Completeness, accuracy, uniqueness, timeliness, and relevance are 5 key metrics for measuring data quality. Completeness ensures all necessary data fields are filled in and there are no missing records or values. Accuracy checks that data values correctly represent real-world information. Uniqueness verifies each data point is only recorded once to avoid duplicates. Timeliness evaluates whether data is current and available when needed. Relevance determines if the data is pertinent to organizational needs and objectives.

1. 5 KEY DATA QUALITY
METRICS
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COMPLETENESS
ACCURACY
UNIQUENESS
TIMELINESS
RELEVANCE
1.
2.
3.
4.
5.
Completeness means you are sure that there are no missing
records and that no records have missing data parts.
All available data entry fields are completed and this is a must.
Completeness indicates if there is enough information to come up
with conclusions.
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WHAT IS DATA QUALITY?
Data quality is a kind of measurement of the adequacy and
usefulness of a given data sets from different perspectives. In the
business world, data need to be high quality in order to be used as a
basis for making business decisions.
Do your data accurately represent the “real world” values?
Accuracy applies to whether the data values stored for a person or
object are the correct values.
To be accurate, data values must be the right value and must be
represented in an unambiguous shape.
Ensure that there no data duplicates reported.
Uniqueness is a dimension that means you are sure no thing will be
recorded more than once. Of course, you need to define “the thing”
first.
Uniqueness minimizes the risk of examining outdated information.
Timeliness is the degree to which data present the reality from a given
point in time. This metric shows whether the information is available
when it is needed.
There are two core aspects of timeliness:
- Frequency
- Currency
Is all the data relevant to your organization needs and expectation? This
question is what the relevance metric is all about.
Here are 2 important points of the Relevance dimension:
- Do the data meet the fundamental needs for which they were collected?
- Can the data be used for additional purposes (e.g., a market sizing
process)?