A few of the evaluation principles requirements for data quality tools
and the holes present while implementing these tools usually lead to
failure of quality projects and data cleansing. However, while
implementing data quality improvement in an organization, it is important
to use the necessary tools:
Implementing data quality improvement with the following tools
•
Removing, analyzing and connecting data: The first and the foremost
step for a good data stewardis to connect all the data and load the data
into the application. There are various ways to load the data into the
application and viewing the data can help build connectivity for the
data.
• Data profiling: after the data has been loaded in the
application, the data quality management performs the step of data
profiling in which a statistics of the data is run. These statistics
include min/max, number of the missing attributes and average. This
helps to determine the relationship between all the data. Data profiling
also serves to build an accuracy of the columns such as email address,
phone numbers, etc of the various customers.
• Cleansing and
data governance: under data cleansing, the function of data
standardization, transform functions, elimination of spaces, calculation
of the values, identification of incorrect locations take place. Data
governance serves as a useful tool to identify all the missing
information and help adjust the information manually.
•
Duplication of records: this process involves cleaning up and merging
the various records that have been duplicated. This happens if the data
is entered poorly, applications are merged or for various other reasons.
After the duplication process is implemented, it is important to
clarify the attributes that should be kept in priority and the ones that
need a manual clean up.
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