Data Quality Fundamentals

Inf Sid, ETL/Data Architect

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35 Lessons (2h 57m)
    • 1. What is Data Quality?

      5:55
    • 2. Examples of Data Quality

      7:53
    • 3. Can we achieve 100 % Data Quality?

      8:21
    • 4. What can be done to achieve 100% Data Quality?

      7:42
    • 5. How can we measure Data Quality?

      3:24
    • 6. What are Data Quality Dimensions?

      1:39
    • 7. Consistency Data Quality Dimension

      2:26
    • 8. Completeness Data Quality Dimension

      3:11
    • 9. Timeliness Data Quality Dimension

      1:52
    • 10. Uniqueness Data Quality Dimension

      13:03
    • 11. Validity Data Quality Dimension

      1:31
    • 12. Accuracy Data Quality Dimension

      1:18
    • 13. Examples of Data Quality Dimensions

      2:16
    • 14. Data Quality Vs Data Governance

      3:41
    • 15. Introduction to the End to End Data Life Cycle with a case study

      4:50
    • 16. Data Maintenance

      1:53
    • 17. Data Derivation

      2:30
    • 18. Data Usage

      2:17
    • 19. Data Publication

      2:18
    • 20. Data Archival

      2:04
    • 21. Data Purging

      1:53
    • 22. Data Quality Life Cycle

      10:56
    • 23. What is Data Profiling?

      4:26
    • 24. Commonly used data types during Data Profiling

      7:57
    • 25. Data Profiling Vs Data Mining

      1:38
    • 26. What are the different types of Data Profiling?

      6:04
    • 27. Business Expectations on Data Quality

      11:51
    • 28. Impacts and Costs of Low Data Quality - Part 1

      4:05
    • 29. Impacts and Costs of Low Data Quality - Part 2

      7:07
    • 30. How to correct the existing errors in the Data Warehouse?

      11:06
    • 31. How does the Enhance, Transform and Calculate phase or the ETL phase help?

      7:56
    • 32. Data Standardization

      6:51
    • 33. Complete and Corrected Data

      5:56
    • 34. Match and Consolidate the Data

      3:35
    • 35. Different Data Quality Roles in an Enterprise

      5:30

About This Class

Data quality is not necessarily data that is devoid of errors. Incorrect data is only one part of the data quality equation. Managing data quality is a never ending process. Even if a company gets all the pieces in place to handle today’s data quality problems, there will be new and different challenges tomorrow. That’s because business processes, customer expectations, source systems, and business rules all change continuously. To ensure high quality data, companies need to gain broad commitment to data quality management principles and develop processes and programs that reduce data defects over time.

Much like any other important endeavor, success in data quality depends on having the right people in the right jobs. This course helps you understand key concepts, principles and terminology related to data quality and other areas in data management.