Data Integrity: Why is it important?
Data is nothing more than another word for information. Data is one of the most valuable assets of any company. Many claims that it is changing the face of the world. Data can help companies benefit in several ways. It can help them boost their revenue. They can use it to build themselves up more efficiently or target their audience with personalized ads. They can even find a cure to a disease. The better quality of data a company has, the more successful it is likely to become. This is where data integrity becomes key. Businesses are asking “What is data integrity exactly and why is having clean and relevant data so important for a business?”
What is Data Integrity?
Data integrity is data’s accuracy, completeness, and quality as it’s maintained over time and across formats. Preserving the integrity of your company’s data is a constant process.
It’s worth noting that data integrity isn’t the same as data security, although the two concepts are related. Data security involves protecting data from both external and internal threats and maintaining the privacy of its subjects. This contributes to the data’s integrity by ensuring those threats haven’t compromised it.
Why is it important?
Your organization is most likely flooded by large and complex datasets from many sources, both historical data and real-time streaming data. And you want to confidently make data-driven decisions that improve your business performance.
Incorrect or incomplete data can lead to bad decisions which can cost you significant time, effort, and expense. Plus, the loss of sensitive data – especially if it ends up in the hands of criminals – can mean enduring and wide-ranging negative impacts.
Here are the key benefits of establishing and maintaining data integrity as part of your data governance framework:
- Supporting accurate data insights and decisions.
- Protecting your customers’ and other data subjects’ information such as personally identifiable information (PII), financial records, and usage data.
- Helping ensure regulatory compliance such as General Data Protection Regulation (GDPR).
Types of Data Integrity
- Physical: Applies to hard copies and digital files, especially in a disaster (e.g., flooding, fire, power outages), as related to the safeguarding of data during storage and retrieval
- Entity: Focused on the characteristics of the tables that are used to store and connect data in relational databases
- Domain: In a database, refers to the suitable values that a column may contain (e.g., constraints on format or the amount of data entered)
- Referential: A set of procedures for how data should be stored and used to ensure consistency and accuracy and prevent duplication, or prohibit the entry of data that does not apply
- User-defined: Rules and restrictions created by a user to meet their specific requirements
- Logical: Protects data while in use in relational databases
Data integrity risks
An assortment of factors can affect the integrity of the data stored in a database. A few examples include the following:
- Human error: When individuals enter information incorrectly, duplicate or delete data, don’t follow the appropriate protocols, or make mistakes during the implementation of procedures meant to safeguard information, data integrity is put in jeopardy.
- Transfer errors: When data can’t successfully transfer from one location in a database to another, a transfer error has occurred. Transfer errors happen when a piece of data is present in the destination table, but not in the source table in a relational database.
- Bugs and viruses: Spyware, malware, and viruses are pieces of software that can invade a computer and alter, delete, or steal data.
- Compromised hardware: Sudden computer or server crashes, and problems with how a computer or other device functions are examples of significant failures and may be indications that your hardware is compromised. Compromised hardware may render data incorrectly or incompletely, limit or eliminate access to data, or make information hard to use.
How to ensure Data Integrity?
Preventing the above issues and risks is reliant on preserving data integrity through processes such as:
Data entry must be validated and verified to ensure its accuracy. Validating input is important when data is provided by known and unknown sources, such as applications, end-users, and malicious users.
Remove Duplicate Data
It is important to ensure that sensitive data stored in secure databases cannot be duplicated onto publicly available documents, emails, folders, or spreadsheets. Removing duplicated data can help prevent unauthorized access to business-critical data or personally identifiable information (PII).
Data backups are crucial to data security and integrity. Backing up data can prevent it from being permanently lost and should be done as frequently as possible. Data backups are especially important for organizations that suffer ransomware attacks, enabling them to restore recent versions of their databases and documents.
Applying appropriate access controls is also important to maintaining data integrity. This is reliant on implementing a least-privileged approach to data access, which ensures users are only able to access data, documents, folders, and servers that they need to do their job successfully. This limits the chances of hackers being able to impersonate users and prevents unauthorized access to data.
Always Keep an Audit Trail
In the event of a breach occurring, organizations must be able to quickly discover the source of the event. An audit trail allows businesses to track what happened and how a breach occurred, and then find the source of the attack.
Data integrity is not an easy one-time process. Instead, it is an ongoing procedure that requires regular commitment to ensure that your company’s information is safe and accurate. Apart from regulatory compliance, data integrity has numerous other benefits for your company. For instance, it allows you to have access to reliable data to make business decisions.
Additionally, data integrity means that you have trustworthy datasets, and you can recover or trace information back to its origins when needed. There are so many risks and factors that can easily alter or corrupt your data, so it is important to ensure that you maintain data integrity via strong data security systems to ensure data quality.