Q. What is the issue with inconsistent values for policy types in different legacy systems?

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Q. What problem is associated with multipurpose fields that can be used differently by various departments?

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Q. What integration issues arise in an auction company when the same customer is both a buyer and a seller?

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Q. Why do integration problems often arise in legacy systems?

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Q. What is the primary goal of data cleansing for a data warehouse?

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Q. Where does data pollution typically occur in the context of a data warehouse?

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Q. Why is it challenging to address data pollution problems from old operational systems?

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Q. How does data aging affect data quality in source systems?

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Q. What is a potential issue related to heterogeneous system integration in the context of data quality?

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Q. How does a good database design contribute to data quality?

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Q. What is the role of entity integrity and referential integrity rules in preventing data pollution?

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Q. What can be a result of incomplete information at the time of data entry for an entity?

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Q. How does the entry of generic values into mandatory fields affect data quality?

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Q. What role does data verification play in preventing data corruption?

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Q. In a data warehouse, why is deduplication of names and addresses essential?

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Q. What are some inherent problems with entering names and addresses in the multiple field format?

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Q. What is a key challenge in recasting name and address data into the multiple field format?

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Q. How can costs related to bad decisions based on routine analysis be estimated?

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Q. Data cleansing tools primarily serve which two main functions for improving data quality?

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Q. What are some features of error discovery functions in data cleansing tools?

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