Data Governance Basics: Definition, Factors, Framework & Books

Last Updated on: April 28th, 2023, Published on: April 28th, 2023

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In today’s data-centric world, managing data effectively is essential to the success of any organization. It’s not just about collecting and storing data; it’s about ensuring that the data is accurate, secure, and readily available to those who need it.

As we all know, data governance can be a complex topic with many nuances to consider. That’s why we’ve put together this blog to help you navigate the basics of data governance in an easily digestible manner.

In this blog, you will understand:

Table of contents

  1. What is data governance?
  2. 9 fundamental aspects of data governance
  3. The essential framework you need to know
  4. 7 must-read books for understanding the basics of data governance
  5. Let’s recap!
  6. Technical metadata: Related reads

Ready to explore them? Let’s get started.

What is data governance?

Data governance is a set of processes, policies, and standards that ensure the proper management, usage, and protection of an organization’s data assets. It aims to maintain data quality, security, and compliance while enabling seamless integration and accessibility of data across the organization.

And there are some fundamental aspects of data governance that you need to be aware of. Let’s take a look at them.

9 fundamental aspects of data governance

Why does it matter to you? The following fundamental aspects of data governance will help you manage data effectively and efficiently within your organization.

  • Data governance objectives
  • Data governance framework
  • Data stewardship
  • Data quality
  • Metadata management
  • Data lineage
  • Data cataloging
  • Data privacy and security
  • Data integration

Now, let’s take a closer look at each one of them.

1. Data governance objectives

The primary objectives of data governance are to improve data quality, ensure data privacy and security, establish data standards, and streamline data management across the organization. Additionally, it also aims to enhance data-driven decision-making and collaboration among different teams.

2. Data governance framework

A data governance framework includes the policies, processes, and tools that help manage and control data assets. And this framework should cover some essential elements without fail.

They are:

  • data ownership
  • data quality
  • data lineage
  • data cataloging
  • data classification
  • data privacy and security

3. Data stewardship

Data stewards are responsible for implementing and enforcing data governance policies and procedures. All right. But what do they actually do? Data stewards ensure data quality, monitor data usage, and facilitate communication between data owners, data users, and other stakeholders.

4. Data quality

Data quality is non-negotiable. Why? One of the main goals of data governance is to improve data quality by establishing data validation rules, data cleansing processes, and monitoring data quality metrics. Good data quality leads to better decision-making and increased trust in the data.

5. Metadata management

Metadata is data about data that provides context and meaning to data assets. Managing metadata is crucial to ensuring data is easily discoverable, understandable, and usable.

6. Data lineage

Data lineage traces the data’s journey from its source to its final destination, including any transformations or processes it undergoes. Understanding data lineage helps maintain data quality and supports data integration efforts.

7. Data cataloging

A data catalog is a central repository of information about an organization’s data assets. And it includes metadata, data lineage, and data quality information, making it easier to discover, understand, and use data assets.

8. Data privacy and security

Data governance must ensure that sensitive data is protected and complies with relevant regulations, such as GDPR (General Data Protection Regulation) or LGPD (Brazilian General Data Protection Law - in English). This includes implementing data access controls, data masking, and encryption.

9. Data integration

Integrating data from various sources is crucial to breaking down data silos and enabling a holistic view of the organization’s data. Data governance supports data integration by providing standardized data formats, data lineage information, and data quality assurance.

By understanding these fundamentals, organizations can implement effective data governance. This ensures that data is of high quality, secure, and compliant with regulations while being easily discoverable and usable. With good data governance practices, organizations can improve decision-making, collaboration, and overall efficiency.

Very well, you now have a good understanding of data governance and its essential aspects. So, let’s set our sights on an essential framework.

The Essential Framework You Need to Know

In this section, let’s look at the framework consisting of key factors you need to consider when implementing a data governance program.

Without any further ado, let’s dive right into it.

1. Data Governance Framework

  1. Purpose 👉 Define the goals and objectives of your data governance program.
  2. Roles and Responsibilities 👉 Assign data owners, data stewards, and other stakeholders.
  3. Policies 👉 Develop clear, concise data policies to guide data management, usage, and security.
  4. Processes 👉 Establish processes for data validation, cleansing, and quality monitoring.
  5. Standards 👉 Set data standards for naming conventions, data formats, and data models.
  6. Tools and Technologies 👉 Select appropriate tools and technologies to support data governance activities.
  7. Metrics and KPIs 👉 Measure the effectiveness of your data governance program using relevant metrics and KPIs.

2. Data Quality

  1. Dimensions 👉 Understand the different dimensions of data quality, such as accuracy, completeness, consistency, timeliness, and uniqueness.
  2. Validation Rules 👉 Create data validation rules to ensure the data conforms to your organization’s standards.
  3. Cleansing 👉 Implement data cleansing processes to correct or remove inaccurate, inconsistent, or duplicate data.
  4. Monitoring 👉 Regularly monitor data quality metrics to identify and address any issues.
  5. Continuous Improvement 👉 Use data quality insights to improve data collection, storage, and processing practices.

3. Data Stewardship

  1. Role Definition 👉 Clearly define the role and responsibilities of data stewards within your organization.
  2. Collaboration 👉 Encourage data stewards to collaborate with data owners, data users, and other stakeholders.
  3. Training and Education 👉 Provide necessary training and resources for data stewards to effectively manage data assets.
  4. Issue Resolution 👉 Set up processes for data stewards to identify, escalate, and resolve data issues.
  5. Communication 👉 Ensure data stewards facilitate communication and knowledge sharing between different teams.

4. Data privacy and security

  1. Regulations 👉 Understand the data privacy and security regulations relevant to your organization, such as GDPR and LGPD.
  2. Data Classification 👉 Classify data based on sensitivity and risk levels.
  3. Access Controls 👉 Implement access controls to restrict unauthorized access to sensitive data.
  4. Data Masking and Encryption 👉 Use data masking and encryption techniques to protect sensitive information.
  5. Incident Management 👉 Develop a plan for handling data breaches or security incidents.

By breaking down the key aspects of data governance into a structured approach, you can create a solid foundation that’s tailored to your organization’s unique needs. With this framework, you’ll be on your way to implementing an effective data governance program in no time. Period.

7 must-read books for understanding the basics of data governance

The following books will help you gain an in-depth understanding of data governance, and equip you with the requisite knowledge.

  1. “Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program” by John Ladley 👉 This book provides a comprehensive guide to designing, implementing, and sustaining a data governance program.
  2. “The Data Governance Imperative” by Steve Sarsfield 👉 This book discusses the importance of data governance and offers practical advice for implementing a successful data governance program.
  3. “Data Management for Researchers: Organize, maintain and share your data for research success” by Kristin Briney 👉 This book covers various aspects of data management, including data governance, and is specifically geared towards researchers.
  4. “The DAMA Guide to the Data Management Body of Knowledge” (DAMA-DMBOK) by DAMA International 👉 This comprehensive guide covers all aspects of data management, including data governance, and is an excellent resource for professionals in the field.
  5. “Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success” by Robert S. Seiner 👉 This book introduces the concept of non-invasive data governance, which focuses on formalizing existing data governance practices rather than implementing new, disruptive processes.
  6. “Data Governance: Creating Value from Information Assets” by Neera Bhansali 👉 This book provides an overview of data governance concepts and offers practical guidance for implementing a data governance program in your organization.
  7. “Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things” by Bernard Marr 👉 While not exclusively focused on data governance, this book discusses the strategic importance of data management and governance in the context of big data and analytics.

These books cover various aspects of data governance, from conceptual understanding to practical implementation. By reading these books, you can develop a solid foundation in data governance and better understand its importance in your organization.

All right. What have you learned so far?

Let’s recap!

5 key points to keep in mind as you wrap up the blog:

  1. Data governance is a set of processes, policies, and standards that ensure the proper management, usage, and protection of an organization’s data assets.
  2. Key objectives of data governance include improving data quality, ensuring data privacy and security, establishing data standards, streamlining data management, and enhancing data-driven decision-making.
  3. Important aspects of data governance include the data governance framework, data stewardship, data quality, metadata management, data lineage, data cataloging, data privacy and security, and data integration.
  4. A basic thinking framework for the key aspects of data governance involves:
    • Data Governance Framework 👉 Defining purpose, roles, policies, processes, standards, tools, and metrics.
    • Data Quality 👉 Understanding dimensions, creating validation rules, implementing cleansing, monitoring, and continuous improvement.
    • Data Stewardship 👉 Defining roles, encouraging collaboration, providing training, setting up issue resolution processes, and facilitating communication.
    • Data Privacy and Security 👉 Understanding regulations, classifying data, implementing access controls, using data masking and encryption, and managing incidents.
  5. Books to help you understand the basics of data governance. Such as “Data Governance” by John Ladley, “The Data Governance Imperative” by Steve Sarsfield, and “The DAMA Guide to the Data Management Body of Knowledge” (DAMA-DMBOK) by DAMA International.

By understanding and implementing data governance principles, your organization can effectively manage its data assets, ensure data quality and compliance, break down data silos, and support data-driven decision-making. And that’s all.

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