What’s data classification and why is it important?

Not all data deserves the same treatment. A public blog post, an internal email, and a customer’s credit card number might all live in your system — but treating them the same is a quick way to create risk. While some information can be shared freely, others absolutely can’t.

Data classification helps draw out the differences between your datasets by tagging sensitive content, adding metadata to provide more context, and ensuring you always know how to keep information safe. We’ll explore why classification is so powerful and how you can make the most of it.

What’s data classification?

Data classification is a strategy to organize data based on its importance, sensitivity, or business value. Organizing data allows you to apply the right security controls, access protections, and prioritize resources for higher-value assets.

Practically speaking, classification works by attaching metadata or labels to datasets or files. These additional bits of information indicate if data is sensitive or confidential. From there, connect your labeling system to downstream security tools that automatically apply stronger protection standards to your most private information.

By directly connecting to your security systems, data classification becomes a standard part of effective governance.

Why data classification is important: Data classification benefits for compliance and security

Whether it's an industry-specific standard like HIPAA or a broad regulation like GDPR, data protection regulations require companies to know what data they have, where it is, and who has access to it. Classification (alongside other strategies like data discovery and data governance) lets you automatically apply the right level of security to data throughout your company.

Instead of manually tagging all the content ingested, classification systems can automatically apply tags that activate additional security measures. Strong classification systems are foundational for compliance and security, helping to:

• Support compliance: Classification transforms your internal policy into automated systems that tag and apply security controls on datasets. Tagged content keeps its metadata even after archiving, keeping you compliant throughout the entire lifecycle.
• Strengthen security: Identifying sensitive datasets through classification ensures they receive the right level of security protection, like additional access restrictions or monitoring.
• Improve audit readiness: If you need to answer questions about where your data lives and what security controls it has, a strong classification system will give you an audit-ready overview of your ecosystem.
• Reduce breach risk: Tagging and restricting data reduces the likelihood of an unauthorized user accessing that content. And, if there is a breach scenario, additional access restrictions will prevent further lateral movement.

Types of data classification

Your business ingests a wide range of data, spanning across structured, semi-structured, and unstructured content. Within each of these categories, individual datasets may vary from highly sensitive to public information. It’s this inconsistency that calls for different types of data classification.

Here are the main types of data classification strategies:

• Content-based classification: Examines the details of data including its internal columns and rows to find sensitive content. 
• Context-based classification: Infers sensitivity based on where data is coming from or the metadata it contains. 
• Automated classification: Uses machine learning to classify data at scale, tagging information as it’s ingested by your business. Teams may use classification as part of a security data lake strategy, keeping all the content they introduce as secure as possible.
• Manual classification: Humans classify your data as it flows into your company. Businesses use this only for small datasets or highly specialized, unstructured data. 

Many data classification systems will use a range of these strategies, rather than just specializing in one. For example, a tool might automatically classify both content and context-based data, blending three approaches. 

Data classification levels

As data becomes more sensitive, the security protections you apply become more rigorous. While you might have different internal names for classification levels, most organizations use a 4-tier system. 

Here are the main levels of classification and the type of data you’ll find in each.

Public data

Public data is any information that people can view via the internet or locally. Information in this tier is actively egressed by your company and doesn’t need additional security as it isn’t sensitive. Producing and publishing a marketing report is an example of data you’d find in the public domain. 

Internal data

Internal data is content only meant for employees, but not anything that contains sensitive information. A memo from one employee to another or an email message would fall into this category. You have some protections to limit public exposure but don’t need to create elaborate access controls for this tier.

Confidential data

The confidential data layer is for private, internal information. To access content in this tier, an employee would have to have the right level of clearance. When building out a permissions system, you can give certain users additional access to confidential content, like giving a vendor access to the documents related to their contract with your business. 

Restricted data

Restricted data is the most sensitive information your business stores; only users with a high level of internal authorization are able to access this content. If data in this category were to leak, it would cause damage to your reputation and break your compliance obligations. To make sure that doesn’t happen, you’ll need to apply strict access restrictions and monitoring. 

What’s the data classification process?

Although the exact tools and systems you use to implement data classification may vary, most companies follow a similar, high-level process:

1. Identify data sources: Map out the SaaS tools, databases, and file systems you use for data curation and ingestion so you know which sources to monitor for incoming data.
2. Define classification categories and levels: Outline the different classification levels and tags you’ll use in your system and what each of those means for security downstream.
3. Apply labels and metadata to datasets: When information enters your organization, use classification systems to automatically apply the correct label and metadata to its content.
4. Monitor and update classifications: Review your data ecosystem to check that details are correctly handled. If any governance regulations change, review your internal policies to make sure you stay aligned.

Data classification and security use cases and examples

If a business uses data, they’re likely using an organizational system. Especially in highly regulated industries, classification is central to protecting data at scale.

Here are some quick examples about how different industries use classification:

• Financial services: Identify and protect PII and transaction data to align with PCI DSS and other regulatory requirements.
• Healthcare: Classify patient and healthcare data to meet HIPAA standards.
• Retail and e-commerce: Protect payment data and sensitive customer information without impeding internal analytics.
• Government: Restricting access to sensitive information and automatically applying access controls.
• Cloud migration projects: Understanding the need to handle datasets according to their sensitivity labels.

How Fivetran enhances data classification for governed data access

Data classification goes hand in hand with data integration, security, and governance. By knowing the specifics in your organization, you’ll be able to apply the right strategies to keep everything visible and safe. Automatically enforcing policies helps meet regulatory requirements and keeps your entire organization audit-ready.

With Fivetran, you can achieve complete visibility into the management and movement of your data. From source to destination, understand what changes were made, what metadata was applied, and who has access. Fivetran ensures that the information you access is always high-quality, consistent, and tracked across its entire lifecycle.
Get started with Fivetran for free to strengthen your data governance workflows. 

FAQs

What is data classification based on?

Data classification relates to the sensitivity of the content you ingest. Highly confidential data will receive a tag or metadata that automatically applies stricter access controls. The more sensitive, the more steps you’ll take to keep it away from unauthorized users. 

What are the C1, C2, and C3 data classification categories?

C1, C2, and C3 are classification tiers, with C1 being low sensitivity information and C3 being highly confidential data. Organizations may extend this system to include C4 for more granularity when applying sensitivity labels. 

How does data classification relate to the GDPR?

The General Data Protection Regulation (GDPR) is an EU law that requires any business located in the EEA or doing business with individuals in the area to follow strict data protection standards. Data classification helps locate data that is relevant to the GDPR (like personal and financial information) and protect it.

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