A framework to implement data cleaning in enterprise data warehouse for robust data quality
Kamran Ali, Mubeen Ahmed Warraich
Abstract
Kamran Ali, Mubeen Ahmed Warraich
Abstract
every day, every hour, every minute, every second trillion of bytes of data is being generated by enterprises especially in telecom sector. To achieve level best decisions for business profits, access to that data in a well-situated and interactive way is always a dream of business executives and managers. Data warehouse is the only viable solution that can bring that dream into a reality. The enhancement of future endeavors to make decisions depends on the availability of correct information that based on quality of data underlying. The quality data can only be produced by cleaning data prior to loading into data warehouse. So correctness of data is essential for well-informed and reliable decision making. The framework proposed in this paper implements robust data quality to ensure consistent and correct loading of data into data warehouse that necessary to disciplined, accurate and reliable data analysis, data mining and knowledge discovery.
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every day, every hour, every minute, every second trillion of bytes of data is being generated by enterprises especially in telecom sector. To achieve level best decisions for business profits, access to that data in a well-situated and interactive way is always a dream of business executives and managers. Data warehouse is the only viable solution that can bring that dream into a reality. The enhancement of future endeavors to make decisions depends on the availability of correct information that based on quality of data underlying. The quality data can only be produced by cleaning data prior to loading into data warehouse. So correctness of data is essential for well-informed and reliable decision making. The framework proposed in this paper implements robust data quality to ensure consistent and correct loading of data into data warehouse that necessary to disciplined, accurate and reliable data analysis, data mining and knowledge discovery.
Key concepts: Data warehouse, Computer science, Data quality, Database, Data governance, Correctness, Byte, Quality (philosophy)