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DATA WAREHOUSING FUNDAMENTALS: A Comprehensive Guide for IT Professionals by Paulraj Ponniah

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13.6. CHAPTER SUMMARY

  • Data quality is critical because it boosts confidence, enables better customer service, enhances strategic decision making, and reduces risks from disastrous decisions.

  • Data quality dimensions include accuracy, domain integrity, consistency, completeness, structural definiteness, clarity, and many more.

  • Data quality problems run the gamut of dummy values, missing values, cryptic values, contradicting values, business rule violations, inconsistent values, and so on.

  • Data pollution results from many sources in a data warehouse and this variety of pollution sources intensifies the challenges faced when attempting to clean up the data.

  • Poor data quality of names and addresses presents serious concerns to organizations. This area is one of the greatest challenges.

  • Data cleansing tools contain useful error discovery and error correction features. Learn about them and make use of the tools applicable to your environment.

  • The DBMS itself can be used for data cleansing.

  • Set up a sound data quality initiative in your organization. Within the framework, make the data cleansing decisions.

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