What Is Data Integrity?

Data integrity refers to the accuracy, completeness, and quality of all data points across different formats at any point in its lifecycle. Contrary to popular belief, data integrity is not the final goal of operational efficiency but a series of processes and strategies your organization implements to safeguard against data corruption and ensure that data remains reliable at all times.

Why is data integrity important?

The importance of data integrity continues to increase exponentially, especially as more organizations rely on data integration and collection for their decision-making processes. Think about your own company and how much it relies on data: Information about the ins and outs of your company helps you predict consumer behavior, assess market activity, and develop tailored marketing and sales strategies.

In addition, data integrity is vital in protecting private and sensitive information, particularly those of your clients. If you are an MSP who works with different clients in the healthcare or financial industries, data integrity is a key aspect of your service. Your clients must feel confident that their personally identifiable information (PII), such as their full legal name, Social Security number, credit card information, etc., is protected against threat actors. This means that data integrity is part and parcel of all regulatory compliances and protocols, including HIPAA and GDPR, among others.

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Types of data integrity

Essentially, data integrity ensures the usability of your data for core business analytics. It covers various strategies that protect these datasets from being corrupted by human error, malware, hacking, or compromised hardware or software. These strategies must cover these types of data integrity:

  • Physical integrity. This refers to protecting data’s accuracy, correctness, and wholeness from where it is stored and retrieved, usually in hardware or removable storage devices. Issues like network outages, power interruptions, and storage erosion can cause inaccurate data storage and retrieval.
  • Logical integrity. This ensures the data’s consistency through relational databases. This type of integrity can be further differentiated into four categories.
    • Entity integrity. This stores data within relational tables and relies on unique keys and values to identify data points, ensuring the same data isn’t listed multiple times.
    • Referential integrity. This is a series of processes that ensures the consistency of your data storage and usage. Database structures ensure the presence of matching records in linked tables to prevent dirty data.
    • Domain integrity. These processes guarantee data accuracy within a domain, defined by a specific set of values to ensure the preciseness of data elements.
    • User-defined integrity. These are any rules about data created by users to meet specific requirements. This type of data integrity is used when other integrity processes cannot adequately meet your IT security needs.

Data integrity vs. data quality

Data quality is a subset of data integrity, and both are needed to help you realize better business outcomes. Data quality refers to the reliability of your data, but data integrity requires that data be complete, accurate, consistent, and in context.

For example, your MSP or IT enterprise may have high-quality data, such as a database of customer names and addresses, but these data points may or may not be relevant to your specific service.

When creating your data integrity plans, it is highly recommended that you guarantee the quality and integrity of all your data points.

Data integrity vs. data security

Data security involves safeguarding data from unauthorized access and preventing data corruption. Data integrity, on the other hand, refers to the accuracy and validity of data points.

Both security and integrity are essential in any robust data management strategy.

Ensuring data integrity in your organization

Develop and implement data auditing strategies

Human error is one of the easiest ways to lose data integrity but also one of the easiest to control. In addition to your data collection strategies, it is recommended that you develop data auditing plans. Have someone who specifically looks for and removes redundant or duplicate data, especially with sensitive data points.

Keep yourself updated with cybersecurity threats

Hackers and cybercriminals may not necessarily target the integrity of your data. Instead, most are intent on stealing information to receive a financial reward, such as in ransomware attacks. However, data could probably lose its integrity when being stolen. As such, you must regularly update yourself on the latest IT trends and threats.

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Regularly backup your data

Robust backup software helps you maintain data integrity by preventing it from being permanently lost due to malware. For example, IT management vendors, such as NinjaOne, offer business backup software that protects your critical business data with flexible solutions in a single pane of glass.

Implement access controls

Ensure data integrity by only granting access to privileged information to a few users. This significantly limits the chances of insider threats from stealing, hacking or damaging your data.

Build a culture of security

Data integrity must be practiced at all levels of your organization, from entry-level professionals to C-level management. Building a security culture includes regular training seminars and access to various educational materials. As much as possible, make sure that everyone in your company understands the importance of data integrity so you can all work together to maintain it.

Protecting data integrity

Data integrity is an ongoing process that must always be prioritized in all your business development plans. Regardless of the specific type of service you provide, it is crucial that your organization keeps your and your client’s information safe, complete, accurate, and of the highest quality.

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