Guide to Data Integrity

Data integrity refers to accuracy, completeness, reliability, and consistency of your data. From data collection to transformation and storage, data integrity is about preserving the data and maintaining its accuracy. Data integrity builds trust within your organization and with your customers and stakeholders.

Before we get into the benefits of data integrity, let’s take a look at its four essential elements.

• Accuracy: Your data should be free of errors, which can happen whenever you type a field or command in your database or CRM system, or while transferring data between systems.
• Completeness: Your data should have all the necessary parts you need to make the best decisions and meet your customers’ needs. A missing price entry in your sales system, for instance, can translate into lost sales and lost customers for life.
• Reliability: Reliable data is data that remains stable over time, giving you similar results when conditions stay the same. Unreliable data can lead to incorrect conclusions in your research, analysis, and decision-making.
• Consistency: Your data is consistent if it is represented the same way across systems and over time unless you change or update it intentionally.

In 1998, after nine months in orbit, NASA’s Mars Climate Orbiter burned up as it entered the Red Planet’s atmosphere. The fiery destruction of the $327 million spacecraft was the result of a simple unit conversion error between engineering teams — one team used metric units, the other imperial.

The lesson of that disaster is that inconsistent, inaccurate, or incomplete data can cost your organization time, money, and reputational damage. A recent survey of businesses by Salesforce and Tableau found that a common sentiment among business leaders is a fundamental distrust of their own data, often stemming from ongoing concerns about its accuracy, reliability, and relevance. The mistrust can chip away at a company’s bottom line.

Clean, trusted data is also a prerequisite for AI-powered apps. Eighty-six percent of analytics and IT leaders agree that AI's outputs are only as good as its data inputs. In other words, the ability to capitalize on AI starts first and foremost with ensuring the quality and integrity of your data.

Data integrity requires that all data remains consistent and accurate through all phases of its lifecycle.

Physical integrity has to do with the accuracy of your data where it is stored. Power failures and natural disasters, for example, can compromise your systems and their data. Once your data's physical integrity is compromised, you may find it very challenging to recover usable data. You likely want redundant storage systems, disaster recovery plans, and environmental controls (e.g., temperature and humidity regulation) to protect your data’s physical integrity.

Logical integrity ensures that your data remains accurate, consistent, and reliable within your digital systems. Logical integrity is supported by four principles.

• Entity integrity: Relational databases use tables with rows to organize data. Entity integrity ensures that there are no duplicate rows inside a table and that no important entries are null. For instance, there are no duplicate records of the same customer, and the necessary fields, such as name or phone, are filled in.
• Referential integrity: Referential integrity keeps the relationships between tables consistent. For instance, a foreign key in one table must match a valid primary key in another. This principle prevents orphan records and maintains data relationships.
• Domain integrity: Domain integrity defines acceptable data types, formats, and ranges that make up a valid dataset. For example, a column for dates should only accept valid date entries.
• User-defined integrity: User-defined integrity lets you set custom rules and constraints beyond the standard database integrity rules. These rules are specific to your application or business, such as a rule that prohibits negative values in a sales field.

Building and maintaining data integrity — data that is accurate, complete, reliable, and consistent — is not merely a nice-to-have. It’s a necessity if you want to stay competitive and compliant, build trust within and outside your company, and avoid reputational damage. Below are the top three benefits of data integrity.

Clean, trusted data is essential in every industry. One obvious example is healthcare, where research and clinical data drives decisions about patients, treatment plans, and the development of new therapies. Financial institutions depend on accurate, trustworthy data for financial projections, risk management, and compliance. And NASA relies on data precision and accuracy for the success of its missions and the survival of its spacecraft and crews.

If you invest the time and effort to build trust in your data, you can not only turn it into a competitive advantage but also increase employee productivity. According to Gartner, high-quality data can increase productivity by as much as 20% . Imagine if IT didn’t have to rush to validate or clean the necessary data before every meeting and instead could focus on innovation. Or if your analytics teams finally found the time to dig up insights and drive predictions.

The benefits of trustworthy, high-quality data can ripple across your entire organization.

Data accuracy and reliability have long been essential elements for data-driven decisions and for running a business effectively. Recent advances in AI and agentic AI are now driving the need for higher quality data to train large language models and ground AI algorithms.

Launching AI and agentic AI apps without trustworthy data is like pouring diesel into a gas tank. Your car may start but it will stall as soon as you shift into drive. For your AI apps, bad data can translate into unreliable chatbot suggestions for service teams, incorrect product recommendations for marketing, and inaccurate forecasts for leadership.

As a business leader, you are the custodian of your customers’ information and data. Whether you maintain personally identifiable information (PII) such as Social Security numbers, financial records, or patient data, there’s an obligation — and often a regulatory requirement, such as the General Data Protection Regulation (GDPR) — to safeguard and maintain it. The cost of non-compliance can be astronomical. Not too long ago, Facebook owner Meta was fined 1.2 billion euros for transferring the personal data of European users to the U.S. without sufficient data protection.

Your data can be damaged or corrupted while stored or transferred. Below are some of the risks.

• Human error: Incorrect data entry or accidental deletion can undermine the integrity of your data. To counteract this challenge, consider using automated data entry and validation tools.
• System errors: Outdated software, incompatible tools, or data silos can lead to inconsistencies and inefficiencies in data management. Consider data management tools or platforms. Also, reducing silos through centralized data platforms can reduce the number of data errors.
• Cybersecurity breaches: Threats such as malware, hacking, or phishing attacks can corrupt or steal data, compromising its integrity. Employ cybersecurity measures, including firewalls, encryption, multi-factor authentication, and regular security audits to help protect the integrity of your data.

By identifying these risks and implementing proactive measures, your organization can mitigate threats to data integrity and instead have reliable, quality data.

Access management only lets authorized individuals access and modify your data. Without these controls in place, your data could be vulnerable to unauthorized changes that could hurt its accuracy and reliability.

Key access policies and technologies that safeguard data integrity include:

• Role-based access controls (RBAC): Ensure that users have access only to the data and systems necessary for their role, reducing the risk of accidental or malicious modifications.
• Access audits and monitoring: Regularly reviewing and monitoring access logs can help you detect and address unauthorized access attempts or unusual activities.
• Encryption and authentication: Using encryption to protect data during transmission and storage can help it stay secure — combined with authentication mechanisms to verify user identities before anyone accesses the data.

Data integrity and data security are closely linked — maintaining accurate and consistent data is essential for security. Compromised data integrity can create vulnerabilities and make your systems more susceptible to unauthorized access and breaches.

When your data integrity is compromised, inaccurate or inconsistent data can create holes where potential attackers could have an opening into your systems. For example, manipulated data can bypass your authentication mechanisms and create vulnerabilities in your organization’s system. That tampered data can also be difficult to identify, which then makes identifying potential security threats more difficult.

Below are some practices to secure data while maintaining its integrity.

• Implement data validation processes: Regularly validate data to detect anomalies and ensure accuracy — this helps you maintain both integrity and security.
• Establish comprehensive backup strategies: Keep secure and up-to-date backups so you can restore compromised data. Backups can help you preserve both integrity and operational continuity.
• Adopt a zero-trust security model: Assume all interactions are potential threats until verified. With access controls and this approach, you can improve data security while preserving data integrity.
• Use secure collaboration tools: Make it easy to share data access with tools designed to help you collaborate securely.
• Real-time monitoring: Use monitoring tools to detect and respond to any unauthorized attempts to access or alter data.

Poor data will slow you and your teams down and cost your company money. Stay on top of data integrity to reach data-based decisions quickly, power your AI apps, stay compliant with regulations, and keep your company’s reputation intact.