Running an organization in a tech-powered world is full of challenges. Safeguarding data from potential thieves, optimizing performance, and maximizing your operation are all integral to attaining success. Data is an exceptionally valuable and important aspect of any organization. Whether it covers customer information, operations, or personnel, maintaining the integrity of your data is critical to success. Utilizing (and reinforcing) proper data integrity protocols is key to ensuring your company can prosper, avoid falling victim to a data breach, and mitigate data issues in a reasonable timeframe. In this article, we’ll take a look at data integrity and the four different types of data integrity frameworks that can lead a company to fantastic data management.
Data Integrity Defined
At a glance, data integrity is merely how accurate, consistent, and serviceable your organization’s data is at any given time. The flow of data through any organization needs to be secure and tightly controlled. Many individuals within an organization, whether they are staff or management, will often access data. As they access the data, it might be moved across different platforms or transmitted somewhere. As it’s being used, stored, and retrieved, mistakes and errors can occur. These errors can ruin the integrity of the data. When data is mishandled, it leads to myriad issues that can harm companies (and, by extension, their clients). So, what do you do to introduce and reinforce data integrity at your place of business? First, using backups and reinforcing the physical integrity of your data is crucial. Next, you need to reinforce the logical integrity of your data. It’s best to take a four-pronged approach to the problem of maintaining data integrity at your company. The four branches of data integrity to focus on in this area are domain, entity, user-defined, and referential integrity. Although it sounds somewhat like something you’d read about in a Statistics class, these four areas are critical when it comes to keeping your data usable, secure, and optimized.
The first step to crafting robust logical integrity is working with reinforcing your domain integrity. In broad terms, domain integrity refers to ensuring accuracy in every bit of data you have within your organization. For data to be valid, all of the fields should contain relevant and valid values. That’s the core tenet of domain integrity: ensuring this remains the case across all of an organization’s data. Setting values in the fields properly so that any data entered into them is accurate means you can protect domain integrity right from the start. By protecting domain integrity, you’re already setting yourself up for higher quality data before it even enters your system.
Where domain integrity involves accuracy, user-defined integrity acts as an additional layer of defense for your data. In this step, the users themselves define specific rules for the data. Let’s say you need to set up/ reinforce a compliance rule or define a parameter for accessing the data. This is the part of the data integrity process where you would do that. User-defined integrity also covers metadata, which is the information that describes your data such as the date created, platform, and publisher. Getting the user-defined aspects of your data right is crucial to better overall data integrity and offers a better way to understand your data in a broader context as you work with it over time.
Reinforcing your data integrity standards requires time, skill, patience, and a keen understanding of your organization’s data needs. Entity integrity plays a pivotal role in this regard. Entity integrity is useful for stopping your data from containing duplicate, incomplete, or null information. This means you won’t have any null values (especially in the case of names or address information that might be similar) in your data. This, in turn, renders your data clean and efficient. It also prevents data corruption and other integrity issues that could put your data at risk.
The final part of the data integrity equation is bolstering referential integrity. Referential integrity covers how your data gets stored and retrieved. It also governs data usage to a certain extent. With the right levels of referential integrity assurance at your organization, you can set up a series of best practices for storing, retrieving, and accessing data. Since data integrity encompasses other aspects of data usage—like quality and accuracy—having strong referential integrity is a must. The same ideas can apply to any database. Referential integrity is enforced by the database management system, through the use of foreign keys. When a foreign key is violated, the database management system will generate an error. It needs to link up with a primary key to ensure everything is working properly. If it doesn’t, it’ll throw an error. This ensures that the data in a table is consistent with the data in all related tables. In the end, referential integrity is probably one of the most important parts of the overall data integrity framework, so taking the right steps to ensure it is critical to making your data work for you.