Making the correct business decisions highly depends on the quality of your data. And, it impacts your ability to solve problems or reach goals. Sounds serious, huh? It is – therefore, you must measure the quality of data using the correct metrics.
Your data quality has a large impact on your strategic decision making. If you have low-quality data, you’re doomed to make poor decisions. Meanwhile, high-quality data helps you make the right decisions and ensure the success of your business.
Fun fact: companies that focus on improving data quality typically have a 15-20% increase in revenue. At this point, you’re probably sold and thinking: so, how can I tell whether my data quality is good enough? And, how should I measure it?
Measuring data quality is all about understanding what data quality attributes are and choosing the correct data quality metrics.
To determine the value of your data, here are a few data quality characteristics to check:
Quite a lot of metrics to keep track of, isn’t it? Luckily, you can use Whatagraph, a data reporting tool, to keep track of how multiple marketing channels are performing. Here’s how a report created with Whatagraph looks like:
Here are the top metrics companies use to measure the quality of their data:
This metric allows viewing how the number of errors in a single 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, it’s likely the quality of your data is growing.
Data transformation is the process of converting data from one format to another. Issues that arise during the process suggest there are problems with the quality of your data.
Knowing the exact number of failed data transformations helps you learn more about your overall data quality. Also, keep in mind that if the transformation process is taking too long, it’s likely that your data is flawed.
This metric shows the number of empty fields in your data set or displays data recorded in the wrong field. Once you’ve got the number, you should track how it changes in time.
Emails bouncing back to you suggest the low quality of your data. Typically, emails get sent to wrong addresses and are bounced back due to missing or outdated information.
Dark data is data acquired through various computer network operations but can’t be used in any manner to derive insights or for decision making. Having a large amount of it suggests the overall quality of your data is low.
A common sign of data quality issues is when the amount of data you use remains the same, while the cost of your data storage increases, or vice versa.
Finally, the amount of time spent deriving results from a data set can help you with identifying your data quality.
You can measure the quality of your data by determining how much time your team spends deriving results from an existing data set. Although many factors impact this data quality metric, data quality issues often slow down the process of generating important information.