7 Important Characteristics of Data Quality and Metrics to Track


Data has been important for making calculated decisions and keeping businesses and organizations in check. But, what about data quality? In this article, we discuss the 7 important characteristics of data quality and the metrics to track it. Stay with us as we share information on how you could challenge and test the characteristics of data quality.

7 Important Characteristics of Data Quality and Metrics to TrackWhat is the importance of data quality?

Data quality plays the most significant role in managing and conducting business efficiently. Decisions made based on hunches and experience can no longer hold value in the face of data. In Data Quality Service you will Read more to find out the 7 characteristics of data quality and the metrics to track it.

Here are the 7 characteristics of data quality and the metrics to track it.

For better understanding, we have chosen the 7 elements of data quality along with their characteristics and examples of metrics.

1. Consistency

The element of consistency removes room for contradictory data. Rules will have to be set around consistency metrics, which include range, variance, and standard deviation.

2. Accuracy

It is a necessity for DQ data to remain error-free and precise, which means it should be free of erroneous information, redundancy, and typing errors. Error ratio and deviation are two examples of accuracy metrics.

3. Completeness

The data should be complete without any missing data. To deliver cloud data quality tools, all data entries should be complete with no room for lapses. The completeness metric is defined as the percentage of complete data records.

4. Auditability

The ability to trace data and analyses the changes over time adds to the Data Quality dimensions of audibility of data. An example of audacity metrics is the percentage of the gaps in data sets, modified data, and untraceable and disconnected data.

5. Validity

Quality data in terms of validity indicates that all data is aligned with the existing formatting rules. An example of a validity metric is the percentage of data records in the required format.

6. Uniqueness

There will be no overlapping of data and it will be recorded only once. The same data may be used in multiple ways, but it will remain unique. Uniqueness metrics are defined by the percentage of repeated values.

7. Timeliness

For data to retain its quality, it should be recorded promptly to manage changes. Weekly over annually, tracking is the solution to timeliness. An example of timeliness metrics is time variance.


It is vital to bear in mind that data quality is an ongoing process and it cannot be achieved overnight. Instead, it has to possess a sense of continuity to achieve the desired results. If you are looking for the Best Data Quality Service.Uarrow for managing and getting your data correctly, get in touch with us to learn more.