What is data governance? Explaining its importance, benefits, and key points for success

As the importance of data in business continues to grow, companies are strongly required to manage their data appropriately. Data governance is one effective means of managing data efficiently and securely.

This article explains not only the basic concept of what data governance is, but also its importance, benefits, and key points for success. If you are considering utilizing data within your company, please read through to the end.

What is Data Governance?

Data governance refers to the frameworks and rules for appropriately managing and maximizing the use of the data held by an organization. A key characteristic of data governance is that it not only maintains data quality and consistency but also plays a role in preventing unauthorized use and security risks.

Specific elements of data governance include:

• Establishing a data management structure

• Clarifying who is responsible for data management

• Formulating rules regarding data usage

Implementing these measures provides accurate and reliable data, leading to improved operational efficiency and faster decision-making. Furthermore, data governance contributes to legal compliance and risk reduction, making it a crucial element for companies to operate their business stably.

The Importance of Data Governance

Data governance is an indispensable initiative in the modern business environment. Without proper data management, problems such as decision-making based on incorrect information and decreased productivity can occur.

In this regard, introducing data governance ensures data accuracy and consistency, enabling operational efficiency and high-precision decision-making. It also facilitates security measures and regulatory compliance, minimizing various risks.

Additionally, providing data in a reliable form can enhance trust with customers and business partners. Thus, in an era where the volume and variety of data held by companies are increasing, the importance of data governance is growing more than ever.

Typical Initiatives in Data Governance

Data governance encompasses a wide range of activities. This section introduces some representative initiatives in data governance.

Data Quality Management
Data quality management is the process undertaken to ensure accurate and reliable data. Specifically, it includes preventing data entry errors and deleting duplicate data. These tasks help maintain high data quality, which in turn leads to improved accuracy in data analysis and decision-making.

Data Access Management
Data access management involves clarifying access rights to data and restricting the scope of use based on those rights. This reduces the risk of internal misconduct and information leaks, enhancing data security. Furthermore, recording access logs to monitor usage patterns facilitates easier tracking in the event of trouble.

Formulating Data Management Policies
A data management policy defines the basic principles and rules regarding the collection, storage, and use of data. Creating such a policy clarifies who handles data for what purpose, enabling consistent data management throughout the organization. Thus, formulating a data management policy is a key initiative when implementing data governance.

Compliance with Laws and Regulations
Various data protection regulations exist globally, including the GDPR (General Data Protection Regulation) and the CCPA (California Consumer Privacy Act). By complying with these laws and regulations, companies can avoid penalties and sanctions, while also improving trust with customers and partners. Moreover, establishing a data management system that complies with regulations makes it easier to support global business expansion.

Benefits of Data Governance

What specific benefits can a company gain by implementing data governance? This section explains three representative benefits.

Improved Operational Efficiency and Decision-Making Accuracy
Introducing data governance ensures data quality and consistency, streamlining business processes. For example, utilizing accurate and organized data not only reduces wasteful tasks and verification work but also enables quick and precise data-driven decision-making. As a result, overall organizational productivity improves, directly strengthening the company’s competitiveness.

Risk Reduction and Compliance
Proper data management minimizes the risks of unauthorized access and information leaks. Additionally, by establishing a system that complies with regulations like the GDPR and CCPA mentioned earlier, companies can avoid penalties and sanctions, and be recognized as trustworthy organizations. This builds external credibility and maximizes corporate value.

Maximizing Data Value and Strengthening Competitiveness
Data governance transforms data from a mere information resource into a strategic asset. Properly managed data helps discover new business opportunities, improve services, and enhance customer experience. Effectively utilizing data allows a company to differentiate itself from competitors, leading to a long-term competitive advantage.

4 Steps to Implement Data Governance

To achieve data governance, it is necessary to proceed with the appropriate steps. This section explains the steps to implement data governance in four phases.

Step 1: Understand the Current Data Environment
The first step in starting data governance is to accurately grasp the data environment within the organization. Identify what data exists and who manages and uses it. Also, clarifying the challenges in your company’s data environment will help pinpoint areas for future improvement.

Step 2: Set Specific Goals
Next, define the goals and objectives you want to achieve through data governance.

For example:

• Improving data quality

• Strengthening security

• Complying with laws and regulations

• Improving operational efficiency

Set goals according to the organization’s needs. By establishing specific goals and sharing them among stakeholders, all project members can proceed with their work based on a common understanding.

Step 3: Formulate Policies and Rules
Once the goals for data governance are set, the next step is to formulate policies and rules that define data usage standards and management methods. Specifics include setting data access permissions, retention periods, security measures, and clarifying the purposes of use. At this point, it is crucial to share the policies in an easily understandable way for all employees and build a system that ensures organization-wide compliance.

Step 4: Operate Data Governance
After formulating specific policies and rules, it’s time to fully implement data governance. Carefully check whether your company’s data is being managed appropriately and promote efficient and secure data utilization.

Introducing IT tools can be an effective option for practicing data governance effectively.

Here are typical examples of tools useful for data governance:

• Data Quality Management Tools (Check data accuracy, consistency, completeness, etc.)

• Access Management Tools (Control access rights to data)

• Data Catalog Tools (Organize data location, owner, metadata, etc.)

As shown, IT tools come in various types. Therefore, it is important to select the optimal tools based on your company’s situation and the objectives of your data governance.

Key Points for Successful Data Governance

There are several points to keep in mind to ensure the success of data governance. Let’s look at what specific aspects require attention.

Leadership from Top Management and Company-wide Cooperation
For data governance to succeed, top management must take the lead. When executives, the company’s leaders, understand the importance of data utilization and present a clear vision to employees, it promotes a change in awareness throughout the organization. Building a company-wide cooperative structure where different departments work together on data governance is also a critical point.

Formulating Clear Policies and Enforceable Rules
Data governance requires the formulation of specific and enforceable policies and rules. Vague rules risk causing confusion among employees, so it is essential to clearly define data usage purposes, responsibilities, security standards, and more.

At the same time, employee education to instill these rules throughout the organization is indispensable. If it is difficult to handle this solely in-house, requesting training from external experts can also be an effective option.

Establishing a System for Continuous Monitoring and Improvement
Data governance is not a one-time setup but requires ongoing operation and continuous improvement. Therefore, regularly check data quality and compliance with rules, and review the content as necessary. By repeating this cycle, you can maximize the effectiveness of data governance, leading to long-term success in data utilization.

Related Terms to Data Governance

So far, we have explained data governance in detail, but there are several other terms that are easily confused with data governance. Finally, we introduce three related terms.

Data Management
Data management refers to the efficient control of the entire process from data collection and storage to usage and disposal. While data governance is the framework that sets rules and policies, data management can be seen as the specific process that implements and operates those rules. As such, data governance and data management are clearly distinguished as different concepts, so it’s important to understand their respective meanings.

Data Stewardship
Data stewardship refers to the roles and activities responsible for maintaining and managing data quality, security, and integrity within an organization. Specifically, data stewards ensure data accuracy and consistency, and oversee that data is used in accordance with data governance policies. In other words, data stewardship is one of the means to maximize the effectiveness of data governance.

Data Lifecycle Management
Data lifecycle management is a method for managing the series of processes from data creation to disposal. While data governance provides the guidelines on “how data should be managed,” lifecycle management involves the concrete operations following those guidelines. For example, by deleting unnecessary data and storing data appropriately, organizations can achieve efficient data utilization and establish an operational system that meets regulatory and security requirements.

Conclusion

This article explained not only the basic concept of what data governance is, but also its importance, benefits, and key points for success.

By implementing data governance, companies can enjoy various benefits such as risk reduction and maximizing data value. Re-read this article to understand the specific initiatives and practical methods involved.