What metrics indicate data quality?
What metrics indicate data quality?
Blog Article
Data quality is a matter of concern for organizations in this data-driven world to make competent decisions. Poor quality of data leads to drawing incorrect conclusions, missing opportunities, and waste of priceless resources. But how do we measure the quality of data? A few important key metrics will enable organizations to make necessary evaluations and improvements in their data quality.
Key Metrics to Measure Data Quality
1. Accuracy
Precision, also called accuracy, refers to the extent that actual data values reflect true values or an authoritative source. For instance, if an address is on file for a customer that includes a bad address, deliveries will not be properly fulfilled. Organisations should check their information against authoritative sources on a regular basis to ensure precision .
2. Completeness
Completeness would imply whether the data that should be there actually exists. Incomplete data may lead to biased analyses B2B Database and poor decisions. For instance, a lack of complete information about a customer's contact in a database may limit marketing. An organization should set a standard on what fields are required, then regularly audit their data to ensure completeness.
3. Consistency
Consistency refers to the fact that information is always the same. An inconsistent state for more than one similar information about any source, being kept differently in different departments, should not exist. For example, suppose there is a customer's phone number with dashes in one system and without dashes in another; this can confuse people. For consistency, periodic synchronization of data should be done along with proper data governance policies.
Additional Metrics
4. Timeliness
Timeliness: The data should be updated and available when needed. Dated information could lead to missed opportunities for which speed is of the essence, as maybe required by business. For instance, a business decision that must be taken today may need today's sales number. Organizations have to have a mechanism to update and refresh their data periodically.
5. Validity
Validity ensures that the data falls within defined parameters or format. For instance, a date of birth should not include dates in the future. An organization can ensure validity using validation rules at the time the data is input.