Business decisions in large enterprises and startups are increasingly being made based on data. Data is now being generated and collected across several layers of business operations. The data extracted is used to fine-tune work processes, create metrics to determine business performance and better understand the prevailing market environment.

With the increased importance and dependence on data for business viability, it has become critical that the data that is being used has integrity. Understanding how to maintain data integrity in a database is crucial for ensuring accurate and reliable information. This means that every business has to ensure that the data on which it draws insights for its operations is reliable, accurate, and dependable.

Best Practices to Maintain Data Integrity

The section below highlights some of the practices that can be used to achieve Data Integrity, you can think of it as some sort of checklist that will take you closer to having data that is authentic and truthful.

The Steps for maintaining Data Integrity are:

  1. Always Validate Input Data
  2. Implement Access Controls
  3. Keep an Audit Trail
  4. Always Backup Data
  5. Adopting Security Best Practices
  6. Educate your Workforce
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1) Always Validate Input Data

Input data should always be validated before it is allowed into your data storage system. Validation is the process of checking data to make sure it is correct and useful. Data should be checked for accuracy regardless of the source of the data, be it data from end-users of an application, internal systems, or external sources.

2) Implement Access Controls

Access Controls - Data Integrity
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Access to data should be tightly regulated to ensure that only those with the proper authorizations have access to data. A least privileged security model should be used in which access is only granted on a need-to-know basis.

Broad access such as administrative rights of entire systems should seldom exist. Instead, employees should have access to only data that enable them to perform their specific job roles. Data should be isolated so that incidences of unauthorized access are pretty much non-existent.

3) Keep an Audit Trail

Audit Trail - Data Integrity
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It is important to maintain an audit trail mechanism that can track the source of data changes. In the event of a data breach, it is vital to know the source of the breach, the documents or data that may have been accessed, and how the breach was possible.

An audit trail should be generated through an automated process in which individuals do not have access to tamper with the results of the audit trail.

It should also have the ability to track data events such as create, delete, update, etc. along with the time the events occurred and the individual that triggered them. A well-managed audit trail can help a lot in the case of investigating a data breach.

4) Always Backup Data

Backup Data - Data Integrity
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Having regular, reliable, and timely backup of data systems is essential to ensure that data can be recovered in the event of data loss. Data loss may be occasioned by hardware failure, software bugs, or even ransomware attacks. A backup process ensures that your organization will not suffer from permanent data loss. Regular data backups are crucial for maintaining data integrity, but knowing how to ensure data integrity during the backup process is equally important.

5) Adopting Security Best Practices

Best Security Practices - Data Integrity
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The security of systems that contain your data should be checked regularly. Software patches should be installed in a timely fashion, and known security vulnerabilities of software packages should be mitigated.

Physical access to data centers or server farms should be restricted to only authorized personnel. Authentication systems should also be used so that only individuals who have been authenticated according to their access level can have access to data.

6) Educate your Workforce

Educating Workforce - Data Integrity
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The employees in your organization should be trained always to maintain the integrity of data in all work processes. A culture of sound data management should be established whereby individuals adhere to Data Integrity guidelines and team members are encouraged to always handle data in a way that ensures the consistency and reliability of data.

7) Remove Duplicate Data

It is critical to ensure that sensitive data in secure databases cannot be copied into publicly accessible documents, emails, folders, or spreadsheets. Duplicate data removal can assist in preventing unwanted access to business-critical data or personally identifiable information (PII).

The Crucial Role of Data Integrity in Successful Projects

Businesses have to ensure that they are not using wrong, faulty, or altered data for their data science projects as relying on data that lacks integrity is a recipe for disaster since the insights derived will be amiss and any strategies built on top of them will likely fail in the real world. Data Integrity is, therefore, to be seen as an integral part of any successful data science workflow because the information derived from data ultimately relies on the accuracy of the input data.

Regardless of how expensive or elaborate enterprise data modelling tools are, they cannot in and of themselves provide and authenticate that the insights or suggestions generated are not skewed if the source data has been tampered with or corrupted. In this article, you will be introduced to the concept of Data Integrity, what it means, why it is important, the methods through which you can ensure/maintain Data Integrity within your organization.

Factors Affecting Data Integrity

The integrity of data recorded in a database can be affected for a variety of reasons. The following are a few examples:

  • Error due to human error: Data integrity is jeopardized when people enter information erroneously, duplicate or delete data, fail to follow proper protocols, or make mistakes during the implementation of procedures designed to protect data.
  • Errors in the transfer: A transfer error occurs when data cannot be effectively transferred from one database location to another. In a relational database, transfer errors occur when a piece of data is present in the destination table but not in the source table.
  • Bugs and Viruses: Spyware, malware, and viruses are types of software that can infiltrate a computer and change, erase, or steal information.
  • Hardware that has been compromised: Significant failures include sudden computer or server crashes, as well as issues with how a computer or other device performs, which could indicate that your hardware is compromised. Compromise hardware might cause data to be rendered inaccurately or incompletely, limit or remove data access, or make information difficult to use.

The following steps can simply be taken to reduce or remove data integrity risks:

  • Limiting data access and modifying permissions to prevent unauthorized parties from making changes to data
  • Validating data, both when it’s collected and when it’s utilized, to ensure that it’s accurate.
  • Using logs to keep track of when data is added, edited, or deleted is a good way to back up data.
  • Internal audits are carried out on a regular basis.
  • Using software to spot errors.

Data integrity vs. data security vs. data quality

TermDefinition
Data QualityThe dependability and accuracy of data. The data must be accurate, reliable, complete, unique, and timely to be useful.
Data SecurityThe infrastructure, policies, and tools used to ensure that only authorized applications and users can access data and that it is being used in a business-compliant manner. Data is also preserved and backed up in case of loss, theft, or malfeasance.
Data IntegrityA broader concept that encompasses aspects of data quality and security. It ensures proper retention, appropriate destruction, and compliance with industry and government regulations.

In summary, data quality refers to the accuracy and reliability of data, data security involves protecting data from unauthorized access and ensuring its preservation, while data integrity ensures that data is retained, destroyed appropriately, and complies with relevant regulations.

Conclusion

In this article, you were educated on what Data Integrity is, the need for Data Integrity in data workflows, and the ways through which an organization can enshrine the principles of Data Integrity to reap the benefits of accurate and reliable data. An in-depth overview was provided on the steps that can be taken to make sure that data has Integrity throughout its entire lifecycle.

It is important to also note that Data Integrity can be supported at the software level, for example, databases can enforce the Integrity of values on columns and rows. Since software packages can provide some level of Data Integrity checks, care should be taken to choose software that encourages Data Integrity. One of such platforms that supports Data Integrity is Hevo Data.

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Ofem Eteng
Technical Content Writer, Hevo Data

Ofem Eteng is a dynamic Machine Learning Engineer at Braln Ltd, where he pioneers the implementation of Deep Learning solutions and explores emerging technologies. His 9 years experience spans across roles such as System Analyst (DevOps) at Dagbs Nigeria Limited, and as a Full Stack Developer at Pedoquasphere International Limited. With a passion for bridging the gap between intricate technical concepts and accessible understanding, Ofem's work resonates with readers seeking insightful perspectives on data science, analytics, and cutting-edge technologies.

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