as Social Security Number, to each record in an HR database, there are no duplicate entries.
3. Example of Referential Integrity
Referential integrity ensures consistency between related tables in a relational database by requiring that foreign keys match primary keys on corresponding tables. For instance, if you have an orders table referencing customer IDs from the customer’s table, referential integrity prevents the addition of an order with an invalid customer ID.
4. Example of Domain Integrity
Domain integrity, or attribute-level validation, enforces valid entries for individual columns based on predefined rules or constraints. For example, a date of birth column in a patient database may require entries to be valid dates and within an acceptable age range.
5. Example of User-Defined Integrity
User-defined integrity refers to custom business rules that are not covered by other types of data integrity. An example might include ensuring that employees’ salaries fall within the appropriate pay scale for their job titles and experience levels.
Related content: Read our guide to data anomaly detection
Data Integrity Examples in Different Sectors
6. Data Integrity in Healthcare: Patient Records
In the healthcare sector, keeping data integrity for patient records is vital for proper diagnosis and treatment. For example, medical professionals rely on accurate electronic health records (EHRs) to access patients’ medical history or allergies. Discrepancies or errors can lead to incorrect treatments or prescriptions with potentially life-threatening consequences.
7. Data Integrity in Finance: Transaction Data
Financial institutions rely on precise transaction data for decision-making processes, such as risk assessment and fraud detection.
An example that illustrates data integrity’s importance is when banks use Know Your Customer (KYC) protocols to authenticate customer identities and prevent illicit activities like money laundering. Ensuring the accuracy of customer information helps financial organizations maintain regulatory compliance while protecting their business and reputation.
8. Data Integrity in Education: Student Records
Educational institutions require accurate student records for various purposes such as enrollment management, academic progress tracking, and grant distribution.
An excellent example is universities that use Integrated Postsecondary Education Data System (IPEDS) reports. IPEDS reports make it possible to analyze institutional performance metrics, but this assumes the university has accurate student demographic information.
By maintaining high levels of data integrity, educational establishments can make informed decisions regarding resource allocation and policy implementation.
Examples of Data Integrity Issues and Risks
Here are a few data integrity issues and risks that are faced by many organizations.
9. Human Error
Human error is a significant cause of data integrity problems. Mistakes made during manual entry or processing can result in inaccurate or inconsistent information stored in databases. For example, a healthcare professional might accidentally input incorrect patient details, or a financial analyst could misinterpret transaction records.
10. Cyber Attacks
Cybersecurity threats, such as malware or phishing attacks, pose serious risks to data integrity. Malicious software may corrupt files or alter critical information within databases without detection. Likewise, unauthorized access by hackers could lead to tampering with sensitive records, affecting business operations.
11. Compromised Hardware
Compromised hardware, including storage devices like hard drives or solid-state drives (SSDs), can cause loss or corruption of vital information due to physical damage or wear-and-tear over time. This issue may also arise from power outages affecting servers that host essential applications.
12. Data Transfer Errors
Transfer errors can compromise data integrity when moving large volumes of data between systems. For instance, data engineers might encounter issues during the ETL (Extract, Transform, Load) process or when migrating to a new database management system.
Learn more in our detailed guide to data integrity issue
Resolving Data Integrity Issues with IBM Databand
Implement end-to-end observability for your entire solutions stack so your team can ensure data integrity by identifying, troubleshooting, and resolving problems.