Data integrity is critical in ensuring the reliability and trustworthiness of data over its entire lifecycle, from the moment it is generated or collected, transferred, stored, backed up, archived, or used in performing analysis. Data Integrity ensures that data is accurate, consistent, and can be trusted. This is crucial for businesses that rely on data for decision-making, analysis, and other critical operations. Maintaining data integrity requires a disciplined approach and adherence to best practices.
Data integrity can describe the state of data, whether valid or invalid, accurate or inaccurate, and the processes through which an organization tries to attain a higher level of data integrity, such as data validation, error checking, and outlier detection. It also usually goes hand in hand with data security, as it seeks to maintain the overall consistency and reliability of data. In the event of a malicious or unauthorized change in data, a system that protects data integrity should be able to answer questions like what data changed, who changed the data, when they changed the data, what permission level was required, etc.
In this post, we’ll explore six best practices for managing data integrity:
- Encrypt your data
- Implement data backup and recovery procedures
- Train employees on data management best practices
- Use data analytics tools
- Use data validation techniques
- Implement robust data governance policies
Encrypt your data
Encryption involves transforming data into an unreadable format using encryption algorithms. Think of it as using a safe with a key. It ensures that even if an attacker gains access to the data, they cannot decipher or modify it without the key. This helps maintain the integrity of the data by safeguarding it from unauthorized access or alterations.
While some could argue that encrypting your data is tied to data security (which aims to protect data from unauthorized access or malicious attacks) rather than true data integrity, it plays a critical role in maintaining data integrity and is a necessary first step. Encrypting data at the lowest level helps prevent unauthorized modifications or tampering. This step adds an extra layer of protection to ensure that data remains intact and trustworthy.
Implement data backup and recovery procedures
Data loss and corruption are a significant threat to data integrity. Data can be lost due to system failures, natural disasters, cyber-attacks, and human error. Planning, implementing, and testing data backup and recovery procedures is essential for maintaining data integrity.
Data backup involves creating and storing a copy of data in a secure location. Data recovery involves restoring data from a backup in the event of data loss. Every organization should establish a data backup and recovery plan that includes procedures for backing up data regularly, backup retention, and testing backup systems.
While developing a backup strategy, it is a good idea to start building a Business Continuity plan considering the recovery time objective (RTO) and recovery point objective (RPO). RTO is the time it takes to restore data after an incident. RPO, on the other hand, is the amount of data you can lose without significantly impacting operations.
Train employees on data management best practices
Employees play a crucial role in maintaining data integrity. Responsible for entering and managing data, their actions significantly impact data accuracy and reliability. This makes training employees on data management best practices essential for any organization.
Employees should be aware of their roles and responsibilities regarding data management. Additionally, they must understand the consequences of not adhering to best practices. Training should cover topics such as data entry, data validation, data security, and data privacy. It should also include procedures for reporting data errors and incidents.
Use data analytics tools
Data analytics tools can help organizations maintain data integrity by identifying patterns, trends, and anomalies in data. Analytics tools can also help identify data errors and inconsistencies that may not be apparent through manual review. There are several data analytics tools – from basic spreadsheet software to sophisticated machine learning algorithms – that organizations can utilize based on their needs and the complexity of their data.
Typically, these tools are easy to implement and give a graphical view of the data. Once implemented, minimal time and knowledge are necessary to monitor trends and gain visibility into a large data set’s integrity.
Use data validation techniques
Data validation is the process of ensuring that data is accurate, consistent, and complete. These are typically implemented in the application layer, with a potential second layer in the database or storage level. There are several data validation techniques that organizations can use to maintain data integrity. These include:
- Field validation: Field validation ensures that data entered into a specific field meets predefined criteria, such as data length, type, and format.
- Range checks: Range checks ensure data falls within a specific range or threshold, such as dates, currency amounts, and numeric values.
- Cross-field validation: Cross-field validation ensures that data entered into different fields is consistent and logically correct.
- Duplicate checking: Duplicate checking ensures you have not duplicated data within or across multiple datasets.
Using data validation techniques helps identify and prevent errors before they cause significant issues. It also ensures that data remains consistent and accurate throughout its lifecycle.
Implement robust data governance policies
Data governance policies are guidelines and procedures that govern how data is collected, stored, processed, and shared within an organization. Strong data governance policies are essential for maintaining data integrity, ensuring data is consistent, accurate, and reliable, and meeting regulatory compliance requirements.
Data governance policies should include procedures for data quality control, data security, and data privacy. They should also define roles and responsibilities for data management, establish standards for data formatting and storage, and outline data backup and disaster recovery procedures.
Implementing robust data governance policies requires collaboration between IT, legal, and business teams. It’s also essential to periodically review and update data governance policies to remain relevant and effective.
Knowing where to start
Maintaining data integrity is essential for organizations that rely on data for critical operations and decision-making. When beginning (or strengthening) your data integrity journey, experts recommend starting with implementing the first three (encryption, backup, and user training) since, without those, a single incident could be business-ending. As your data strategy becomes more mature, the remaining items on the list, analytics, validation, and governance, will allow a higher level of integrity and leverage your data for business advantage.
Ensuring consistency for a sound data strategy starts with your data team implementing the right technology. A secure data platform that enables a trustless data process where a 100% audit of your data is possible ensures “what you put in is what you get out.” This further allows administrators and cybersecurity officers to operate within a system of guaranteed integrity with the perfect blend of immutability and transparency. This allows everyone to concentrate on ensuring they have the safest procedures to get the most value from the data.
