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Common Data Quality Metrics to Measure Data Quality

The quality of your data determines the quality of your business decisions and the ability to solve problems and reach goals. That’s why you need to measure it with the right data quality metrics and work on it to improve it.

Why Is Data Quality Important?

Your data quality has a direct impact on your strategic decision making. If you have poor quality data, you’ll make poor decisions that can cost you time and money.

On the other hand, high-quality data helps you to make the right decisions to ensure the success of your business.

In fact, companies that focus on improving the quality of data have reached 15% to 20% increased revenue. So, how to know if your data quality is good or bad? How to measure it?

Measuring data quality includes understanding the data quality attributes and using the right data quality metrics.

Characteristics of Data Quality

To determine the value of your data, you need to understand the following data quality characteristics:

  • Accuracy – measuring how accurately your data corresponds to reality. When it comes to the financial area, data quality is either accurate or not because the number of pennies and pounds in an account is a specific number.

    This characteristic is important for large organizations with high penalties for failure. The ratio of data to errors is a common data quality metric to measure accuracy.
  • Completeness – measuring if all the necessary data is found in a precise dataset. It indicates whether there’s enough data to make conclusions. An example of a data quality metric to measure completeness is the number of empty values.
  • Consistency – measuring if two data values derived by different data sets are conflicting with each other. The percent of values that match across various reports/records is a common data quality metric for consistency.
  • Timeliness – measuring the accuracy of data at a specific time period. This attribute of data quality measures the time between the moment you expect the data and the moment you can actually use it. A typical metric to measure timeliness is data time to value.
  • Integrity – measuring if the data remains the same as it moves between multiple systems. Usually, data stored in separate systems affects data integrity. The goal is to make sure there are no unintended data errors. Typical metric to measure integrity is data transformation error rate.
  • Validity – measuring how good data complies with required value attributes. For instance, making sure the day, month, and year numbers conform to the same format.

data reporting

Common Data Quality Metrics to Measure Data Quality

Here are data quality metrics most commonly used by companies to measure the quality of their data:

1.The Ratios of Data to Errors

This metric allows you to see how the number of errors you have in one data set corresponds to the size of the data set. Common data errors include redundant, incomplete, or missing entries.

If you have fewer errors while the size of your data set grows or stays the same, your data quality is likely improving.

2.Data Transformation Error Rates

Data transformation is the process of converting data from one to another format. Problems with this process indicate problems with data quality.

By knowing the number of failed data transformation operations (or if they took too long to complete), you can learn more about your overall data quality.

3.Number of Empty Values

This metric shows the number of empty fields you have within a data set or what data was recorded in the wrong field. The next step would be to track how this number changes in time.

4.Email Bounce Rates

Email bounces are usually caused by poor data quality. The reason they occur is because missing data, errors, or outdated data makes emails to be sent to wrong addresses.

5.Amounts of Dark Data

This data can’t be used properly due to problems with data quality. So, if you have large amounts of dark data, it means you have more data quality problems.

6.Data Storage Costs

A common sign of data quality problems is when the amount of data you use is the same while the cost of your data storage increases, or vice versa. Storing data that you’re not using can happen due to data quality problems.

7.Data Time-to-Value

You can measure the quality of your data by determining how much time your team needs to derive results from an existing data set. Even though many factors impact this data quality metric, problems with data quality often slow down the effort to generate important information from data.

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