Data Quality Dimensions Overview
There’s simply no way around it: data is a critical part of nearly every kind of enterprise. Without data to analyze, informed decision making becomes practically impossible.
But, not all data is equal. The quality of the data an organization collects can have a huge impact on how useful it is (or isn’t) for driving business decisions. Being aware of the most important data quality characteristics and utilizing the right data quality tools are a couple of the ways organizations can avoid being misled by poor-quality data.
In a moment, we’ll discuss data quality dimensions and how they relate to data quality. First, however, let’s take a step back and cover some of the basics. At this point, one might wonder “what is data quality and why is it important?” If you’re wondering “what is data quality,” you are probably not alone — it’s a complex topic, after all.
Data quality is essentially a measurement of how useful data is. The definition of high-quality data varies slightly depending on the scenario, but it’s most commonly defined as data that accurately depicts what’s happening in the real world and is a good match for the purpose for which it’s being used.
Most often, data quality is measured by considering factors like the information’s accuracy, its timeliness, and its relevance to its intended use within the enterprise. Tracking data quality metrics such as these can help an organization determine whether or not the data it relies on is telling a reliable story.
Developing an effective data quality framework can make it easier to identify anomalies or other red flags that might indicate the data is of poor quality. This could include multiple checkpoints throughout the data pipeline to provide plenty of chances to catch any issues. Moreover, it could help in adequate data quality management.
What are Data Quality Dimensions?
Now that we’ve established a general definition of data quality, let’s take a closer look at data quality dimensions, what they are exactly, and why they’re important. For even more detailed information about this topic, consider reviewing a data quality dimensions PDF guide. We have numerous resources that can help break down data quality dimensions and make them easier to understand.
Data quality dimensions are a set of parameters that indicate the quality of data. If data remains within these parameters by meeting all the standards outlined by the dimensions, it can be considered high-quality data. If the data falls outside of the parameters, it is not high-quality data.
There are 6 dimensions of data quality that are essential considerations when determining data quality. These 6 dimensions are as follows:
Using these 6 dimensions, an enterprise can not only determine whether the data it’s using is of a high enough quality to be considered useful — it can also identify exactly where the issue lies so it can be corrected. Continually validating data quality via multiple checkpoints throughout the data pipeline is one of the best ways for an organization to give itself plenty of warning when data fails to meet the standards of the 6 dimensions of data quality.
The 6 Dimensions of Data Quality
Now that we've answered the question “what are the 6 dimensions of data quality,” we can more closely examine the dimensions of data quality with examples.
First and foremost, quality data is accurate. At the most basic level, an organization’s data is next to useless if the information is simply incorrect. Inaccuracies in the data are not always immediately obvious, so it’s extremely helpful to have some kind of structured method of checking data for accuracy at multiple points along the data pipeline.
The second of the data quality dimensions is completeness. Data is complete if it presents a complete picture of whatever information is being gathered. High-quality data is not missing essential details that might change the way the data is interpreted.
If the data is not consistent with other information an enterprise has gathered, it is most likely not high-quality data. Relying on outlying data is usually a mistake because anomalies in the data can’t be relied upon to tell an accurate story.
The freshness of the data also has an impact on its quality. Outdated information is not reliable. Unless the data is up to date and reflects the most current state of affairs, it should not be considered quality data.
Data validity refers to how usable the data actually is. If data is formatted correctly and adheres to the applicable business rules, an enterprise can consider it to be valid data.
The uniqueness of data also plays a part in determining its level of quality. When verifying the quality of data, an organization should always pose the question: does this information appear elsewhere in the database, or is this its only instance?
For almost every enterprise, verifying the quality of data is of the utmost importance. That’s why it is so essential to implement the necessary types of data quality checks throughout the data pipeline. By monitoring the pipeline closely, an organization can greatly improve the value of its data.
Data Quality Dimensions Examples
Accuracy, completeness, consistency, freshness, validity, and uniqueness are 6 of the most common data quality dimensions examples. There are other dimensions that could be relevant depending on the intended use for the data, but these 6 are some of the most important in general. If an enterprise is using its data to inform a specific area of business that requires the data to be held to different standards, the data quality dimensions definition might change slightly.
For instance, another data quality dimension that is sometimes pertinent is timeliness. Data quality timeliness examples include data that arrives late or drifts. By using multiple types of data quality checks, an enterprise can increase the odds of successfully detecting data that is not timely (and therefore not quality data). Understanding the data observability criteria necessary for modern data environments is critical. Data observability can help organizations to manage data quality adequately.
Timeliness is just one example of a data quality dimension enterprises could use. Each different organization should consider its specific data analysis needs and choose to utilize the dimensions that make the most sense according to the scenario.
Data Quality Dimensions
The 6 most widely agreed upon data quality dimensions were established by DAMA (Data Management Association International). The reasons for establishing a standard 6 data quality dimensions was to eliminate confusion stemming from the fact that there was previously no universally accepted definition of data quality dimensions.
The data quality dimensions DAMA outlines, such as data quality accuracy, are applicable to the majority of data management situations. However, other dimensions that have not been officially established by DAMA, such as data quality timeliness, can also prove useful for some purposes.
Standard Data Quality Dimensions
Even though there are many different ways to measure the quality of data, having a set of 6 data quality standards that are universally acknowledged makes it much easier to verify data quality accuracy, completeness, consistency, and more. Hopefully, our explanation of these 6 data quality dimensions with examples can make it easier to verify data quality.
Here are some final tips for effectively tracking and qualifying data:
- Keep plenty of checkpoints in place throughout the data pipeline. This allows for ongoing data validation that can alert organizations to any irregularities in the data with plenty of warning.
- If an issue with the data is detected, the failure should be immediately reported, along with the reason the problem occurred. If possible, a solution to the problem should also be included in the report.
- Include all necessary contextual information along with the report of the failure in the data. Unless it’s understood in context, the problem will likely be difficult to address correctly.
These are some of the best tactics an enterprise can implement to decrease the likelihood of being misinformed by data that falls below the acceptable data quality standards.