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What Is Data Governance?

Data governance refers to a method of defining the people in an organization that have the power to manage data assets, as well as how these assets can be put to use. In this definition of data governance, the processes, technologies, and people involved in maintaining governance data all play an important role.

Data Governance vs. Data Management vs. Data Stewardship

Data governance is different from data management, primarily because the latter involves managing database information and all other data crucial to an organization’s functioning. Data governance is a key element of data management, so the two are different yet inseparable.

Data governance is also different from data stewardship in that data stewardship specifically outlines the activities connected with ensuring the accuracy and control of data. Data governance, on the other hand, focuses on roles, organization, strategy, and policies.

Why Do Organizations Need To Govern Data?

To Avoid Inconsistent Data Silos

Data silos often result in key data that cannot be accessed by stakeholders. Also, when data is siloed, the quality of security stewardship can suffer, mainly because one security policy covering a certain silo may not be as effective as another.

Common Data Definitions

Data governance introduces common ways of defining different categories of data, allowing different teams to use common language when discussing data.

Data Map

With data governance, you get the ability to see where all data is located in relation to the entities that use it.

Improved Data Quality

With appropriate data governance, the accuracy, consistency, and completeness of data are ensured.

Increased Analytics Accuracy

Data governance ensures the quality and structure of your data-sets, resulting in more accurate and, ultimately, actionable data analytics.

Implement Policies That Help Prevent Data Errors and Misuse

Data governance not only enhances your data integrity and data quality but also gives an organization more control over how it manages and disseminates data.

Compliance with Data Privacy Issues

There is no shortage of data compliance regulation that companies have to conform to in the modern business environment. With a data governance system, it is easier to identify and protect your data because it—and the methods of securing it—can be explicitly defined.

Who Is Responsible for Data Governance?

Chief Data Officer (CDO)

The CDO oversees the management, gathering, and use of data, including data analytics. Therefore, they play a key role in data governance.

Data Governance Manager and Team

An organization may choose to identify a specific data governance manager, as well as a team to support them. This may be particularly helpful when compliance issues come into play because proper governance protects the organization.

Data Governance Committee

A data governance committee can be assembled from current employees, and they do not all necessarily have to come from the IT team. Any stakeholder with an understanding of the importance of the control and implementation of data can play an integral role.

Data Stewards

Data stewards are in charge of executing the actions necessary to properly protect data. Due to new data security challenges, data stewards may be a valuable tool in managing data governance.

Components of a Data Governance Framework

A data governance framework needs to include:

  1. A plan to ensure the accuracy, consistency, and completeness of data
  2. The ability to locate data in relation to the entities that need to use it, making it readily available to them
  3. Methods of using data and securing it that can be replicated across the entire organization
  4. Structures that ensure data policies support compliance with government regulations
  5. Ways to keep data both confidential and secure

Data Governance Initiative Inclusions

Data Mapping and Classification

When data is mapped, the way it flows into, through, and out of an organization is organized and understood. The classification of data helps identify the different types of data, enabling the organization to provide adequate protective measures for each type.

Business Glossary

The way different types of data are defined can get nebulous without data governance. On the other hand, with proper governance, all in the organization can stay on the same page regarding what they call data and the processes used to manage it.

Best Practices for Data Governance Initiatives

The top 10 best policies for data governance initiatives include:

  1. Setting clear goals
  2. Defining ownership
  3. Defining goals
  4. Understanding benefits
  5. Analyzing the current state of data management
  6. Analyzing the changes made to policies and their effectiveness
  7. Deriving a roadmap to success
  8. Developing a comprehensive data governance program
  9. Implementing the data governance program
  10. Monitoring and controlling the program

Data Governance Challenges

Lack of Data Leadership

Specifically organizing how data is controlled is often passed between various people without any one person taking the lead.

Understanding Business Value of Data Governance

Stakeholders need to be made aware of how data governance directly impacts the business bottom line.

Recognizing the Need/Pain Caused by Data

Data that is not properly governed can result in lawsuits, inefficiencies, inaccuracies, and breaches that significantly impact the health of the business.

Senior Management Support, Sponsorship, and Understanding

For senior management, data governance should be a core issue, as opposed to a tangential, low-priority initiative.

Budgets and Ownership

Data governance requires the allocation of funds, as well as individual people taking ownership of the policies and practices that result.

People Think IT Owns the Data

The data of an organization is owned and managed by all who are affected by it—namely, everyone. Too often, it is assumed IT “owns” the data.

Lack of Data Documentation

Data often exists without being tracked or categorized. This can result in a lack of documentation, leaving the organization exposed to compliance challenges.

Resources To Apply to Data Governance

Often, an organization does not know which resources—both computational and human—should be applied to data governance. The lack of clarity results in weak or otherwise inadequate governance.

Pillars of Data Governance

What are the pillars of data governance?

Data Stewardship

Data stewardship involves implementing actions related to data governance. These may include identity and access management (IAM) and IAM tools, as well as steps taken to remain in compliance with government mandates.

Data Quality

The standards regarding what makes quality data have to be defined. Also, the flow of data needs to be monitored while its integrity is maintained.

Master Data Management

Master data management involves the organization working together to make sure its master data is accurate, semantically consistent, uniform, and properly overseen.

Use Cases

Examples of how data has been managed successfully, either within the organization or within another company, can be helpful in governing data. Also, key concerns like security stewardship and data analytics should come into play when deciding how to best implement data governance for the benefit of the organization.

How Fortinet Can Help

The Fortinet IAM resources are composed of tools that ensure proper data governance planning and execution. FortiAuthenticator sees to it that only the proper individuals are allowed to access sensitive data. With FortiToken, the organization can confirm the validity of users’ identities. With FortiToken Cloud, you get multi-factor authentication (MFA) as a service. 

With these tools, data governance is easier to plan and roll out for the benefit and security of the organization.