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Data Governance Analyst

Data Governance Analysts work to ensure that data is properly managed and controlled throughout an organization. They are responsible for developing and implementing data governance policies and procedures, as well as ensuring that data is properly classified and used. Data Governance Analysts also work to identify and mitigate data risks, and to ensure that data is used in a way that is consistent with the organization's business objectives.

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Data Governance Analysts work to ensure that data is properly managed and controlled throughout an organization. They are responsible for developing and implementing data governance policies and procedures, as well as ensuring that data is properly classified and used. Data Governance Analysts also work to identify and mitigate data risks, and to ensure that data is used in a way that is consistent with the organization's business objectives.

Responsibilities

Data Governance Analysts typically have the following responsibilities:

  • Develop and implement data governance policies and procedures
  • Classify and manage data assets
  • Identify and mitigate data risks
  • Ensure that data is used in a way that is consistent with the organization's business objectives
  • Work with other stakeholders to ensure that data is properly managed and controlled

Education and Training

Data Governance Analysts typically have a bachelor's or master's degree in computer science, information systems, or a related field. They also typically have several years of experience in data management or data analysis.

There are a number of online courses that can help you learn about data governance. These courses can teach you the basics of data governance, as well as the skills and knowledge you need to be successful in this career.

Skills and Knowledge

Data Governance Analysts should have the following skills and knowledge:

  • Strong understanding of data governance concepts and principles
  • Experience in data management and data analysis
  • Excellent communication and interpersonal skills
  • Ability to work independently and as part of a team
  • Problem-solving and critical thinking skills

Career Growth

Data Governance Analysts can advance their careers by taking on more responsibilities or by moving into management positions. They may also choose to specialize in a particular area of data governance, such as data security or data quality.

Transferable Skills

The skills and knowledge that Data Governance Analysts develop can be transferred to other careers in data management, data analysis, and information technology.

Day-to-Day

The day-to-day work of a Data Governance Analyst can vary depending on the organization and the specific projects they are working on. However, some common tasks include:

  • Developing and implementing data governance policies and procedures
  • Classifying and managing data assets
  • Identifying and mitigating data risks
  • Ensuring that data is used in a way that is consistent with the organization's business objectives
  • Working with other stakeholders to ensure that data is properly managed and controlled

Challenges

Data Governance Analysts can face a number of challenges in their work. These challenges can include:

  • The need to stay up-to-date on the latest data governance trends and technologies
  • The need to work with a variety of stakeholders who may have different perspectives on data governance
  • The need to balance the need for data security with the need for data access

Projects

Data Governance Analysts may work on a variety of projects, such as:

  • Developing and implementing a data governance framework
  • Classifying and managing data assets
  • Identifying and mitigating data risks
  • Ensuring that data is used in a way that is consistent with the organization's business objectives
  • Working with other stakeholders to ensure that data is properly managed and controlled

Personal Growth

Data Governance Analysts can experience a great deal of personal growth in their careers. This growth can include:

  • Developing a deep understanding of data governance concepts and principles
  • Gaining experience in data management and data analysis
  • Improving their communication and interpersonal skills
  • Developing problem-solving and critical thinking skills

Personality Traits and Personal Interests

Data Governance Analysts typically have the following personality traits and personal interests:

  • Analytical and detail-oriented
  • Strong communication and interpersonal skills
  • Ability to work independently and as part of a team
  • Interest in data management and data analysis
  • Desire to learn and stay up-to-date on the latest data governance trends and technologies

Self-Guided Projects

There are a number of self-guided projects that you can complete to better prepare yourself for a career as a Data Governance Analyst. These projects can include:

  • Developing a data governance framework
  • Classifying and managing data assets
  • Identifying and mitigating data risks
  • Ensuring that data is used in a way that is consistent with the organization's business objectives
  • Working with other stakeholders to ensure that data is properly managed and controlled

Online Courses

There are many online courses that can help you learn about data governance. These courses can teach you the basics of data governance, as well as the skills and knowledge you need to be successful in this career. Online courses can be a great way to learn about data governance at your own pace and on your own schedule.

Online courses can help you learn about data governance through lecture videos, projects, assignments, quizzes, exams, discussions, and interactive labs. These learning tools can help you develop the skills and knowledge you need to be successful in this career.

Are Online Courses Enough?

Online courses alone are not enough to follow a path to a career as a Data Governance Analyst. However, they can be a helpful learning tool to bolster your chances of success for entering this career.

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Salaries for Data Governance Analyst

City
Median
New York
$177,000
San Francisco
$153,000
Seattle
$165,000
See all salaries
City
Median
New York
$177,000
San Francisco
$153,000
Seattle
$165,000
Austin
$132,000
Toronto
$145,000
London
£82,000
Paris
€66,000
Berlin
€63,000
Tel Aviv
₪402,000
Singapore
S$95,000
Beijing
¥388,000
Shanghai
¥201,000
Shenzhen
¥309,000
Bengalaru
₹710,000
Delhi
₹560,000
Bars indicate relevance. All salaries presented are estimates. Completion of this course does not guarantee or imply job placement or career outcomes.

Path to Data Governance Analyst

Take the first step.
We've curated 24 courses to help you on your path to Data Governance Analyst. Use these to develop your skills, build background knowledge, and put what you learn to practice.
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Reading list

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Is not a beginner's guide; rather, it deals with deeper topics within data modeling and database design. It covers advanced topics such as dimensional modeling, data warehousing, and performance tuning with real-world case studies.
Comprehensive guide to data modeling for machine learning. It covers the different types of data models, the principles of data normalization, and the techniques for creating and maintaining data models. It also discusses the different tools and technologies that can be used for data modeling.
Comprehensive guide to data modeling. It covers the different types of data models, the principles of data normalization, and the techniques for creating and maintaining data models. It also discusses the different tools and technologies that can be used for data modeling.
Does a good job in providing a thorough introduction to data modeling and database design. It describes the different data modeling techniques and provides a step-by-step guide on how to create a data model. It is helpful for those who want to learn the basics of data modeling and database design and how to apply them in practice.
Provides a comprehensive overview of Microsoft Purview, including its features, benefits, and use cases. It valuable resource for anyone looking to learn more about how Purview can help them manage and govern their data.
Practical guide to data modeling for big data. It covers the different types of data models, the principles of data normalization, and the techniques for creating and maintaining data models. It also discusses the different tools and technologies that can be used for data modeling.
Provides a practical approach to data modeling. It does not go too much into the theoretical details but instead focuses on providing a step-by-step guide on how to create a data model. It covers the different types of data models and how to use them, as well as how to design and implement a database.
Guide to data modeling using UML. It covers the different types of data models, the principles of data normalization, and the techniques for creating and maintaining data models. It also discusses the different tools and technologies that can be used for data modeling.
Practical guide to data modeling for the Internet of Things. It covers the different types of data models, the principles of data normalization, and the techniques for creating and maintaining data models. It also discusses the different tools and technologies that can be used for data modeling.
Covers the basics of data modeling and database design. It starts with an introduction to data modeling and then covers the different types of data models and how to use them. Finally, it discusses how to design and implement a database.
Practical guide to data modeling for business intelligence. It covers the different types of data models, the principles of data normalization, and the techniques for creating and maintaining data models. It also discusses the different tools and technologies that can be used for data modeling.
Practical guide to data modeling. It covers the different types of data models, the principles of data normalization, and the techniques for creating and maintaining data models. It also discusses the different tools and technologies that can be used for data modeling.
Focuses on data modeling using Microsoft SQL Server 2012. It covers the different features of SQL Server 2012 that can be used for data modeling, such as the new table types and columnstore indexes. It also provides a step-by-step guide on how to create a data model in SQL Server 2012.
Focuses on data modeling using Oracle. It covers the different features of Oracle that can be used for data modeling, such as the new table types and columnstore indexes. It also provides a step-by-step guide on how to create a data model in Oracle.
Focuses on data modeling using MongoDB. It covers the different features of MongoDB that can be used for data modeling, such as the new table types and columnstore indexes. It also provides a step-by-step guide on how to create a data model in MongoDB.
Is an introduction to data modeling with UML. It covers the different types of UML diagrams and how to use them to create a data model. It also provides a step-by-step guide on how to create a data model using UML.
Quick and easy beginner's guide to data modeling that explains the fundamentals in a simple way. Through practical examples, it describes the different types of data models and how to use them.
Covers the compliance capabilities of Microsoft Purview. It provides guidance on how to use Purview to meet regulatory compliance requirements.
Provides a hands-on guide to using Microsoft Purview. It includes step-by-step instructions on how to perform common tasks.
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