NEWS: Congruity360 Launches Next Generation of Classify360 Platform, Enhancing Insights, Actions, and Comply

Read The Press Release!

How Does Data Classification Work?

More Arrow

Data, over the last several decades, has become a critical asset for organizations across all sectors and industries. The ability to properly manage and leverage this data can significantly enhance operational efficiency, strategic decision-making, and overall business growth. However, before employing effective data management strategies, a key foundational step is understanding what data classification is and how it works.

Data Classification Explained

Data classification is the process of categorizing data into different types, sensitivity levels, and relevance for an organization. The classes are typically defined based on data security needs, regulatory requirements, business value, or a combination of these factors. Common categories include public, confidential, proprietary, and personally identifiable information (PII).

Data classification allows organizations to apply appropriate security controls based on the sensitivity of the data, comply with regulatory standards, enhance business intelligence, and optimize their data management strategy. By understanding how data classification works, organizations can better safeguard their crucial data assets while maximizing their value.

How Data Classification Works

Data classification starts with creating a policy that outlines criteria for each category, considering data nature and required protection. The policy can then be applied manually or automatically, with users categorizing based on the policy or software scanning and categorizing based on rules.

Manual Classification

Manual classification, also known as user classification, involves individuals classifying data based on their understanding of the data classification policy. It requires a deep understanding of the data and company policies. While prone to errors, it allows for nuanced decision-making. Proper training is crucial to ensure accuracy and effectiveness.

Automated Classification

Automated classification uses software to categorize data, reducing reliance on individuals and minimizing human error. It efficiently handles large volumes of data and can flag sensitive information. Regular updates ensure alignment with evolving business needs and regulatory changes. This consistent approach helps organizations categorize and safeguard data assets effectively.

Hybrid Method

A hybrid approach to data classification combines automated software with manual oversight, offering accuracy, efficiency, and flexibility. Automated methods handle most of the classification process, while human input ensures nuanced understanding for ambiguous or complex data. This hybrid method provides consistency and security while maximizing the utility of data assets. Understanding data classification in a hybrid model helps optimize data management strategies for organizations.

Data Classification Examples

There are numerous examples of data classification in the modern business environment, which clearly illustrate how data classification works. Let’s consider a few:

  1. Public Data: This is information that is open and accessible to the general public. An example would be a company’s website contents, marketing materials, press releases, etc. These types of data require minimal security measures as their disclosure poses no risk to the organization.
  2. Internal Data: This type of data is meant for internal use within an organization. Examples include internal memos, organizational charts, or internal policies. While this data may not be highly sensitive, it requires a certain level of security to prevent unnecessary exposure.
  3. Confidential Data: Confidential data is information that, if disclosed to unauthorized individuals, could cause harm to an organization or its stakeholders. Examples include strategic plans, financial reports, or trade secrets. This data type requires a high level of security controls.
  4. Regulated Data: This category includes data that is subject to regulatory compliance requirements. Examples of regulated data are patient records in healthcare (subject to HIPAA) or customer data in banking (subject to GDPR). This data requires stringent security measures and controls to ensure regulatory compliance.

Each of these examples demonstrates how different types of data require varying levels of protection. Understanding how data classification works in these contexts is crucial for managing data security and compliance effectively.

Adopting a Data Classification Solution

When implementing a data classification solution, organizations must consider several key factors. Understanding how data classification works is crucial, as it helps define the project scope and objectives. The chosen solution should align with specific needs, considering data type, volume, sensitivity, and compliance requirements. Staff training is necessary for clear understanding and responsibility. Technical requirements include infrastructure, resources, and personnel. Ongoing management, auditing, and refinement are essential for continuous data classification. By considering these aspects, organizations can adopt an efficient and secure data classification solution.

Subscribe to Get More
Data Gov Insights In Your Inbox!

Subscribe Now

Learn More About Us

Classify360 Platform

Learn More

About Congruity360

Learn More

Success Stories

Learn More

Ready for actionable insight into the DNA of your data?