There are several dimensions of the quality of the data used. This list continues to grow as the data grows in size and diversity. However, some core dimensions remain constant in the source data.
- measures the extent to which the accuracy of the data values are correct – and very important for the ability to draw accurate conclusions from your data.
- means the completeness of all data elements that have real value.
- Consistency focuses on uniform data elements in a different data sample, with values derived from the known reference data domain.
- address the fact that the age of the data should be fresh and current, with values up to date across the board.
- The uniqueness shows that each record or element represents a once in a data set, helping avoid duplicate.
The main features of the data quality management
A good data quality program on a system with a variety of features that help increase the confidence of your data. If you're looking for best data quality management services then you can browse various online sources.
First, data cleaning duplicate records help correct, non-standard data representation and data types that are not known.
Cleansing enforces the rules standardize the data needed to provide insight from your data set. It also establishes a hierarchy of data and reference data definitions of data to adjust according to your unique needs.
Profile data, action monitoring and clean up the data, the validation of the data used standard statistical measures, relationships unravel and verification of data on a suitable description. Profile data will shape trends steps to help you find, understand and potentially expose inconsistencies in your data.