What is Data Integrity? A Complete Guide

Drive Precise Decision-Making with Business Data Integrity

Data — it’s simply everywhere. And organizations have more and more of it every day.

While data can be instrumental in making strategic decisions, it’s no good if it’s incomplete or inaccurate. Poor-quality data can also increase in volume as companies grow and evolve, further complicating things.

So how do organizations prevent bad data?

The key: achieving and maintaining data integrity.

This guide will walk through everything you need to know about data integrity — including what it is, why it’s critical, and how to uphold it as your company grows — to drive precise decision-making and prevent data loss.

What is data integrity?

Data integrity is the accuracy, consistency, and completeness of data over its lifecycle. While there are many reasons why an organization prioritizes data integrity, it’s often the result of implementing a data security practice for meeting compliance regulations — such as the European Union’s General Data Protection Regulation (GDPR). Organizations then maintain data integrity through various processes, policies, and rules.

Regardless of why or how an organization maintains data integrity, doing so is critical for several reasons.

Importance of data integrity

Data integrity is essential because it protects your organization from making wrong decisions due to inaccurate or incomplete information. A recent survey of 2,190 global IT and business decision-makers found that only 35% have high trust in their organization’s use of analytics. Focusing on data integrity can help leaders have more confidence in their data as well as avoid making costly mistakes.

There are two other crucial aspects of data integrity, and those are data quality and data security.

Data quality: an essential component of data integrity

Although many people lump data quality in with data integrity, the two terms differ.

Data quality refers to a dataset’s accuracy, reliability, and general cleanliness, meaning quality data is:

Complete: the present information is a large percentage, if not all, of the required data.
Unique: it’s free of redundant or irrelevant entries.
Valid: conforms to syntax and structure requirements.
Timely: up-to-date.
Consistent: represented in a standard way throughout the dataset.

Data integrity goes further than data quality, requiring that information be all of the above and contextual, meaning it must also be:

Integratable: organizations must be able to integrate data into their other systems.

Enrichable: businesses can add more meaning to internal data by enriching it with data from external sources.

Bottom line: data with integrity can be quality data, but not all quality data has integrity.

Data security: a method for maintaining data integrity

Data security is another term people use synonymously with data integrity. Some examples of data security are multi-factor authentication, encryption, tokenization, or data masking.

While data security and data integrity play an essential role in achieving the other, data security relates more to data protection and often results in data integrity. In other words, data security is one method an organization can employ to maintain data integrity.

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Types of data integrity

Now that you understand what data integrity is, why it’s crucial, and how it differs from quality and security, it’s time to dive into how to attain and maintain it. To achieve data integrity, companies must uphold both physical and logical integrity.

Physical integrity

As you might suspect, physical integrity safeguards your organization’s devices that store and utilize data. Natural disasters, power outages, server erosion, or even attacks at office locations can all put physical integrity in danger. By securing these areas, organizations can limit the damage of physical compromise.

Logical integrity

Unlike physical integrity, logical integrity focuses on maintaining the correctness or rationality of data. As an organization distributes data across relational databases, logical integrity preserves the data’s intended state, protecting against human error and potential hacking.

There are four ways to maintain logical integrity:

Entity integrity: This method verifies that each row in a dataset has a unique and non-null primary key value. Security and data teams can use primary keys — the unique values that identify pieces of data — to ensure that each table’s rows and columns remain intact and accurately tagged, even during transformations and translations.
Referential integrity: Referential integrity focuses on standardizing how an organization stores and uses data. Data teams may use this approach by inserting rules into a company’s data infrastructure to prevent data elimination, duplication, or unauthorized changes.

Domain integrity: Domain integrity refers to the accuracy of each piece of data within a particular domain or table. It defines the proper data type, format, and amount, which helps users input and interpret data correctly.
User-defined integrity: Sometimes, the above three methods aren’t enough to protect data, so users establish their own requirements for handling specific data use cases.

Focusing on physical integrity and the four methods of logical data integrity are critical to maintaining accurate and complete data and preventing data loss.

Data integrity risks

As companies gather and store more data, preserving integrity becomes more complicated and attracts a host of risks.

Here are a few situations that can compromise data integrity:

Human error: Unfortunately, human error is one of the most significant risks to data integrity. Users may enter data incorrectly, duplicate data, fail to follow specific protocols, or make mistakes when taking steps to protect data quality and security.
Data transfers: Data transfers — sending information from one organization or business unit to another — regularly happen, even in small organizations. While transfers may not typically present any integrity issues, there is the possibility of transposing or erasing data in the process.

Bugs, viruses, and cybersecurity threats: Cyberattackers constantly brainstorm new ways to steal information, whether through phishing, whaling, smishing, or email compromise. During these attacks, hackers can install spyware, malware, or viruses that can drastically alter how a computer works, granting them view, edit, and delete permissions on specific datasets.
Compromised hardware: Even as sophisticated as computers and servers are now, they are still prone to crashes, and device failures can cause data to load improperly, hinder employees from accessing the data they need to, or generally make data more challenging to use.

While these risks can have some terrible repercussions, the good news is that organizations can take measures to curtail them.

How to reduce risks and ensure data integrity

There are plenty of ways to decrease risks to data quality and security while boosting data integrity:

Validate data: There are several points in the data engineering process where someone can compromise data integrity, which is why validation steps are critical. Data teams should validate data upon entry and when they push it out to various applications to confirm there are no duplications or alterations in the process. They should also take extraordinary precautions when dealing with personally identifiable information (PII) or other sensitive business data.
Backups: Companies should mandate automatic backups to help recover data if any breaches or accidental deletions occur.

Access controls: Updating permissions, changing passwords frequently, and restricting data access lessens the chances of data compromise. Organizations should take a least-privilege access approach, granting access only to the platforms, documents, folders, and servers employees need to do their jobs.
Audit trails: Monitoring when someone modifies, removes, or adjusts data can reveal unusual behavior, pinpoint the source of that activity, and enable security teams to control and address it.

Each of these mitigations is easier said than done, but there are plenty of software solutions that can help you keep your data integrity in check.

Get started with data integrity

Data integrity can seem like an elusive goal, especially if you’re dealing with incomplete or inaccurate data, but it’s entirely attainable.

When it comes to improving your CRM data hygiene quickly and dramatically, Demandbase’s Data Integrity solution can help. With AI-powered data enrichment and standardization, you can clean up accounts, contacts, and leads, fill in missing data, and enhance sales territory assignments. Demandbase’s Data Integrity gives you a fully up-to-date picture of your pipeline and your customers, freeing up your team to work on strategic projects that propel your go-to-market (GTM) engine forward.

See how Demandbase’s Data Integrity can help you maintain your CRM data’s accuracy, consistency, and completeness by exploring our service today!

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