Data Governance Basics: Definition, Factors, Framework & Books

Updated August 17th, 2023

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The basics of data governance refer to the foundational principles and practices that ensure the quality, consistency, security, and appropriate use of an organization’s data.

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 article to help you navigate the basics of data governance in an easily digestible manner.

Let’s dive in!

What is data governance?
14 Fundamental aspects of data governance
The essential framework you need to know
Phases of data governance
7 must-read books for understanding the basics of data governance
Let’s recap!
Technical metadata: Related reads

Let’s start with the first basic concept of data governance, which is “What is data governance?”

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/basic aspects of data governance that you need to be aware of. Let’s take a look at them in this article.

14 Fundamental aspects of data governance

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

Basics of data governance:

Definition and purpose
Data governance objectives
Data governance framework
Data stewardship
Data quality
Metadata management
Data lifecycle management
Data policies and procedures
Data lineage
Data cataloging
Data privacy and security
Data integration
Data architecture and technology infrastructure
Regulatory and compliance Issues

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

1. Definition and purpose

Data governance is a discipline in the field of better quality data management. It deals with the overall management of the availability, integrity, and security of the data employed in an organization. The purpose is to ensure that data serves its intended use, is consistent across various departments, and has safeguards against misuse.

2. 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.

3. 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

We will cover all of the above in brief in this section.

4. 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.

5. 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.

6. 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.

7. Data lifecycle management

This deals with the various stages data goes through, from creation to deletion. By managing this lifecycle, organizations can ensure that data is effectively archived, retired, or purged when no longer necessary, thus optimizing storage costs and ensuring data relevancy.

8. Data policies and procedures

This involves setting clear guidelines about data acquisition, storage, usage, and disposal. It also includes defining the roles and responsibilities of individuals in the data governance framework.

9. 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.

10. 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.

11. 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.

12. 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.

13. Data architecture and technology infrastructure

Effective data governance requires a strong foundation in terms of infrastructure and architecture. This ensures that data is stored, retrieved, and processed efficiently and reliably.

14. Regulatory and compliance issues

Many industries are subject to strict regulatory requirements regarding data. Data governance ensures that an organization’s data management practices are in compliance with these regulations, thus avoiding potential fines and reputational damage.

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.

Now, we 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

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

2. Data quality

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

3. Data stewardship

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

4. Data privacy and security

Regulations 👉 Understand the data privacy and security regulations relevant to your organization, such as GDPR and LGPD.
Data Classification 👉 Classify data based on sensitivity and risk levels.
Access Controls 👉 Implement access controls to restrict unauthorized access to sensitive data.
Data Masking and Encryption 👉 Use data masking and encryption techniques to protect sensitive information.
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.

Phases of data governance

Data governance is a systematic approach to managing and ensuring the quality, security, and appropriate use of an organization’s data. As such, it is a multi-faceted initiative that unfolds in a series of key phases to build an effective governance framework.

Main phases of data governance:

Awareness and recognition
Planning and roadmap development
Design and definition
Implementation
Monitoring and control
Enhancement and evolution

Let us look into each of the above aspects in detail:

1. Awareness and recognition

Before any initiative can begin, there needs to be an acknowledgment of the need. This phase is about recognizing the importance of data as an asset and the necessity of governing it effectively.

It involves creating a sense of urgency around data issues and challenges, highlighting the risks of poor data quality or misuse, and promoting the benefits of data governance to gain organizational buy-in.

2. Planning and roadmap development

With the importance of data governance recognized, the next step is to chart a course forward.

This phase involves assessing the current data landscape, identifying stakeholders, setting clear objectives, and outlining a strategic roadmap for the data governance initiative. It’s also crucial to define roles, responsibilities, and the governance structure during this stage.

3. Design and definition

Now that there’s a plan, it’s time to detail how governance will actually work. This phase involves designing the data governance framework, defining policies, standards, and procedures.

It also involves the creation of data definitions, taxonomy, and classification, as well as setting up data stewardship roles and responsibilities.

4. Implementation

With a clear design in hand, the actual governance processes can be put into practice. This involves the actual rollout of the data governance framework, ensuring that all relevant policies and procedures are followed, setting up necessary technology tools and platforms, and ensuring that all stakeholders are informed and engaged.

5. Monitoring and control

Once implemented, it’s essential to ensure that the governance framework is working as intended. This phase involves regularly auditing and monitoring data practices against set standards, tracking compliance, resolving data issues, and ensuring that the governance processes are adhered to consistently across the organization.

6. Enhancement and evolution

Data governance is not a one-time effort; it’s a journey that needs to adapt over time. This phase is about continuous improvement. It involves reviewing and updating the governance framework based on feedback, changing business needs, and emerging technologies or regulatory requirements.

The aim is to ensure that the data governance initiative remains relevant and effective in an ever-changing data landscape.

The journey of data governance is iterative, and organizations might often find themselves revisiting earlier phases as they learn and adapt to the evolving needs and challenges of data management.

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.

“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.
“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.
“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.
“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. (Read our article on What is DAMA-DMBOK Framework and How to Adopt It.)
“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.
“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.
“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 data governance books, you can develop a solid foundation in data governance and better understand its importance in your organization.

Let us now recap what we have learned so far.

Let’s recap!

5 key points to keep in mind as we wrap up this article:

Data governance is a set of processes, policies, and standards that ensure the proper management, usage, and protection of an organization’s data assets.
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.
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.
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.
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.