Data pollution

1. Give examples of four types of data quality problems.

2. What is the problem related to the reuse of primary keys? When does it usually occur?

3. Describe the functions of data correction in data cleansing tools.

1. Name five common sources of data pollution. Give an example for each type of source.

2. List six types of error discovery features found in data cleansing tools.

3. What is the “clean as you go” method? Is this a good approach for the data warehouse environment?

Assume that you are the data quality expert on the data warehouse project team for a large financial institution with many legacy systems dating back to the 1970s. Review the types of data quality problems you are likely to have and make suggestions on how to deal with those.


1. How does the data warehouse differ from an operational system in usage and value?

2. Explain briefly how the information from the data warehouse promotes customer relationship management.

As a data warehouse consultant, a large bank with statewide branches has hired you to help the company set up a data quality initiative. List your major considerations. Produce an outline for a document describing the initiative, the policies, and the procedures.

1. Discuss the common sources of data pollution and provide examples.

2. You are responsible for the selection of data cleansing tools for your data warehouse environment. How will you define the criteria for selection? Prepare a checklist for evaluation and selection of these tools.


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