How to Measure Data Quality – 7 Metrics to Assess the Quality of Your Data
Businesses today are increasingly dependent on an ever-growing flood of information. Whether it is sales records, financial and accounting data, or sensitive customer information, the accuracy and adequacy of a company’s ability to measure data quality is critical. If portions of that information are inaccurate or incomplete, the effect on the organization can range from embarrassing to catastrophic.
1.The ratio of data to errors
This ratio offers an unequivocal way to measure data quality. Briefly stated, it consists of tracking the number of known errors – such as missing, incomplete, or redundant entries – within a data set relative to the overall size of the data set. If you find fewer errors while the size of your data stays the same or grows, you know that your data quality is improving. The disadvantage of this approach is that there might be errors of which you aren’t even aware. You don’t know what you don’t know. In this respect, the ratio of data to errors can potentially provide an overly optimistic view of data quality.
2.Number of empty values
Empty values often indicate that important information is missing, or that someone has used the wrong field to record it. This is a relatively easy data quality problem to track. You simply need to quantify the number of records within a data set containing empty fields and then monitor that value over time. It’s important, of course, to focus on data fields that significantly contribute to overall value. An optional memo field, for example, might not be a good indicator of data quality, whereas an essential value like a zip code or phone number corresponds more closely to the overall completeness of data sets.
3.Data transformation error rates
Problems with data transformation – that is, the process of taking data that is stored in one format and converting it to a different format – are often a sign of data quality problems. If a required field is null, or if it contains an unexpected value that does not conform to business rules, then it’s likely to trigger an error during the transformation process. By measuring the number of data transformation operations that fail (or take unacceptably long to complete) you can gain insight into the overall quality of your data.
4.Amounts of dark data
Problems with data transformation – that is, the process of taking data that is stored in one format and converting it to a different format – are often a sign of data quality problems. If a required field is null, or if it contains an unexpected value that does not conform to business rules, then it’s likely to trigger an error during the transformation process. By measuring the number of data transformation operations that fail (or take unacceptably long to complete) you can gain insight into the overall quality of your data.
5.Email bounce rates
Digital marketing campaigns can only be successful if you’re working with a high-quality email list. Customer and prospect data can decay quickly, leading to poor-quality data sets and poorly performing campaigns. Low data quality is one of the most common causes of email bounces. They happen because errors, missing data, or outdated data cause you to send emails to the wrong addresses.
6.Data storage costs
Are your data storage costs rising while the amount of data that you use stays the same? This can often be an indicator of data quality issues. If you are storing data without using it, it could be because the data has quality problems. If, conversely, your storage costs decline while your data operations stay the same or grow, you’re likely improving on the data quality front.
7.Data time-to-value
How quickly is your team able to turn data into business value? The answer can reveal a lot about the overall quality of your data. If data transformations generate a lot of errors, or if human intervention and manual cleanup are required, that can be a sign that your data quality is not what it should be. While many different factors affect data time-to-value, data quality problems are one of the most common points of friction.
The metrics that make the most sense for you to measure will depend upon the specific needs of your organization, of course. These are just guidelines for measuring data quality. Precisely offers data quality solutions that support data governance and compliance initiatives and produce a complete, single, and trusted view of your data.