U.S. Student Aid Data Warehouse Evaluation.docx U.S. Student Aid Data Warehouse Evaluation DAT/390 U.S. Student Aid Data Warehouse Evaluation The goal here is to provide overall quality data that is consistent. So
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U.S. Student Aid Data Warehouse Evaluation.docx U.S. Student Aid Data Warehouse Evaluation DAT/390 U.S. Student Aid Data Warehouse Evaluation The goal here is to provide overall quality data that is consistent. So our data evaluation strategy is to focus on information that is most relevant and valuable. We then tailor our focus there and then begin by utilizing data cleaning and what this does is helps us to ensure the data is correct and no errors are identified. The plan is also to continue to monitor for errors to avoid any potential inconsistencies that may lead us to query incorrect data. We also plan to standardize our process which allows us to solidify a good entry point and avoid duplicate data. Once we have thoroughly cleaned our data warehouse our focus is to continue to provide accurate data. One way to accomplish this is by investing in tools and software™s in which we can clean our data warehouse in real-time. This is crucial because the ability to recognize any flaws in real-time allows us to come up with more immediate solutions which in turn ensures that we can provide more quality data. Another data evaluation strategy would be to search for duplicates which save a tremendous amount of time when you have to analyze the data. It also keeps data more organized which is helpful when you are required to query the warehouse for results. The use of automation tools is also an effective strategy because these software™s can create scripts and rules that can test the database. These automated tests can he. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .. .. . . . . . . . . . . . . . . . . . .. . . . .
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