April 1, 2024

What is Data Quality?

Data quality is more than just a trend —it's a critical factor that can make or break organizations.

Bigeye Staff

Data quality is more than just a trend —it's a critical factor that can make or break organizations. 

Consider this: a minor error in Google Maps once led to the accidental demolition of a house in Texas. In another instance, NASA suffered a $125 million loss when different teams used incompatible measurement systems, causing the failure of a spacecraft.

These examples underscore the profound impact that data quality can have on our lives and businesses. Data quality is not just about accuracy; it's about trust, efficiency, and effective decision-making. In this article, we'll delve into the depths of data quality, exploring its definition, importance, and practical tips for ensuring high-quality data in your organization.

What is Data Quality?

Data quality refers to the reliability, accuracy, and completeness of data for its intended purpose. 

Essentially, it ensures that data is good enough to support the tasks and processes it's used for. Data quality includes many aspects, and all of these together determine how good the data is overall.

Who Owns Data Quality in an Organization

Data quality is primarily the responsibility of data engineering and data platform teams within an organization. While this allocation of ownership seems natural, data engineers often perceive data quality as an additional task rather than a core responsibility.

The real challenge lies in striking the right balance between speed and performance, on one hand, and ensuring quality and reliability, on the other. The goal is to minimize the burden effectively, fostering an environment where data engineers can navigate the complexities of ensuring data quality without compromising efficiency.

Adopting advanced data quality tools can enable better processes for data profiling, cleansing, monitoring, and governance. Tools such as Bigeye offer automated mechanisms to identify data anomalies, pipeline errors, reducing manual efforts.

Why is Data Quality Important?

Informed Decision-Making

Reliable, accurate, and complete data is essential for making informed decisions. Businesses that rely on data to make strategic choices must be confident in that data, to minimize the risk of decision making. 

Operational Efficiency

Poor data quality can lead to inefficiencies in operational processes. When data is inaccurate or incomplete, it can result in wasted time and resources as employees spend time and money to fix issues caused by poor data.

Customer Satisfaction

Inaccurate or incomplete data can lead to customer dissatisfaction. For example, if a customer's order is lost due to data errors, it will lead to a frustrated customer and even lost business.

Regulatory Compliance

Many industries are subject to strict regulatory requirements regarding data quality. Non-compliance can lead to legal consequences and financial penalties.

Reputation and Trust

Data quality also impacts an organization's reputation and the trust that customers and stakeholders place in it. Organizations that consistently provide accurate and reliable information are able to build trust with their customers and partners.

Data Quality Dimensions

A data quality dimension is a specific aspect or characteristic of data that is used to evaluate its quality. These dimensions help organizations assess the reliability, accuracy, and usability of their data.

By evaluating data quality across these dimensions, organizations can identify areas for improvement and implement strategies to enhance the overall quality of their data.

Let’s take a look at each of the six data quality dimensions, and how you can evaluate your data using each one. 


Completeness refers to the extent to which data is whole, meaning it contains all the necessary attributes and values. Incomplete data can lead to inaccurate analyses and decisions. For example, a customer database missing contact information would be incomplete.

In the example in the table we can see at least one missing field for each of the rows: either a first name, last name, email address or the phone number. While a phone number might be one of those attributes that are not always available, the missing data with the first three attributes indicates that data is likely incomplete.

CustomerID FirstName LastName Email Phone Address
1 Robert Plant robertplant@email.com 555-123-4567

123 Main St

2 Grace graceslick@email.com 555-987-6543 456 Elm Rd
3 Saul Hudson

789 Oak Ave

4 Tyler steve@email.com

890 Pine Ln

5 Janis Joplin


100 Maple Dr


Accuracy is the measure of how closely data reflects the real-world information it represents. Accurate data is free from errors, omissions, and inconsistencies. Inaccurate data can lead to misguided decisions and costly mistakes.

OrderID CustomerName Product Quantity Price TotalAmount
1 Robert Plant
Laptop 2 800 1600
2 Grace Slick Mobile Phone 3 400 1200
3 Saul Hudson Tablet 1 600 400
4 Steven Tyler Smartwatch 4 150 550
5 Janis Joplin TV 1 1000 1100
6 Headphones -2 50 -100
7 Alice Cooper Mouse 2 90
8 Joe Satriani Keyboard 3 30 90

In this example, you can identify various inaccuracies:

  • Inaccuracy in Quantity: Order 6 has a negative quantity which doesn't make sense in a real-world scenario.
  • Inaccuracy in Price: Order 7 has a missing price making it difficult to assess whether the TotalAmount is correct.
  • Inaccuracy in Total Amount: The total amount for Order 5 appears to be calculated incorrectly as it should be 1000.


Consistency concerns the uniformity of data across various sources and instances. When data is consistent, it ensures that different parts of an organization are working with the same information. Inconsistencies can result in misunderstandings and errors.

EmployeeID EmployeeName Address Salary EmploymentStatus
1 Robert Plant 123 Main St 60,000 Active
2 Grace Slick 456 Elm Road 55000 Active
3 Saul Hudson 789 Oak Ave 62000 Active
4 S. Tyler 890 Pine Ln 58000.00
5 J. Joplin 1000 Cedar St $54,000 Active
6 Alice Cooper 10 Oak Avenue 63000 Active

There are a few inconsistencies that we can see on the table:

  • EmployeeName column sometimes has a full name and sometimes first initial and the last name
  • Address column has abbreviated street names such as “St” and somewhere in full such as “Road”
  • Salary column has inconsistent formatting


While you may think that formatting could fall under consistency as well, data formatting issues are so common that they deserve a section of their own. One of the most frequent examples is date, but also data types such as mixing integers and strings or booleans and strings. Formatting refers to the structure and organization of data. Consistent formatting is important for data compatibility and ease of analysis. Inconsistent formatting can lead to data integration challenges and increased processing time.

EmployeeID EmployeeName JoiningDate Salary EmploymentStatus
1 Robert Plant 2022-04-15 60000 Active
2 Grace Slick 06/30/2023 55000 Active
3 Saul Hudson 12-MAR-2023 62000 Active
4 Steven Tyler


58000 Inactive
5 Janis Joplin


54000 Active
6 Alice Cooper

30th July 2019

63000 Inactive

Each date in the JoiningDate column has a different date format.


Uniqueness ensures that there are no duplicate records within a dataset. Duplicate records can lead to overcounting, skewed analytics, and errors in reporting.


Timeliness pertains to the age of the data. Data should be current and relevant for its intended use. Outdated data can result in misinformed decisions, especially in dynamic environments.

And, poor data timeliness can have consequences ranging from financial losses and inefficiencies to safety risks and missed opportunities. Timely and up-to-date data is crucial for informed decision-making, efficient operations, and ensuring the well-being of individuals and organizations.


Accessibility is a dimension that focuses on how easily and quickly data can be retrieved and used. Inaccessible data can hinder decision-making processes and create bottlenecks in operations.

Addressing data accessibility issues typically involves implementing efficient data storage and retrieval systems, utilizing user-friendly interfaces, establishing access controls that balance security and usability, and ensuring proper documentation and metadata. Accessibility is crucial for organizations to maximize the value of their data and enable users to make informed decisions and take prompt actions.

How is data quality different from data integrity?

Data quality and data integrity are related but different concepts. 

Data quality is mainly about accuracy, completeness, and consistency, among other aspects. On the other hand, data integrity is specifically about keeping data accurate and consistent throughout its life. It involves processes and technologies that protect data from unauthorized changes.

In summary, data quality focuses on making sure data is accurate and suitable for its purpose, while data integrity is about protecting data from tampering or corruption.

How is data quality different from data observability? 

Data quality is a traditional, all-encompassing term that is focused on  fixing data issues in a reactive manner. Data quality refers to the general state of your data: how healthy is it?

In contrast to data quality, data observability constantly surveys the state of the data pipeline and proactively diagnoses issues. Data observability platforms can help to ensure data quality.

Prevention Before Mitigation

When it comes to data quality, prevention is often more effective and efficient than mitigation. 

Preventing data quality issues at the source is far less costly and time-consuming than trying to clean and correct data after it has already entered the system. In addition if the data quality issues are not identified on time, it will lead to misled further action.

Some of the first steps to start preventing data quality issues include:

  • Implementing clear and consistent data entry standards to ensure that data is recorded accurately and uniformly from the beginning.
  • Using data validation rules to prevent the entry of invalid or inconsistent data. For instance, you can use regular expressions or predefined value ranges to validate data entries.
  • Establishing data governance policies and practices that define roles, responsibilities, and processes for maintaining data quality.
  • Training employees on the importance of data quality and provide them with the tools and knowledge needed to enter data accurately.
  • Implementing automated checks and validations within data entry forms and systems to catch and prevent data quality issues in real-time.

Data Pipeline Monitoring

Effective data quality management often involves the monitoring of data pipelines, the processes that transport and transform data from various sources to its destination. Bigeye is a modern data observability platform designed to help organizations monitor and manage their data pipelines effectively by providing real-time insights into data pipelines.


From ownership and importance to dimensions and tools, understanding data quality is crucial for any organization looking to leverage its data effectively. By focusing on data quality, you can ensure that your data is not just reliable but also valuable, enabling you to make better decisions and drive business success.

Let's continue the conversation about data quality and how it can transform your organization.

Request a demo here.

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Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
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The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
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Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
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