Dimensions of Data Quality
Data quality is an assessment or a perception of data’s fitness to fulfill its purpose. Simply put, data is said to be high quality if it satisfies the requirements of its intended purpose.
The quality of data can be measured by six dimensions: Accuracy: Data accuracy is defined as the degree with which data correctly reflects the event in question or the ‘real world’ object.
Consistency: Data is said to be consistent if all the systems across the enterprise reflect the same information.
Completeness: Data completeness is the expected comprehensiveness. Data is considered complete if it meets the expectations.
Uniqueness: Each data record should be unique, otherwise the risk of accessing outdated information increases.
Timeliness: It references whether the data is available up to date at the time someone is attempting to use.
Validity: Data is valid if it conforms to type, format and range of its definition.
Bad data is inaccurate, unreliable, unsecured, static, uncontrolled, noncompliant, and dormant. Certainly, no one wants to go to the wall in such fashion, and businesses will work their heads off to improve the quality of their data as they seek to make good in a rather competitive digital age.
Importance of a digital solution to manage data quality
A Good Data Quality tool has to be implemented to ensure the quality of the data. DQ tools remove the errors, redundancies and other issues effectively. They tackle numerous tasks such as data mapping, ingestion, integration and metadata discovery. So identifying the right data quality management solution is vital for every business.
DQLabs, an AI augmented data quality platform that helps you to collect accurate and relevant data from various sources. It empowers organisations to detect and solve data quality issues without much human efforts.
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