ness structural definiteness, clarity, and many more. enhances strategic decision making, and reduces risks from disastrous decision Data quality is critical because it boosts confidence, enables better customer s Data quality problems run the gamut of dummy values, missing values, cryptic v Data quality dimensions include accuracy, domain integrity, consistency, Data pollution results from many sources in a data warehouse and this variety of s of pollution intensifies the challenges faced when attempting to clean up the d contradicting values, business rule violations, inconsistent values, and so on. . Poor data quality of names and addresses presents serious concerns to org . Data cleansing tools contain useful error discovery and error correction features La about them and make use of the tools applicable to your environment. This area is one of the greatest challenges. The DBMS itself can be used for data cleansing. . Master Data Management (MDM) initiatives provide a means for ensuring data qu . Set up a sound data quality initiative in your organization. Within that frame, make the data cleansing decisions. in the data warehouse. . REVIEW QUESTIONS 1. List five reasons why you think data quality is critical in a data warehouse. 2. Explain how data quality is much more than just data accuracy. Give an example 3. Briefly list three benefits of quality data in a data warehouse. 4. Give examples of four types of data quality problems. 5. What is the problem related to the reuse of primary keys? When does it usually occur? 6. Describe the functions of data correction in data cleansing tools. 7. Name five common sources of data pollution. Give an example for each type of source. 8. List six types of error discovery features found in data cleansing tools. 9. What is the "clean as you go" method? Is this a good approach for the data warehouse environment? 10. Name any three types of participants on the data quality team. What are their functions?