Data Quality vs Data Integrity: Why You Should Even Care

Nimrod Iny
Oct 26, 2022

Data Quality vs Data Integrity: Why You Should Even Care

With the rapid development in the modern world, data has become the most important asset in any organization. As a result, companies exponentially grow their databases and process them to make various business decisions. However, handling large amounts of data is complex, and organizations must continuously work on data security to reduce the risk.

On the other hand, organizations can not depend on any data they collect. Therefore, data should be trustworthy and up to the required quality to make good decisions based on them. Otherwise, it will have a negative impact on the organization. For example, IDC researches indicate that 68% of organizations believe disparate data negatively impacts their organization.

Data quality and data integrity are two of the most used terms to describe the condition of the collected data. Organizations use these two terms to describe the data's accuracy, context, and consistency before using it for decision-making. So, this article will discuss what data quality and integrity stand for and why we need to pay attention to them.

What is Data Quality, and Why Should You Care?

Data quality is a crucial part of data integrity. It refers to the reliability of the data, and there are 5 characteristics to measure data quality. Therefore, a quality dataset should have all 5 characteristics:

  • Complete - The available data should be a large percentage of the total data needed.
  • Unique - The dataset should not have any redundant or extraneous entries.
  • Valid - The collected data should comply with the structure defined by the business requirements.
  • Timely - Data should be up to date for the required usage.
  • Consistent  - All the data records should be consistently represented throughout the dataset.

Data quality is essential for any organization since the collected data implicates decision-making. The whole organization can collapse if using low-quality data for decision-making. So, let's discuss some of the most common data quality issues we find in datasets.

Data Quality Risks

  • Using Incorrect Data - This is one of the most common mistakes made by organizations. This can happen due to human error, ambiguity, or expired data. For example, using data from the Asian region to predict European sales will give a completely different result and create a disaster within the organization.
  • Duplicate and Incomplete Data - As mentioned in the data quality characteristics, organizations need to ensure that datasets do not have duplicate or incomplete records before using them for data processing.
  • Inconsistent Formats and Patterns - If you extract data from multiple sources, different patterns or formats can be used for the same data records. For example, there will be confusion if date records have 2 formats, like dd/mm/yy and mm/dd/yy.
  • Measurement Unit Mismatches - This is another common issue in datasets similar to inconsistent patterns. You need to ensure all the data records are in the same measurement unit, especially regarding length, volume, quantity, weight, etc.
  • Missing Dependencies - Some data fields can be empty if their dependent fields are empty. For example, the State field can be open if there is no value in the Country field.

What is Data Integrity, and Why Should You Care?

Data integrity indicates whether the data is accurate, consistent, complete, and contextual. In other words, data integrity defines whether the data set is valid or not to its owner. It is a combination of 4 main pillars, including data quality.

  • Data Integration - Data can be collected from multiple sources like databases, legacy systems, warehouses, etc. But, regardless of the source, all the data should be seamlessly integrated into a single view.
  • Data Quality - The dataset should be complete, unique, valid, updated, and consistent.
  • Data Enrichment - Data should be enhanced using external sources to give a complete view for effective decision-making.
  • Location Intelligence - Adding a layer of richness and complexity to the dataset with location insight and analytics to make it more actionable.

Data Integrity Risks

Many factors can affect the integrity of a dataset. Here are some of the most common data integrity issues we can notice in organizational datasets.

  • Human Errors - A dataset can have many issues related to human errors. Sometimes, users may enter information incorrectly, create duplicate records, accidentally delete, or even make mistakes during data collection.
  • Transfer Errors - Data transfer errors can occur when we transfer data from one location to another. Sometimes data packets can get lost during the communication creating empty records on the receiver side. Also, transfer errors can happen if the receiver is not ready to accept all the data attributes. For example, the data sender can be a NoSQL database while the receiver is a MySQL database.
  • Bugs and Viruses - Viruses and bugs can delete, change and manipulate data.
  • Compromised Hardware - you need to ensure that required hardware resources are available to process and store data. If not, there can be network, storage, and server issues, causing incorrectly saved and processed data. 

However, you can easily avoid most of these issues by following simple things like e creating backups, using error detection software, proper access control, and using logs and data validation.

What Data Integrity is not?

The term data integrity is often misused with data security and data quality. But, all these 3 terms have unique meanings and are not substitutable. So, let's see how data quality and security differ from data integrity.

Data Security

Data security is all about protecting the data. Organizations use various processes, tools, software, and personnel to increase data security and minimize the effects of malware attacks. Increasing data security helps organizations reduce the damage and easily recover from data breaches.

On the other hand, data integrity ensures that the available data is accurate, consistent, complete, and contextual. It is a combination of multiple aspects of data, and data security is another subset of data integrity that ensures data is protected from outsiders.

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Data Quality

As explained earlier, data quality defines a dataset's completeness, uniqueness, validity, timeliness, and consistency. In addition, it is one of the 4 main pillars of data integrity. So, data quality is not similar to data integrity. It is only a subset of data integrity.

Data Integrity and Compliance

Data integrity plays a significant role in organizations' compliance with data protection regulations like GDPR. Data integrity ensures most of the rules in data protection regulations are covered by the organization and helps to avoid significant penalties for violating regulations. As an organization, it is essential to ensure that you follow these regulations since multiple violations can put an organization entirely out of business.

However, you do not need to worry about ensuring data integrity and compliance with data protection regulations all by yourself. Instead, you can use DSPM tools like Polar Security to prevent data vulnerabilities and compliance violations.

Secure Your Data with Polar Security

This article discussed data quality and integrity while highlighting their differences and risks. As an organization, it is critical to ensure data integrity to comply with data protection regulations and avoid any penalties for violating them.

You can utilize DPSM platforms like Polar Security to automatically and continuously map your cloud data, track its movements and get actionable recommendations to mitigate data vulnerabilities and compliance violations.

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