Data Classification

What is data classification?

Data classification is the practice of evaluating and organizing data into categories for more efficient retrieval, management, security, and more. Data can be classified based on the type of data, how it is stored, risk level, use, and attributes that describe the data, among others. 

Data classification can be completed manually, but organizations typically use automation to categorize data. More recently, artificial intelligence (AI) accelerates data classification while developers rely on data classification to improve AI innovations and AI-driven outcomes.

Data management and data governance are two domains where data classification plays an important role. Additionally, as regulations like GDPR, CCPA, and HIPAA and standards such as PCI and SOX require governance of sensitive information, financial data, and Personally Identifiable Information (PII), data classification is the foundation of sound data governance.

Benefits of data classification

Organizations, even small ones, generate massive amounts of data. Some data is highly sensitive, such as internal financial data, payroll information, and customer details. Other data have lower importance and sensitivity, such as company contact information and product specifications.

Quickly differentiating data via data classifications creates efficiencies in data-related processes, from who has access for analysis and visualization to whether data is subject to specific regulatory rules.

Key benefits of data classification include:

• More effective data management by organizing, storing, and enabling accessibility to data based on importance, risk level, or other attributes. 

• Increased data security by providing extra protections to the most sensitive data, such as encryption and controlled access. 

• Improved compliance and data governance by streamlining efforts to protect sensitive data, identify data governed by regulations, and enable compliance with regulatory requirements.

• Faster data analysis by helping data consumers find data more quickly and ensuring the right data is used for a given application.

• Increased operational efficiency by aligning data importance with the cost and effort to manage and protect data. 

Types of data classification

Data classification has many frameworks. Think of how the government classifies data as confidential, secret, and top secret. That might not work for a manufacturing company, which might have public, internal, confidential, and restricted data classifications to categorize data in increasing levels of sensitivity.

How an organization classifies data, however, falls into three types of approaches:

1. Content-based classification inspects data for sensitive information and labels it accordingly. 

2. Context-based classification classifies data by how it is used.

3. User-based classification relies on a role such as a data steward to review and categorize data based on their knowledge of the content, context, and other unique concerns.

One or a combination of these approaches, among other needs, will help organizations determine the best framework for data classification. Regardless of the choices, effective data classification relies on building a robust data governance program to ensure its tracking, maintenance, and ongoing success.

Data classification and data governance

Data governance defines how data should be gathered and used within an organization and depends on data classification to enforce related policies and guidelines for specific data types. 

With guidelines in place and data classified, data governance efforts will provide insights into how data is used, identify which data requires which types of rules, procedures, and controls, and enable compliance with laws and industry standards.

Best practices for data classification

A data classification process ensures that goals are defined, standards are in place, and efforts are prioritized. Best practices for classifying data include:

1. Define the program’s goals and objectives.

2. Determine data classification types and levels, such as risk levels and use categorizations, and publish those data classifications in a data catalog for continued improvement and flexibility.

3. Create classification processes to detail which data is to be evaluated and how automation can assist with data classification.

4. Create detailed data categories with flexibility to classify data under multiple categories and levels within categories.

5. Determine how each category relates to data access and compliance.

6. Deploy a solution for continuous monitoring and maintenance of data classification efforts as business needs change, more data comes online, and new requirements emerge.

Alation: Automated data classification

Alation solves the challenge of data classification by automatically discovering and classifying sensitive data at scale and providing centralized data governance. 

Key features include:

• Publish and manage data governance and data classification policies for transparent and actionable data classification processes.

• Scale policy application and enforcement across the enterprise, with automation to speed up data governance practices.

• Protect important and sensitive data by masking data from view or denying access based on role.

• Increase compliance at the user level with clear insights, categorizations, and classifications to inform responsible data use.

Alation turns data classification into a powerful tool for data governance, risk management, compliance, security, and more.

Next steps: Learn more about metadata

Dive deeper into data classification and how a data catalog can help by using the following resources: