There are many definitions of data quality but data is generally considered high quality if it is “fit” for the intended use in operations, decision making, forecasting and planning. Alternatively, data is deemed of high quality if it correctly represents the accurate information to which it refers.
This frees you up to pursue your core revenue generating business activities.
People’s views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. This is when data cleansing may be required in order to ensure data quality.
During the process we provide data analysis and once complete, we formulate a complete analysis of the project.
Data cleansing is the process of detecting and correcting duplicated or inaccurate records from a database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, merging or deleting the dirty data.
For example, a company that would like to transmit and receive purchasing and invoices with other companies might use data mapping to create data maps from a company’s data to standardized messages for items such as purchase orders and invoices.
The purpose of these statistics is usually to find out whether existing data can be easily used for other purposes.
Data mapping is used as a first step for a wide variety of data integration including the data transformation between:
