Data integrity vs data quality: What's the differences?
Data quality vs data integrity: Both are different parts of data management. The article also explains what they stand for, how they are closely related, how to ensure data quality and integrity together.
Subbu
Feb 3, 2025 |
9 mins
What is data quality?
Data quality is ensuring that a dataset meets organization’s requirements for accuracy, consistency, and completeness to be good enough for decision making. The major focus of data quality is to make the data usable and trustworthy, meeting its intended purpose when it’s required.
How would you measure data quality – data quality is usually measured based on quality dimensions like accuracy, consistency, uniqueness, integrity, timeliness, and validity. for example, the data must be up to date and have all fields filled up or it should meet all predefined constraints & formats.
The tools we use for data quality are Talend, IBM Infosphere, Informatica data quality, Experian, and other data cleansing and processing tools.
What is data integrity?
Data integrity is how accurate, consistent, and reliable the data is throughout its lifecycle. it means that data isn’t altered or modified between its creation and deletion, while the data goes is processed, stored, or transformed. Data quality is a part of data integrity.
Anything could affect data integrity – from security threats and data attacks to data inconsistencies to data collection errors. For example, a company is collecting customers data including their personal identification numbers, contact details, and banking information. A cyberthreat happens, affecting and altering this data, which not only corrupts the information and changes how the company sees the users, but also tarnishes the company reputation.
This might make data integrity look like data security; but both are different. Data security measures exist to protect data access from unauthorized users.
There isn’t a specific tool for data integrity. But, there are many other auditing, security, and quality management tools you can use for ensuring data integrity. Some of these are
Change tracking tools and in-built integrity checkers in databases.
Database management systems to set up user-based access and manage compliance.
Data profiling tools like Talend or Informatica, and Splunk to set up automated alerts.
ETL tools for data verification and validation during data collection or transformation.
Differences between data quality and data integrity
Factors | Data quality | Data integrity |
Definition | Delivering high-quality, accurate, valid, and consistent data to meet end users’ goals. Even though data quality and integrity shares properties, they still might mean different things. For example, accuracy in data quality means data should reflect real-world values. Completeness in data quality means no missing fields. | In addition to being accurate, consistent, and complete, data integrity also deals with data being secure & unmodified throughout the lifecycle. Accuracy in data integrity could mean that data should be the same as of its original entry and should be untampered. Completeness in data integrity means that there is no value loss during a transfer. |
Purpose | Data quality exists helps data meet its intended purpose – serving analytics, helping with data-driven decision-making. | Data integrity’s purpose is to ensure that are no unintended changes to the data throughout its lifecycle. |
Components | Data quality has 3 major components. Accuracy – data is same as of its real-world values. Completeness – all fields are present. Timeliness – Records are up-to-date and available on time Consistency – data remains the same across all systems. | Data integrity has three components: Physical integrity: is related to data accuracy while being stored or retrieved. Protects the data from physical threats and cyberattacks. Logical integrity: data accuracy in logical sense (whether the structure is accurate and makes sense) Referential integrity: ensures consistency of values between two tables |
Examples | A customer’s name is written incorrectly, which leads to validation failures and misidentifications during communication. | A customer’s past orders are missing from the master records, which could have been accidentally deleted during system migration. |
Why it matters? | Sees to it whether the data served for business decision making is relevant and reliable.
IT/data teams spend less time cleaning data & more time improving them.
The cost being paid for bad data is high; wrong decisions, misled campaigns, and tarnished business reputation.
| This is essential to adhere with data protection laws like GDPR.
Ensures that the data isn’t manipulated or altered & prevents unauthorized accesses.
Prevents data loss, which could be otherwise detrimental to the company. |
How to ensure data quality and data integrity at the same time?
It is possible to ensure both data quality and integrity at the same time. But why do you need an interconnected approach, when both have different purposes and intent? It’s because of the way both data quality and integrity are connected. If data quality is good, but integrity is low -> data is vulnerable to be attacked. If data integrity is high, but quality is low -> data is safe, but it’s not qualified for business analysis. But there are also challenges in maintaining both.
Data could be secured and protected; data decay is real, and data could become outdated if not updated regularly.
Compliance expenses like GDPR violations can be hefty, which could go up to 4% of the annual revenue of an organization’s revenue, if data accuracy or protection is compromised.
When there is lack of centralized governance policies, each team handles data differently. This causes lots of inconsistencies.
Manual data entry errors are still prominent & lack of access control could exacerbate this.
Transferring data from one system to another need to be handled properly.
Strategies needed to ensure both data integrity and data quality
Data governance framework: Start with data governance as this is the base for both data quality and integrity. It brings accountability and standards to data quality and secures data through access control and compliance measures.
Data formats standardization: If there are no predefined standards and data formats, then standardizing them could help. This way, there will be consistency between systems, reduced format errors, and prevents data corruption.
Data backup & recovery plans: A backup & recovery plan is not only to have a copy of data for worst-case scenarios. It could also help when there are missing data or data loss.
Data cleaning & enrichment: Data needs to be frequently cleaned and standardized before use. This would remove errors, redundancies, and incomplete data which helps with improving quality and maintain consistency which improves integrity.
Automated data validation: There are automated data validation tools that check the accuracy, consistency, errors, and missing values during any change, while also preventing unauthorized entries.
Audit logs monitoring: Audit logs can tell you who had access to the data recently & who modified it – something would help with data integrity. It also protects and ensures data accuracy while entry.
Final thoughts
Evolving regulations and data challenges one side, fragmented systems and teams' other side. How do an organization make the best use of its data while overcoming classic challenges like data quality and integrity problems – with regular data profiling, auto-validation, meta-data management, governance framework, and audit and analysis.
Our data engineering team has faced many unique cases like this, where they have scoped out the problem, devised the right solution, and fixed it with cost-effective resources. If you would like to set up the base for data-driven decision making, let’s help you start with the discovery call, analyze your data integrity and data quality practices, and suggest the best solutions.
