Thought leadership
May 22, 2024

Your Guide to Data Quality Metrics

Not all data is created equal.

Adrianna Vidal

Not all data is created equal. The quality of your data is crucial for making accurate, reliable, and actionable decisions. Data quality metrics are essential tools that help assess and maintain the high quality of data within an organization. These metrics provide quantifiable measures that offer insights into various aspects of data quality, including accuracy, completeness, consistency, reliability, and timeliness. By leveraging these metrics, organizations can ensure their data is trustworthy and valuable for decision-making processes.

Why is data quality important?

We have already written extensively about data quality and its importance.

Without data quality, we can’t have meaningful outcomes.

Here’s why:

Garbage In, Garbage Out

One of the most widely recognized maxims in the world of data is "garbage in, garbage out" (GIGO). This encapsulates the fundamental idea that the quality of output is directly proportional to the quality of input. In the context of data, it means that if you feed your systems with poor-quality data, the results and decisions derived from that data will also be of poor quality.

Consider a scenario where an e-commerce company relies on sales data to optimize its inventory management. If the sales data is riddled with inaccuracies, including incorrect product codes, flawed order information, and missing details, the company's inventory management system will generate erroneous results. This can lead to overstocking, understocking, increased operational costs, and poor customer service due to delivery delays. Poor data quality has a cascading effect, negatively impacting multiple facets of the business.

For this reason, data quality is not merely a matter of technical concern but is fundamentally a strategic concern for organizations that rely on data to drive their decision-making processes. Inaccurate or unreliable data can lead to misguided strategies, wasted resources, and missed opportunities. To mitigate these risks, organizations need to understand the factors that can negatively impact data quality and employ data quality metrics to maintain data integrity.

What can negatively impact the quality of data?

Data quality can be compromised in various ways, often due to the following factors.

Data entry errors

One of the most common sources of data quality issues is human error during data entry. Even the most meticulous data entry professionals can make occasional mistakes, leading to inaccuracies in databases. This can include typographical errors, transposition errors, or misinterpretation of data.

The typical solution to this problem is setting up the system where the human errors are reduced to a minimum. 

Data integration problems

Many organizations use multiple systems and databases to store and manage data. When data is transferred or integrated between these systems, it can lead to inconsistencies or data format issues. If the integration process is not well-managed, it can introduce errors into the data such as no ingestion for a certain period of time, partial ingestion or even a duplicate ingestion. 

Outdated data

Data has a shelf life, and when organizations fail to update their datasets regularly, they risk working with outdated information. This is especially problematic in industries where conditions change rapidly, such as stock trading or public health. Decisions made based on stale data can not only be counterproductive, but also .

Data breaches

Data breaches and cyberattacks can compromise data integrity. When unauthorized parties gain access to a system or database, they can manipulate or steal data, rendering it untrustworthy. Data breaches can happen in a variety of ways which include phishing attacks, malware and ransomware, weak passwords, unsecured networks, software vulnerabilities and many more. Many of those, 88% of the cases, happen due to human error which is why security training is necessary for all the employees.

Data transformation errors

Data often needs to be transformed, cleaned, or prepared for analysis. Errors in this process can lead to data quality issues. For example, a simple mistake in converting units can result in inaccurate metrics. To illustrate this example, in 1983 Air Canada’s airplane had ran out of fuel due to the misunderstanding that the fuel amount was in kilograms, while in fact it was in pounds. While this example isn’t entirely related to the big data pipelines, it shows how a simple error can have massive repercussions.

Lack of data quality monitoring

Without proper data quality monitoring in place, organizations may not even be aware of data quality issues until they result in costly errors or operational inefficiencies. Regular monitoring can help detect issues early and take corrective action.

Data quality metrics

To maintain and enhance data quality, organizations use a variety of data quality metrics. These metrics provide a systematic way to assess the accuracy, completeness, consistency, and reliability of data. Let's explore some key data quality metrics:

Percentage of missing values in a column

One crucial metric for assessing data quality is the percentage of missing values in a column. If a dataset has a high proportion of missing values, it can significantly impact the validity of analyses and models built using that data. High missing value percentages can indicate data entry errors or system issues that need to be addressed.

Error rate in numerical data

For datasets containing numerical data, calculating the error rate can be invaluable. This metric measures the extent to which the numerical data deviates from the expected or true values. It helps identify inconsistencies and inaccuracies in the data.

Delay in data updates

In scenarios where data needs to be updated regularly, monitoring the delay in data updates is crucial. Data that is not refreshed in a timely manner can lead to decisions based on outdated information. This metric helps ensure that data is current and relevant. Some factors that contribute to delays in data updates include batch processing, data extraction frequency, data transfer latency, data loading time and more.The goal here is to minimize this delay for the use cases that require real-time or near-real-time data access. In order to achieve that, you can implement strategies such as streaming data processing, event-driven architectures and also the optimization of the workflows and pipeline monitoring.

Count of duplicate records

The count of duplicate records refers to the number of instances where identical or nearly identical data entries exist within a dataset. Duplicate records can occur in various types of databases or datasets and may result from errors during data entry, system glitches, or other factors. The count of duplicate records is a key metric in data quality assessment and management.

Data range, mean, median, and standard deviation

For numerical data, statistical measures such as data range, mean, median, and standard deviation can provide insights into data quality. These metrics help assess the consistency and distribution of numerical data points. Significant deviations from expected values can indicate data quality issues. This way you can identify extreme values, missing data points and more. It is also the first step towards the exploratory data analysis which allows you to get a feel for the data.

Number of data pipeline incidents

Data pipelines are the systems and processes used to collect, process, and move data from one place to another. Monitoring the number of data pipeline incidents, such as failures or data loss, helps identify areas where data integrity might be compromised. Reducing pipeline incidents can improve data quality.

Table health

Table health is an aggregate metric that refers to the overall well-being of the database table. It may include metrics like the number of missing values, data range, and record consistency within a table. These metrics provide a holistic view of data quality for specific datasets. Some of the factors that contribute to table health could be data integrity, completeness, accuracy, timeliness, performance and more.

Table freshness

Table freshness metrics assess the recency of data. They measure how up-to-date data is and can help ensure that the information used for decision-making is relevant. This is particularly critical in industries where real-time data is essential, such as financial trading or public safety.


Data quality is essential for making informed and effective decisions in today's data-driven world. Poor data quality can result in incorrect conclusions, wasted resources, and missed opportunities. To address data quality issues, organizations should use data quality metrics to assess and maintain data integrity. By tracking metrics like missing values, error rates, and data freshness, businesses can ensure their data is accurate, complete, and up-to-date. This enables them to make better decisions, optimize operations, and achieve success in an increasingly data-centric world.

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Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
Data analyst
Business analyst
Data/product manager
Total cost
Common needs
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
Business intelligence analysts
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
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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