As data volumes continue to grow, a solid classification strategy gains importance


By Ahsan Siddiqui, Director of Product Management at Arcserve. 

A recent study by ESG Research revealed that 82% of organisations have experienced a data breach in the past 12 months—and 42% blame it on poor data classification.

That’s one reason why a well-honed classification strategy is increasingly crucial as data continues to grow in volume and importance. But there are many reasons. A robust classification strategy enables organisations to understand their data better and make informed decisions about storing, accessing, and sharing information.

The first step in crafting a well-honed classification strategy is to understand that all data is not created equal—and is not equally valuable. It’s simply not a smart, secure, or cost-effective strategy to back up and store every scrap of data in your organisation.

But with a good classification strategy, you don’t have to. By classifying data based on attributes such as sensitivity, value, and regulatory requirements, you can establish clear and efficient guidelines for how long they should retain and store each data category. For instance, sensitive customer information might require extended retention periods due to legal obligations, while noncritical operational data might need shorter retention periods.

Classification allows organisations to allocate resources more efficiently, ensuring they dedicate valuable storage space to essential data. Less critical data can be appropriately archived or disposed of when its retention period expires. Ultimately, data classification enables organisations to reduce storage costs, minimise clutter and adhere to compliance regulations.

Conversely, no data classification system exposes organisations to significant risks. When they cannot differentiate between critical and noncritical data, their storage resources can be overloaded with redundant or outdated information, resulting in unnecessary costs. Additionally, organisations may struggle to identify data subject to retention requirements without proper classification, which may lead to noncompliance and legal repercussions.

Furthermore, the absence of a data classification system hampers security, as a recent ESG Research study showed. Without clear guidelines on data handling, employees might inadvertently mishandle sensitive information. They might share it with unauthorised recipients, store it on insecure devices, or neglect encryption. Cybercriminals often exploit these security gaps to access valuable data, which they use for ransom demands or other nefarious purposes.

The many advantages of automation

Companies often encounter challenges when attempting to classify their data effectively. One major obstacle is the sheer volume and diversity of data generated across different departments and systems, making it difficult to establish consistent classification criteria. Additionally, data might be stored in various formats, file types, and locations, further complicating the process and leading to misclassification. Lack of employee awareness and training about the importance of data classification can also hinder adoption.

But there are strategies organizations can implement to address these challenges. First, they can establish a cross-functional team involving IT, data management, legal and compliance experts to define clear classification criteria. This team can ensure the classification system aligns with regulatory requirements and business goals. Second, companies can invest in data classification tools and software that automate the process, reducing human error and ensuring consistency. These tools can scan and analyse data to assign appropriate labels or tags based on predefined rules.

Automation, in particular, offers multiple advantages. First and foremost, it dramatically reduces the potential for human error, a common pitfall in manual classification efforts. This results in a higher degree of accuracy in data classification and reduces the likelihood that sensitive information will be mislabelled or improperly handled. Moreover, automation ensures a consistent classification standard across diverse data sources and types, eliminating discrepancies that might arise from variations in human interpretation or judgment.

Training and awareness programs are crucial as well. Organisations should educate employees about the significance of data classification, the potential risks of mishandling data, and the proper procedures to classify data accurately. This education helps foster a culture of data responsibility throughout the organisation.

Finally, data classification is more than just a one-and-done exercise. It’s an ongoing task that demands continuous monitoring and adjustment. The world doesn’t remain static, nor does your data. Proper classification requires periodic reviews and updates to accommodate changes in data types, regulatory requirements, and business needs. Regular audits and assessments can identify areas where classification may have deviated from standards or new data categories have emerged.

Final takeaway

Data classification is a navigational guide amid escalating data volumes and complexity. A proper classification strategy helps organisations better sort and categorise their data, which leads to easier access, enhanced security, and improved decision-making. In the final analysis, a good classification strategy empowers organisations to harness the true potential of their disparate data and turn chaos into clarity.

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