Data is the world's most valuable resource, so organizations often go to great lengths to protect it. They must ensure it's accurate enough to translate into reliable insights and private enough to avoid costly breaches or corruption. Businesses must understand how to ensure data integrity to achieve that.
Most forward-thinking companies understand the importance of capitalizing on information, but just 26.5% of organizations report being fully data-driven. Data integrity is a key piece to that puzzle many businesses may miss. What is it, and why is it so important? Here's a more in-depth look.
What Is Data Integrity?
Data integrity is the accuracy, consistency, and completeness of an organization's information across its life cycle. Data with integrity remains unchanged and intact each time a business accesses, uses, transfers, or replicates it. Ensuring that integrity across an entire organization can be challenging, considering enterprise data volumes are increasing at a 42.2% annual growth rate.
Many experts divide data integrity issues into two main categories. The first is physical integrity, which revolves around information's accessibility, availability, and reliability in storage and retrieval, mostly focused on storage hardware. The second is logical integrity, which ensures data remains complete and correct in all its contexts, focusing on software, usage, and access.
Integrity isn't strictly a security issue but involves many controls, as threats like breaches and data poisoning affect accuracy, completeness, and availability. Ensuring data integrity also means protecting against internal problems like misconfigurations, software bugs, and human error.
Data Integrity vs. Data Quality
It's important to make a distinction between data integrity vs. data quality. Both concepts help organizations ensure their information is reliable, but there are essential differences between them.
The primary difference is that data quality focuses on keeping information consistent with real life. By contrast, integrity is more concerned with its consistency with its original state as it moves and people access it throughout its life cycle.
Data quality in a customer relationship management (CRM) platform ensures records reflect their real-life names, addresses, and other information. The integrity of that CRM guarantees those records don't change as they move throughout the organization.
While distinct, data quality and integrity are closely related. Organizations that want to avoid the $12.9 million that poor-quality data costs businesses annually must keep their records consistent and complete across their life cycle after ensuring their accuracy. Integrity maintains the benefits that quality initially provides.
Why Is Data Integrity Important?
Maintaining data quality across its useful life is one of the leading reasons why integrity is essential. However, the importance stretches beyond ensuring businesses gain an economic edge from their insights.
Data integrity is also vital for reliable cybersecurity. Guaranteeing integrity requires high transparency into and control of an organization's data storage and usage processes, which helps prevent and respond to breaches. The average breach costs $4.24 million, rising to $4.62 million if ransomware is involved, so this security has financial implications, too.
Similarly, data integrity is important for complying with rising data regulations. Legislation like Europe's General Data Protection Regulation (GDPR) requires organizations to ensure the integrity of customers' personal data to protect it from loss, destruction or damage. Companies in some highly regulated industries may also need to keep intact, reliable backups, making integrity a critical consideration.
How to Ensure Data Integrity
Understanding why data integrity is important is the first step to implementing proper controls. Here are some other crucial measures to ensure organizations keep their information safe.
Clean and Validate Data
Data integrity is most helpful when the original information is as high-quality as possible, so it's important to start with cleaning. Verifying and organizing data will make it easier to validate and manage later by removing errors and standardizing formats from the beginning.
Human error accounts for 50% of data accuracy issues, more than any other source, so it's best to automate this process as much as possible. Machine learning tools can clean, verify and standardize information to minimize the risk of errors before an employee performs the final check to ensure everything is correct.
It's important to continually validate data as it moves through the organization. Routinely check records for changes, losses, or mistakes as people access them, formats change or the company transfers them. Regular validation will prevent inaccuracies or incomplete information from going unnoticed.
Employee error is one of the most common data integrity issues, so it's also important to train workers thoroughly. Everyone should know how their actions can impact the integrity and how that can affect the organization. Similarly, everyone should understand their roles and responsibilities in ensuring accuracy.
Developing habits like double-checking data before inputting it and using organizational controls like passing information to someone else for verification can help prevent errors. Encouraging collaboration is also vital, as having multiple people manage records catches mistakes others might overlook. Organizations can support this by assigning specific integrity responsibilities to each employee.
Even with thorough training, automation is still important. Manual data entry error rates typically fall around 1%, which may not seem significant. However, that can make a substantial difference in a vast database where critical decisions stem from minute details. Automating these tasks will minimize that error rate.
Employ Stringent Authorization Controls
Outsider threats and privilege misuse are other significant data integrity issues. Organizations can address these by enacting strong authentication and authorization protocols.
The principle of least privilege, where every user, app, and system can only access what they need, is the best way to approach authorization. This restriction will minimize the amount of data one party can access, reducing the chances of far-reaching errors. It will also prevent lateral movement in the event of a breached account.
Of course, access restriction is only effective if an organization also has a secure way of authenticating that people and apps are who they say they are. Passwords alone are insufficient, as people reuse nearly 51% of them across multiple sites, making them vulnerable to credential stuffing. More stringent controls like multifactor authentication (MFA) and tokenization are better alternatives.
Overcome Data Integrity Issues to Stay Secure
Data integrity issues stand in the way of effective analytics and cybersecurity. Organizations that want to protect and capitalize on their digital information must follow these steps to ensure its accuracy. If they can do that, they can transform into truly data-driven businesses to succeed in the changing, tech-centric market.
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