Nowadays, data is increasingly available, which has led businesses to make use of it in data-driven business decisions.
More and more organizations have been working to become data-driven to harness the data for their profitability and outperform competitors in the market.
With endless data sprawl produced by businesses each day, companies are having difficulty managing it, significantly affecting the entire enterprise.
In such cases, the importance of data integrity comes into the picture. As data increases exponentially, businesses rely more on data integrity which is relevant to mitigate security risks and leverage it to make informed decisions.
This article will feature the fundamentals of data integrity, its types, ways to secure it, and its importance for businesses.
What is data integrity?
The term data integrity refers to the state and a process.
Data integrity as a state ensures the accuracy, consistency, completeness, and quality of data throughout its lifecycle. This process begins with its creation, acquisition through observation and analysis, and archival or termination.
On the other hand, data integrity as a process defines measures to maintain the accuracy and validity of all data in a database.
As data security threats increase, more cases of a company’s sensitive data are becoming compromised. According to the 2021 IBM report, data breach costs can rise from $3.86 million to $4.24 million.
Businesses of all sizes can become vulnerable. Thus, securing data integrity is a core focus of many enterprise data security solutions.
Data integrity can be compromised in various ways, including:
- Human error
- Viruses (e.g., malware, bugs, hacking, and other cyber threats)
- Inconsistencies across all formats
- Compromised hardware
- Collection and transfer errors
Two types of data integrity
Securing data integrity requires an understanding of the two types of data integrity:
Physical integrity
Physical integrity refers to the processes that ensure the accuracy and wholeness of data when it is stored and retrieved. This type of integrity is compromised through:
- Human error-caused issues
- Storage erosion
- Electromechanical faults
- Design flaws
- Power outages
- Natural disasters, radiation, and environmental extremes
- Extreme temperatures
- Various types of cybersecurity attacks
Logical integrity
Logical integrity ensures that data remains unchanged while being used in different ways in a relational database.
It has four subsets when dealing with relational databases.
Entity integrity
Entity integrity uses primary keys and unique values that ensure that no data element is duplicated or null, which has no value in the relational database.
Referential integrity
Referential integrity refers to the processes that ensure the data remains stored and used in a database uniformly.
Rules are embedded into the database structure about how foreign keys are used, ensuring only authorized changes, additions, or data deletions can be made.
Domain integrity
Domain integrity is a series of processes that ensures the accuracy of pieces of data within a domain.
It may also include constraints and other measures that limit the format, amount, and type of data that can be entered.
User-defined integrity
User-defined integrity refers to the rules and constraints established by the user to align with the organization’s specific requirements.
Specific business rules are often implemented to strengthen other data integrity measures further.
How can businesses secure data integrity?
Companies are reliant on data integrity to prevent various data issues and risks. Regular error-checking and validation routines are essential to ensure data isn’t compromised between updates and transfers.
Here are some tips to secure data integrity in your organization:
Data cleaning and maintenance
As data is processed continually, staff also continue to work with the system. With these constant system changes, companies regularly erase error systems to ensure data is secured.
Data entry training
Data entry training is important since the users will be dealing with various issues, such as malicious attempts.
Thus, users must be knowledgeable about managing the data and keeping them accountable for securing and keeping the efficiency of the data.
Data validation
Human error is unavoidable. For this reason, data validation rules are important to implement regularly to better control and restrict data values that users store in the system.
Organizations do this practice to prevent any accidental modifications as well as to improve their data analytics.
Why data integrity matters
Harnessing the importance of data integrity significantly helps businesses in many ways.
With data integrity, companies can avoid data loss, and corrupt and compromised data, which considerably harm the business.
Data integrity should be included in the core security solutions. Companies can benefit from it by ensuring the validity and accuracy of data and the system’s performance.
It is worth noting that secured and quality data helps businesses to make data-driven decisions. Thus, data security is one of the solutions that a company must practice to achieve specific business goals.
