Data Governance: Understanding Data Quality and Normalization
Data normalization best practices are essential for organizing and standardizing data effectively. In this course, you will gain an understanding of data quality dimensions, roles and responsibilities, and best practices for ensuring data quality.
What you'll learn
Enterprises struggle with data quality issues and seek data normalization for more effective data management. In this course, Data Governance: Understanding Data Quality and Normalization, you’ll gain the ability to understand and apply best practices for ensuring data quality and normalization.
First, you’ll explore data quality dimensions, including key concepts, principles, and terminology related to data quality. Then, you'll learn about the impact of poor data quality and the roles and responsibilities of data governance.
Next, you’ll explore data normalization importance and best practices, the process of standardizing, and how to organize data effectively.
Finally, you'll briefly see the implementation of normalization techniques.
When you’re finished with this course, you’ll have the skills and knowledge of data quality and normalization best practices needed to identify, monitor, and resolve data quality issues, as well as to apply data normalization best practices to your work.
Table of contents
- Module Overview 1m
- Standardizing and Organizing Data Effectively 4m
- Benefits of Data Standardization and Organization 1m
- Importance of Data Normalization 3m
- Normalization Forms 3m
- Data Normalization for Databases and It's Impact on Companies 3m
- The Application of Normalization Rules 2m
- Validating and Refining Normalized Data 3m
- Data Refining 4m
- Documenting the Normalized Data Structure 5m
- Demo: Implementation of Normalization Techniques 4m
- Module Summary 1m