Data is one of the most valuable assets in any organization. Whether you manage customer information or proper computer classification is important to ensure the company, efficiency, security and compliance that oversee the huge dataset. But where do you start when you can handle hundreds or thousands of files, databases and systems?
Why this guide is required that you required computer classification will undergo it, steps to classify your organizational data, and best practices that help you handle them effectively.
Why Data Classification Matters
Before we get into the “how,” let’s discuss the “why.” Classifying data is more than just tagging files; it’s about structuring your data in a way that facilitates better management, security, and decision-making.
Key Benefits of Data Classification
- Improved Data Security
Not all data is created equal. Some information is highly sensitive, such as customer financial data or internal strategies, while other data may be less critical. Classification helps assign appropriate security measures to safeguard sensitive data against unauthorized access or breaches.
- Regulatory Compliance
Data regulations, such as GDPR, CCPA, and HIPAA, require organizations to handle specific types of data with care. Classification ensures compliance by identifying which data falls under these regulations, reducing the risk of fines and penalties.
- Data Optimizations and Cost Savings
By categorizing your data, you can eliminate redundancies and prioritize storage for critical datasets. This ensures efficient use of often-expensive storage solutions, such as cloud data warehouses.
- Enhanced Decision-Making
Decision-makers can better act on classified data, knowing which datasets are complete, current, and relevant for analysis.
Now that we know why it’s essential, let’s jump into the process of data classification.
Steps to Classify Data in Your Organization
Step 1 Identify Your Data Sources
The first step is understanding where your data resides. Modern organizations leverage multiple sources, such as customer relationship management (CRM) systems, email servers, file-sharing software, databases, and even paper documents.
To ensure you don’t miss anything, perform a data audit to map your organization’s data landscape. Use tools such as data discovery software to uncover locations where data may reside.
Pro Tip: Create a data inventory or catalog to keep tabs on your data sources and their respective owners.
Step 2 Define Data Categories and Sensitivity Levels
Next, determine how you’ll categorize and classify the data. Although every organization’s needs are unique, a common approach is to classify data by type and sensitivity level.
Common Data Categories
- Business Data: Includes contracts, sales data, and business strategies.
- Customer Data: Email addresses, transaction details, and purchase history.
- Employee Data: Payroll, performance reviews, and medical records.
- Intellectual Property: Trade secrets, patents, and proprietary processes.
Sensitivity Levels
- Public Data (Low Sensitivity): Information that can be shared freely without risk, such as marketing materials.
- Internal Data (Moderate Sensitivity): Data intended for internal use, like team project documentation.
- Confidential or Restricted Data (High Sensitivity): Includes customer data, financial records, and legal documents that must be tightly controlled.
Clearly define each category and sensitivity level to avoid ambiguity during the classification process.
Step 3 Implement Data Classification Tools
Manually tagging and classifying data is inefficient and error-prone, especially for large organizations. Utilize automated data classification tools equipped with features like machine learning to streamline the process.
Recommended Tools for Data Classification
- Microsoft Purview
Designed for enterprises, this tool helps businesses manage and classify vast datasets across Microsoft solutions.
- Varonis
A robust platform offering visibility into where your sensitive data is stored and ensuring it’s properly classified.
- Spirion
This tool specializes in automating the discovery and classification of sensitive information.
These tools can detect and classify data in real time, allowing for a seamless and automated workflow.
Step 4 Apply Metadata and Tagging Standards
Tagging involves assigning specific labels (metadata) to datasets for easy identification and sorting.
Examples of Metadata Tags
- Department (e.g., HR, Sales, IT)
- Data Sensitivity (e.g., Public, Confidential)
- Retention Period (e.g., 1 Year, Permanently)
Using standardized tags ensures consistency across your organization. Consider integrating metadata tagging into your existing workflow by implementing it during manual data upload or using automation rules.
Step 5 Set Policies and Access Controls
Now that the data is classified, it’s vital to establish policies on how it should be handled. Set access controls to ensure only authorized employees can view certain types of data. For example, an HR manager may get access to payroll records, but other teams shouldn’t.
Access Control Solutions
- Leverage role-based access control (RBAC)
- Use encryption for highly classified or sensitive files
- Implement multifactor authentication for accessing sensitive data
Step 6 Monitor, Maintain, and Update
Data classification is not a one-and-done process. Data is dynamic and constantly growing, which means classifications can quickly become outdated. Make ongoing efforts to monitor and revise your data classifications as your organization evolves.
Maintenance Best Practices
- Schedule regular audits to verify that classified data remains accurate.
- Update classification policies in response to changes in compliance regulations.
- Continuously train employees on data handling and classification protocols.
Data Classification Best Practices
To maximize the effectiveness of the data classification process, keep these best practices in mind.
- Start Small: If classifying your entire database feels overwhelming, begin with a specific department or project and gradually expand the effort.
- Educate Your Team: Classification is much easier when everyone in the organization understands its purpose and follows the same guidelines.
- Use Automation Effectively: While tools can save time, configure them properly to meet organizational requirements.
- Document Your Criteria: Keep a clear record of the categories, sensitivity levels, and processes to create consistency across teams.
Building a Framework for Long Term Success
Data classification adds structure, security, and insights to your organization’s information landscape. By categorizing data, setting clear policies, and leveraging automation, you can future-proof your business against risks while unlocking opportunities tied to optimized data use.
Looking to simplify your data management strategy? Start implementing these data classification steps today and empower your team to work smarter.
Step 1: Identify the Types of Data Your Business Collects
The first important step in computer classification is to understand what types of data from your organization. This includes both structured and unnecessary data, and can include customer information, financial items, employee data and more. By identifying these different types of data, you can then prioritize that is most important for classification and security.
In addition, it is important to note whether your organization collects sensitive or regulated data as a personally identifying information (PII) or the health care records. This type of data requires extra care when it comes to classification and security measures to follow rules such as GDPR or HIPAA.
Step 2: Categorize data based on sensitivity and risk
After identifying the types of data your organization collects, the next step is to categorize them based on their sensitivity and potential risk. This will help you determine which data requires stricter classification and protection measures.
Some common categories used for data classification include:
- Public: Data that can be freely shared with anyone without any restrictions.
- Internal: Data that is only accessible to employees within the organization.
- Confidential: Highly sensitive data that should only be accessible to authorized personnel.
- Restricted: Extremely sensitive data that should have limited access and strict security measures in place.
Take some time to carefully assess each type of data your organization collects and determine which category it falls under. This will help you establish proper data management protocols and ensure that your organization is adhering to industry regulations and standards.
Data Management Best Practices
In addition to classifying data, there are a few best practices that organizations should follow when it comes to managing their data:
- Have a Clear Data Management Policy: A clearly defined policy outlining how data is collected, stored, accessed, and shared within the organization is essential for effective data management. This policy should be well-communicated to all employees and regularly reviewed and updated as needed.
- Implement Regular Data Backups: It’s important to back up your data on a regular basis in case of unexpected events such as hardware failures, cyber attacks, or natural disasters. This ensures that your data is always accessible and minimizes the risk of loss.
- Restrict Access to Sensitive Data: Not all employees need access to all types of data in an organization. It’s important to restrict access to sensitive data only to authorized individuals who have a legitimate need for it. This helps prevent unauthorized access and protects the privacy of individuals.
- Use Secure Storage and Transfer Methods: Data should be stored and transferred using secure methods, such as encrypted storage and secure file transfer protocols. This ensures that sensitive data remains confidential and protected from potential breaches.
- Implement Data Retention Policies: It’s important for organizations to have policies in place for how long data will be stored and when it should be deleted or destroyed. This helps prevent unnecessary storage of sensitive data and reduces the risk of it being compromised.
