4 Ways to Measure Data Quality
Data Quality Measurement – 4 Metrics to Consider
There are lots of good strategies that you can use to improve the quality of your data and build data best practices into your company’s DNA. Although the
technical dimensions of data quality control are usually addressed by engineers, there should be a plan for enforcing best practices related to data quality
throughout the organization. After all, virtually every employee comes into contact with data in one form or another these days. Data quality measurement is everyone’s responsibility.
Assessing data quality on an ongoing basis is necessary to know how well the organization is doing at maximizing data quality. Otherwise, you’ll be investing
time and money in a data quality strategy that may or may not be paying off.
To measure data quality – and track the effectiveness of data quality improvement efforts – you need, well, data. What does data quality assessment look like in practice? There are a variety of data and metrics that organizations can use to measure data quality. We’ll review of few of them here.
Database entry problems
In cases where you are working with structure datasets, you can track the number of database entry problems that exist within the datasets. The fewer data quality problems you have to start with, the faster you can turn your data into value. A few of these measurements include the ratio of data to errors and the number of empty values.
The ratio of data to errors
This is the most obvious type of data quality metric. It allows you to track how the number of known errors – such as missing, incomplete or redundant entries – within a data set corresponds to the 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.