Data — it’s your most critical asset. According to Domo, 2.5 quintillion bytes of data are created daily. It’s no wonder that finding and identifying data is one of the most complex and challenging processes organizations face along their data protection journeys. With the average total cost of a data breach hitting $3.92 million in 2019, companies must discover and classify their data as a foundational component of their data security and data privacy strategies.

What Is Data Discovery and Classification?

Data discovery and data classification go hand in hand. Data discovery is the process of scanning your environment to determine where data (both structured and unstructured) resides — e.g., in database and file servers that could potentially contain sensitive and/or regulated data.

Data classification, which follows the data discovery process, is more complicated. It’s the process of identifying the types of data within the discovered data sources using a predefined set of patterns, keywords or rules and assigning classification labels to that data. For example, if you work at a health insurance company, you would use medical identifier patterns to search for sensitive healthcare information.

Why Is Data Discovery and Classification Important?

Put simply, if you don’t know what data you have and where it lives, you can’t protect it effectively, which means your data is vulnerable. In addition, data classifications inform how you should treat and protect your data, including the policies you need to place around it, and guide the prioritization of your data protection and risk mitigation activities. Finally, it helps identify data that is governed by regulations and enables you to implement the controls required to achieve compliance.

Common Barriers to Effective Data Protection

Given the myriad strategic, tactical, business and technical reasons for performing data discovery and data classification, why isn’t every company doing it? Well, it’s complicated.

Operationally, discovering and classifying structured and unstructured data in a unified way across the cloud and on-premises locations is a complex process due to the scale, types of data, and underlying architectures and platforms. It’s also challenging to establish and maintain a coherent approach across the different environments and assign labels consistently across all the data. Without that consistency, the effectiveness of these processes is limited at best.

Moreover, data is constantly changing and moving, which means it needs to be tracked and reclassified regularly and continuously. Your business changes and evolves over time, which can complicate your data discovery and data classification efforts when introducing legacy (or, conversely, new) technologies. Lastly, with so many new regulations coming into effect — especially data privacy regulations — it’s hard to keep up with, centralize and manage all the compliance requirements for data protection.

In part two of this series, we’ll explore some tips and best practices to help companies strategically plan and implement a flexible approach to data discovery and classification.