Imagine your company’s data is altered or deleted, and you have no idea how to find out how this happened and who is behind this data loss. Data loss can significantly impact your data-driven business decisions. Data integrity is therefore very important. In this article, we’ve explored what data integrity is, why it’s important, and how enterprises can protect it.
So let us get started.
What is Data Integrity?
Data integrity refers to the accuracy and consistency of the data. Data integrity is a critical factor to consider when creating databases and maintaining it through the business process. A good database will enforce data integrity whenever required.
If, for example, a user accidentally enters a phone number in the text field and your website enforces data integrity, it will warn them not to make such mistakes.
- Physical integrity: Physical integrity offers protection of the accuracy of data that is stored and retrieved. Imagine that natural disasters, power outages, or hackers disrupt your database services. In that situation, physical integrity is compromised, but the accuracy of data is maintained by applications programmers and internal auditors to prevent human error.
- Logic integrity: Local integrity maintains data integrity despite being used differently in a relational database. Your data is protected from human error and hackers by logic integrity. However, it differs from physical integrity. Logic integrity consists of four types:
- Entity integrity: Entity integrity relies mainly on creating primary keys – the unique values that identify pieces of data to ensure that data is not listed more than once and that no field in a table is empty. Additionally, entity integrity is a feature of the relational system that allows data to be linked and used in different ways.
- Referential integrity: Referential integrity refers to a series of processes that ensure your data is stored and used uniformly. Rules embedded in the database’s structure about the foreign keys are used to make sure only appropriate changes, additions, or deletion of data takes place. Rules include constraints that eliminate duplicate data entry and guarantee accurate records, as well as disallowing entry of data that does not apply.
- Domain integrity: Domain integrity refers to the collection of processes that ensure data is accurate in every domain. Here domain refers to a set of acceptable values that a column in a table should have. Additionally, it includes constraints and other measures that limit the type, amount, and format of data entered.
- User-defined integrity: User-defined integrity involves the rules that the users create to fit their needs. There are times when entity integrity, referential integrity, and domain integrity aren’t enough to protect all the business data. It is important to consider all business rules and integrate different data integrity measures.
Difference between data integrity and data security
Most of us confuse data integrity and data security. Data security refers to the protection of data. On the other hand, data integrity implies the trustworthiness of the data.
Data security mainly focuses on the different ways to reduce the risk of leaking intellectual property, business documents, healthcare data, emails, trading secrets, and much more.
There are many data security tactics you can use such as permission management, data classification, identity access management, threat detection, and security analytics.
Why is Data Integrity Important?
Imagine taking an important business decision with data that is entirely or partially incorrect. Organizations make data-driven business decisions based on the available data.. The decisions taken without maintaining data integrity can affect the company’s business goals and plans.
According to KPMG International, many senior executives do not have a high-level trust in how their organization uses data, analytics, or AI.
According to the statistics:
- Only 35% of senior executives trust how their organizations use data and analytics.
- 92% of the senior executives are concerned about the negative impact of data and analytics on the reputation of the organization.
- 62% of the senior executives state that technology functions bear the responsibility when any machine goes wrong.
Importance of Data Integrity
Performance: Minimizing or removing incomplete and duplicate records helps to improve database performance.
Reliability: You need to be sure that the data your employees use to make business decisions is reliable. Integrating data integrity processes helps ensure the reliability of your data.
Access: Your employees should be able to access the data to do their work. When you know that your hardware and software that supports all your business data and information is reliable, you also know that your employees will be able access the data as and when needed.
While data integrity is not the same as data security, you cannot have data integrity if your data is leaked or stolen.. If your company has data security issues, it can damage its reputation.
Data Integrity Threats
There are chances that the data integrity is compromised because of human error or any malicious activities. Data that’s accidentally altered during the transfer from one device to another, for example, can be stolen or misused by hackers.
Some of the common threats that can alter the state of data integrity are:
- Human error
- Unintended transfer errors
- Misconfigurations and security errors
- Malware, insider threats, and cyberattacks
- Compromised hardware
So how do you know when your data is genuine? The following are some characteristics of data with integrity.
When someone is working on a presentation or document that is dependent on data, it is important to have accurate data available and accessible. Without the proper access to the critical data, your business may come to harm as it will impact your decision making. This might result in your organization losing its competitive edge.
Traceability: With the technology change, you can trace every touchpoint you make with your lead or customer. This is done with a data point. The data can inform the decision-makers, highlight the red flags, deficiencies, or limitations. Therefore, you need to make sure these data touchpoints are accurate.
Reliability: Having reliable and consistent business metrics against the company goals and competition is one of the best ways to gain success and build a reputation.
How to Preserve Data Integrity?
Here we have mentioned a checklist that will help you preserve data integrity and minimize the risk of data theft in your organization:
1. Validate input: When a data set is supplied by a known or unknown source, you need input validation. You need to verify and validate the data to ensure that the input is accurate.
2. Validate data: It is very important to check and certify that your data process is not corrupted. Determine the specifications and key features important to your business and make sure the input is correct.
3. Remove duplicate data: Sensitive data from a secure database can easily find a way on a document, spreadsheet, email, or in shared folders where employees can set it without proper access. Therefore, it is important to clean up duplicate data.
Many small companies do not have a team to take care of data security and integrity. In such times the use of the below-mentioned tools can help them clean up duplicate files on a hard drive or cloud.
Here is a list of tools that help to remove duplicate data from your system:
- Clone files checker
- Duplicate images finder
- Easy duplicate finder
- Duplicate cleaner
4. Back up data: Once you have removed the duplicates, you need to make sure that your data is backed up. Data backup is an important part of the process when you are trying to preserve data integrity. Backing up is important to prevent permanent data loss.
The next question that might come to your mind is, how often should you back up your data? As often as possible. You need to know that having a backup is critical. It helps you when your computer is hit with a ransomware attack. Additionally, you need to ensure your backup is not encrypted.
5. Access controls: Access controls is one of the most popular data security practices to preserve data integrity. People who do not have proper access to the data or malicious intent can be very dangerous for your organization and the data.
Therefore it is important to implement a least privilege framework where only the users who need access to data get the same. It is the most successful access control framework used by many organizations.
The most sensitive servers need to be isolated and bolted to the floor or wall, and only the people who access should have an access key making sure that the keys to the kingdom are kept safe and secure.
6. Keep an audit trail: Whenever there is a data breach, it’s critical to track down the sources. Often referred to as an audit trail, it offers an organization the breadcrumbs that pinpoint to the source problem.
What data integrity isn’t
With so much talk about data integrity, it is easy for it’s true meaning to be muddled. Data security and data quality are often wrongly substituted for data integrity, but these terms have different meanings.
Data integrity is not Data Security.
Data security is a collection of measures taken to protect the data from getting corrupted, or stolen. It incorporates the use of systems, processes, and procedures that keeps the data inaccessible to others who may misuse it. Data breaches in data security can be small and easy to contain or large and capable of causing serious damage to the organization.
Data security is one of the many facets of data integrity. While data integrity mainly focuses on keeping data and information intact and accurate until it exists, data security aims to protect the information from outside cyber-attacks. Unfortunately, data security is not broad enough to include the required processes for keeping the data unchanged over time.
Data integrity is not Data Quality.
Does the data in your database meet the company standards and needs of your business? Data quality answers these questions with the processes that measure your data’s age, relevance, accuracy, completeness, and reliability.
Like data security, data quality is also a part of data integrity and is very important compared to other facets of data integrity. Data integrity encompasses every aspect of data quality and then implements rules and processes that govern how your data is entered, stored, transferred, and more.
Protecting your company’s data integrity using traditional methods can seem like a daunting task. However, using a secure, cloud-based data integration platform allows a modern alternative that offers a real-time view of all your data.
A few years back, it was difficult to collect data. However, today with the advancement of technology, it is no longer an issue. We can connect so much data. However, the most responsible thing to do is to preserve data integrity. So now, management can confidently make data-driven decisions that align with their organization’s goals.