What is data integrity? Definition, Best Practices & More

What is Data Integrity?

Before we delve deep into the nitty-gritty details about data integrity, let us start by understanding what data integrity is.

Data is known to be one of the most treasured assets of any given company. It can help the company boost its revenue, help target audiences with personalized ads, among many other benefits. A company’s success depends on the type of data it possesses and how well it adheres to the best practices of data integrity. The better the data quality, the more successful the company will be.

Data integrity describes the state of data as being valid, accurate, and consistent throughout its lifetime. Any compromised or lost data is of less importance to enterprises. Data integrity is the understanding of any digital information’s health and maintaining it.

There are also dangers associated with data loss; hence any enterprise needs to maintain data integrity. Data can be altered in several ways. When transferring data, ensure it is intact and unchanged. Always rely on validation processes error-checking approaches to maintain data integrity of any reproduced or transmitted data. Data integrity can be easily achieved with an SSL certificate. However, a site holder for website security can choose any SSL from the SSL product portfolio. Different certificates are suitable for different usage.

For example, a single domain SSL secures a single domain, while a multi-domain certificate secures multiple domains and subdomains. It is much easier to maintain data integrity through high-level encryption.

Data integrity as a process and as a state

It is easy to confuse data integrity because it can be defined as either a process or a state. Data integrity as a process refers to approaches used to ensure that all the data found in a database is accurate and valid. In this case, validation and error-checking fall under the data integrity process. On the other hand, data integrity as a state means the data set is accurate and valid.

Case for data integrity

One of the reasons why an enterprise should maintain data integrity is that data integrity ensures there is

1. Connectivity
2. Recoverability- whenever anyone is working on a deal, projection, or presentation, they should ensure they have the correct data in the right place. Failing to do so can be harmful to the business.
3. Searchability- to keep your organization at the top, ensure standard business metrics against the organization’s goals and the competition.
4. Traceability from its source. Having a data point ensures all touchpoints you make with a customer are accessible. The data can point out red flags or limitations or notify decision-makers.

Also, data integrity intensifies steadiness and performance while simultaneously improving maintainability and reusability.

Data is involved in the decision-making processes of enterprises. Still, it needs to go through several processes and changes from its raw form to layouts useful when identifying relationships and facilitating informed decisions.

Ways in which data integrity can be compromised include:

1. Errors during transfers, which may include accidental alterations when transferring data from one device to another.
2. Human errors, whether intentional or unintentional.
3. Compromising devices physically or misconfigurations.
4. Malware/viruses, bugs, hacking, and other cyber-attack threats.
5. Compromised hardware, for example, disk or device crash.

Data backup and replication are critical for data integrity because only some of the compromises mentioned above can be prevented by data security. To minimize such risks;

1. Validate data- check critical specifications that are important for your company before validating data.
2. Validate input- input validation is vital, whether your data is from a known or unknown source.
3. Eliminate duplicate data- remove any stray or replicated data because it may find a way to employees who are not authorized to access it.
4. Back up data- regular data backup helps in preventing permanent data loss.
5. Keep audit trails- these audits help the company to detect the source of the problem.
6. Access controls- only employees authorized to access the data should have the credentials to do so.

Database of Data Integrity:

There is a total of four types of data integrity in the database.

1. Entity integrity

A database has tables, rows, and columns. The three elements ought to be numerous for data accuracy in primary keys but not more than necessary/needed. The elements should neither be null nor the same. A clear example is the employees’ database with primary key data for their name and a precise employee’s number (a payroll number.)

2. Referential integrity

A database with foreign keys is a second table that may denote a primary key table in the given database. Foreign keys may have data that is null or may be shared. An example is that employees may belong to the same department and share roles.

3. Domain integrity

All values and categories in databases are always set, including nulls like N/A. This type of integrity of a database refers to the usual ways used when inputting and reading this data. An example is a database that uses monetary values to include cents, and dollars means that three decimal places are disallowed.

4. User-Defined integrity

There are usually groups of data created by users away from domain, referential, and entity integrity. For example, whenever any employer comes up with a column to fill in corrective action of employees, the data is user-defined.

Data integrity vs. data security

Data integrity and data security are related, and each plays a vital role in attaining the other. For example, data security protects data from corruption or any illegal access, and it is needed to achieve data integrity.

Data integrity is the accuracy and validity of data rather than data protection. In short, data integrity is the desired outcome of data security. Whether intentional or unintentional compromise of data, data security is needed when maintaining data integrity.

Modern enterprises need data integrity for the effectiveness and accuracy of business processes and decision-making. Also, many data security programs rely on data integrity.

Data integrity is known to be critical but highly manageable for modern organizations. It can be achieved through constraints in database integrity, data backup and duplication, validation processes, and other protocols.

Conclusion

When your organization’s data gets deleted or changed, it will negatively affect business decisions that rely on the data. This is why data integrity is essential and needs to be preserved at all costs. If you fail to do so, your data will be compromised, leading to expensive data audit trails to check for errors and recover the lost data. Therefore, data security is also a key element in data integrity. This article has given some insights into data integrity and how it relates to data security.