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 a 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:
- Accuracy
- Completeness
- Consistency
- Freshness
- Validity
- Uniqueness
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 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.
Why Does Data Quality Matter?
Data quality matters because of several reasons:
- Organizations gain a competitive edge by using high-quality data, which helps them stay current with market trends. For example, they're able to customize products relevant to customer needs and this gives them an edge against competitors.
- Having good quality data allows you to adhere to industry standards, thus avoiding non-compliance and legal issues.
- When data is accurate and relevant, it allows you to make informed decisions.
- High-quality data help reduce errors which prevent unnecessary use of resources.
- Good quality data helps you win and retain customers because you can offer personalized services and products. As a result, you improve customer satisfaction.
- Poor quality data can affect your finances. For example, incorrect financial reports or missed opportunities can lead to revenue loss.
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. It’s not enough just to understand the importance of your data’s quality. You need 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 the data conveys correct 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.
Completeness
Completeness measures whether 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 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 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 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. Some templates 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 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 particularly noteworthy metric is completeness. Completeness isn’t as obvious of a metric as something like accuracy. However, data can be completely true but still misleading 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.
Data Quality Metrics to Measure
Below is a list of key metrics you can use to measure data quality:
Error Ratio
This checks the number of errors relative to your dataset. If the error grows as your data grows, then it could signal data quality issues. Common errors include incomplete or duplicate entries.
Null Rate
Here, you're checking the number of null values relative to the dataset and calculating the percentage. For example, if you have 100 null entries in a dataset with 10000 entries, then you have a 1% null rate. This is important because you can set thresholds and ensure that you have sufficient data. It can also help to identify potential gaps in data collection.
Data Storage Cost
This metric calculates the cost per gigabyte of the stored data. The cost affects the retention and deletion of data. High cost may push you to optimize your data quality by removing redundancy or you may accidentaly delete useful data and affect the completeness. Thus, it is prudent to establish a balance between quality and cost.
Data Downtime
Downtime captures the total time data is inaccessible or unavailable. For example, if data is inaccessible for one hour, then the downtime is one hour. Data unavailability can lead to economic losses and customer dissatisfaction.
Best Practices for High Quality Data
Here are a few strategies you can use to maintain high quality data:
- Have data validation rules to ensure that your data meets all the requirements.
- Regularly audit and update your data to counter any recurrent issues and to ensure it keeps up with the changing market.
- Profile your data to identify anomalies and inconsistencies. That way, you're able to identify and solve issues like missing values, duplicates or invalid formats.
- Use data quality management and monitoring tools to track and visualize data quality trends.
- Train and educate your staff on proper data entry and manage and on the importance of data quality.
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