Issues with data accuracy and consistency exist across all businesses and can result in minor disturbances as well as substantial business complications.
In this data-oriented age, when a vast quantity of data is being generated and stored, it is becoming increasingly important to preserve the integrity of the information that’s gathered. 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.
In this article, we’ll dive into data integrity, its different types, importance, and the factors that impact it. Lastly, we’ll look at different ways that can help ensure data integrity.
Let’s get started by defining data integrity.
What is Data Integrity in a Database?
Data integrity is defined as the maintenance, assurance, accuracy, consistency of data over its entire life-cycle i.e. throughout its design, implementation, and usage stages.
The term data integrity refers to the overall 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 of data pertaining to regulatory compliance.
Data integrity 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 a predefined set of business rules. For instance, the rules dictate to filter out the data with an incorrect date or time value.
Why is it important to maintain data integrity? The importance of data integrity is also evident when creating relationships between disparate data elements. It ensures that the data transferring from one stage to another is accurate and error-free. This way, when the information is written into the database, its trustworthiness is implied regardless of the duration for which it is stored or the frequency of access.
Data Integrity Vs. Data Quality Vs. Data Security: What’s the Difference?
People often confuse data integrity with data security and data quality. However, these three are related but different concepts. Let’s start by defining what is data security and integrity.
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 take account of the numerous procedures essential for keeping your information unaffected over time. Similarly, data quality is also 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 ensures maintaining data integrity in a database. In doing so, it applies a set of rules on a specific or complete dataset, and stores it in the target database. Moreover, a component of data quality is data accuracy that specifically refers to the correctness of values stored. 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.
Example of Data Integrity of a Database Table
Data integrity 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 the above image. While salary of all employees is in integer. One employee has a salary in alphanumeric characters. Since Salary table only accepts INT, this value will not be accepted by the database. This is one way the data is protected by the database using domain-level data integrity.
Types of Data Integrity
Data integrity is applied in all database models and 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 also make it unmanageable for data operators to obtain information from a database.
It concerns with 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 make sure 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 a range of ways.
It denotes a series of procedures that ensure proper and consistent storage and usage of data. Referential integrity is what ensures that only the required alterations, additions, or removals happen via rules implanted into the database’s structure about the way foreign keys are used.
These rules might include conditions that remove duplicate data records, warrant that data is precise, and/or prohibit recording data that is not suitable.
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 that restrict 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 of the rules defined by the operator to fulfill their specific requirements. At times entity, referential, and domain integrity are not enough to refine and secure data. Time and again, particular business rules must be considered and integrated into data integrity processes to meet enterprise standards.
Why is Data Integrity Important?
Data integrity is an essential constituent of data integration. If the ‘integrity’ of data is maintained, it means that data values stored within the database are consistent in relation to the data model and/or data type. For instance, to preserve data integrity, numeric columns or cells shouldn’t include textual information.
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.
Factors Affecting Integrity in A Database
Several factors impact the integrity of the data stored in a database, including:
Entering or managing data manually increases the chances of errors, duplications, or deletion. Often, the entered data fails to follow the apt protocol or the errors in 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 Preserve Data Integrity in a Database
Here are some of the best practices of data integrity that can minimize or eliminate the risks of data breach and ensure accuracy, assurance, and completeness throughout the database.
- Limit data access 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 back up 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.
Defending 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 the integrity of data.