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5 Data Quality Metrics Executives Should Track

Rafael M. Dourado

Professional looking at his laptop to check his data quality metrics

As a business leader, you know that data is one of your most valuable assets. The right information helps you unlock opportunities to innovate. For example, Programmers helped a major fast-food chain discover the ideal coupon combo by looking at customer buying habits. Data also allows you to target inefficiencies, suspicious activity, and redundancies in your company’s operations.

But do you know how to measure the quality of your data? Low-quality data prevents you from innovating and becoming more efficient. Plus, it makes insights you uncover from generative AI and other technology unreliable. In this blog post, we’ll explore the importance of data quality and uncover several key data quality metrics.

The Importance of Data Quality

Data directly informs your company’s strategic decisions. That’s why your organization needs to have high-quality data. Out-of-date or otherwise inaccurate information leads to poor judgments, wasting your company’s valuable resources and time.

Having the best data possible is crucial to discovering key insights. And if you want to get started using generative AI for analytics, data quality is absolutely foundational. So, how can you determine the reliability of your information? Targeting the right data quality metrics is a productive way to start.

Defining Data Quality Metrics

Data quality metrics are measurements that evaluate the legitimacy of your company’s information. You can get a broad view of data quality by combining several indicators we’ll discuss in the next section.

It is essential to track data quality proactively. If your organization only looks at metrics once unreliable data is already a problem, it is too late to reverse ill-informed decisions.

According to the SD Times, only 17% of software developers say their companies are “data savvy” or “data driven.” This is a concerning sign of how few organizations carefully consider how they ingest, organize, and use information. Continuously monitoring the state of data quality through metrics is an important step in avoiding the mistakes many companies (including some of your competitors) make.

What Are Examples of Data Quality Metrics?

Accuracy, completeness, auditability, consistency, and validity are all examples of data quality metrics. Together, these measurements will give you a macro-level view of how trustworthy, uniform, and comprehensive your information is. Below, learn more about each of these metrics and how to measure for them.


The degree to which information represents actual events, amounts, or statistics is data accuracy. Data from unreliable sources may have input errors or may not be updated regularly, causing accuracy issues.

How Do You Measure Data Accuracy?

As your team or an automated system reviews the accuracy of your data, you can create a percentage point comparing correct values with the amount of information in total. So, for example, if three data points are accurate out of four overall, that would be a score of 75% accuracy.

While we all strive for 100% accuracy, there will always be some (hopefully small) number of errors in your data. Be sure your teams have a realistic idea of the data accuracy score they are targeting, whether it be 95%, 99%, or any other figure.

White, male employee in blue shirt with numerous braclets checking data quality metrics on two different laptops


Data completeness is a metric that determines if a dataset has all the relevant information. Missing data prevents you from having a full picture of events or conditions before taking action. This could cause you to make inefficient decisions or stop you from seeing an emerging pattern.

How Do You Measure Data Completeness?

A percentage point representing how much information you have versus total necessary information can measure data completeness. For example, if you have six out of the ten data points you need, that is a 60% completeness rating.

In some cases, it may be very clear what missing data looks like. For example, let’s say an HR department wants to leverage insights from an employee survey. However, an employee forgot to answer one of the survey questions. That is missing information, which lowers the overall data completeness.

Other times, it may not be evident how many data points are necessary for a particular set. When that happens, be sure to consult with team leads and others with specialized knowledge to narrow down that figure.


Can you see who updated different data points in your system and when? Data auditability measures the amount of information in your system that has a full and transparent edit history.

There are two important questions that auditability answers: When was the information updated, and by whom? Knowing that a particular data point was last updated seven years ago, for example, gives you a heads up that it might be out of date. However, if the last edit was last week, you’re working with more up-to-date information.

It is also important to know who (or, in the case of automation, “what”) updated your datasets. Even though 92% of business leaders report that embracing automation is imperative, many companies still rely partially on manual processes. And as long as companies need employees to input information, human error will always be a problem. Knowing who entered incorrect data can help you track patterns of input error. It can also enable you to be more proactive in helping workers who consistently have trouble.

How Do You Measure Data Auditability?

To track data auditability, figure out the percentage of data points for which you have a comprehensive edit history. If, for example, 9 out of 10 pieces of data have fully tracked edits, then your organization has a data auditability score of 90%.

Black laptop on wooden table with data related to B2C company on it


In many organizations, an individual data point may reappear in several different datasets or platforms. Data consistency indicates how fully these copies match each other.

Contradictory information between your teams could cause them to come to wildly different conclusions. As a business leader, you cannot afford this level of disorganization and disagreement between groups meant to support each other.

Ideally, all your data would be in one central platform that each team accesses. Data silos, or separate databases only accessible to individual departments, generally cause fractured information and insights. However, many business leaders find themselves in companies with this divided infrastructure. 451 Research recently found that 25% of companies have an astonishing 50+ data silos. So, if data points must reappear in many different places, they should at least be consistent.

How Do You Measure Data Consistency?

One way to measure data consistency is by creating a percentage of how many data points are the same across all sets and platforms. So, if 80 out of 100 data points are the same everywhere they appear, then your data consistency score is 80%.


Consistently formatting data points is an important part of organizing information. Data validity tracks the amount of information that conforms to your formatting preferences. For example, a transportation company tracking how far their drivers travel each day will want all measurements to appear in either miles or kilometers, never both.

If data appears in many configurations, it will be difficult to compare these points later. Using our example from the last paragraph, it is completely understandable that one department may prefer miles and the other kilometers. This is especially true if they are based in different countries. However, for the sake of clarity, these teams must conform to the same measurement.

How Do You Measure Data Validity?

To measure data validity, create a percentage point that compares the amount of data that conforms to your formatting requirements to the total number of data points. For example, if 72 out of 100 pieces of information are formatted correctly, then that is a data validity score of 72%.

Final Thoughts

Too often, business leaders accept data at face value without considering its accuracy, when it was updated, and how full of a picture this information gives them. Luckily, the data quality metrics above can help you avoid these common pitfalls. A keen eye for data accuracy, completeness, and other measurements allows you to make optimal decisions and respond dynamically to emerging trends. Learn how we were able to collect and deliver high-quality data that gave a company 100% fleet visibility


Of course, it may be difficult for your organization to begin pinpointing where it is in terms of data quality and how effectively it is leveraging information. Programmers can help with a Data Platform Maturity Assessment, which allows you to see how close your company is to enjoying the benefits of a fully realized analytics platform. From there, we can remove barriers such as data silos and issues with data ingestion that separate your teams from the best information possible. Learn more about our data analytics services to begin leveraging high-quality data today.


You also might be looking to improve data quality to gain better insights from generative AI. Download our e-book today to learn how to improve the quality of your data for GenAI. Then, discover our generative AI services to begin infusing this technology into your data infrastructure.

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