Issues with data accuracy and consistency exist across all businesses and can result in minor disturbances and substantial business complications. Comprehending the basics of data integrity and how it works is the initial step in retaining the quality of your data and keeping it safe. This article will dive into data integrity explore how it’s different from data quality management and how a data integration software can help define or constrain some aspects of the business. Lastly, we’ll look at the methods that can help ensure data integrity in a database.
What is Data Integrity?
Data integrity is the accuracy, completeness, and reliability of data. It can be specified by the lack of variation between two instances or consecutive updates of a record, indicating that your information is error-free. It also corresponds to the security and integrity controls and methods of regulatory compliance.
Data integrity in a database is preserved by an array of error-checking and validation procedures, rules, and principles executed during the integration flow designing phase. These checks and correction procedures are based on predefined business rules. For instance, the rules dictate to filter out the data with an incorrect date or time value.
The question then arises, why is it imperative to maintain data integrity in a database?
The importance of maintaining data integrity in a database is evident when creating relationships between disparate data elements. It ensures that the data transferring from one stage to another is accurate and error-free.
Data Integrity vs. Data Quality vs. Data Security
People often confuse data integrity with data security or data quality. However, these three are related but different concepts.
Data security concerns measures taken to protect enterprise data from misuse. It includes using methods and techniques that make your data inaccessible to undesired parties or making selected data accessible to the desired parties. Data security breaches can threaten the existence of an organization. On the other hand, data integrity deals with the accuracy and completeness of data present in the database.
The end goal of data security is to protect your data from external or internal breaches. Thus, it is one of the many aspects of data integrity, but it isn’t extensive enough to account for the numerous procedures essential for keeping your information unaffected over time. Similarly, data quality is another facet of data integrity, albeit a major one.
Data quality ensures that the data stored in your database is compliant with the organization’s standards and requirements. In other words, it maintains integrity in a database. In doing so, it applies a set of rules to a specific or complete dataset and stores it in the target database. Moreover, data quality is data accuracy, which refers explicitly to the correctness of stored values. Data integrity vs. data accuracy can be understood by seeing data integrity as an umbrella term, whereby data accuracy is one of the many categories.
Integrity of Data In a Database Table
Data integrity in a database covers all aspects of data quality and advances further by executing several rules and procedures that oversee how information is entered, deposited, transmitted, and more.
Consider this example of data integrity. While the Salary of all employees is an integer, one employee has a salary in alphanumeric characters. Since the Salary table only accepts integers (INT), the value 697abc will not be accepted by the database. This one-way data is protected by the database using domain-level data integrity.
Let’s look at the two methods that help ensure data integrity.
Types of Data Integrity
Data integrity is applied in all databases can be categorized into two main types:
Protecting data against external factors, such as natural calamities, power outages, or hackers, falls under the domain of physical integrity. Moreover, human faults, storage attrition, and several other problems can make data operators unable to obtain information from a database.
It concerns the rationality of data present within the relational database. Logical integrity constraints can be categorized into four types:
It depends on the making of primary keys or exclusive values that classify data items. The purpose is to ensure that data is not recorded multiple times (i.e., each data item is unique), and the table has no null fields.
Entity integrity is a critical feature of a relational database that stores data in a tabular format, which can be interconnected and used in various ways.
It denotes a series of procedures that ensure proper and consistent data storage and usage. Referential integrity ensures that only the required alterations, additions, or removals happen via rules implanted into the database’s structure about how foreign keys are used.
These rules might include conditions that remove duplicate data records, warrant that data is precise, and prohibit unsuitable recording data.
It’s an assortment of procedures that ensures the precision of every data item is maintained in a domain. Here, a domain is defined as a set of suitable values that a column is permitted to enclose.
Domain integrity encompasses rules and other processes restricting the format, type, and volume of data recorded in a database. It ensures that every column in a relational database is in a defined domain.
It comprises the rules defined by the operator to fulfill their specific requirements. Entity, referential, and domain integrity are not enough to refine and secure data. Time in time again, particular business rules must be considered and integrated into data integrity processes to meet enterprise standards.
Importance of Integrity in Data
Data integrity in a database is essential because it is a necessary constituent of data integration. If data integrity is maintained, data values stored within the database are consistent about the data model and type. Thus, reliable insights can then be gained from the data model so users can make informed business decisions.
Here are some examples of data integrity at risk:
- An attempt to enter a phone number in the wrong format.
- A developer accidentally tries to insert the data into the wrong table while transferring data between two databases.
- An attempt to delete a record in a table, but another table references that record as part of a relationship.
- A user accidentally tries to enter a phone number into a date field.
These are just a few examples of data integrity being at risk. However, all these and more can be avoided easily. For instance, numeric columns or cells shouldn’t include textual information to preserve data integrity. Plus, for data to be complete, its features such as business rules, relationships, dates, definitions, and lineage must be accurate.
Data integrity helps ensure that the data stored in your database can be found and linked to other data. This guarantees that your entire data set can be recovered and searched whenever needed. It strengthens the stability of data, offers optimum performance, and makes it reusable and maintained easily.
Now that you are aware of the importance of data integrity and the two methods to ensure data integrity, let’s move on towards factors that hinder integrity.
Factors Affecting Integrity in A Database
Several factors impact the integrity of the data stored in a database, including:
Manual data entry increases the chances of errors, duplications, or deletion. Often, the entered data fails to follow the apt protocol, or the mistakes in the manual entry can extend to the execution of processes, hence corrupting the results. All these issues put data integrity at risk.
A transfer error occurs if the data is not successfully transferred from one site within a database to another. These errors usually occur when a data item exists in the target table but is absent from the source table within a relational database.
Bugs and Viruses
Your data’s integrity can also be compromised due to spyware, malware, and viruses invading a computer and altering, deleting, or stealing data.
How to Ensure Integrity of Data in a Database
Here are some of the best practices of data integrity that can minimize or eliminate the risks of data breaches in a database. The common methods used for data integrity check include:
- Limit access to data and change permissions to constrain modifications to data by unapproved parties.
- Focus on data validation to ensure the accuracy of data when collected or integrated.
- Maintain a regular backup of data.
- Use logs to monitor when data is entered, altered, or erased.
- Conduct systematic internal audits to ensure that information is up to date.
Protecting the integrity of your critical business data using conventional methods can look like a difficult task. However, modern data integration tools provide an efficient alternative, offering real-time error detection and debugging.
With cutting-edge data integration platforms like Astera Centerprise, you can attach numerous source data applications and get access to all of your enterprise data in one place. It offers all the features you need to kick-start your data integration project, consolidate incongruent data sources, and generate an integrated view of your enterprise’s information assets while maintaining data integrity.