ETL Process Modeling In DWH Using Enhanced Quality Techniques

Large organizations have a lot of data. The data can be stored in many formats including data bases and unstructured file. This data bases must be collected, compared and made to work as a seamless whole but the different databases communicate well. A Data warehouse is an integrated collection of subject- oriented data in the support of decision making. The integration of data sources is achieved through the use of ETL (Extract, Transform and load) process. It is therefore extensively recognized that the appropriate design of ETL process are key factors in success of Data Warehouse Project. Data warehouse is used to provide effective result from multi- dimensional data analysis. Defective data lead to break downs in the supply chain, poor business decisions and inferior customer relationship management. So data quality is the degree to which data meet the specific needs of the customer. The accuracy and correctness of the results depend on the quality of the data. Improving the quality of data is important in data warehouse because it is used in the process decision support which requires accurate data. This project presents a data warehouse construction with quality decision support system to “Manage results for an organization using customer care center”. Organization used to maintain customer care to support and handle customer queries, to maintain details of customers, to provide frequent information regarding to their premiums, loans. This project determines a detail report such as how many customers are there in an Organization. How many customers paid full premiums, what are their dues, total amount paid? Which locations customer exists? How many customers are more valued customers? Total amount credited in organizations quarterly, what percent is gain/loss. In this paper we take source as flat files, relational tables and the data is extracted in staging area and then it is loaded in to a data warehouse. The different five themes frame our analysis is: Integration, Implementation, Intelligence, and Innovation and quality. The factors Definition conformance, completeness, validity, accuracy, non- duplication, accessibility applied on data warehouse dynamically to improve the performance of data warehouses.