Data Governance: Understanding Data Quality and Normalization from Pluralsight

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.

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.

What's inside

Syllabus

Course Overview

Evaluate Data Quality Best Practices

Evaluate Normalization Best Practices

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops essential skills for data management, including identifying and resolving data quality issues, organizing data effectively, and applying data normalization techniques

Taught by Naveed Tauhid, recognized for their expertise in data quality and normalization

Relevant to both industry professionals and students pursuing careers in data management and analytics

Covers best practices for ensuring data quality and normalization outlined by industry experts

Provides a strong foundation for beginners seeking to build a solid understanding of data quality and normalization

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Governance: Understanding Data Quality and Normalization with these activities:

Organize and Review Course Materials

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Regularly organize and review your notes, assignments, and other course materials to enhance retention and understanding of key concepts.

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Gather all of your course materials in one place.
Organize your materials by topic or module.
Review your materials regularly.

Review Project Management Fundamentals

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Review key concepts and terminology related to data quality and normalization, such as data quality dimensions, roles and responsibilities, and best practices.

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Go over your notes from a prior data management course.
Read through articles and blog posts on data quality and normalization.

Participate in a Data Quality and Normalization Hackathon

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Challenge yourself and apply your skills by participating in a hackathon focused on data quality and normalization, where you can collaborate with others to solve real-world data challenges.

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Find a data quality and normalization hackathon to participate in.
Form a team or work individually.
Develop a solution to the hackathon challenge.
Present your solution to the hackathon judges.

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Join a Study Group for Data Normalization

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Enhance your learning by joining a study group with peers to discuss concepts, share insights, and work on exercises related to data normalization.

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Find a study group for data normalization.
Attend the study group meetings regularly.
Participate actively in discussions and exercises.

Attend a Workshop on Advanced Data Normalization Techniques

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Expand your knowledge of data normalization best practices by attending an in-person or online workshop led by industry experts.

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Find a workshop focusing on advanced data normalization techniques.
Register for the workshop.
Attend the workshop and participate actively.
Follow up with the workshop organizers or speakers if you have any questions.

Create a Data Quality Plan for a Real-World Dataset

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Apply data quality best practices to identify and resolve data quality issues in a real-world dataset, and create a plan to ensure data quality in the future.

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Choose a dataset that you are interested in.
Identify the data quality issues in the dataset.
Develop a plan to resolve the data quality issues.
Implement your plan and evaluate the results.

Contribute to an Open-Source Data Quality or Normalization Project

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Gain practical experience and contribute to the community by working on an open-source project related to data quality or normalization, allowing you to learn from and collaborate with others in the field.

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Find an open-source data quality or normalization project to contribute to.
Review the project's documentation and codebase.
Identify an area where you can contribute.
Make a pull request to the project with your contribution.

Write a White Paper on Data Quality Best Practices

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Solidify your understanding of data quality best practices by writing a comprehensive white paper summarizing key concepts and practical implementation techniques.

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Research data quality best practices.
Write an outline for your white paper.
Write the first draft of your white paper.
Get feedback on your white paper from peers or experts.
Revise and finalize your white paper.

Develop a Data Governance Framework for Your Organization

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Demonstrate your ability to apply data governance principles by creating a comprehensive framework that defines roles, responsibilities, and best practices for data quality and normalization within your organization.

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Research data governance best practices.
Create an outline for your data governance framework.
Develop the first draft of your data governance framework.
Get feedback on your data governance framework from stakeholders.
Revise and finalize your data governance framework.
Implement your data governance framework within your organization.

Career center

Learners who complete Data Governance: Understanding Data Quality and Normalization will develop knowledge and skills that may be useful to these careers:

Data Governance Analyst

A Data Governance Analyst helps organizations improve data quality and use data more effectively. This course can teach you the best practices for data quality and normalization, which are essential skills for this role. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Data Governance Analyst

Data Architect

A Data Architect designs and builds data architectures that support the organization's business needs. This course can help you build a foundation in data quality and normalization, which are important concepts for this role. You'll learn how to create data architectures that ensure data quality and integrity, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Data Architect

Data Engineer

A Data Engineer builds and maintains data pipelines that move data between different systems. This course can teach you the best practices for data quality and normalization, which are essential skills for this role. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Data Engineer

Data Scientist

A Data Scientist uses data to solve business problems. This course can teach you the best practices for data quality and normalization, which are essential skills for this role. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Data Scientist

Database Administrator

A Database Administrator manages and maintains databases. This course can teach you the best practices for data quality and normalization, which are essential skills for this role. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Database Administrator

Information Security Analyst

An Information Security Analyst protects an organization's data and information systems from unauthorized access and use. This course can teach you the best practices for data quality and normalization, which are important concepts for this role. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Information Security Analyst

IT Auditor

An IT Auditor examines an organization's information systems and data to ensure they are compliant with regulations and policies. This course can teach you the best practices for data quality and normalization, which are important concepts for this role. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for IT Auditor

Project Manager

A Project Manager plans and executes projects to achieve specific goals. This course may be useful for this role, as it can teach you the best practices for data quality and normalization, which are important concepts for managing data-related projects. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Project Manager

Quality Assurance Analyst

A Quality Assurance Analyst tests software and systems to ensure they meet quality standards. This course may be useful for this role, as it can teach you the best practices for data quality and normalization, which are important concepts for testing data-related software and systems. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Quality Assurance Analyst

Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course may be useful for this role, as it can teach you the best practices for data quality and normalization, which are important concepts for developing data-related software applications. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Software Engineer

Systems Analyst

A Systems Analyst analyzes and designs business systems. This course may be useful for this role, as it can teach you the best practices for data quality and normalization, which are important concepts for analyzing and designing data-related business systems. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Systems Analyst

Technical Writer

A Technical Writer creates documentation for software and systems. This course may be useful for this role, as it can teach you the best practices for data quality and normalization, which are important concepts for documenting data-related software and systems. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Technical Writer

Web Developer

A Web Developer designs and develops websites. This course may be useful for this role, as it can teach you the best practices for data quality and normalization, which are important concepts for developing data-driven websites. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Web Developer

Business Analyst

A Business Analyst analyzes business needs and develops solutions to meet those needs. This course may be useful for this role, as it can teach you the best practices for data quality and normalization, which are important concepts for analyzing data-related business needs and developing data-driven solutions. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Business Analyst

Data Analyst

A Data Analyst collects, analyzes, and interprets data to help organizations make better decisions. This course may be useful for this role, as it can teach you the best practices for data quality and normalization, which are important concepts for collecting, analyzing, and interpreting data. You'll learn how to identify and resolve data quality issues, and how to apply data normalization best practices to your work.

See salaries and explore the career path for Data Analyst