What are Data Quality Metrics?
Nearly every organization relies on data to inform business decisions like cost optimization. Companies invest huge amounts of time and resources into gathering data as effectively as possible. But, all that data is useless unless it’s high quality data. That’s why data observability is so important. Data observability is the practice of assessing the quality of the data entering your data system. Without data observability platform, enterprises would have a difficult time differentiating accurate, useful data from low quality data that shouldn’t be trusted.
So how do you measure the quality of data? You need to use the right system of measurement to define your data’s quality. There are several data quality metrics that data observability experts commonly use to rate data quality. If you need a quick refresher on data quality metrics definition, just think of them as a set of characteristics that demonstrate the quality of data. There are six critical dimensions of data quality - these data quality examples make up the most important metrics for measuring data quality:
These are some of the most important data quality KPIs examples you’ll likely come across in the data observability field. You can also use the same kinds of measurements for data entry metrics.
If you’re someone who’s new to data observability and you’d like to get started with data quality work, you’ll need a solid understanding of metrics like these. You’ll also probably need to learn about data quality metrics as they relate to the most popular tools in the industry. For example, it will be helpful to be familiar with Salesforce data quality best practices.
Data Quality Metrics Scorecard
Society’s increased value on digital media, commerce, and interactions has led to the rise of many data pipeline tools and platforms tIt’s not enough just to understand the importance of your data’s quality. You actually need to be able to verify it in a quantifiable way, like with a data quality score calculation or a data governance scorecard. Data quality metrics are a useful way to calculate a meaningful data quality health score for your data.
Let’s break down a typical data quality metrics scorecard piece by piece:
- Accuracy - accuracy measures whether or not the data conveys true information. It demonstrates the data’s ability to reflect the real world correctly. Your data needs to be providing you with information that is accurate and factual.
- Completeness - completeness measures whether or not the data includes all the information needed for the data to serve its intended purpose. The data you use needs to be presented in its full context. If data isn’t complete, it isn’t quality data.
- Consistency - consistency measures whether or not the data differs depending on the source. If different sources are measuring the same thing but recording different data, that’s a strong indicator that you don’t have quality data.
- Uniformity - uniformity measures whether or not the data is all presented in the same format. The data needs to be presented using all the same units of measurement.
- Relevance - relevance measures how useful the data is for its intended purpose. Different tasks obviously require different types of data, so you should always be using the right data to inform the decisions at hand.
The metrics on this data quality scorecard example are some of the most common, but they aren’t necessarily the only metrics you could use.
Data Quality Metrics Dashboard
Keeping track of data quality metrics can be challenging. Without the right tools, it’s nearly impossible. Using an effective data quality metrics dashboard is essential for verifying data quality reliably.
The best data quality dashboard examples include intuitive features that make it simple to monitor the quality of your data. There are also templates that make it easy to add structure to your data quality assessment. You can use a data quality dashboard template to integrate data quality measurement with other tools, like a data quality dashboard excel spreadsheet.
The quality of your data has a major impact on your organization’s decision making capabilities. Investing in an excellent data quality metrics dashboard can help you be sure the data you’re using is quality data.
Data Quality Metrics Completeness
Every metric you use to measure the quality of your data is important in its own way. Data quality accuracy, completeness, consistency, uniformity, and relevance all play an integral part in telling you about the quality of your data and ensure appropriate data quality management. Each one should be carefully considered as part of your data quality measurement framework.
One metric that’s particularly noteworthy is completeness. Completeness isn’t as obvious of a metric as something like accuracy. However, data can be completely true but still mislead because there was a piece of the picture missing. When data is verifiably complete, you can trust there isn’t any additional nuance to the information that you aren’t aware of.
If you’re choosing the kinds of data quality measurement tools to use, remember to consider how well they can check your data for completeness. Of course, you shouldn't neglect other factors like accuracy, consistency, uniformity, or relevance. The next time someone asks you “what is data quality metrics?” you can tell them it hinges on metrics such as these.