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Conclusion

Data quality tools are available to enhance the quality of the data at several stages in the process of developing a data warehouse. Cleansing tools can be useful in automating many of the activities that are involved in cleansing the data- parsing, standardizing, correction, matching, transformation and householding. Many of the tools specialize in auditing the data, detecting patterns in the data, and comparing the data to business rules. Data extraction and loading tools are available to translate the data from one platform to another, and populate the data warehouse.

In the initial stages of data warehouse development the sources of the data should be examined. Questions should be asked of the data source that would enable the developer of the warehouse to know what problems exist with the data. Once these problems have been isolated, the warehouse builder could determine which features of the data quality tools address the specific needs of the data sources to be used. The matrix that has been developed will guide the warehouse developer towards the tool that would be appropriate for the data sources that will eventually populate the warehouse. Once the proper tools have been identified, the second matrix could be used compare price, platform, and special features of each tool. The two matrices work together to enable the data warehouse developer to efficiently choose the software tool suitable to the data sources that are to be used in the warehouse.