Six Challenges of Data Classification and How to Overcome Them

Data classification is a vital component of a robust data security strategy, especially in a world where data volumes and security threats continue to escalate. Effective data classification not only improves security but also streamlines compliance and optimises data management. However, many IT teams struggle to implement classification systems that are accurate, scalable, and integrated with existing workflows. Here I want to address six key challenges we see that IT teams face with data classification and suggest strategies to address them.

1. Volume and Complexity of Data

Modern businesses generate massive amounts of data across multiple systems and applications. For IT teams, the sheer volume of data creates a formidable challenge to classify it all effectively. Compounding this issue is the growing diversity of data formats—structured and unstructured—which can include anything from spreadsheets and emails to multimedia files and complex datasets.

Manual data classification at scale is mission impossible, even for the most security conscious organisation. That’s why automation is the key to managing large data volumes. Machine learning and artificial intelligence can streamline the process by automatically tagging and categorising data based on patterns and context analysis. Automated tools can quickly analyse large data sets and identify sensitive information, making it possible for teams to classify data in real-time as it’s created or modified. To maximise effectiveness, businesses should select classification tools designed to handle a wide range of data formats, both structured and unstructured.

2. Lack of Clear Classification Policies

Even with the best tools, data classification efforts can fall short without a clear framework. When classification policies are vague or poorly defined, inconsistencies in data handling can arise, potentially leading to security gaps or compliance issues. Teams need a solid understanding of what constitutes sensitive, confidential, or restricted data to categorise information consistently.

As such, developing clear, comprehensive classification policies is essential. These policies should outline specific categories based on data sensitivity, business impact, and compliance requirements. Classifications such as "Public," "Internal Use," "Confidential," and "Restricted" offer a foundational approach. Involving stakeholders from IT, legal, compliance, and business units ensures the framework aligns with organisational needs. Regular policy reviews are also necessary to adapt to new types of data, changing regulations, or evolving business requirements.

3. User Resistance

Employee cooperation is critical to a successful data classification program. However, some users, or realistically the majority of users, may perceive classification tasks as burdensome or unnecessary, leading to low compliance or errors. This resistance can be especially prevalent when employees lack a full understanding of data classification’s importance or feel the added steps slow down their workflow. And, let’s be honest, asking people to classify each and every asset they just won’t fly in most organisations.?

While education and empowerment are the best strategies to overcome user resistance, having tools in place to classify at scale can significantly reduce reliance on employees. Training sessions that emphasise the importance of data security and the role of classification in protecting sensitive information can increase buy-in. Additionally, deploying user-friendly AI-powered tools that minimise, or even remove, the effort required for manual tagging or allow for real-time prompts can make the process seamless. When employees understand how classification contributes to overall data security, they are more likely to engage actively and mindfully with the process.

4. Integration with Existing Systems

One major challenge IT teams face is integrating data classification into the organisation’s existing IT infrastructure, especially when legacy systems are involved. Older systems may lack compatibility with modern classification tools, creating friction and limiting visibility into sensitive data across the organisation.

That’s why businesses need to seek out classification tools designed for integration with various platforms, including cloud storage services, SaaS applications, and on-premises systems. Additionally, consider using API-based solutions that facilitate integration across diverse environments. In cases where legacy systems don’t support seamless integration, gradual migration to newer, more compatible systems may be necessary. Taking a phased approach allows organisations to adopt classification systems without compromising existing operations or security protocols.

5. Resource Constraints

Effective data classification requires more than just the right technology—it also demands skilled personnel and ongoing financial investment. Smaller IT teams or companies with limited budgets may struggle to implement and maintain a robust classification system, which can lead to missed opportunities for improved data management and security.

As such, businesses need to prioritise automation to reduce the manual burden on IT staff. Automated classification tools can significantly lower operational costs while ensuring consistent application of classification policies. Additionally, consider phased implementation, beginning with the most sensitive data and expanding as resources allow. Partnering with a managed service provider (MSP) can also be a cost-effective way to access expertise and technology without having to build a dedicated in-house team.

6. Evolving Data Regulations

Data protection regulations such as GDPR, CCPA, and HIPAA require strict data handling and classification to protect sensitive information. However, keeping up with evolving regulatory requirements can be a challenge, especially for organisations operating in multiple jurisdictions. Failure to stay compliant not only poses security risks but can also result in substantial legal penalties.

By implementing a classification system that supports regulatory compliance from the start, businesses can save time and resources. Classification tools that map specific data types to regulatory requirements are invaluable. For example, some tools are designed to recognise personally identifiable information (PII) and health-related data, which are often subject to stringent protection standards. Regular compliance audits and updates to classification policies will also ensure that businesses stay aligned with the latest regulatory requirements.

Data classification may seem like it presents a host of challenges to businesses, but the benefits are well worth the effort. By implementing a clear framework, investing in automation, and securing buy-in from employees, organisations can overcome these obstacles and create a data environment that is both secure and efficient. As the digital landscape continues to evolve, data classification will only grow in importance as a foundational component of modern data security strategies.

With the right approach, businesses can effectively manage data across diverse environments, reduce risk, and stay ahead of the curve in an increasingly complex regulatory landscape. Whether you’re starting from scratch or enhancing an existing framework, a strong data classification system is a key driver in building trust and resilience in the modern workplace.