How to Maintain Data Integrity in Your Organisation? 

Business decisions in organisations of all sizes are increasingly being made based on data. Data is created and collected across many different layers of operations. The data extracted is used to streamline operations, better understand the market, and generate metrics to determine performance. With the increased focus placed upon data to maintain business operations today, it is crucial that the data used has integrity. Every organisation must ensure that the data it uses is accurate, reliable, and dependable. It must have data integrity.  

What is Data Integrity? 

Data integrity refers to the process of ensuring and maintaining the accuracy and consistency of data throughout its lifecycle. Data integrity can be defined as the accuracy, reliability, and trustworthiness of data over its entire lifecycle, from the moment it is collected or generated, transferred, stored, backed up, archived, or used in performing analysis. Data integrity answers the question of whether data is consistent, accurate, and can be trusted.  

Data has integrity if it can be proven that the contents of the data in question has not been compromised or corrupted, be that through human error or malicious activity. Data integrity is used in the industry to describe the state of data; is it accurate or inaccurate, valid, or invalid. It can also be used when describing the processes through which you try to achieve data integrity for your data such as error checking, data validation, outlier detection, and more.  

The 2 Types of Data Integrity 

There are two types of integrity that ensure data integrity – physical and logical. In relational and hierarchical databases, both are collections of methods and procedures that maintain data integrity. Let’s take a closer look at the two types of data integrity: 

Physical Integrity 

Physical integrity is the safeguarding of data’s correctness and completeness during storage and retrieval. Physical integrity becomes jeopardised when disasters take place, like when the power goes out or malicious actors interrupt database functionality. System programmers, data processing managers, applications programmers, and internal auditors may be unable to access accurate data due to storage erosion, human mistakes, or a variety of other issues. 

Logical Integrity 

In the context of a relational database, logical integrity ensures the data remains complete when used in a variety of ways. Logical integrity protects data from both human error and hackers, but in a different way to physical integrity. Logical integrity can be broken down into four categories. Let’s take a closer look: 

1. Entity integrity 

Entity integrity relies on the generation of primary keys so that data isn’t listed more than once and that no field in the database is null. The primary keys are the unique values that identify pieces of data and are the core of entity integrity. It’s key in relational systems, which store data in tables that are often linked and used in a number of different ways. 

2. Referential integrity 

Referential integrity refers to a control on the database design that ensures each foreign key in a table points to a unique primary key value in another table. This helps maintain data quality and ensure that no data is lost. Referential integrity also ensures that data is saved and used consistently. Only appropriate deletions, changes, or additions of data are made following rules contained within the database’s structure regarding how foreign keys are used. These rules ensure proper data entry, may include limits that prevent redundant data entry, and/or prohibit the entering of irrelevant data. 

3. Domain integrity 

Domain integrity basically refers to the collection of processes that ensure the accuracy of each piece of data in a domain. In this context, a domain is a set of permitted values that a column can hold. Constraints that limit the kind, format, and amount of data entered might be included within domain integrity. 

4. User-defined integrity 

User-defined integrity involves the constraints and rules created by the user to fit their own needs. When it comes to data security as a whole, referential, entity and domain integrity are not always enough. User-defined integrity adds an extra layer. The organisation’s rules should often be considered and included within data integrity safeguards. 

The Importance of Data Integrity 

The dependence on data to drive business decisions is becoming commonplace. Smart organisations are heavily investing in their data workflows but at the same time want to ensure that the insights generated are accurate. The trustworthiness of data is key for business executives to have confidence in the insights gathered by data science teams. A sound culture of data integrity within an organisation helps generate confidence in data processes and ensures integrity of the data held within the business.  

The importance of data integrity cannot be underestimated. A thorough data integrity regime eliminates insider threats, human error, misconfiguration, malware, security errors, and more. It also provides an audit trail which helps when it comes to understanding the data flowing through an organisation. Data integrity provides a level of quality assurance where procedures and control measures are put in place to ensure best practices are adhered to in the management of data according to a set of predefined rules. This process ensures a more robust data operation that continuously produces actionable suggestions that can be pursued for measurable success. 

What are the Factors Affecting Data Integrity? 

There are a number of factors that can affect the integrity of data recorded in a database. Let’s take a look at a few examples: 

• Human error: Data integrity is jeopardised when human error comes into play. Integrity is compromised when a human accidently enters information incorrectly, delete or duplicate data, make mistakes during the implementation of procedures designed to protect data, or fail to follow proper protocols. 

• Errors in data transfer: When data is not effectively transferred from one database location to another a transfer error occurs. In a relational database, transfer errors happen when data is present in the destination table but not in the source table. 

• Viruses and bugs: Malware, spyware, and viruses can infiltrate a computer and erase, change, or steal sensitive and valuable information. 

• Compromised hardware: Sudden server crashes, computer failures or poor performance could indicate that your hardware has been compromised. Compromised hardware could cause data to be rendered incompletely or inaccurately, remove, or limit data access, or make information difficult to use. 

If you believe your data integrity has been affected, the following steps can be taken to remove or reduce data integrity risks: 

• Modifying permissions and limiting data access to prevent unauthorised personnel from accessing or making changes to data. 

• Validating data, both when it’s utilised and when it’s collected, to ensure that it is accurate. 

• Using logs to keep a record of when data is edited, added, or deleted is one of the best ways to ensure that data is backed up. 

• Ensure that internal audits are carried out in a timely manner. 

• Use the latest and most appropriate software to spot errors. 

The 6 Best Practices to Maintain Data Integrity 

To ensure that data integrity can be achieved, best practices in handling and securing data should be followed. The best approach is to standardise and streamline these processes throughout the organisation instead of leaving it to individuals or specific teams. There should be a unified and a holistic approach to maintaining data integrity. With that in mind, let’s take a closer look at a few steps which will ensure the integrity of data within your organisation: 

Implement Access Controls 

Implement access controls. Access to data held within your organisation should be tightly regulated. Only those with a need to know, or need access, basis should be granted access to specific data sets. A least privileged model of security should be adopted where access is only granted to those that need it. Staff should only be given access to the data they need to perform their jobs. Any other data held within the organisation that they don’t need to access should be blocked off. Not only does this limit the amount of damage that can be caused by a data breach, but it also reduces the amount of people who can access sensitive data within the company.  

Always Keep an Audit Trail 

Maintaining an audit trail mechanism that can track the source of data changes is key. If a data breach occurs, it is important to know the source of the breach. It’s also important for pinpointing the data or documents that have been accessed, and most importantly, how the breach began. An automated process should be implemented to conduct a security audit. Removing human intervention is important so that the results can’t be tampered with. The audit trail should also be able to track data events such as create, update, delete, etc., as well as the time of the event and the individual who triggered them. An in-depth audit trail can help when it comes to investigating a data breach and determining how to remedy the problem.  

Validate Your Input Data 

Always validate your input data, especially before it is allowed into your data storage system. Validating your data is the process of checking data to make sure it is useful and correct. Data should always be checked for its accuracy regardless of the source. 

Never Forget to Always Backup Your Data 

Ensuring that you have a regular, reliable, and timely backup of data systems allows for data to be recovered in the event of a breach. Data loss can occur in the event of hardware failure, ransomware attacks or software bugs. A backup procedure ensures that your organisation will not be the subject of permanent data loss. 

Follow and Implement All Security Best Practices 

The security measures on the systems that contain your data should be checked regularly. Software patches should be implemented as quickly as possible while known security vulnerabilities of software packages should be mitigated. Physical access to server farms or data centres should be restricted to only authorised staff. Authentication systems should also be used to ensure that only those who need access are given access.  

Educate Your Employees 

Educating your employees on cybersecurity risks and how to maintain the integrity of data in all work processes is key. Adopting a culture of sound data management should be established whereby employees adhere to data integrity guidelines. Staff should also be educated on basic social engineering scams like phishing, and sophisticated cybersecurity attacks like ransomware attacks, or on malware designed to steal personal data or intellectual property which will ensure they can remain vigilant and are able to notice the threats.  

How to Maintain Your Data Integrity with RiskXchange 

Maintaining data integrity has become a key part of IT security in organisations all around the world. A range of tactical steps can be put in place to minimise the risks that could lead to your organisation’s data becoming compromised. Although it is almost impossible to completely eliminate all risks, RiskXchange offers various tools and strategies that can be applied to minimise risk and bolster security.  

RiskXchange is the global standard for enterprise and third-party cyber risk score ratings and cyber risk analysis. RiskXchange provides a simple, automated, and centralised risk management solution that enables organisations to manage their own cyber risk score as well as ensuring their suppliers and third-party partners meet any rules, regulations, or requirements. 

RiskXchange provides a 360° view of the Enterprise Cyber Risk Posture using AI Machine Learning. A simple, clear, and informative dashboard enables senior executives to see in real time their Enterprise and Third-Party Cyber Risk Score position, helping them to make informed and measurable business risk decisions. 

The RiskXchange platform enables the centralised sharing of risk score data upstream and downstream for simple, oneto-many exchange of cyber risk data. RiskXchange uses powerful machine learning capabilities to map an enterprise’s ecosystem and determine the cyber risk rating score and posture of multiple degrees of relationships to the prime enterprise. This information is also very beneficial in providing visibility of the industry average cyber risk score and peer benchmarking for competitive advantage. 

Data Integrity FAQs 

Get in touch with RiskXchange to find out more about how to maintain data integrity in your organisation.