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What Is Data Quality? (And How to Measure Data Quality With 7 Metrics)

Data quality is a measurement of how useful a set of data is for achieving a particular purpose. It’s good to track data quality for a couple of reasons — primarily, it lets you see if your data is actually benefiting you. You can measure data quality by calculating some different data quality metrics, including:

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What is data quality?

Data quality refers to the overall usefulness of your data. It’s a measurement of how effective that data is for whatever you intend to use it for.

Data quality is commonly used in relation to lead or client data. You want that data to help you market and sell to different leads. So, in that context, data quality is a measurement of how accurate and helpful for marketing your customer data is.

Why track data quality?

The primary reason to measure data quality is simple: You want to ensure that your data is accurate and useful. Needless to say, collecting unhelpful data is a waste of your time, and building your marketing and sales efforts on inaccurate data can outright sabotage your campaigns.

Data quality is also a useful measurement for choosing third-party data providers. As time goes on, first-party data is proving way more valuable than third-party data. That doesn’t mean it’s not nice to have third-party data sometimes, though. It can help flesh out your existing first-party data.

You can get that third-party data from providers. To help you pick the best data provider for your data enrichment needs, you can ask several candidates to perform a data test, where they provide third-party data on a customer you already have, and you see how well it lines up with the data you already have on that customer.

Performing data tests this way can help you find the best data provider for your needs, depending on which one ends up delivering the highest-quality data.

How to measure data quality: 7 data quality metrics

Now that we’ve covered the definition and reason for using it, let’s talk about how to measure data quality.

Basically, you measure data quality by looking at a variety of different metrics. What are these metrics? Well, that depends on your specific needs. The metrics you use will often depend on the type of data you have and your intended use for it.

That said, there are a handful of data quality metrics that it’s always a good idea to track. We’ll cover seven of those metrics in brief below.

Data quality metric What it means How to calculate it
Error rate The percentage of your data that consists of errors # of inaccurate data points / total # of data points x 100
Rate of coverage The percentage of the info you want that appears in your dataset Amount of desired info that appears in your dataset / total amount of desired info x 100
Empty value rate The percentage of expected information missing from your data # of blank fields in your dataset / total # of fields in your dataset x 100
Relationship consistency How well related pieces of data match up List all inconsistencies found in dataset
Format consistency How consistently your data is formatted across the dataset List all inconsistencies found in dataset
Duplicate rate The percentage of your data that consists of duplicates # of duplicate data entries / total # of data entries x 100
Data precision How precise your data points are (Subjective — no set method of measurement)


Read on to learn more about each metric.

1. Error rate

The first metric to look at, and probably the most important one, is error rate. Error rate simply measures what percentage of your data consists of errors.

This is obviously vital to understanding data quality. Inaccurate data is the worst kind of data to have because not only will it not help you, but it will actively harm your business strategy.

How to measure: To find error rate, look at the total number of data points in a dataset. Then find out how many of those points are inaccurate. From there, just calculate what percentage of all the data points are inaccurate.

2. Rate of coverage

Another useful data quality metric to check out is rate of coverage. This metric looks at what percentage of the information you’re interested in appears in your data.

So, let’s say your dataset looks at all the pest control businesses in your city. What percentage of those businesses are actually represented in the dataset? Answering this question can help you determine how widely applicable your data is.

How to measure: You’ll have to decide in advance exactly which metrics you’re interested in learning or tracking. Then just count how many of those metrics appear in your dataset and convert it to a percentage.

3. Empty value rate

Empty value rate is slightly similar to rate of coverage. It measures how much information is missing from your data. That is, of the data that you attempted or expected to obtain, how much is absent?

How to measure: The easiest way to measure this is to track how many fields in your dataset are blank. Then compare that to the total number of fields to calculate the percentage. This is your empty value rate. It helps you see how complete your dataset is and where there are significant gaps you need to address.

4. Relationship consistency

Often, certain pieces of data will directly connect to other ones. For example, if your marketing team hands off some leads to your sales team, the number of leads the marketing team records transferring should match up with the number of leads the sales team reports receiving.

Relationship consistency is a measure of how well related pieces of data match up. In the above example, if the sales team reported a different number of leads than the marketing team, that shows an inconsistency. Measuring the number of inconsistencies in your data can help you determine its overall quality.

How to measure: There’s no exact metric for this. You simply list all the inconsistencies you find in the data.

5. Format consistency

There’s another type of consistency you can measure — format consistency. This refers to consistency in the way your data is formatted. Sometimes, different datasets will end up formatted in different ways, and you want to make sure everything is on the same wavelength.

For example, maybe you’re tracking data on company names and emails. If one dataset formats company names by removing terms like “Inc.” and “LLC,” but another database always includes those terms, your data tools may not recognize that those two datasets belong to the same company.

Locating any formatting inconsistencies is another way to see the quality of your data. 

How to measure: Just like relationship consistency, there’s no set metric for this. You just list the inconsistencies you find, and that’s all there is to it.

6. Duplicate rate

Duplicate rate refers to the percentage of your data points that are duplicates. Duplication isn’t uncommon in datasets — you’ll often find that some of the same pieces of information appear several times. Those duplicates can clutter up the datasets, so you’ll want to remove them.

How to measure: To calculate duplicate rate, just compare the number of duplicate entries to the total number of data entries in the dataset. The percentage you get will be your duplicate rate.

7. Data precision

Finally, it’s worth assessing data precision. This is sort of a subjective metric, so it’s up to you how you want to measure it. Essentially, though, it’s a measure of how precise your dataset is.

For example, let’s say one data point you have is the number of employees at a given company. Does the data point give you a range or a broad estimate, like “200–300 employees?” Or does it provide an exact number, like “273 employees?”

Having more precise data is obviously helpful, so the more precise your data is, the higher your data quality will be.

How to measure: As stated above, this is a totally subjective metric. There’s no established way of measuring it — you can either come up with your own way of doing it, or you can just record general observations rather than trying to use a direct measurement.

Measuring the metrics that affect your bottom line.

Are you interested in custom reporting that is specific to your unique business needs? Powered by MarketingCloudFX, WebFX creates custom reports based on the metrics that matter most to your company.

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Get help organizing and analyzing your data with WebFX and Nutshell

As you verify the quality of your data, you’ll also want somewhere to store that data. When it comes to customer data, your best option is a customer relationship management (CRM) platform like Nutshell. With Nutshell, you can easily gather, categorize, and analyze data.

You can also get help from WebFX with your data analytics. We’ve been building data-driven marketing campaigns for over 28 years, and we know exactly how to do the same for you. Not only can we help you learn from your data, we can help you implement those insights into your marketing.

To get started with us, just call 888-601-5359 or contact us online today!

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