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Data Integrity vs Data Quality: What’s the Difference?

Idan Novogroder
Idan Novogroder Author

Idan has an extensive background in software and DevOps engineering....

January 29, 2024

Data integrity and data quality sound so similar that you’re bound to get confused. Data integrity is all about the quality and consistency of data throughout its lifecycle. Data quality, on the other hand, refers to the correctness, completeness, consistency, and reliability of your data.

In this article, we take a closer look at these two concepts to note the most important differences and understand the role each plays in delivering accurate, reliable, and trustworthy data to organizations.

Data integrity vs. data quality: How are they different?

Data integrity ensures the data’s overall completeness, correctness, consistency, accessibility, and security. These criteria, when considered together, define the reliability of the organization’s data. 

Data quality measures the level of data integrity and, as a result, its dependability and appropriateness for intended purposes using specific metrics and dimensions. 

Data quality and integrity are critical to any data-driven organization that uses analytics to make business decisions, gives self-service data access to internal stakeholders, and delivers data to customers.

Let’s dive into the specifics of these two terms.

Data integrity

An organization usually starts by setting out processes, rules, and standards that control how data is gathered, stored, accessed, changed, and used. This is done to ensure a high level of data integrity. 

Throughout the data lifecycle, teams can utilize a variety of technologies and cloud environments to preserve data integrity in line with their set data governance principles. This entails developing, updating, and enforcing policies, regulations, and standards to prevent mistakes, data loss, data corruption, mistreatment of sensitive or regulated data, and data breaches.

Data integrity can be classified into two types: 

  1. Physical data integrity 
  2. Logical data integrity

Physical data integrity is the preservation of data completeness, accessibility, and correctness while data is at rest or in transit. Natural catastrophes, power failures, human mistakes, and cyberattacks all put data’s physical integrity in danger.

Logical data integrity focuses on the preservation of data consistency and completeness when it’s accessible by many stakeholders and applications across departments, disciplines, and locations. 

Common steps taken to ensure logical data integrity include:

  • Removing data duplicates (entity integrity)
  • Controlling the storage and use of data (referential integrity)
  • Data preservation in an appropriate format (domain integrity)
  • Ensuring that data fits the unique or industry-specific criteria of an organization (user-defined integrity)

Data quality

Data errors inevitably happen, even to the best of us. This is why we need data quality dimensions and metrics. These parameters help us to understand the utility and efficacy of a dataset, including its correctness, completeness, consistency, validity, uniqueness, and timeliness.

Data quality monitoring is essential for identifying data quality issues and determining whether your data is appropriate to serve its intended purpose – this is called fitness for use. Data quality has grown more important as data processing has become more tightly linked to business processes and organizations use data analytics to drive business decisions.

Data quality management is an important part of the overall data lifecycle management process (also called the master data management process). The efforts to improve data quality are frequently linked to data governance initiatives that ensure data is formatted and used consistently across an organization.

Data quality can be measured across six data quality dimensions:

  1. Accuracy – Can the data be proven to be true, and does it represent real-world knowledge?
  2. Completeness – Does the data include all relevant and accessible data? Are there any missing data or blank fields?
  3. Consistency – Are related data values consistent across locations and environments?
  4. Validity – Is data obtained in the proper format for its intended use?
  5. Uniqueness – Is the data unique? Are there any duplicate records or data points overlapping with other data?
  6. Timeliness – Is data current and easily accessible when needed?

Using data quality tools, teams can achieve a high score across all six dimensions, which prove that a dataset is trustworthy, easy to use, and useful.

Data Quality Data Integrity
Definition Data quality measures the level of data integrity and, as a result, its dependability and appropriateness for intended purposes using specific metrics and dimensions. Data integrity
ensures the data’s overall completeness, correctness, consistency, accessibility, and security. These criteria, when considered together, define the reliability of the organization’s data.
Goal Ensures that data is accurate, relevant, and suitable for the intended purpose, allowing for informed decision-making and operational efficiency. Ensures data security and trustworthiness by avoiding unauthorized changes and preserving data dependability.
Methods Data cleansing, data profiling, data standardization, and data governance. Encryption, checksums, access restrictions, and data validation.
Scope Entire data lifecycle. Entire data lifecycle.
Impact Data quality difficulties lead to erroneous insights, faulty decision-making, and organizational inefficiencies. Data integrity problems can result in data corruption, loss, or illegal access, putting data dependability and security at risk.

Data integrity examples

To clarify the concept of data integrity and how it differs from data quality, here’s an example:

Imagine a financial database that holds a wealth of transaction data. Data integrity guarantees that the data related to each transaction – the transaction amount, date, and parties involved – stays constant and correct in the database.

Access controls and data validation checks prohibit any unauthorized modifications or changes to these transaction records, successfully maintaining the data’s integrity.

Data quality examples

What about data quality? Here’s a good example illustrating this concept:

Imagine an e-commerce platform with a customer database. Data quality is here to guarantee that customer information stored in that database is accurate, complete, and up to date. 

Verifying the customer’s identity, contact information, shipping address, and preferences on a regular basis is part of this process. And it’s definitely in the best interest of the e-commerce company, which can use high quality data to personalize client experiences, launch targeted marketing efforts, and improve customer support.

Benefits of good data integrity

A company that is able to maintain data integrity enjoys the following advantages:

  • Greater speed with which data may be recovered in the case of a breach or unplanned outage
  • Keeping unauthorized access and data tampering at bay
  • Achieving and maintaining compliance more efficiently
  • Unlocking tangible business value from data via data-driven decision-making

The greater the completeness, accuracy, and consistency of a dataset, the more informed business intelligence and business processes become. As a result, leaders are more prepared to define and execute goals that benefit their company while also increasing employee and consumer trust.

Machine learning and data science applications benefit substantially from robust data integrity. When an underlying machine learning model is trained on reliable and accurate data records, it performs better when generating business predictions or automating operations.

Benefits of good data quality

Data quality management increases consumer trust and confidence in data when it’s delivered via data analytics initiatives such as business intelligence dashboards or machine learning-powered applications. If data consumers make decisions based on poor quality data, the entire business is at risk. 

Here are the advantages that teams gain from ensuring data quality at all stages of the data lifecycle to avoid data corruption:

The Data Lifecycle from Ingestion, Transformations, Testing & Deployment to Monitoring & Debugging

1. Data collection 

This is the most susceptible point in terms of quality since, in most cases, we don’t own the source of the data. We wouldn’t know if something went wrong during the gathering procedure before the data entered the data lake. It’s vital to evaluate data quality and ensure that data quality issues such as erroneous or inconsistent data don’t cascade into our ETLs.

2. Storage

When data is managed in silos and storage is dispersed, consistency issues become the norm. We must validate the consistency of the data from the various sources and ensure that any consistency problems are rectified before going on to the next phases of the lifecycle.

3. Processing 

The last step is to prepare your data for use by curating, deduplicating, and conducting any other preparation that the application requires. We anticipate outcomes in terms of both data and metadata since such preprocessing activities are meant to improve data quality and produce data sets appropriate for analysis. After preprocessing, we must check that the data meets our expectations.

4. Evaluation

We design and execute data pipelines at this point. When we develop those pipelines for machine learning or business intelligence purposes, we must be able to assess the quality of such models throughout the development or improvement phases.

5. Production and maintenance

Even if we’ve completed data quality validation at all phases of the data lifecycle, testing is still a must. We’re not only talking about before deployment into production but also afterwards as a type of monitoring to guarantee data quality remains in good shape.

Why data quality management is important

How to ensure both data quality and data integrity

Data cleansing

Data cleaning is the practice of correcting data quality concerns and inconsistencies uncovered during data profiling. This should be part of every data quality framework. Data cleaning involves several steps, and one of them is deduplication to ensure that multiple data entries don’t end up across several locations by accident. 

Data governance

Data governance is developing a framework and set of processes to control and assure data quality across the company. It outlines data management roles, responsibilities, rules, and processes. 

It’s an essential step to improve data integrity and ensure that the organization benefits from high data accuracy and overall quality. The practice seeks to create responsibility and ownership of data and ensure that data-related choices are made in a purposeful and coordinated manner.

Data standardization

This is the process of converting fragmented data assets and unstructured big data into a standardized format that assures the data is full and ready for use, independent of the source. Business rules are used to ensure datasets correspond to an organization’s requirements and demands while standardizing data.

Data enrichment

Data enrichment aims to introduce updates and information into an organization’s current database to enhance accuracy and add missing information. Building on current data enables improved company choices and consumer interactions.

Data encryption

Encrypting sensitive data involves encoding it in a way that can only be decoded with the required decryption key. Encryption is used to safeguard data during transmission and at rest (for example, while stored in databases or on drives). Overall, the approach improves data security.

Data entry controls

Data entry controls are procedures used to reduce mistakes and maintain data correctness throughout the data entry process. Drop-down menus, data pickers, input masks, and validation checks are all examples of how we can help users enter data in the right format and within acceptable limits.

Data validation and verification

The process of validating the correctness and completeness of data during data entry or data import is called data validation. It entails using established data validation rules or limits to ensure that the data fulfills certain requirements and is legitimate. Data verification and validation go hand in hand, with data being cross-checked and certified as correct through other sources or procedures.

Access control

Data access is restricted to authorized people or users, depending on their responsibilities and permissions. Limiting access is the best method to avoid unwanted data alterations, deletions, or tampering, protecting data integrity and security.

Audit trails and logs

Audit trails and logs document specific data changes and access activities. They keep track of who accessed the data, when it was accessed, and what modifications were done. Audit trails are essential for data consumption monitoring, helping us to spot potential security breaches and make data quality inquiries.

Error handling mechanisms

Error handling methods outline processes and procedures for quickly identifying, reporting, and resolving data inconsistencies or errors. When data errors are discovered, the necessary steps are taken to repair the data or to commence a data cleansing procedure.

Regular backup and recovery plans

Regular data backups involve creating copies of data at predetermined times. Data can be recovered from backups in the case of data loss due to hardware failures, system crashes, or cyberattacks. A well-defined data recovery strategy guarantees that data is retrieved quickly and correctly.

What is more important: data integrity or data quality?

Data integrity and data quality are both critical parts of data management, and they’re closely linked to each other. Overall, the importance of data integrity vs. data quality depends on the context and use case.

For example, data integrity may play a more important role in scenarios where you need data for vital operations, financial transactions, medical records, or legal processes. It’s critical to ensure the accuracy and security of data to avoid potential issues or legal concerns caused by compromised data.

When data is used for analytical purposes, business intelligence, or decision-making, data quality becomes more important. To derive useful insights and make informed decisions, accurate, full, and relevant data is required.

In truth, both of these concepts are inextricably linked and equally important to an organization’s success. Focusing on them is beneficial because data integrity and quality contribute to a trustworthy data environment that efficiently supports diverse business tasks.

Bonus: Data version control helps to achieve and maintain high data quality

Automating data version control processes using tools

Continuous integration and continuous data deployment are automated procedures that rely on the capacity to discover data errors and prevent them from cascading into production. Ideally, you should do data quality tests whenever necessary.

This is when version control solutions like lakeFS might come in handy.

To facilitate automated data quality checks, lakeFS has zero-copy isolation, pre-commit and pre-merge hooks. It also interfaces with data quality testing solutions that provide the above-mentioned testing logic, allowing you to test your data effortlessly and at every critical stage of the data lifecycle.
Discover how lakeFS allows CI/CD on data lakes.


While data integrity and data quality are similar concepts, their areas of focus and techniques differ. That’s why it’s still worth making a data integrity vs. data quality comparison and differentiating between the two.

Data integrity is a core feature of database management systems and is crucial for ensuring an organization’s data’s general credibility.

Making good decisions, enhancing operational efficiency, maintaining compliance, and supporting data-driven initiatives all require high quality data. Poor data quality can lead to incorrect assumptions, bad decision-making, and inefficiencies inside a company.

Data integrity assures data’s dependability and security, whereas data quality ensures data’s correctness, completeness, and appropriateness for its intended purpose. Both are essential for organizations to have reliable and useful data for decision-making and commercial success.

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