Data integrity is the totality of a dataset’s validity and consistency over its entire life cycle. In other words, it refers to the correctness and trustworthiness of data. It can be applied to any dataset, from personal information to business records.
Data is valid if it contains only correct (accurate) information and can be trusted (reliable). It must also be complete, with all required fields filled in the same way (consistent) and not missing any information (complete). Only then can you ensure that the dataset is valuable to the organization.
Data integrity ensures that your organization can meet its goals, respond to market changes effectively, continue delivering on its promises to customers, and keep up with the global competition. It’s critical for businesses of all sizes – from small to large enterprises – to ensure that their data is accurate, complete, secure, and accessible.
Other Definitions Include:
- “The overall accuracy, completeness, and consistency of data. It is maintained by a collection of processes, rules, and standards implemented during the design phase.” (Talend)
- “The assurance that digital information is uncorrupted and can only be accessed or modified by those authorized to do so.” (TechTarget)
- “A critically important aspect of systems which process or store data because it protects against data loss and data leaks. Maintaining the integrity of your data over time and across formats is a continual process.” (Qlik)
Types of Data Integrity Include:
There are several types of data integrity, based on hierarchical and relational databases’ use processes and methods:
- Physical integrity: Physical integrity protects the accuracy of data as it’s stored and retrieved. Natural disasters, power outages, or cyberattacks can compromise physical integrity. Human error, storage erosion, and so on can make it impossible for data processing managers, system programmers, applications programmers, and internal auditors to obtain accurate data.
- Logical integrity: Logical integrity safeguards data from being corrupted by different uses of the data in a relational database. It protects data from human error and cyber threats by looking at it from a logical perspective. Here are four subtypes that help you do that:
- Entity integrity: Uses primary key values to uniquely identify records saved within a table in a relational database management system (DBMS). It prevents them from becoming duplicated. It also ensures they cannot be null because then you wouldn’t be able to uniquely identify the record if the other fields in each row were identical.
- Referential integrity: Refers to the rules that dictate how data is maintained between two tables. A few rules include removing duplication, ensuring input data is accurate, and more. It maintains consistency throughout the system.
- Domain integrity: A set of rules that define what can go into a particular field in a table. These rules may include restrictions on the size of the value allowed, whether you should accept only certain characters, and how the values should be formatted.
- User-defined integrity: These checks can be used to enforce business rules and constraints that you cannot enforce through referential, domain, and entity integrity. It can include anything you need specifically for your organization’s data. Once applied, your data management tool can verify the data and make it specific to your use case.
Benefits of Data Integrity Include:
- Ensures that your data is accurate, which in turn helps you make better data-driven decisions based on the ground reality of the business
- Helps protect sensitive customer and business information and lowers potential risk
- Ensures your business complies with data regulations provided by the government or other institutions
- Helps you solidify your Data Management strategy as it continuously validates the organization’s data
- Provides increased control and accessibility over your data, as you know where it’s stored, how it’s stored, and who manages it
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