Data inconsistency occurs when similar data is kept in different formats in more than one file. When this happens, it is important to match the data between files. Sometimes, files duplicate some data. When information like names and addresses are duplicated, it may lead to a compromise in data integrity.
What Is Data Integrity?
Data integrity occurs when the data in a database are consistent. Every organization relies on data integrity to ensure that they have reliable and accurate information. The information must also be consistent with real-world events.
When an organization has strong data integrity, the data represents real information. For example, it provides accurate information about a patient’s address and phone number after they have moved.
Data Inconsistency Is Caused By Redundancy
Redundant data is a problem because it can create unreliable information. One person may change the value in one file but not in another file. This is a problem for companies that rely on accurate data.
Let’s say that a hospital has a system for file processing, but several files for one patient are kept separately. If a professional changes the patient’s address in one file but not in any others, the patient’s bill, or insurance information could be sent to the wrong destination. If you do not have the right phone number on each file, you may find yourself running into problems regarding which one to call.
How to Prevent Data Redundancy
Several methods can prevent data redundancy. For one, planning better structures for databases can prevent data from being present in several files. In some cases, this is not possible. Normalizing a database is the next step. The process involves cleaning up the tables in the database.
The goal of preventing data redundancy is to ensure that information is in one place. It is not scattered throughout the database.
Sometimes an organization tries to normalize a database. They may not be able to clean up all the redundant information. When this happens, one may see a data anomaly.
Sometimes, the organization needs to step in and ensure that employees are trained properly. Data input can be tricky, and untrained employees may not realize they are adding new information rather than changing old information.
How to Prevent Data Inconsistency
People approach data inconsistency in two ways. One way to fix the problem is through central semantic storage. This requires a lot of logging and storing rules. It also involves creating a central area for information. The process can be difficult.
You can also use the master reference store approach. This process seeks to centralize the data. This means that there are strict rules about where the database stores information. The goal of this approach is to have more control over important data. It may require more resources than other methods.
Every organization wants its data to be accurate and reliable. From insurance companies to tech companies, organizations all across the country need to know the information they have collected is useful.