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Data Quality Metrics You Should Track and Measure

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Low-quality data is one of the major reasons why companies miss out on revenue-driven opportunities and make poor business decisions. So, what can be done about it?

Read this post to find which data quality metrics every business should track and measure to realize their full potential.

Why is data quality important?

The answer to this question is as simple as that – the better the quality of your data, the more benefits you can get from it. In other words, data quality is important because it helps businesses acquire accurate and timely public information to manage service effectiveness and ensure the correct use of resources.

As discovered by IBM, in the US alone, businesses lose $3.1 trillion due to poor quality data annually. What’s important to mention, the impact is not only financial. Bad data wastes your team's time, leads to customer dissatisfaction, and drives out top employees by making it impossible to perform well.

All these issues call for an effective way to track and access the collected public data in order to make sure it’s of the highest quality. Allen O’Neill already stressed the importance of ensuring consistency in the quality of data in his informative guest post on our blog, stating that “If your data isn’t of high enough quality, your insights will be poor; they won’t be trustworthy. That’s a really big problem”.

On the other hand, some potential advantages of high-quality data include:

  • Easier analysis and implementation of data
  • More informed decision-making
  • A better understanding of your customers’ needs
  • Improved marketing strategies
  • Competitive advantage
  • Increased profits

What are the 6 dimensions of data quality?

Now that you have a proper understanding of why data quality is essential, we can dive into explaining each of the data quality dimensions that together define the overall value of collected public information.

Organizations agree that data quality can be broken down into 6 core categories:


Defining question


Is all the necessary data present?


How well does this data represent reality?


Does data match across different records?


How well does data conform to required value attributes (e.g., specific formats)?


Is the data up-to-date at a given moment?


Is this the only instance of data appearing in the database?


A data set can be considered complete only when all the required information is present. For instance, when you ask an online store customer to provide their shipping information at checkout, they will only be able to move on to the next step when all the required fields are filled in. Otherwise, the form is incomplete, and you might eventually have problems delivering a product to the right location.


Data accuracy represents the degree to which the collected public information describes the real world. So, when wondering if the public data you got is accurate, ask yourself: “Does it represent the reality of the situation?” “Is there any incorrect data?” “Should any information be replaced?”


A large number of organizations tend to store information in various places, and maintaining synchronicity between them is one of the integral steps toward ensuring the data is of high quality. In case there's even a slight data difference between two records, unfortunately, your data is already on a path to losing its value.


Validity is a measure that determines how well data conforms to required value attributes. For example, when a date is entered in a different format than asked by the platform, website, or business entity, this data is considered invalid.

Validity is one of the dimensions that are easy to access. All that has to be done is a check if the information follows certain formats or business rules.


As the name suggests, timeliness refers to the question of how up-to-date information is at this very moment. Let’s say specific public data was gathered a year ago. Since it is very likely that new insights were already produced during that time, this data can be labeled as untimely and would need to be updated.

Another essential component of timeliness is how quickly the data was made available to the stakeholder. So, even if it is up-to-date within the warehouse but cannot be used on time, it is untimely.

It is extremely important that this dimension is constantly tracked and maintained. Untimely information can lead to wrong decisions and cost businesses time, money, and reputation.


The information can be considered unique when it appears in the database only once. Since it is not rare to see data being duplicated, it is essential to meet the requirements of this dimension by reviewing the data and ensuring none of it is redundant.

Data quality metrics you should measure and track

Let’s agree – understanding the dimensions of data quality doesn’t seem that hard. However, having this knowledge is still not enough to adequately track and measure the quality of your data. While dimensions give us a general idea of why they are important, data quality metrics define how specifically each of the dimensions can be measured and tracked over time. Thus, the six dimensions should be instantiated as metrics, also referred to as database quality metrics or objective data quality metrics, that are specific and measurable.

For instance, a typical metric for the completeness dimension is the number of empty values. This data quality metric helps to indicate how much information is missing from the data set or recorded in the wrong place.

Talking about the accuracy dimension, one of the most obvious data quality metrics is the ratio of data to errors. This metric gives businesses an opportunity to track the number of wrong entries, such as missing or incomplete values, in relation to the overall size of the data set. If you find fewer data errors while your data size grows, it means that the quality of your data is improving.  

Check out this table for more examples of data quality metrics for each of the six dimensions:


Sample data quality metrics


Number of empty values, number of satisfied constraints


Ratio of data to errors, degree to which your information can be verified by a human


Number of passed checks to the uniqueness of values or entities


Number of data violations, degree of conformance with organizational rules


Amount of time required to gather timely data, amount of time required for the data infrastructure to propagate values


Amount of duplicated information in relation to the full data set

Keep in mind: data quality metrics that will be the most suitable for your use case will depend on the specific needs of your organization. The essential thing is to always have a data quality assessment plan to make sure your data fits the needed quality standards.

Putting data quality metrics into practice

A typical data quality assessment approach might be the following:

  • Identify which part of the collected public data must be checked for data quality (usually, information critical to your company's operations).
  • Connect this information to data quality dimensions and determine how to measure them as data quality metrics.
  • For each metric, define ranges representing high or low-quality data.
  • Apply the criteria of assessment to the data set.
  • Review and reflect on the results, and make them actionable.
  • Monitor your data quality periodically by running automated checks and having specific alerts in place (e.g., email reports).

How web scraping can ensure data quality

As you might know, web scraping is the ultimate way of gathering the needed public data in large volumes and at high speed. But scraping is not only about collecting. It is also about verifying, choosing the most relevant data, and making the existing data more complete.

So, how exactly does web scraping ensure data quality?

When performing web scraping with high-quality scraping tools, users get the possibility to retrieve timely and accurate public data even from the most complex websites. For instance, Oxylabs’ E-Commerce Scraper API is known for its AI & ML-driven built-in features. These specifications allow the scraper to adjust to website changes automatically and, eventually, gather the most up-to-date data almost effortlessly.

Additionally, reliable scraper APIs are also powered by proxy rotators, giving you a chance to prevent unwanted blocks, which significantly increases your likelihood of getting all the public data you need and, in turn, satisfying the completeness dimension.

Other benefits of web scraping that help improve data quality include:

  • Request tailoring on country or city-level
  • Delivering clean and structured data you can rely on
  • Collecting data from thousands of URLs for a complete dataset

Let’s wrap up

Data is undoubtedly one of the most valuable resources for today’s businesses. It presents actionable insights, provides new opportunities, and, if used by companies correctly, allows them to stay on top of the competition. However, data is only useful when it is of high quality. This means that businesses should start paying more attention to tracking the quality of information they use by constantly having a data quality strategy in place.

In today’s blog post, we provided a detailed explanation of the six data quality dimensions that together define the overall value of assessed data, as well as listed a number of data quality metrics that can be used to measure and track the quality of this data.

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