Monday, 27 October 2014

The necessary tools needed to implement data quality management

data quality management

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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