5 Data Quality Metrics Executives Should Track

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 Out-of-date or otherwise inaccurate information leads to poor judgments, wasting your company’s valuable resources and time.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.

Accuracy

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.

Completeness

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.

Auditability

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%.