Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is a critical aspect of data management that ensures the accuracy, completeness, and consistency of data. Organizations must understand why data integrity is critical because it combines features of data quality and security, and it is essential for the consistent reuse of data and data-driven operations. Data integrity is achieved through a continual process of gathering and maintaining accurate, consistent data across multiple sources, teams, and formats. This process involves quality control, integration, validation, de-duping, preservation and storage, privileged access, cybersecurity, and audits.
It’s important to note that data integrity is not the same as data security. Data security is the practice of protecting data from unauthorized access or theft. Any unintended changes to data as a result of storage, retrieval or processing operation, including malicious intent, unexpected hardware failure, and human error, is a failure of data integrity . Data integrity can be compromised by errors, damage, loss, change, duplication, and corruption of data.
What is Data Integrity?
Data integrity is a concept and procedure that assures an organization’s data is accurate, full, consistent, and valid. Organizations who adhere to the process make sure their databases have accurate and up-to-date data in addition to ensuring the integrity of the data.
The significance of data integrity in preventing data loss or leakage cannot be overemphasized. To protect your data from dangerous outside sources, you must first guarantee that inside users are managing the data correctly. By implementing adequate data validation and error checking, you can ensure that sensitive data is never miscategorized or inappropriately stored, potentially exposing you to risk.
In SQL databases, data integrity means guaranteeing that each row of a table is uniquely recognized so that data may be retrieved independently. Constraints on columns are required to accomplish this (constraints are sets of rules). Data constraints serve to ensure data integrity by preventing erroneous data from being entered into the database’s base tables.
Good data integrity processes also adhere to all safety and regulatory requirements. The FDA’s abbreviation ALCOA precisely describes data integrity criteria.
ALCOA is an acronym that stands for:
(A) Attributable: Attributable data indicates that companies should know how and by whom data is created or obtained.
(L) Legible: Legible data indicates that organizations should be able to read and interpret it, and the records should be permanent.
(C) Contemporaneous: This aspect of data integrity requires companies to understand how data appeared in its initial condition and what happened to it during the various stages of its lifecycle.
(O) Original: Understanding data’s source systems and the capacity to retain source data in its original state are required for this component of data integrity.
(A) Accurate: Accurate data is error-free and adheres to the protocols of the applications that use it.
What is the importance of data integrity?
Data integrity assurance benefits firms in a variety of ways. It ensures that your data is simple to retrieve, search, trace (to the source), and connect. Securing the legitimacy and accuracy of data improves the stability and performance of your data management systems. Implementing data integrity methods improves data reusability and maintenance.
Many businesses prioritize data in their decision-making process. However, data from credible sources must go through several adjustments and techniques before it can be used in meaningful formats. These practical formats can assist you in identifying linkages and making smarter judgments. As a result, investing in data integrity is a key concern for modern businesses. Improving data quality also results in a 20–40% boost in revenue.
Types of Data Integrity
To ensure data integrity, it is important to understand the two main types of data integrity: physical integrity and logical integrity. In this blog post, we will explain what these types are and how they can be maintained.
Physical integrity deals with the storage and retrieval of data. It ensures that data is not corrupted or damaged by external or internal factors. For example, physical integrity can be affected by power outages, disk failures, network errors, natural disasters, or malicious attacks.
To maintain physical integrity, some of the best practices are:
- Always backup data to prevent data loss or corruption due to system failures or disasters.
- Use data encryption to protect data from unauthorized access or modification .
- Use antivirus software, firewall, and data loss prevention tools to prevent malware infections or cyberattacks .
- Monitor and test your systems regularly to detect and fix any errors or vulnerabilities.
Logical integrity ensures that data makes sense in a specific context. It ensures that data follows the rules and constraints that define its structure and meaning. For example, logical integrity can be enforced by domain, entity, referential, and user-defined rules .
- Domain rules specify the range of values that a data element can have. For example, a domain rule can ensure that an email address has a valid format.
- Entity rules specify the uniqueness and identity of a data element. For example, an entity rule can ensure that each customer has a unique ID number.
- Referential rules specify the relationships between data elements. For example, a referential rule can ensure that a foreign key in one table matches a primary key in another table.
- User-defined rules specify the business logic and requirements that apply to the data. For example, a user-defined rule can ensure that a customer’s credit limit is not exceeded.
8 Ways to reduce Data Integrity Risk
1. Implement Quality Control Measures
A company should designate individuals and procedures to implement these steps. These individuals ensure that all other employees follow the security and data management policies. These enforcers aid in the risk evaluation of data integrity. To analyze security solutions, you can select data stewards to monitor the provenance of data sources.
2. Establish an Integrity Culture
Promoting an integrity culture minimizes data integrity risk in numerous ways. It aids in keeping employees honest about both their own work and the efforts of others. Workers in a data-driven culture are also more likely to report situations where others take shortcuts or fail to perform their responsibilities in the many different facets of data integrity.
3. Regularly clean and backup your data
Data integrity requirements are maintained by frequently removing identical data and backing up the data. Duplicate data complicates the database and increases the danger of valuable data being leaked.
Backing up your data on a regular basis protects you from harmful intrusions. Your organization can save money by using market tools to clean and backup your data. So, increase the quality of your data by cleansing it and backing it up at regular intervals.
4. Generate an audit trail
An audit trail is a very effective technique for lowering the risk to data integrity. Audit trails are crucial for understanding what happened to data at different times in its history, including its creation and subsequent transformation or use.
5. Improve the Efficiency of Your Software Development Process
A solid software development lifecycle allows you to keep track of quality-focused tasks throughout the process. Examining this lifespan enables firms to address structural adjustments that can improve the quality of their operations. All technology in your company must be built, skilled, verified, and tested on a regular and appropriate basis. So, always fine-tune your quality processes to prioritize data integrity.
6. Removal of known security Vulnerabilities
Security flaws must be fixed in order to lessen the chance that maintaining data assets would compromise its integrity. To identify known security vulnerabilities and put preventative measures in place, this approach to risk reduction needs subject-matter expertise. To actually make this effort, technology such as security patches is also needed.
7. Place the appropriate validations
What sources you use to collect data is irrelevant. Prior to entering your systems, you must handle and properly process this raw data. You can clean up your data using a variety of data validation techniques to guarantee the highest quality possible. These includes:
- Source re-verification ensures that data transit does not alter the meaning of the data.
- Continual source-to-source verification is used to make sure that problems in legacy systems are not carried over to the new system.
- keeping track of every issue in one location for better management.
- Validating external data that enters your system by performing data checks
8. Create Process Maps for All Critical Data.
It is vital to develop process maps for critical data to govern how, by whom, and where the data are used. Data assets can be better controlled when organizations map them – ideally before they are used. In addition to ensuring compliance with regulations, these maps are essential for implementing security measures.
Using conventional ways to maintain the integrity of your company’s data can seem like a daunting task. Platforms for secure, cloud-based data integration provide a more recent option while also giving you a real-time view of all of your data. You can connect several source data applications and access all of your company’s data in one place using market-leading cloud integration technologies.