Data Governance vs Data Classification: 5 Key Differences
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Data governance is a personalized, community-centered approach towards data enablement. Gartner defines data governance as a way to “specify decision rights and accountability to ensure appropriate behavior as organizations seek to value, create, consume, and control their data, analytics, and information assets.”
On the other hand, data classification is a pivotal aspect of data governance. It is the systematic and methodical process of categorizing data into distinct types based on their sensitivity, significance, and regulatory requirements.
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In this article, we will understand:
- How is data classification relevant in data governance?
- The 5 key differences between data governance and data classification
- Sources to explore for data classification
Let’s dive in!
Table of contents
- How is data classification relevant in data governance?
- Difference between data governance and data classification
- Crafting a data governance strategy with a data classification perspective
- Where can you find out how data should be classified?
- Summarizing it all together
- Related reads
How is data classification relevant in data governance?
Data classification in data governance refers to the process of categorizing data assets based on their sensitivity, criticality, and usage. It involves labeling or tagging data to indicate its level of confidentiality, access restrictions, and handling requirements. The purpose of data classification is to ensure that data is appropriately managed, protected, and accessed based on its classification level, as defined by the organization’s data governance policies.
The data classification process typically involves the following steps:
- Identifying data categories
- Defining data classification levels
- Labeling and tagging data
- Implementing access controls
- Data handling guidelines
- Compliance and regulatory requirements
Let us understand each of them in detail:
1. Identifying data categories
The first step is to identify the various data categories within the organization. For example, data may be classified into categories such as public, internal, confidential, and restricted.
2. Defining data classification levels
Each data category is assigned specific classification levels based on its sensitivity and importance. For instance, public data may have the lowest classification level, while confidential data may have the highest level of classification.
3. Labeling and tagging data
4. Implementing access controls
Data classification guides the implementation of access controls. Access permissions are set based on the data’s classification level, ensuring that only authorized personnel can access sensitive or restricted data.
5. Data handling guidelines
Data classification also helps define data handling guidelines. For example, it may specify encryption requirements for highly classified data or outline specific procedures for sharing confidential information with external parties.
6. Compliance and regulatory requirements
Data classification takes into account any regulatory or compliance requirements related to data privacy and security. It helps ensure that data is managed in line with relevant laws and industry regulations.
But, what is the difference between data governance and data classification?
In the previous section, we learned how data classification is relevant in data governance. Now, let us understand how data classification is different from data governance.
Data governance and data classification are related concepts but serve different purposes within the broader data management framework. Here are the key differences between thew two concepts:
1. Definition and purpose
- Data governance: Data governance refers to the overall framework of policies, processes, and guidelines that govern the management, protection, and utilization of an organization’s data assets.
- It is a strategic approach to ensure data is managed effectively, securely, and in compliance with regulations.
- Data governance focuses on defining data ownership, data stewardship, data access controls, data quality standards, and data lifecycle management.
- Data classification: Data classification, on the other hand, is a specific aspect of data governance.
- It involves categorizing data assets based on their sensitivity, confidentiality, and criticality.
- Data classification’s primary purpose is to label or tag data to indicate how it should be handled, accessed, and protected based on its classification level.
- Data governance: Data governance covers a broad range of activities related to data management, data security, data privacy, data compliance, and data quality across the entire data lifecycle. It is a comprehensive approach to ensure data is effectively governed and used as a valuable organizational asset.
- Data classification: Data classification has a narrower scope and is primarily concerned with labeling and categorizing data assets based on their importance and sensitivity. It does not encompass all the aspects of data governance but plays a crucial role in guiding data handling and access control decisions.
- Data governance: Data governance activities include defining data policies, data standards, data access controls, data retention policies, data stewardship roles, data quality management, and compliance management.
- Data classification: Data classification activities involve identifying data categories, defining classification levels, labeling or tagging data with the appropriate classification, and implementing access controls based on the data’s sensitivity.
- Data governance: Data governance focuses on ensuring that data is managed responsibly, securely, and in alignment with the organization’s strategic goals and regulatory requirements.
- Data classification: Data classification focuses on identifying the sensitivity of data and establishing guidelines for data handling, access, and protection based on its classification level.
- Data governance and data classification: Data classification is a part of data governance. While data governance provides the overall framework for managing and governing data, data classification is a specific practice that contributes to effective data governance. Data classification guides data governance in determining access controls, data protection measures, and compliance requirements based on the data’s sensitivity and classification level.
In summary, data governance is a comprehensive approach to managing data effectively and responsibly, while data classification is a specific aspect of data governance that involves categorizing data based on its sensitivity and determining how it should be handled and protected.
Together, data governance and data classification work hand in hand to ensure data is governed, protected, and utilized optimally to support the organization’s objectives.
Crafting a data governance strategy with a data classification perspective
In this section, we will try to create a data governance strategy that focuses specifically on data classification. This strategy will detail the crucial steps, protocols, and best practices that organizations should follow to ensure the proper classification of data within their governance framework.
Here’s what it involves:
- Data inventory and cataloging
- Define classification levels
- Establish a data classification policy
- Designate data stewards
- Implement data classification tools
- Map data classification to data security policies
- Regular auditing and compliance
- Training and Awareness
- Continual improvement
Let us understand each of the above aspects in detail:
1. Data inventory and cataloging
First, catalog all the data that exists in your organization. This includes understanding the various sources of data, their formats, and how it flows within your organization. This step will enable you to have a comprehensive overview of the data you’ll need to classify.
2. Define classification levels
Define clear, simple, and universally applicable data classification levels. They may include categories like public, internal, confidential, and highly confidential, or other categorizations that make sense for your organization. Make sure these levels reflect the security measures required for each category.
3. Establish a data classification policy
Formulate a policy outlining the principles, roles, and responsibilities in the data classification process. Include guidelines on who can classify data, how it should be classified, and who has access to each class of data.
4. Designate data stewards
Assign data stewards who will be responsible for data classification. These individuals will have a deep understanding of the data and its potential use and risks, as well as the authority to assign classification levels.
5. Implement data classification tools
Leverage data classification tools to automate the process of classifying data. These tools can use machine learning algorithms to identify, categorize, and tag data automatically.
6. Map data classification to data security policies
Link your data classification to your data security policies. The security measures should align with the sensitivity of the data. For example, highly confidential data should be protected with higher levels of security.
7. Regular auditing and compliance
Set up regular audits to ensure that data classification is done correctly, and any discrepancies are corrected. Make sure that you comply with all regulatory requirements related to data classification and data protection.
8. Training and awareness
Carry out regular training and awareness programs to ensure that all employees understand the importance of data classification, how to handle data based on its classification, and the penalties for non-compliance.
9. Continual improvement
Regularly review and update the classification policies and tools based on changing data landscapes and regulatory environments. Incorporate feedback from audits, regulatory bodies, and employees for continual improvement.
This data governance strategy is solely oriented towards data classification, ensuring that all the data in your organization is appropriately classified and subsequently managed. The benefits of this strategy include enhanced data security, improved regulatory compliance, and better data management, leading to more reliable data-driven decisions.
Where can you find out how data should be classified?
Determining how data should be classified generally involves a combination of internal policies, industry standards, and legal or regulatory requirements.
Here are some common places to find this information:
- Internal data classification policy
- Industry standards and best practices
- Legal and regulatory requirements
- Data protection officer or IT department
Let us understand each of them in detail:
1. Internal data classification policy
Most organizations should have an internal data classification policy or guideline. This policy will outline the categories of data, the types of information that belong in each category, and the corresponding handling procedures. The policy should be readily accessible to all employees, often via the company’s intranet or policy handbook.
2. Industry standards and best practices
Certain industries have established standards and best practices for data classification.
For example, the financial and healthcare industries have standards like PCI DSS (Payment Card Industry Data Security Standard) and HIPAA (Health Insurance Portability and Accountability Act) respectively.
Trade associations and professional organizations often provide resources and guidance about these standards.
3. Legal and regulatory requirements
Laws and regulations at the local, national, and international levels can dictate how certain types of data must be classified and handled.
For instance, GDPR (General Data Protection Regulation) in the European Union has specific requirements for handling personal data. Legal counsel or a compliance officer can provide guidance on these requirements.
4. Data protection officer or IT department
In many organizations, the Data Protection Officer (DPO) or IT department is responsible for data classification and can provide guidance on how data should be classified.
Remember, effective data classification requires ongoing effort and should be part of a larger data governance strategy. It’s important to review and update your classifications regularly to ensure they remain relevant as your organization, and the data it handles evolves.
Summarizing it all together
Today, businesses generate, manage, and utilize a substantial volume of data, making its effective management critical. So, implementing a comprehensive data classification strategy is integral to this endeavor. It enables organizations to prioritize their resources, bolster security measures, and adhere to compliance regulations based on the value and sensitivity of data categories.
By assigning data to different tiers, data classification not only aids in reducing potential risks associated with data breaches but also significantly contributes to enhancing operational efficiency and informed decision-making. It serves as the foundation upon which robust data governance frameworks are built, ensuring that data assets are managed in a way that is secure, efficient, and compliant with all relevant laws and regulations.
In conclusion, data classification indeed serves as a pivotal step in data governance. It provides a structured approach to managing and protecting an organization’s data assets, aligning them with the company’s operational requirements, strategic goals, and legal obligations.
As the volume of data within organizations continues to grow exponentially, the role of data classification becomes increasingly crucial. This process enables organizations to prioritize data security measures, optimize storage strategies, ensure regulatory compliance, and make more effective, data-driven decisions.
In essence, data classification forms the backbone of robust data governance, setting the stage for responsible and effective data management. By proactively categorizing and securing data, organizations are not only protecting their critical information assets but also paving the way for a data-centric future.
Data governance vs data classification: Related reads
- 18 Data Validations That Will Help You Collect Accurate Data
- Benefits of Moving On-Premise to Cloud: Empowering Your Business for Success
- The Benefits of GDPR Compliance and Data Governance: Protecting Your Data and Your Business
- Data Quality Measures: Best Practices to Implement
- Data Privacy vs Data Security: How & Why They Aren’t Same?
- Data Management 101: Four Things Every Human of Data Should Know
- What is Data Stewardship?
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